Utlity of Multi Criteria Analysis in Large Infrastructure Development Project

7/29/2019 Utlity of Multi Criteria Analysis in Large Infrastructure Development Project

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UTLITY OF MULTI CRITERIA ANALYSIS INLARGE URBAN INFRASTRUCTURAL

DEVELOPMENT

(A CASE STUDY OF URBAN AND ENVIRONMENTIMPROVEMENT PROJECT)

SUBMITTED BY:MANOJ KUMAR SIGDELM.Sc. URBAN PLANNING

2062 BATCH

TRIBUHVAN UNIVERSITYINSTITUTE OF ENGINEERING

DEPARTMENT OF ARCHITECTUREURBAN PLANNING PROGRAMME

PULCHOWK CAMPUS, LALITPUR.

NEPAL

February, 2008


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CERTIFICATE

This is to certify that this entitled UTILITY OF MULTI CRITERIA ANALYSISIN LARGE URBAN INFRASTRUCTURAL PROJECT: A CASE STUDY OF

URBAN and ENVIRONMENT IMPROVEMENT PROJECT submitted by

Manoj Kumar Sigdel has been examined and it has been declared successful for the

fulfillment of the academic requirement towards the completion of the Master of

Science Course in Urban Planning.

Dr. Sagar Prasai.

(Thesis Supervisor)

Date:


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DECLARATION

I declare that this dissertation has not been previously accepted in substance for any

degree and is not being concurrently submitted in candidature for any degree. I state

that this dissertation is the result of my own independent work/investigation, except

where otherwise stated. I hereby give consent for my dissertation, if accepted, to be

available for photocopying and understand that any reference to or quotation from my

thesis will receive an acknowledgement .

Manoj Kumar Sigdel

February, 2008


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ACKNOWLEDGMENT

This work of thesis is a result and encouragement of many people to whom I express

my sincere acknowledgement. Foremost, I express my sincere gratitude to Dr. Sagar

Prasai , my thesis advisor for professional guidance and continuous encouragement in

every stage of work delivering in depth knowledge, and critical suggestions moreover,

for sharing profound knowledge. The credit for any good about this thesis is attributed

to his guidance.

I am highly indebted to Prof. Dr. Sudarshan Raj Tiwari, Prof. Dr. Jib Raj Pokharel

and Asst. Prof. Sanjay Upreti; program coordinator of MSc urban planning for their

regular suggestions and comments. The way they have guided and the inputs they

have during the thesis preparation are the most valuable achievements.

My profound gratitude goes to Mr. Shashi Bhattarai , for his suggestion, constructive

discussions and providing necessary software and reference documents. His

encouragement and continuous support through out this period is life time memorable.

I express my gratitude to Dr. Mahendra Subbha, Mr. Shiva Hari Sharma; Project

Director of Urban and Environmental Project & Mr. Grija Prashad Gorkhali , for

their support in providing references, project related documents and data which were

very useful in thesis preparation.

I am very grateful to Integrated Consultants Nepal (P.) Ltd . for their continuous

support during this period. Also the generosity goes to all the respondents for

providing valuable informations that are very useful in the building the research to

this stage.

I owe a special debt of thanks to all the friends especially to Mr. Dhiraj Pokhrel and

Rabindra Shrestha for their suggestions, support, and necessary arrangements.

Thanking You,

Manoj Kumar Sigdel


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ABSTRACT

This thesis attempts to analyze the utility of MCA in large urban infrastructural

investment as it is not effectively applied for such projects in Nepal. The paper, in its

attempt to show that multi-criteria analysis might be a reliable tool in the context of

Nepalese planning processes, tries to point out the repercussions of decision making

process at different level which directly undermined the projected goals of such

projects in the past. During the process of research, the WT and AHP methods was

applied on different criteria used in selection process of UEIP towns. It attempts to

analyze the ranking of towns by the change of weights in criteria and sub-criteria in

sensitivity analysis. In the process of defending this thesis, this research showed that

intervention from several external criteria, which directly affected the entire

implementation of projects, led to a reduction in the utility of multi criteria analysis.

Also the research found that the actual decision making process rested on the decision

makers hierarchy. Finally this research draws the conclusion that multi criteria

analysis is an appropriate decision support tool for rational judgments in decision

making but the wide spectrum of criteria should be selected to avoid distortions in the

decision process. Moreover, the research also calls in for the application of AHP in

MCA since the results obtained through this method quite tally with the one drawn by

WT method. AHP, which focuses on qualitative data and subjective judgment, is

more suitable for a country like Nepal where there is a substantial dearth of

quantitative data.

Key words: Decision, Multi - Criteria Analysis, Analytical Hierarchy Process, Urban,

Infrastructure Development.


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ABBREVIATIONS/ ACRONYMS

ADB Asian Development Bank

AHP Analytic Hierarchy Process

BC Before Christ

CBO Community Based Organization

CBS Centre Bureau of Statistics

CoTL Commitment of Town Leaders

DDC District Development Committee

DoP Degree of Participation

DTLR Department for Transport, Local Government and the Region

DUDBC Department of Urban Development and Building Construction

DWSS Department of Water Supply and Sewerage

EP Economic Potential

GoN Government of Nepal

H High

HMG/N His Majestys Government of Nepal

IC Institutional Capacity

INGO International Non Governmental Organization

KUDP Kathmandu Urban Development Project

KVMP Kathmandu Valley Mapping Project

LSGA Local Self Governance Act

LwK Linkage with Kathmandu

M Moderate

MAUT Multi Attribute Utility Theory

MAVT Multi Attribute Value Theory

MCA Multi Criteria Analysis

MCDA Multi Criteria Decision Analysis

MCDM Multi Criteria Decision Making

MIT Massacheutte Institute of Technology

MLD Ministry of Local Development

MoF Ministry of Finance

MOPE Ministry of Population and Environment

MPPW Ministry of Physical Planning and Works

MuAN Municipal Association of Nepal

NGO Non Governmental Organization


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NM Not Much

NPC National Planning Commission

NWSC Nepal Water Supply Corporation

Ok Okay

OPP Opportunities to Physical Planning

PA Project Advisor

PCO Project Coordination Office

PIU Project Implementation Unit

PPP Public-Private Partnership

PPPUE Public Private Partnership for Urban Environment

PtoK Proximity to Kathmandu

S Strong

SC Steering Committee

SEU Subjectively Expected Utility

TDC Town Development Committees

TDF Town Development Fund

TOR Terms of Reference

UDLE Urban Development through Local Effort

UEIP Urban and Environmental Improvement Project

UEVP Urban and environmental Infrastructural View Point

UGR Urban Growth Rate

VDC Village Development Committee

VH Very High

VS Very Strong

W Weak

WofP Willingness of People

WT Weighted Table

PSC Project Steering Committee


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Table of Contents

CERTIFICATE.......................................................................................................................i

DECLARATION ...................................... ....................................... ...................................... ii

ACKNOWLEDGMENT.......................................................................................................iii

ABSTRACT..........................................................................................................................iv

ABBREVIATIONS/ ACRONYMS........................................................... ............................ v

1. Inroduction............... ............................................... ........................................... ................ 1

1.1 General Background ...................................... ........................................... ................ 1

1.2 Problem Statement..................................... ........................................ ....................... 2

1.3 Scope and Limitations of the Study..........................................................................4

1.4 Research Questions ................................... ............................................ ................... 4

2. Research Methodology ....................................... ......................................... ...................... 5

3. Literature Review ....................................... ............................................ ........................... 8

3.1 History of Decision Making ........................................ ............................................. 8

3.2 Decision Theory ..................................... ........................................... ..................... 12

3.2.1 Normative Theory of Planning................................................. .......................... 13

3.2.2 Synoptic and Incremental Planning Theory ............................................. .......... 13 3.3 Theory of Decision Making....................... ........................................ ..................... 14

3.3.1 Subjectively Expected Utility (SEU) ................................... .............................. 14

3.3.2 Differentiation and Consolidation (Diff Con Theory) ................................... .... 15

3.4 Decision Making Problematics.................................... ........................................... 21

3.4.1 The Classification Problem..................................... .................................... ....... 21

3.4.2 Uncertainities in Decision Making................... ..................................... ............. 22

3.5 Role of Government, Planning and Legislation ....................................... .............. 23

3.6 Multi Criteria analysis ...................................... ........................................ .............. 24

3.6.1 Brief History of Multi Criteria Analysis ......................................... ................... 24

3.6.2 Basic Concept........................................ ........................................... .................. 24

4. Brief Introduction of UEIP ........................................ ............................................... ....... 30

4.1 Overall Goals and Objectives ............................................. .................................... 31

5. Analysis ..................................... ........................................... ........................................... 33

5.1 Selection of UEIP Towns ..................................... ........................................... ....... 33


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5.1.1 Frame Work Assessment................................................. ................................... 34

5.1.2 Preparation of Survey Tools................................... ......................................... ... 35

5.2 Result.... ........................................... ........................................... ............................ 36

5.2.1 Weighted Table ......................................... ........................................... .............. 36

5.2.2 Analytical Hierarchy Process...... ....................................... ................................ 39

5.2.3 Sensitivity Analysis............................ ........................................... ..................... 39

5.3 Comparision on ranking of Towns .................................. ....................................... 40

5.4 Decision Assessment:....... ........................................... ........................................... 44

6. Findings ............................................ ............................................ ................................... 49

6.1 Utility of Multi Criteria Analysis ........................................ ................................... 49

6.1.1 Weighted Table ......................................... ........................................... .............. 49

6.1.2 Analytical Hierarchy Process...... ....................................... ................................ 50

7. Conclusion and Recommendation ........................................ ........................................... 56

7.1 Recommendation... ........................................ ....................................... .................. 57 7.2 Recommendation for further research .................................... ................................ 58

BiblioGraphy ....................................... ........................................... ........................................ i

Annexure


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List of Tables

Table 1: Overview of Diff Con Theory16

Table 2: Uses of AHP at different sectors of planning.....28

Table 3: Selected Towns for UEIP .33

Table 4: Criteria for selection of UEIP Towns.34

Table 5: Details of Criteria and Weight Distribution under Normal Condition...35

Table 6: Multi Criteria Analysis by Simple Weighted Mean Method.37

Table 7: Multi Criteria Analysis by AHP Method...38

Table 8: Criteria and sub-criteria distribution for sensitivity analysis.40

Table 9: Score obtained by towns under three conditions41

Table 10: Sensitivity Analysis type 1through AHP method42Table 11: Sensitivity Analysis type 2through AHP method....43

Table no 12: Ranking of UEIP towns based on WT method...49

Table 13: Ranking of UEIP towns based on AHP method......50

Table 14: Ranking of UEIP towns through sensitivity analysis 1 based on AHP

method.51

Table 15: Ranking of UEIP towns through sensitivity analysis 2 based on AHP

method..52

Table 16: Comparision of rank obtained from different methods53

List of Figures

Figure 1: Framework Development of the Research Work...5

Figure 2: Analytical Hierarchy Process (AHP) for UEIP towns..29

List of Charts

Chart 1: Details of Criteria and Weight Distribution under Normal condition36

Chart 2: Comparison of UEIP towns in three different cases..41

List of Maps

Map 1: Strategic Location UEIP Project..31


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1. INRODUCTION

1.1 GENERAL BACKGROUND

Nepal is predominantly an agricultural country, but agricultural land has been

changing rapidly due to unchecked urbanization in the areas near its cities. At the

same time, land for non-agricultural use increased by 45,200 hectares to 95,700

hectares and then to 119,160 hectares from 1961-62 to 1991-92 and then to 2001-02

respectively (Centre Bureau of Statistics (CBS), 2002). In 1961, the government, for

the first time, defined urban as settlements having 5,000 or more population, with

urban facilities such as markets, industrial establishments, school, offices, etc. In that

year, there were 16 towns with a population of 5,000 and more. This figure increased

to 23, covering 6.4 per cent of the total population in 1981. In 1991, the number

increased to 58 urban areas with a population of 10,000 and more, covering 9.2 per

cent of the total population, which rose to 13.9 per cent in 2001 (Ministry of

Population and Environment (MOPE), 2003).

Urbanization is now changing the face of the Nepal dramatically. The rapid rate of

urbanization during the past two decades has created unprecedented pressure on

Kathmandu and a number of cities in the Terai. Apart from the obvious health issues,

population growth, rapid urbanization and industrialization brought in its wake a new

dimension to the inadequate infrastructure and services in Nepal. Urban areas are

undergoing considerable growth in employment, infrastructure and basic services,

which is likely to have pervasive impact on the quality of urban life and, of course,

the environment.

As the country now develops towards a more urban economy, the economic growth

and sustainable development of its existing and newly emerging urban settlements is

becoming increasingly important in enhancing the economic well being of the nation.

Local governments are thus faced with new and far more complex urban challenges.

Rapid population growth from various reasons have resulted in serious deficiencies of

basic urban services such as water supply, drainage, sewerage, solid waste

management, urban road system etc. This situation has led to the degradation of urban

environment causing serious health problem and thereby deteriorating the quality of


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the urban life. There has been a growing concern over the uncontrolled growth of

major cities including Kathmandu and the simultaneous inadequacy in the provisions

of physical and social infrastructure affecting the entire urban milieu.

Serious deficiencies of infrastructure and services like sewage management are the

commonest issues in the urban area of Nepal causing environmental problems in

terms of public health and sanitation. Apart from meeting the current deficits, the

future infrastructure and services requirement for the growing urban population

provides the major policy challenges for both the national and local governments. To

address the above problems and in a bid to devolve power to local government, the

Ninth plan (1997-2002) brought up some policies on urban development and

environmental amelioration and to support decentralization and strengthen local

governance through local development.

In order to meet the goals as envisaged in the ninth plan and to overcome urban

infrastructure deficits, the Ministry of Physical Planning and Works (MPPW) (the

then Ministry of Housing and Physical Planning) and the Asian Development Bank

(ADB) signed a Memorandum of Understanding on November 5, 1999 to launch the

"Urban and Environmental Improvement Project (UEIP) Technical Assistance. The

project, in a strategic move, was launched in 2000 for improving the urban

environment, facilitating towards sustainable urban development through

infrastructural development of 9 municipalities (Banepa, Dhulikhel, Panauti,

Bharatpur, Ratnanagar, Hetauda, Bidur, Kamalamai, and towns of Dhading Besi)

located in vicinity of the Kathmandu valley. Its objective was to improve urban

environment and prevent the inevitable new problems looming over the Kathmandu

valley.

1.2 PROBLEM STATEMENT

The background is sufficient to show that it is exigent for the government of Nepal to

take strong and immediate steps for the management of haphazard growth and the

burgeoning population pressure within Kathmandu. This requires systematic planning

intervention, policies and large projects. In past decades, several efforts were made to

improve the urban sector of Nepal especially in Kathmandu but the present haphazard

urban environment by itself is a testimony to the failure of such endeavors. Urban


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sector problems are of diversified in nature. This means it has to incorporate different

criteria (like social, technical, institutional, political, financialetc) for driving

towards sustainability. Functional failure of one criterion, as a natural corollary,

leaves serious repercussions on the entire urban environment.

Notable examples of failure precipitated by the inability of a coherent addressing of

the aforementioned criteria include the projects Kathmandu Urban Development

Project (KUDP) and Kathmandu Valley Mapping Project (KVMP), which were

funded by World Bank and European Union respectively. In the case of these

projects, the donor agencies executed the project through Project Implementation

Unit (PIU) where only technical assistance was provided by expertise with relatively

minimum input of municipality. Though the project ensured the timely

implementation of project during that period but in long run, this has to be handled bylocal permanent staff. At this point the project was failed because of lack of technical

expertise and low sense of obligation about that project putting a big question mark

over the sustainability of large donor funded projects. (Urban and Environmental

Improvement Project (UEIP), 2001). From the past experience it should be realized

that analysis of several criteria should be meticulously done while deciding the

implementation of large donor funded projects.

Another major factor that influence the haphazard urban development is mainly

through uncoordinated decisions between the concerned institutions and of course

individuals (like donor agencies, central government, local government and line

agencies). The influence of different forces like (political, donors, personal

interestetc) derails the project due to the contradictory impulses of prioritization

and implementation. Trend of carrying a decision process within short period of time

without studying infrastructural projects from different criteria and unawareness of

the possible outcomes and risk lies behind its implementation causes the failure of

project.

Thus, planners are confronted with the necessity of taking into account the impacts

typically analyzed by other disciplines. Though it is felt and several plans and policies

are brought up for infrastructural development, but this effort are done individually by

the institutions creating doubts over long term sustainability of such endeavors.

Meanwhile there is lagging of coordination among the institutions working in


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infrastructural development sector. Conflict of perspectives among the varied actors is

an unvarnished truism. In this situation, the necessity of Multi Criteria Analysis

(MCA) is felt for decision making purposes solely to draw the concern of different

actors within an umbrella, and to come up with single objectives and goals.

Objectives of the Research

Main Objectives

To analyze utility of multi criteria analysis in large urban infrastructure

investment.

Secondary Objectives

To analyze the process of decision making at different levels for urban

infrastructural development.

To suggest ways of integrating multi criteria, multi actor decision making process

in urban infrastructure investment.

1.3 SCOPE AND LIMITATIONS OF THE STUDY

This study will analyze the utility of multi criteria analysis and present decision

making process in urban infrastructure investment project.

The study will suggest the appropriate method/ software for the multi criteriaanalysis for decision making in urban infrastructure investment project.

A typical case of MCA application will be demonstrated on Urban Environment

Improvement Project utilizing best available resources and time of research.

The depth of the study will be limited through low information regarding the

topics and availability of data.

The study and outcome thus obtained will be from an individual effort hence

resource management and mobilization may limit rigorous study.

1.4 RESEARCH QUESTIONS

How rational or pragmatic is the use of Multi Criteria Analysis in large

infrastructure investment project?

What does an integrated model of multi criteria/ multi actors decision process

contain in the context of Nepal?


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2. RESEARCH METHODOLOGY

The study has followed a descriptive method and interpretive type and the structure of

the study mainly depends following factors:

1. Desk Study

This was the preliminary state where all the available data, documents, maps and

reports related to project (UEIP) were studied and its main objective was to extract

and compile as much possible information before hand so as to streamline the future

course of action.

2. Framework development

Figure 1: Framework Development of the Research Work


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3. Literature Review

Various journals, literatures, books and other available sources of information concern

with decision theory, urban infrastructure and literature regarding multi sectoral

project with application of MCA are thoroughly studied.

4. Data Collection

Data were collected through primary survey and secondary sources as well. Only key

informant interviews were conducted thus only the qualitative data were collected and

data were extracted from secondary sources.

At the preliminary stage, data were collected through concerned institutions and

also experts involved during selection of UEIP towns were requested to fill up the

matrixes of selection criteria against short listed towns for multi criteria analysis

(MCA) of UEIP towns.

At the final phase, the sets of questionnaire were prepared discussing with thesis

coordinator then key informant surveys were done with the experts involved in

selection of UEIP towns, relevant organizations and steering committee to

determine what factors and which actor plays the key role in decision making

process during selection of nine UEIP towns.

Institutions visits and discussion were done accordingly.

5. Data presentation and Analysis

Subjective criteria setting was conducted for multi criteria analysis, to avoid the

deviation of results, the criteria adopted were taken same as it was considered by

the professionals for towns selection.

MCA Software

For the analysis, formulation of hierarchy model was first prepared and the ranking of

towns were done through weighted mean table method and by multi criteria software;

expert choice based on theory of Analytical Hierarchy Process (AHP).

The weighted table method is the simplest form of MCA and easy to understand. Here

the alternatives were scored for each comparison criteria and the summation were


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matrix based. The criteria were weighted because as they were not equally valued by

individuals.

The Analytical Hierarchy Process (AHP) based multiple criteria analysis starts from

building tree like structure with criteria at higher level and sub criteria were at the

lower level. Objective of evaluation lies at the top and the options or alternatives to be

evaluated are placed at the lowest level of the hierarchy. The importance or weight to

the factors can either be directly applied or could be generated by making pair-wise

judgment between the various factors. The sensitivity analysis was also done through

this method changing the weight of criteria and sub criteria.

The result thus obtained from MCA was reviewed on the basis of respondent

survey. The purpose was to find out the involvement of multi actor/ hierarchical

decision making process in the UEIP towns.

6. Draft final research

Draft report on the research incorporating all the study data analysis and result

were prepared and submitted for comments.

7. Final research report preparation

Based upon the comments received modifications on the draft research is made to

final thesis.


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3. LITERATURE REVIEW

3.1 HISTORY OF DECISION MAKING

The history of decision making is long, rich and diverse The following timeline

represents only a small sample of the people, events, research and thinking that

contributed to the subject and any dates are approximate. A sypnotic version of the

history of decision making as put forward by Buchanan and Cornell (2006) can be

handy in this regard:

Prehistory For millennia, human decisions guided by interpretations of entrails, smoke, dreams

and the like; hundreds of generations of Chinese rely on the poetic wisdom and

divinations instructions. The Greeks consult the Oracle of Delphi. Prophets and seers

of kinds peer into the future.

6th Century BCPrinciple of non willful action: letting events take their natural course Confucius

says decisions should be informed by benevolence, ritual, reciprocity, and filial piety.

5th Century BCMale citizens in Athens, in the early form of democratic self-government, make

decisions by voting.

4th Century BC

Plato asserts that all perceivable things are derived from eternal archetypes and are

better discovered through the soul than through senses.

Aristotle takes an empirical view of knowledge that values information gained

through the senses and deductive reasoning.

399 BC

In early jury-trial decisions, 500 Athenian citizens agree to send Socrates to his death.

333 BCAlexander the Great slices through the Gordian knot with his sword, demonstrating

how difficult problems can be solved with both strokes.


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49 BCJulius Caesar makes the irreversible decision to cross the Rubicon, and a potent

metaphor in decision making is born.

9th Century

The Hindu-Arabic number including the zero, circulates throughout the Arab empire,stimulating the growth of mathematics.

11th CenturyOmar Khayyam uses the Hindu-Arabic number system to create a language of

calculation, paving the way for the development of agenda.

14th CenturyAn English friar proposes what became known as Occams razor, a rule of thumb

for scientists and others trying to analyze data: the best theory is the simplest one thataccounts for all evidence.

17th Century

Stable keeper Thomas Hobson presents his customers with an eponymous choice:

the horse nearest the door or none.

1654Prompted by a gamblers question about the problem of points, Blaise Pascal and

Pierre de Fermat develop the concept of calculating probabilities for chance events.

1660Pascals wager on the existence of God shows that for a decision maker the

consequences, rather than the likelihood, of being wrong can be paramount.

1738

Daniel Bernoulli lays the foundation of risk science by examining random events

from the standpoint of how much an individual desires or fears each possible outcome.

19th CenturyCarl Friedrich Gauss studies the bell curve, described earlier by Abraham de Moivre,

and develops a structure for understanding the occurrence of random events.

1907Introduces the net present value as a decision making tool, proposing that expected

cash flow be discounted at the rate that reflects an investments risk.


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1921Frank Knight distinguishes between risk, in which an outcomes probability can be

known (and consequently insured against), and uncertainty, in which an outcomes

probability is unknowable.

1938Chester Barnard separates personal from organizational decision making to explain

why some employees act in the firms interest rather than their own.

1944

John von Neumann and Oskar Morgenstern in Game Theory describe a mathematical

basis for economic decision making like most theorists before them, they take the

view that decision makers are rational and consistent.

1947Herbert Simon argues that because of the costs of acquiring information, executives

make decisions with only bounded rationality they make do with good enough

decisions Rejects the notion that decision makers behave with perfect rationality.

1950s

Research at the Carnegie Institute of Technology and MIT led to the development of

early computer-based decision support tools.

1951Kenneth Arrow introduced the Impossibility Theorem which holds that there can be

no set of rules for social decision making that fulfils all the requirements of society.

1952

Harry Markowitz demonstrates mathematically how to choose diversified stock

portfolios so that the returns are consistent.

1960sEdmund Learned, C. Roland Christensen, Kenneth Andrews and others develop the

SWOT (strengths, weaknesses, opportunities and threats) model of analysis, useful for

decision when time is short and circumstances complex.

1961

Joseph Hellers term catch-22 becomes popular shorthand for circular, bureaucratic

illogic that thwarts good decision making.


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1965Corporations use IBMs System/360 computers in stat implementing management

information systems.

Roger Wolcott Sperry begins publishing research on the functional specialization of

the brains two hemispheres.

1968

By the end of the 1960s, MCDA attracted the interest of European operations

researchers. Roy (1968), one of the pioneers in this field, introduced the outranking

relation approach; he is considered as the founder of the European school of MCDA.

1970John D.C. Little develops the underlying theory and advances the capability of

decision-support systems

1972Irving Janis coins the term groupthink for flawed decision making that values

consensus over the best result Michael Cohen, James March and Johan Olsen

publish A Garbage Can Model of Organizational Choice which advices

organizations to search for their information trash bins for solutions thrown out

earlier for lack of a problem

1973Fischer Black, Myron Scholes and Robert Merton show how to accurately value stock

options, beginning a revolution in risk management Henry Mintzberg describes

several kinds of decision makers and positions decision making within the context of

managerial work.

1973

Victor Vroom and Philip Yetton develop the Vroom- Yetton model which explains

how different leadership styles can be harnessed to solve different types of problems.

1979Amos Tversky and Daniel Kaheman publish their Prospect Theory that demonstrates

that the rational model of economics fail to describe how people arrive at decisions

when facing the uncertainties of real life.


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1980sSaaty (1980) first proposed the AHP method (Analytic Hierarchy Process) for

addressing complex decision making problem involving multiple criteria. The method

is particularly well suited for problems where the evaluation criteria can be organized

in a hierarchical way into sub-criteria.

1992

Max Bazerman and Margaret Neale connect behavioural decision research to

negotiations in Negotiating Rationally.

1996Web users start making buying decisions based on the buying decisions of people like

themselves.

2005In Blink Malcolm Gladwell explores the notion that our instantaneous decisions are

sometimes better than those based on lengthy rational analysis.

3.2 DECISION THEORY

Decision implies the end of deliberation and the beginning of action. (William cited

in Buchanan & Connell 2006). Decision making is an every day human function.

Decisions usually concern people (human resources); money (budgeting); buying

and selling (marketing); how to do things (operations); or what to do in the future

(strategy and planning) (Crainer 1999).

A combination of economic, social and technological developments has produced a

situation where people have to make important decisions about their relationships and

family life, their health, and their education and careers. Decisions are also essential at

a societal level. Business and financial institutions are faced daily with decisions

about investment, research and development, and deployment of resources in a

complex and uncertain environment (Ranyard and Crozier 1997).

It is perhaps the social and economic significance of decisions that has resulted in the

considerable influence upon psychological approaches to the study of decision

making of concepts from other disciplines. Theories of planning are broadly speaking

theories of how decisions concerning the community or the society at large are

prepared or should be prepared.


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3.2.1 Normative Theory of Planning

Most of decision theory is normative or prescriptive, because it is concerned with

identifying the best decision to take, assuming an ideal decision maker who is fully

informed, able to compute with perfect accuracy, and fully rational. Normative theory

enables planners in appropriate way of thinking the ethical issues and dealing withthem. Normative literature suggest a wide diversity of approaches to the questions of

what principles should be central and how reasoning about and justification of ethical

choices should takes place (Howe n.d.). The normative ethical theory can be

classified into two groups based on the relation between right action and intrinsic

actions.

3.2.1.1 Deontological Normative Theory:

The word deontological was first derived from Greek word ought and is concerned

with moral rules or the right of process. The theory of ethics is not concerned with the

consequences of action but with the rightness of the act itself. It means that it has no

concern with the out comes of the result.

3.2.1.2 Teleological Normative Theory

On the contrary to deontological theory, teleological ethics is concerned with the

goodness of result. That means this theory has concerned only with the goals of action

or the goodness created from planners decision. In greek teleos means purpose hence

it is also called as consequentiality ethics. Though teleological theories focus on the

consequences but they can vary in other ways concerning whose consequences count

what motivates ethical behaviour and what criteria are made for moral judgments.

3.2.2 Synoptic and Incremental Planning Theory

Synoptic planning is normative which tells method for tackling the problems

rationally. Synoptic planning is thought to have perfect knowledge of possibilities;

i.e., the consequences of all the alternatives. Synoptic and incremental planning are

opposites when it comes to the assumptions concerning information andcommunication.

In synoptic planning, the data base for acquiring perfect information exists at the

outset, thus it require little need for external communication during the planning

process also little learning with bearing on decisions already made, thus in

consequences intermittent feed back are required.


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Incremental planning has incomplete initial data base and required external

communication during the process to improve information, understanding and

agreement; also learning and feed back loop are essential.

The ambition of synoptic planners to be instrumentally rational encourages the

application of technology, analytical techniques ensure that the knowledge is

systematized and put to use efficiently. Perfect information means lot of information,

which would simply cause a mess without suitable techniques for handling the data.

Due to the perfect information the synoptic planners hold the ambition of embarking

on the grand optimization right form the start(Sager 1994). The synoptic approach to

planning is weak on participation but strong on technical issues.

3.3 THEORY OF DECISION MAKING

3.3.1 Subjectively Expected Utility (SEU)SEU theory is a model of rational behaviour, originating in economics and

mathematics. This assumes that decisions should be reached by summing over the set

of alternatives where the utility of each alternative weighted by the subjective

probability of its occurrence. Its elegance and authoritative status provide an incentive

for decision makers to apply it to their own situation. Nevertheless, the value of utility

maximization as a normative choice principle was criticized, for example, Simon

(1957) argued that people can productively adapt to their environment by identifying

actions that are merely suitable for their goals (Ranyard & Crozier 1997). He

proposed the alternative normative principle of satisfying: take the first course of

action that is satisfactory on all important aspects. This principle, he further argued,

could be applied without sophisticated powers of discrimination and evaluation,

powers that humans do not possess. Based on this. he developed his novel concept of

bounded rationality, which encompasses the basic assumption that rationality is

relative to the information processing capacities of the decision maker.

SEU as a model describes how people actually make decisions did not receive

unambiguous support from empirical studies. Edwards and Tversky 1967 summarized

the early research as follows: these studies generally show consistent, orderly, rational

performance; the SEU model is clearly wrong in detail; certain invariances, that

should exist if it were right, do not exist (Ranyard & Crozier 1997). One approach to


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reducing discrepancies between behavior and the SEU theory has been to develop

fresh theories that provide a better account of some of these findings.

3.3.2 Differentiation and Consolidation (Diff Con Theory)

In this theory the decision making process is modeled as one in which a choice optionis gradually differentiated from other alternatives, until it is judged sufficiently

superior in attractiveness to be chosen. The theory not only focuses on this pre-choice

differentiation path, but also considers post-decision consolidation processes.

The basic assumption revovles around the concept that the decision makers intended

goal is not only to choose the best option, but to pick the option that will remain the

best option in the post decision future. An option that is not well enough differentiated

from its competitors may result in, for instance, a reversal of preference, or negative

feelings such as uncertainty, regret, or envy. The following table presents an overview

of the theory.

Table 1: Overview of Diff Con Theory

Phase Stage Process

Recognition of

decision problem

Identification of Alternatives

Goal Decision

Perceptual and

cognitiveidentification

Involve elicitation

Goal adaptation

Holistic

differentiation

Differentiation Screening

Editing

Selection of reference and for

preliminary alternative

Process and structural

differentiation

Problem restructuring


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Phase Stage Process

Differentiation

Decisionconsolidation

Post decision consolidation

Implementation of decision

Post implementation

Out come

Post outcome consolidation

Process and structuraldifferentiation

Problem restructuring

(Ranyard & Crozier 1997)

According to Differentiation and Consolidation Theory, a decision process starts with

identifying decision alternatives, attributes and goals. Once the decision problem is

defined, various types of differentiation processes may take place.

Table above presents phases in Differentiation and Consolidation Theory Decision

makers start with screening the available options, and attempt to eliminate options

that do not qualify for further consideration.

3.3.2.1 Holistic differentiation

A quick holistic process may select a initial choice of alternatives as it comprises of

a quick classification, which occurs automatically, naturally, and thus beyond the

decision makers awareness. Differentiation criteria may stem from, for instance,

experience with similar decisions, intuitive use of schemas, comparison with an

exemplar or prototype, or explicit demands or restrictions in the decision context.

(Beach 1990). Holistic differentiation may thus be enough for making a decision, lead

to a set of options that deserve further deliberation , endowing alternative for

preliminary choice that is further put under a scanner or an option that serves as a

reference in the process that ensues.

At low levels of any type of involvement, decision makers are liable to engage in

holistic differentiation, which provides a differentiated representation of the decision

problem at relatively low cognitive costs. Low involvement information processing


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typically comprises the use of readily available category-based judgments simple

heuristics, or habit-based responses. Such processing results in quick categorizations

of choice options, or leads to an immediate choice (Ranyard & Crozier 1997).

It can be argued that holistic differentiation is not restricted to low involvement

contexts. In high value-relevant involvement decisions, the use of holistic

differentiation can also be taken into account . Activated values may provide cues that

allow for quick categorization processes, which characterize holistic differentiation,

by evoking schemas.

The occurrence of holistic differentiation under conditions of high impression and

relevant involvement depends on the type of evaluation context that is expected.

When the decision maker expects to face an audience with unknown standpoints,

taking a moderate, defensible decision is the optimal strategy. In that case holistic

differentiation is less likely. On the other hand, when the decision maker knows how

he or she will be evaluated, this criterion can be used in a holistic differentiation

process, leading to the categorization of alternatives, and a (preliminary) choice.

3.3.2.2 Process Differentiation

The second type of differentiation is process differentiation. Given that a decision

maker has a set of options, further differentiation may be accomplished by processing

information about the alternatives. Such information may be drawn from memory, or

acquired extrinsically. In this phase one alternative is gradually differentiated so as to

become the chosen one.

Various processing strategies may be used, which is referred to as decision rule

differentiation. Application of decision rules creates information about the degree of

superiority of one alternative over another. A large variety of decision rules have been

described in the decision making literature. Some rules result in elimination of

alternatives early in the selection process.

3.3.2.3 Conjunctive and disjunctive rule of differentiation

According to the conjunctive rule, all alternatives that do not meet a given criterion

level on an attribute are eliminated from the choice set. Some rules require criteria to


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be set by the decision makers, for example criteria of rejection in the conjunctive rule,

or criteria of acceptance in the disjunctive rule.

In the disjunctive rule an alternative is chosen that exceeds a certain criterion level of

attractiveness on an attribute. Such criteria may be varied during the process of

applying a rule, and may thus be used as a tool in accomplishing sufficient

differentiation, which is known as criterion differentiation.

3.3.2.4 Weighted Additive Rule

In weighted additive rule, pros and cons of alternatives are weighted by the

importance of the attributes, and the option is chosen that has the most favourable

weighted score. In general, rules that allow decision makers to make trade-offs

between attribute values (compensatory strategies) are cognitively more effortful than

rules that do not allow compensation, such as the conjunctive rule. Decision strategies

typically comprise combinations of rules, which are often utilized in a bottom-up,

constructive and adaptive fashion (Payne et al. 1992).

Generally, when involvement in a decision is relatively low, there is a greater

probability of simple decision rules. These rules are non compensatory, requiring little

mental gymnastics, and may yet result in a satisfactory decision, given that accuracy

motives are not strongly present (ibid). In high value-relevant involvement decisions,

effort is expended on mapping the decision makers values and goals on the problem.

As long as this mapping is not clear, acceptance criteria (e.g., in a conjunctive rule)

may be relaxed, in order not to drop potentially valuable alternatives. As soon as a

satisfactory mapping is accomplished, values may determine the strictness of the cut-

off criteria, which are used to maintain or reject alternatives.

There are other aspects of process differentiation that might be influenced by

involvement. Process differentiation comprises consideration of information about

choice options. This information may be available in memory, or may have to beacquired. In either case, value-relevant involvement in particular may affect the

search for information.


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3.3.2.5 Structural Rule of Differentiation

In contrast to most decision-theoretic approaches, the interpretation of decision rules

in Differentiation and Consolidation theory not only acknowledges the rules power to

select one alternative as superior, it views decision rules as tools to establish the

degree of superiority of one alternative over others. For instance, the conjunctive rule

provides information about how far from the pass-fail criterion alternatives are.

Decision rules and criterion differentiation are processes that ultimately differentiate

one option sufficiently from its competitors. In conjunction with the application of

decision rules, changes in the representation of the decision problem also take place.

This is known as structural differentiation. Structural differentiation refers to changes

in ones representation of the choice problem. Structural differentiations are of four

types, i.e., restructuring of (1) attractiveness, (2) attribute importance, (3) theinterpretations of facts and (4) the decision problem.

Attractiveness restructuring comprises revision of attractiveness of attribute values of

options. Also, a decision maker may ascribe different degrees of importance to

attributes so as to support a preliminary choice and is denoted as importance

restructuring. Facts may be differently interpreted, reinterpreted, or misinterpreted

during the decision process, i.e., facts restructuring. This may especially occur to the

extent that facts are uncertain, or information is ambiguous. Finally, the decision

problem as such may be changed into problem restructuring. For instance, in

unstructured decision contexts one may look for new alternatives in addition to

evaluating present options (Ranyard and Crozier 1997).

Structural differentiation mechanisms, and in particular attractiveness and attribute

importance restructuring, are related to decision rule and criterion differentiation. Like

the latter processes, the goal of structural differentiation is to achieve sufficient

differentiation such that one alternative can be chosen. Once a decision is made, Diff

Con Theory postulates that differentiation mechanisms continue to operate, which is

referred to as consolidation of the decision. After a choice is made, differentiation of

the chosen option and its competitors is further increased, so as to minimize the

occurrence of regret or cognitive dissonance (Festinger 1957).


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Consolidation comprises many of the differentiation principles that were described for

the pre-decision phase. Thus, once a decision has been made, the decision maker may

also engage in holistic, process and structural differentiation processes. For instance,

attractiveness restructuring may take place by increasing the attractiveness difference

between the chosen and non-chosen alternative on one or more attributes.

Structural differentiation refers to changes in representations of decision problems,

which may concern attractiveness of aspects, attribute importance, facts, or the

problem as such. Because the representations of decision alternatives are contingent

on the decision makers goals, representations are likely to be modeled according to

goals that are elicited by the three involvement types. Involvement type will be related

to aspect attractiveness restructuring. The involvement type-related goals (adhering to

a value, accuracy, and eliciting an appropriate impression, respectively) provide theframe of reference for attractiveness judgments, and thus for attractiveness

restructuring.

The same holds for attribute importance restructuring. The involvement-related goal

structures may determine which attributes are used in a process of restructuring, for

instance in changing relative importance of an attribute that makes a preliminary

chosen alternative more attractive. Facts restructuring is likely at very high levels of

involvement, in particular in high value-relevant involvement decisions. Strong value-

driven motives to support an alternative may be backed up by a biased interpretation

of reality.

Some decision problems bring about problem restructuring resulting in alternative

ways of representing the decision problem, for instance by finding new attributes,

creating new alternatives, or modifying old ones. This kind of fundamental

restructuring may bolster a preliminary choice, which is likely in high value-relevant

involvement decisions. It may also lead to a completely new representation, one that

produces a new choice candidate. This situation may be expected at very high levels

of outcome-relevant involvement decisions, when the decision maker is unable to

solve the problem satisfactorily according to the course of action followed so far. In

that case a relatively objective and creative fresh look at the problem is needed.


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3.4 DECISION MAKING PROBLEMATICS

Decision science is a very broad and rapidly evolving research field at theoretical and

practical levels. The post-war technological advances in combination with the

establishment of operations research as a sound approach to decision making

problems, created a new context for addressing real-world problems through

integrated, flexible and realistic methodological approaches. At the same time, the

range of problems that can be addressed efficiently has also been extended. The

nature of these problems is widely diversified in terms of their complexity, the type of

solutions that should be investigated, as well as the methodological approaches that

can be used to address them.

Providing a full categorization of the decision making problems on the basis of the

above issues is a difficult task depending upon the scope of the categorization. Arather straight forward approach is to define the two following categories of decision

making problems.

Discrete problems involving the examination of a discrete set of alternatives on which

each alternative is described along some attributes. Within the decision making

context these attributes have the form of evaluation criteria..

3.4.1 The Classification Problem

As already mentioned classification refers to the assignment of a finite set of alternatives into predefined groups; this is a general description. There are several

more specific terms often used to refer to this form of decision making problem. The

most common ones are the following three:

Discrimination

Classification

Sorting

The first two terms are commonly used by statisticians as well as by scientists of the

artificial intelligence field (neural networks, machine learning, etc.). The term sorting

has been established by MCDA researchers. Although all three terms refer to the

assignment of a set of alternatives into predefined groups, there is notable difference

to the kinds of problems that they describe.


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In particular, from the methodological point of view the above three terms describe

two different kinds of problems. The terms discrimination and classification refer to

problems where the groups are defined in a nominal way. In this case the alternatives

belonging into different groups have different characteristics, without being possible

to establish any kind of preference relation between them (i.e., the groups provide adescription of the alternatives without any further information).

3.4.2 Uncertainities in Decision Making

Planning is enmeshed with complexity and uncertainty and risk. It begins with the

dilemmas inherent in planning and depicts the complex intergovernmental system as

the medium of planning. (Christensen 1999). Planning is a type of public decision

making and is the process of devising set of actions towards better future for some

public purpose. But how can we know the future so that a good decision could be

made? Thus the dilemmas in planning public projects still exist. Not much research

has so far been done on uncertainty and risk in planning of development projects

which could help to guide studies in this field and assert validity of findings (Samset

1998).

In the absence of relevant research to guide uncertainty of development project, the

possibilities of actual finding of uncertainty factors affecting the projects are also

minimal. But the general uncertainty factors characterized in different ways: to what

extent they were contextual or operational, to what extent they could have been

predicted or not, and to which professional fields they were associated, the financial/

economic, environmental, institutional, political, socio cultural or technological

fields.

Uncertainty, as currently defined, is situations where negative or adverse outcomes,

like losses, accidents, health injuries, or deaths cannot be ruled out. We dont know

that they will happen, but neither can we guarantee that they will not. Inspired by risk

analysts and formal decision making models, we often hope to go a step further andestimate the probabilities of the adverse outcome. In fact, some decision theorists

distinguish between risky prospects, where the probabilities associated with the

possible outcomes are assumed to be known and uncertain prospects, where the

probabilities are not established (Tversky and Fox 1995 cited in Samset 1998).


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Risky decision making is full of so-called biases, that is, deviations of actual behavior

from normative models. One of the most influential biases in decision making is the

preference reversal phenomenon, which refers to the finding that preferences are not

invariant with regard to either the procedures that are used to elicit them or to

alternative problem descriptions. The framing effect is one such bias. In framingexperiments, the description of formally identical problems is varied, thus

highlighting different aspects of them. Typically, valued objects are used and are

presented so as to highlight either their gains or losses.

3.5 ROLE OF GOVERNMENT, PLANNING AND LEGISLATION

Local Self Governance Act, 1999LSGA 2055 (1999) provides the administrative structure for the devolution of

responsibility and empowerment of local community through enhanced localgovernment democracy, and accountability. The arrangements made are listed as;

1. Decentralized municipal planning process should based on the bottom up approach

starting from community level and basically ending at municipal council level.

2. All key development actors- municipalities, local people/communities, NGOs, user

groups, line agencies, DDC, etc., are to be involved in the municipal planning

process.

3. Provision has been made to utilize the expert views/ideas through an advisorycommittee consisting of local intellectuals, experts, etc.

4. Arrangement has also been made for maintaining close coordination between and

among municipalities, government agencies, NGOs and INGOs / donors at local

level in the process of plan formulation mainly to remove duplication of effort.

Town Development Committees (TDCs) have to formulate their plans in

consultation with municipalities.

5. the municipality members are to be accountable to the municipal council - the

highest policy making municipal body - and the actions undertaken and decisions

made by the municipality members (including mayors and deputy mayors) can be

closely scrutinized in its meetings.

6. the local communities, NGOs, user groups etc., as stated earlier, are to be deeply

involved in the planning process;


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3.6 MULTI CRITERIA ANALYSIS

MCDA is an advanced field of operations research providing several advantages from

the research and practical points of view. At the research level, it provides excess of

methodological approaches for addressing a variety of decision making situations.

Many of these approaches are well-suited to the nature of the classification problem.

The major characteristic shared by all MCDA classification approaches is their focus

on the modeling and addressing of sorting problems. This form of classification

problems is of major interest within a decision making context, given that the concept

of preference lies in the core of every real-world decision. Except for the MCDA

approach, the research made in other fields on considering the special features of the

sorting problems is still quite limited. This characteristic of MCDA can be considered

as a significant advantage within a decision making context. (Doumpous 2002).

3.6.1 Brief History of Multi Criteria Analysis

Even from the early years of mankind, decision making has been a multidimensional

process. Traditionally, this process has been based on empirical approaches rather

than on sound quantitative analysis techniques. (Pareto 1896 cited in Doumpous

2002) first set the basis for addressing decision-problems in the presence of multiple

criteria. One of the most important results of Pareto research was the introduction of

the efficiency concept.

During the 1940s and the 1950s Von Neumann and Mor genstern (1947) introduced

the utility theory, one of the major methodological streams of modern MCDA and

decision science in general. These were all studies from US operations researchers.

By the end of the 1960s, MCDA attracted the interest of European operations

researchers too. (Roy 1968 cited in Doumpous 2002), one of the pioneers in this field,

introduced the outranking relation approach; he is considered as the founder of the

European school of MCDA. During the next two decades (1970; 1990) MCDA

evolved both at the theoretical and practical levels.

3.6.2 Basic Concept

Multi Criteria Analysis (MCA) has been developed to introduce multiple inter-

disciplinary aspects to the planning process in this complex decision environment that

are characterized by any mixture of monetary and non-monetary objectives. Multi-

criteria analysis is a tool for decision aid and a mathematical tool to compare different


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alternatives or scenarios against several often conflicting criteria in order to guide the

decision-maker(s) to a rational judgment. (Roy 1985 cited in Department of

Transport, Local Government and the Regions (DTLR) 2001).

Its technique is to a single most preferred option, to rank options and to shortlist a

limited number of options for subsequent detailed appraisal, or simply to distinguish

acceptable from unacceptable possibilities ( DTLR 2001).

The logic of decision making, on which the theoretic approach of MCA is based, lies

in the comparison of the alternative solutions performance in relation with a

consistent set of decision criteria. The evaluation is based on the specification of

particular goals, a structured description of the possible alternative solutions, via

cardinal characteristics, and the comparison of these alternatives.

The final result consists in the visualization of the one optimal solution or a ranking of

possible alternative solutions in relation to their fulfillment of the pre-set goals. The

important component in any decision making is risk and uncertainty. There are many

ways in which risk can be handled in MCA so MCA approach would be broadly

applicable across the range of government and concerned institutions to make policy

recommendation for the sustainable infrastructural development.

MCA has a different philosophical perspective because in real life, a decision is

seldom made on the basis of one criterion, but MCA could be useful for several

criteria at the same time. Therefore decision makers would remain more in control of

the decision environment reducing uncertainty and risk in complex projects. The

oldest field in MCA is so called Multi Criteria Decision Making (MCDM) and it

involves in searching for an alternative that is the most attractive over all the criteria.

MCDA methodology synthesizes the matrix information and ranks the alternatives by

different means. Different methods require diverse types of value information and

follow various optimization algorithms. Some techniques rank options, some identify

a single optimal alternative, some provide an incomplete ranking, and others

differentiate between acceptable and unacceptable alternatives. There are number of

MCDA methods.

Multiattribute Utility Theory (MAUT),


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Analytical Hierarchy Process (AHP)

Weighted Table Method (WT)

MAUT relies on the assumptions that the decision maker is rational (preferring more

utility to less utility, for example), that the decision maker has perfect knowledge, and

that the decision maker is consistent in his or her judgments. The goal of decision

makers in this process is to maximize utility or value. Because poor scores on criteria

can be compensated for by high scores on other criteria, MAUT is part of a group of

MCDA techniques known as compensatory methods. This evaluation method is

suitable for complex decision with multiple criteria and many alternatives. But it is

typically used when quantitative information is known of each of the alternative. (

Doumpous 2002).

3.6.2.1 Analytical Hierarchy ProcessSimilar to MAUT, AHP aggregates various facets of the decision problem using a

single optimization function known as the objective function. The goal of AHP is to

select the alternative that results in the greatest value of the objective function. Like

MAUT, AHP is a compensatory optimization approach. However, rather than utility

and weighting functions, AHP uses pair-wise comparisons of decision criteria to elicit

decision makers values. All individual criteria are paired against all others, and the

results are compiled in matrix form.

Saaty (1980) first proposed the AHP method (Analytic Hierarchy Process) for

addressing complex decision making problem involving multiple criteria. The method

is particularly well suited for problems where the evaluation criteria can be organized

in a hierarchical way into sub-criteria. During the last two decades the method has

become very popular, among operations researchers and decision scientists, mainly in

USA. At the same time, ever, it has been heavily criticized for some major theoretical

shortcomings involving its operation.

The decision maker uses a numerical scale to compare the choices, and AHP moves

systematically through all pair-wise comparisons of criteria and alternatives. AHP

thus relies on the supposition that humans are more capable of making relative

judgments than absolute judgments. Consequently, the rationality assumption in AHP


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is more relaxed than in MAUT and methodological weaknesses of these methods have

been subject to multiple reviews.

AHP models a decision making problem through a process involving four stages:

Stage 1: Hierarchical structuring of the problem. Stage 2: Data input.

Stage 3: Estimation of the relative weights of the evaluation criteria.

Stage 4: Combination of the relative weights to perform an overall evaluation of

the alternatives (aggregation of criteria).

In the first stage the decision maker defines a hierarchical structure representing the

problem at hand. The top level of the hierarchy considers the general objective of the

problem. The second level includes all the evaluation criteria. Each criterion is

analyzed in the subsequent levels into sub-criteria. Finally, the last level of the

hierarchy involves the objects to be evaluated. Within the context of a classification

problem the elements of the final level of the hierarchy represent the choices (groups)

available to the decision maker regarding the classification of the alternatives.

Once the hierarchy of the problem is defined, in the second stage of the method the

decision maker performs pair wise comparisons of all elements at each level of the

hierarchy. Each of these comparisons is performed on the basis of the elements of the proceeding level of the hierarchy. In the second level, all elements (evaluation

criteria) are compared in a pair wise way on the basis of the objective of the problem

(first level of the hierarchy). Then, the sub-criteria of the third level are compared

each time from a different point of view considering each criterion of the second level

of the hierarchy.

For instance, the sub-criteria and are initially compared on the basis of the criterion

then on the basis of criterion etc. The same process is continued until all elements of

the hierarchy are compared. The objective of all these comparisons is to assess the

relative significance of all elements of the hierarchy in making the final decision

according to the initial objective.

For a classification problem the global evaluation for the elements in the last level of

the hierarchy are used to decide upon the classification of an alternative. Since the


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elements of the last level correspond to the pre specified groups, an alternative is

assigned to the group for which the evaluation of the corresponding element is higher.

The application of AHP in different sectors of planning and their corresponding dates

were listed as:

Table 2: Uses of AHP at different sectors of planning

SR .

NO .

YEAR AUTHOR /S APPLICATION

AREAS

O THER TOOL /S USED

1 1990 Arbel A, Orger Y E Banking

2 1992

Benjamin C O, Ehie I C,

Omurtag Y Education

Linear goal

programming

3 2003 Chen S J, Lin L, Industry

4 2002 Crary M et al. Government

Mixed integer

programming

5 1990 Ehie I C et al. Banking

6 1993 Ehie I C Benjamin C O Social

Linear goal

programming

7 1998 Kim J Engineering

8 1994

Ko S K, Fontane D G,

Margeta J Social

Linear programming,

andepsivj;

9 2001

Korpela J, Lehmusvaara A,

Tuominen M Engineering constraint method

10 1999 Lee M et al Industry

11 1999 Lee C W, Kwak N K Social Goal programming

12 1999 Momoh J A, Zhu J Engineering

13 1998 Radasch D K, Kwak N K Engineering Goal programming

14 2003 Su J C Y et al Engineering

15 1999

Weistroffer H R, Wooldridge

B E, Singh R Government

16 1991 Wu J A, Wu N L Personal

17 2003 Yang T, Kuo C Industry

18 1997 Zulch G et al. Engineering

Source: Vaidhya, O.S., 2004


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Figure 2: Analytical Hierarchy Process (AHP) for UEIP towns

3.6.2.2 Weighted Table

Multi criteria Weighted table or weighted summation is a form of MCA and is oftenreferred to as additive weighting. Weighted summation is matrix-based and is

considered as the simplest MCA technique to understand and traditional. Alternatives

are scored for each comparison criteria and then an importance weight is applied to

each criterion. Criteria are weighted because they are not equally valued by

individuals; what is important to one person is not always to another. Therefore, a

range of weights for criteria could also be produced based upon different interest

group concerns (Buselich, 2002 cited in WAN 2005).


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4. BRIEF INTRODUCTION OF UEIP

The conception of UEIP was initiated to address the critical urban environmental

issues of towns in Nepal. For this a memorandum of understanding was signed in

between the Ministry of Physical Planning and Works (MPPW) and the AsianDevelopment Bank (ADB) on November 5, 1999. The project includes nine towns

comprising eight Municipalities (Banepa, Dhulikhel, Panauti, Bharatpur, Ratnanagar,

Hetauda, Bidur and Kamalamai) and the town of Dhadingbesi (Nilkantha Village

Development Committee, VDC) and nearby areas. It is intended to facilitate

sustainable urban development in selected towns by giving priorities in:

1. The provision of environmental infrastructure improvements to secondary urban

centers;

2. Poverty alleviation;

3. Decentralization of authority; and

4. Strengthening local municipal institutional capabilities, especially as they relate to

the recently passed Local Self-Governance Act (LSGA).

5. Generation of productive employment opportunities and increased incomes

resulting from faster, broad-based economic growth;

6. Equitable improvements in basic social services to enhance human development,

resulting in reduction of population growth; and

7. Protection and improvement of the environment to sustain gains.


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Map 1: Strategic Location UEIP Project

4.1 OVERALL GOALS AND OBJECTIVES

The project is designed to achieve the goals which were committed by HMGs (Now

Nepal Government) in ninth plan (1997-2002), the local self governance act, 1999,

and subsequent legislation listed as:

reducing levels of poverty as defined by the number of persons living at or below

the official poverty line;

to aim for greater devolution of responsibility and ownership by urban

municipalities, and community involvement and participation in the planning,

implementation and maintenance of urban infrastructure; to take action to redress gender imbalances within selected municipalities;

to improve the environmental situation and awareness levels within the

municipalities;

to improve levels of urban infrastructure and facilities, and levels of service within

these municipalities; and

Ratnanag

Bharatp

Central

Excluded Towns

Selected Towns

Ka malam

Janakpur

Birgunj

Butwa

Pokhara

Dhading BesiBidhur

Ktm. valley

BanepaPanauti

Dhulikhel

Hetau

Bhimeshwor


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to mitigate the increasing trend of migration to the city of Kathmandu by

stimulating growth in secondary towns having linkage to Kathmandu.

Specifically, the Project's main objectives are to:

Support the Government's policies on decentralization through specific

institutional strengthening and capacity building components targeted at central

and town levels.

Provide support to the UEIP Towns in improving municipal and financial

management, participatory town planning, revenue mobilization and provision of

municipal services.

Improve the environmental conditions and access to municipal services in the

UEIP Towns by means of environmental infrastructure improvements, which

benefit all sectors of the community including the urban poor.

Alleviate urban poverty through specific targeted interventions, such as promoting

improved representation of disadvantaged groups at ward and municipal levels,

skills training and health education programmes, sanitation assistance and micro-

credit enterprise programmes, combined with capacity building of local NGOs and

CBOs to enable them to assist with implementation of the programmes.

Promote improved urban planning and land development practices in the UEIP

Towns and, through land pooling or guided land development solutions, restrain

urban sprawl, congestion and ribbon development, promote public open spaces,

and preserve cultural heritage.

Promote private sector participation in the UEIP Towns by providing institutional

and financial support for the development of public-private partnerships for

municipal services and other revenue generating enterprises.

Improve community health and environmental awareness by providing, through

local NGOs and CBOs, campaigns which are integrated with the actual

infrastructure improvements, to educate the general public on the proper use of

project facilities, the health aspects of proper sanitation and waste disposal and the

need to impose user fees. Special programmes will be targeted at primary schools.

Make the UEIP Towns better places for people to live and work in. The Project

should therefore contribute to reducing the trend of migration to the Kathmandu

Valley.


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5. ANALYSIS

5.1 SELECTION OF UEIP TOWNS

The counterpart professionals selected the project towns accordingly as the strategy to

develop small towns outside Kathmandu valley to help reduce migration within the

valley (UEIP, 2001). During that period, the professionals sought guidance from

Director General of DUDBC regarding the inclusion of more strategic towns on the

east west corridor and border access routes.

Initially, the counter part professionals were suggested to select towns as towns on

the "inner" circle (former names in parentheses) included Kirtipur, Madhyapur

(Thimi), Banepa, Dhulikhel, Panauti, Bhimeshwor (Dolkha - Charikot), Bidur,

Dhadingbesi, Byas (Damauli), Prithivinarayan (Ghorkha), Bharatpur, Ratnanagar

(Tadi), Hetauda and Kamalamai (Sindhulimadi). Towns on the "outer" circle

included Pokhara, Butwal, Birganj and Janakpur for reconnaissance survey. Out of

these 18 selected towns ADB advised the counterpart professionals to exclude the

valley towns. Kirtipur and Madhyapur and the towns Vyas and Prithivi Narayan were

excluded from the professional on the ground of low strategic significance (UEIP,

2000). The selected towns were listed as:

Table 3: Selected Towns for UEIP

S.N O NAME OF T OWNS R EMARKS

1 Bharatpur

2 Hetauda

3 Ratnanagar (Tadi)

4 Kamalamai (Sindhuli Madi)

5 Dhulikhel

6 Banepa

7 Panauti

8 Bidhur

9 *Dhading Besi * Dhading Besi is not a municipality

Source : UEIP 2000


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Note: the recent decision was made for the exclusion of the Dhading Besi from the

project list for not being a municipality. Thus the fund allocated for this project is

transferred to the up Grading of Bishnumati link road.

5.1.1 Frame Work Assessment

Since the objective of the project was to grant loan on nine towns, and the project wasintended to support the decentralization policy through urban improvement, the

framework assessment was based on the developing the criteria for the best selection

of towns. The criteria adopted for analysis were considered from UEIP, Inception

Report 2000 because the selction of UEIP towns were based on these criteria as

reported by counterpart professionals. The MCA and generation of score were done

basically by two methods, with weighted table (WT) and the Analytical Hierarchy

Process (AHP) method.

The frameworks consist of three criteria and ten sub-criteria for selection of

alternatives. The generated information in isolation was integrated with the scoring

system making comparatively easier to judge, looking at the composite indicator

value on alternatives whether the ranking under consideration according to MCA or

not. Further on the score were distributed in four situations very high (VH), high (H),

okay (Ok) and not much (NM) except for institutional capacity. The distribution

system used for institutional system were taken as Very Strong (VS), Strong (S),

Moderate (M) and Weak (W) because for institutional capacity the weakest one isgiven highest priority.

Table 4: Criteria for selection of UEIP Towns.

1. Institutional Capacity (10)

2. Opportunities of Physical Planning (10)

A. Institutional /

technical (30)

3. Degree of Participation (10)

4. Urban Growth Rate (10)

5. Economic Potential (10)

6. Linkage with Kathmandu (10)B. Economical (40)

7. Proximity to Kathmandu (10)

8. Commitment of Town leaders (10)

9. Urban Environment View Point (10)

G O A L

C. Social (30)

10. Willingness of People (10) A L T E R N A T I V E S


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5.1.2 Preparation of Survey Tools

Based on the criteria identified, a matrix chart was developed against short listed

towns and the criteria built therein. The counterpart professionals were requested to

fill up the matrix (See annexure for the sample of the matrix). Also these professional

were requested to mark the relative importance of the criteria to each other.

Analysis of the information was condu

Utlity of Multi Criteria Analysis in Large Infrastructure Development Project

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