A goal programming capital budgeting model under ... A goal programming capital budgeting model

download A goal programming capital budgeting model under ... A goal programming capital budgeting model

of 11

  • date post

  • Category


  • view

  • download


Embed Size (px)

Transcript of A goal programming capital budgeting model under ... A goal programming capital budgeting model

  • Scientia Iranica E (2018) 25(2), 841{851

    Sharif University of TechnologyScientia Iranica

    Transactions E: Industrial Engineeringhttp://scientiairanica.sharif.edu

    A goal programming capital budgeting model underuncertainty in construction industry

    S. Etemadi, H. Koosha, and M. SalariDepartment of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, P.O. Box 91775-1111,Iran.

    Received 13 February 2016; received in revised form 4 December 2016; accepted 19 December 2016

    KEYWORDSCapital budgeting;Time horizon model;Goal programming;Fuzzy AnalyticHierarchy Process(FAHP);Construction industry.

    Abstract. Due to the increase of investments in construction projects and the lack ofpractical models in this area, developing new practical models is essential. In this paper,researchers suggest a new model in which (1) Its assumptions are adopted based on the realworld, (2) Goal programming is used because of the soft nature of the budget constraints,and (3) Risk of variations in cash ows is considered. The presented model chooses themost protable portfolio of projects and determines their respective nancing resources,area under construction, and pre-sale and sale amounts for each period, such that thecumulative cash ow at the end of the time horizon is maximized. The Fuzzy AnalyticHierarchy Process (FAHP) is used to determine the weight of the objectives. The exactsolution to the model is obtained using the ILOG CPLEX software. The presented solutionseems ecient since it yields very small elapsed times to solve exactly the real-world-sizedproblems. Also, the sensitivity analysis is performed and the results are deliberately studiedand analyzed. Parameters, such as pre-sale prices, mean, and variance of the sale price andconstruction costs, are among the highly sensitive parameters. 2018 Sharif University of Technology. All rights reserved.

    1. Introduction

    The construction industry is one of the most impor-tant industries in any country, since it absorbs vastinvestments and is one of the substantial needs ofthe community. Nevertheless, this industry suersfrom huge amounts of capital wastes caused by poorand inecient capital budgeting [1]. Compared toother industries, the absence of nancial control andweak management has led to a set of undesirable andirreversible results. In addition, the bankruptcy levelis signicantly high which can be alleviated with a

    *. Corresponding author.E-mail addresses: se et536@alumni.um.ac.ir (S. Etemadi);koosha@um.ac.ir (H. Koosha); msalari@ferdowsi.um.ac.ir(M. Salari)

    doi: 10.24200/sci.2017.4436

    better cash ow management procedure [2]. Cash owanalysis of construction projects is an important issuein construction industry [3]. Therefore, this paperis dedicated to the capital budgeting of constructioncompanies due to the lack of applicable models in thisarea.

    This research develops a model, which helpsdecision-makers in construction industry to select themost protable investment opportunity from a poolof available opportunities and determine the propernancial resources at the right time. The data froma real construction company is used in order for themodel to be more adaptive to the real world and to beable to quench the industry's expectations.

    Capital budgeting is a process that allocateslimited nancial resources to the available projectsand aims at maximizing the return on investment [4].Capital budgeting is a major problem that managesthe nancial resources. This is usually a long-term

  • 842 S. Etemadi et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 841{851

    problem involving risk complications and needs sig-nicant amounts of investment. The majority ofcapital budgeting decisions are irreversible. Therefore,applying proper capital budgeting models, leading intothe right decisions on choosing the right projects at theright time, is crucial to the survival of the companiesin their uncertain environment [5].

    The rst research on capital budgeting was re-ported by Lorie and Savage [6] with the goal ofmaximizing the Net Present Value (NPV). Weingart-ner [7] presented a mathematical programming modelfor the capital budgeting problem that maximizescumulative cash at the end of the time horizon, knownas horizon models. Unlike the NPV maximizationapproach, the model developed by Weingartner consid-ers dierent nancial resources with dierent interestrates and transfers each period's unused budgets toits next period. In addition, the consideration ofpossible loaning, borrowing, and dierent nancingapproaches is amongst the noteworthy characteristicsof this model. Thus, it is very well adapting to thereal-world situations; therefore, modeling the capitalbudgeting problem through the maximization of thecumulative cash at the end of a pre-specied timehorizon is preferred to the maximization of the NPV [8].

    However, Weingartner's time horizon model facessome critical issues. In particular, the presence of thehard budget constraints in the model is one of theseissues. Considering each period's budget constraintas an internal functional constraint imposed by themanagement is more realistic than considering it asan external constraint imposed by the market [9].Thus, one can consider the budget constraints assoft constraints and minimize the deviation from theideal. To this end, goal programming is the mostcommon and reputable method already suggested forsuch situations.

    As in the literature, all the following researchershave presented their models: Taylor and Keown [10] forpolice department budgeting, Lawrence and Reeves [11]for an insurance company, Mukherjee and Bera [12] forIndia coal industry, and Thizy et al. [13] for a Canadiantelecommunication company. In addition, Badri etal. [14] presented a binary goal-programming model forproject selection in a healthcare institute. Vashishthet al. [15] proposed a binary goal programming modelfor the capital budgeting of a hospital. None of theaforementioned studies considers the loaning, borrow-ing, and dierent nancing approaches with dierentinterest rate options.

    Goal programming model along with the FuzzyAnalytic Hierarchy Process (FAHP) is a supple toolto accomplish the goals under various constraints [16].Hence, neither of them is adequate for considering thenumerical and non-numerical information alone [17].The FAHP prepares a relatively mature explanation of

    the decision process, including personal and imprecisejudgments of the decision-makers [18]. Chang [19]presented the Extent Analysis Method for the FAHPin which triangular fuzzy numbers for pairwise com-parisons are used. Other methods as Modied DigitalLogic (MDL) and Fuzzy Decision-Making Trial andEvaluation Laboratory (FDEMATEL) are used to pri-oritize the importance of various criteria. Chaghooshiet al. [20] proposed the decision-making process ofselecting the most suitable managers for projects usingFDEMATEL method. Rathi et al. [21] developeda project selection approach to determining properSix Sigma projects in automotive companies usingthe MDL method. Ghazimoradi et al. [22] proposeda model based on neural networks to anticipate thesuccess of construction projects depending on thelevel of realization of success factors during the initialphase of a project. Tang and Chang [23] applied thegoal programming and FAHP to a capital budgetingproblem in a small car renting company. However,their model does not utilize the Weingartner's horizonmodel along with the cash ow of the investmentopportunities as the criteria for optimal investmentopportunity selection.

    Another critical issue with the Weingartner'smodel is that it ignores risk and uncertainty. Thoughrisk management and capital budgeting often needto be considered jointly [24], none of the aforemen-tioned studies considers the uncertainty and risk of theprojects seldom inevitable, especially in investment-type projects.

    The main goal of the portfolio optimization isto help the investor optimally allocate nancial re-sources to the investment opportunities, such thatthe risk and uncertainty parameters are accountedfor [25]. Risk and uncertainty can be considered asa Markowitz mean-variance model [26], Capital AssetPricing Model (CAPM) [27,28,29], and Value at Risk(VaR) model [30]. Su and Huang [31] proposed a mean-variance capital budgeting model in which project pa-rameters are regarded as random variables. Babaei etal. [32] formulated the portfolio optimization problemas a multi-objective mixed integer programming inwhich VaR is specied as the risk measure. Beraldi etal. [33] considered Conditional Value at Risk (CVaR) intheir capital budgeting model. Khalili-Damghani andTaghavifard [34] proposed a multi-dimensional knap-sack model for capital budgeting under uncertainty inwhich fuzzy set theory is applied.

    Contrary to the Weingartner's time horizonmodel, in this article, budget constraints are consideredsoft and the capital budgeting problem for constructioncompanies is modeled in the form of goal programming.FAHP is also used to determine the weight of theobjectives.

    In addition, we consider the risk in our model.

  • S. Etemadi et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 841{851 843

    Since the CAPM and VaR models need a number ofdicult-to-estimate parameters in the case of construc-tion companies, they do not seem suitable in this area.Hillier [35] evaluated risky investments by estimatingthe expected values and standard deviations of netcash ows for each alternative investment. Walls [36]dened risk as the standard deviation of returns (i.e.,net present value) of the portfolio of assets. He pointedout that this measure is more precisely dened as astatistical measure of uncertainty. In this study, therisk is considered rather similar to the mean-variancemodel. The only dierence here