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CHAPTER - 2
LITERATURE REVIEW
2.1 Literature Review 17
2.2 Literature survey based on fms layout 19
2.3 Literature survey based on metaheuristics in fms Scheduling 25
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2.1 LITERATURE REVIEW
The problem related to the layout design in FMS is one of the
foremost important issues and this should be resolved suitably at the
beginning of the system design referred from Gunasekaran et al [54]. Of
late, research works have focused on the FMS layout design and FMS
scheduling which have been taken as two separate streams. However,
only a few researchers have highlighted the importance of FMS layout
design with integrated scheduling. Since the hardware used in FMS is
rather expensive, the FMS layout designer should select suitable layouts
duly examining the various alternative layouts. The machine layout
problem implies the arrangement of machines on a factory floor so that
the total time required to transfer material between each pair of
machines is minimized. Apart from time and distance factors, factors
such as handling carrier path, clearance between machines, etc., are to
be taken into account while evaluating the alternative layouts. T. SAWIK
et al [6] presents a multilevel decision model for simultaneous machine
and vehicle scheduling in a flexible manufacturing system. The multilevel
approach proposed for simultaneous machine and vehicle scheduling in
a flexible manufacturing system based on a bi-criterion formulation and
a family of complex dispatching rules, may support decision making in
various FMS environments. Wei Xie et al [7] presents the development
of a novel branch and bound algorithm for the unconstrained minimum
cost rectangular facility layout problem and extensions. They focused on
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continuous facility layout problems and revealed the relation between
continuous facility layout and the Very Large Scale Integrated (VLSI)
module packing problem, for which the shortest path algorithm has been
proposed to decode a sequence-pair representation. X. Li et al [8]
addressed a discrete artificial bee colony with composite mutation
strategies is presented to compensate the defects of the single mutation
scheme which is easy to get into the local best for Partial Flexible
Scheduling System Problem (PFSSP). They propose a discrete artificial
bee colony-based hybrid algorithm, i.e. CDABC, to solve PFSSP (Partial
Flow Shop Scheduling Problems). D. Hajinejad et al [9] proposed
Particle Swarm Optimization (PSO) algorithm for a Flow Shop Sequence
Dependent Group Scheduling (FSDGS) problem, with minimization of
total flow time as the criterion. They revealed that the proposed PSO
algorithm can be used to solve FSDGS problems with different objective
functions such as minimization of total tardiness with minor changes.
They made an assumption based on Group Technology in which, a
separate setup time is not needed for each job of a group. If it needs,
while in processing the desired setup time is supposed to be included.
M. Adel El-Baz et al [10] describes a Genetic Algorithm (GA) to solve the
problem of optimal facilities layout in manufacturing systems design so
that material-handling costs are minimized. They propose an approach
using GAs to solve facility layout problems. The proposed approach
considers different types of manufacturing layout environments. They
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consider the non-polynomial hard (NP-hard) class. Their limitation is
concerned with the problem of complexity which increases exponentially
that depends upon the number of possible machine locations.
2.2 LITERATURE SURVEY BASED ON FMS LAYOUT
In FMS, layout decisions cannot be treated as independent as
they interact with the following decisions:
(i) The number and capacity of the stations;
(ii) The number and capacity of the storage units;
(iii) The material handling system design. Although there are
several studies suggesting a relation between these layout
decisions, there is no comprehensive study to identify the level of
these relationships [6]. Hence, this research area needs due
attention from production researchers.
Afentakis P et al [11] presented a method to solve the physical
layout problems for higher utilization of FMS and studied the impact of
material handling systems on layout design. However, they assumed that
the process planner selects only one route for each part type which is
against the concept of flexibility according to Ying-Chin Ho et al [12].
Since layout decisions in an FMS interact with aggregate
production planning, a study involving the sensitivity of the layout
decision to these would answer the question how to split the design
problem in an FMS. The balanced property is extremely useful in
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developing more efficient models and solution techniques, especially
when this is combined with other special structures, and limited part
flow and storage capacity as per Chittratanawat et al [13]. In addition,
the buffer capacity and location of the storage unit are also important
factors in determining the layout of FMS. Hence, a different approach is
required to integrate storage capacity decisions with layout decisions
highlighted in Hermann J.W et al [14]. The following situations, which
relate to the distance used in layout models, are to be accounted while
designing the layout for FMS discussed in Chuda Basnet et al [15].
a) Line layout problem
b) Loop layout problem
c) Multiple loop problem
d) Layout, storage and MHS selection
e) Optimal system configuration
In the facility layout problem, one considers the physical location
of the non- moving components of an FMS. These non-moving
components like machines, buffer area, automated storage and retrieval
system and the physical layout of the interconnection network that the
AGVs travel. Many FMS facility layout problems are similar to the
problems that are addressed in the general facility layout and location
theory. In this section, however, authors specially focus on facility
layout problems that are typical in an FMS environment.
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Planning problems are long term problems including loading,
grouping, selection of parts for manufacturing in an FMS etc. Most of the
literature is dedicated to FMS planning problems. Resources allocation
problems are the scheduling problems with smaller time horizon. Except
for the heuristic approaches, few authors have worked in this area none
other than Deb et al [16]. The optimization criterion taken into
consideration with regard to facility layout is part travel time. Since
travel time constitutes, next to queuing time and processing time, an
important part of the part lead – time, the minimization of travel time is
an important issue with regard to the logistic performance of an FMS. In
practice, travel time normally shows a deterministic behavior. Queuing
theory is therefore, not used for their analysis. One rather has recourse
to the “standard layout optimization techniques” that have been
developed in the context of general layout problems by Leavary et al
[17].
This fact is illustrated by the studies of Tansel et al [18] and
Hergue et al [19]. In the first study, the optimal location of a central
storage area in an FMS is considered. This storage area can be of two
kinds:
a) It can consist of finitely many discrete unit storage areas
b) It can be continuously located within a given plane
For case (a) the problem can be modelled as a generalized
assignment problem while the problem arising in case (b) can be solved
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by constructing so-called contour sets in a two dimensional plane, as
studied by Francis et al [20]
Shahram Ariafar et al [21] presented a mathematical model for
facility layout in a cellular manufacturing system. It minimizes both
inter-cell and intra-cell material handling costs. A variant of simulated
annealing algorithm is developed to solve the model. They developed
algorithm which executes solutions with better quality and less
computation time in comparison with the benchmarked algorithm.
A. Hadi-Vencheh et al [22] their main objective is to incorporate
qualitative criteria in addition to quantitative criteria for evaluating
facility layout patterns (FLPs). They present a decision-making
methodology based on a simple nonlinear programming model (NLP) and
analytic hierarchy process (AHP).They used a computer-aided layout-
planning tool, spiral is adopted to generate the FLPs, as well as the
quantitative data.
Tai-Yue Wang et al [36] formulated a model solving both inter-
cell and intra-cell facility layout problems for cellular manufacturing
systems which minimizes the total material handling distance on the
shop floor. They presented an Improved simulated annealing algorithm
for the solution of this model.
Hamed Tarkesh et al [45] presented a novel approach to the
facility layout design problem based on multi-agent society where agents’
interactions form the facility layout design. They developed it by
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considering each block of the plant as an agent that must maximize its
utility and that contains both THC and DM’s utility considerations.
S. P. Singh et al [50] focused on current and future trends of
research on facility layout problems based on previous research
including formulations, solution methodologies and development of
various software packages. In this paper, they presented various trends
of facility layout research over the past two decades. Recent facility
layout papers are identified and summarized along with the solution
methodology.
Sadan Kulturel-Konak et al [51] presented the most recent
advancements in designing robust and flexible facilities under
uncertainty. They focused on exploring the way uncertainty is
incorporated in facility design, namely dynamic and stochastic facility
layout problems.
Mirko Ficko et al [52] discussed the design of flexible
manufacturing systems (FMSs) in one or multiple rows. In this regard,
they developed the most favorable number of rows and the sequence of
devices in the individual row by means of genetic algorithms (GAs).
Andrew KUSIAK et al [53], they surveyed facility layout problems
and presented various formulations of the facility layout problem and the
algorithms for solving this problem. Twelve heuristic algorithms are
compared on the basis of their performance with respect to eight test
problems commonly used in the literature. Certain issues related to the
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facility layout problem and some aspects of the machine layout problems
are analyzed. P.Arikaran et al [49], focused on different heuristics for
solving the unequal area facility layout problems. They considered Multi-
objective approaches for developing facility layout software using meta-
heuristics such as simulated annealing (SA), genetic algorithm (GA), ant
colony algorithm (ACO), and concurrent engineering.
Ravi Kothari et al [57], discussed the single row facility layout
problem (SRFLP) which is an NP-hard problem. The authors reviewed
the literature on the SRFLP and commented on its relationship with
other location problems and provided an overview of different
formulations of the problem that appeared in the literature. They
determined exact and heuristic approaches that have been used to solve
SRFLPs, and pointed out research gaps and promising directions for
future research on this problem.
M. Solimanpur et al [100] focused on the single row machine
layout problem in which they assumed different sizes of machines and
the clearance between the machines. The problem is formulated as a 0-1
non-linear mathematical model. They found that the formulated 0-1 non-
linear model is more intractable than the traditional QAP formulation of
facility layout problem.
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2.3 LITERATURE SURVEY BASED ON METAHEURISTICS IN FMS
SCHEDULING
Latest trend of research mainly focused on optimization of various
parameters in industrial management and manufacturing which is
possible through usage of metaheuristics. Betul Yagmahan et al [23]
consider the flow shop scheduling problem with multi-objectives of make
span, total flow time and total machine idle time. Ant colony optimization
(ACO) algorithm is proposed to solve this problem which is known as NP-
hard type. The proposed algorithm is compared with solution
performance obtained by the existing multi-objective heuristics.
Andreas C. Nearchou et al [24] presents a new hybrid simulated
annealing algorithm (hybrid SAA) for solving the flow-shop scheduling
problem (FSSP); an NP-hard scheduling problem with a strong
engineering background with limitation such as prevention is
inadmissible, thus, the operation of each job on a machine requires an
incessant period of time. The hybrid SAA integrates the basic structure of
an SAA together with the features borrowed from the fields of genetic
algorithms (GAs) and local search techniques. The research concluded
that the proposed approach is fast and easily implemented.
Computational results on several public benchmarks of FSSP instances
up to 500 jobs and 20 machines show the effectiveness and the high
quality performance of the approach.
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Marcio M. Soares et al [25] presents the development and use of
genetic algorithm (GA) to MPS problems and they analyzed the
performance of genetic algorithms applied to master production
scheduling problems. The authors have been investigating the
effectiveness of GA to MPS through a comparative study with the
simulated annealing and mixed integer mathematical programming
models. They realized that master production scheduling is still
extremely limited, maybe because this class of scheduling problems
imposes several other restrictions that are not usually present in
traditional shop floor.
Ashkan Ayough et al [26] presented a new model dealing with the
job rotation scheduling problem, which is less studied, focusing on
human characteristics such as boredom. They focused on different
search algorithms, genetic algorithm (GA) and imperialist competitive
algorithm (ICA), designed to conquer the algorithmic complexity of model
and their parameters adjusted using Taguchi’s method.
Shanthi Muthuswamy et al [27] proposed a mathematical
formulation and present a particle swarm optimization (PSO) algorithm.
The solution quality and run time of PSO is compared with a commercial
solver used to solve the mathematical formulation. Their assumption on
scheduling seems to be limitations for their work such as the first batch
processing machine can process a batch of jobs as long as the total size
of all the jobs assigned to a batch does not exceed its capacity. Once the
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jobs are batched, the processing time of the batch on the first machine is
equal to the longest processing job in the batch; processing time of the
batch on the second machine is equal to the sum of processing times of
all the jobs in the batch.
Dar-Li Yang et al [28] considers a single-machine scheduling
problem with both deterioration and learning effects. Their objectives are
to respectively minimize the make span, the total completion times, the
sum of weighted completion times, the sum of the kth power of the job
completion times, the maximum lateness, the total absolute differences
in completion times and the sum of earliness, tardiness and common
due-date penalties.
Yue Xi et al [29] their goal is to minimize total weighted tardiness
on a single machine with sequence-dependent setup time and future
ready time. They proposed two dispatching rules, ATC with ready time
and continuous setup (ATCRCS) and ATC with ready time and separable
setup (ATCRSS).
Moacir Godinho Filho et al [30] reviews the literature regarding
Genetic Algorithms (GAs) applied to flexible manufacturing system (FMS)
scheduling. On the basis of this literature review, a classification system
is proposed that encompasses 6 main dimensions: FMS type, types of
resource constraints, job description, scheduling problem, measure of
performance and solution approach.
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Fardin Ahmadizar et al [31], focussed on the group shop
scheduling (GSS) problem subject to uncertain release dates and
processing times. Their objective is to find a job schedule which
minimizes the total weighted completion time. They consider the
problem based on the chance-constrained programming.
M. Chandrasekaran et al [32] deals with the criterion of make
span minimization for the job shop scheduling of different size problems.
They proposed computational method of artificial immune system
algorithm (AIS) which is used for finding optimal make span values of
different size problems.
Murat Arıkan et al [33] focused on a mixed-integer programming
model which is handled sequentially and solved by a diversification-
strategy-added version of the Hybrid Tabu Search and Simulated
Annealing Algorithm. They tested performance of the algorithm on eight
random-generated problems with different sizes.
P. Paul Pandian et al [34], proposed two kinds of secondary
population, one with set of non-dominated solutions and another with a
set of inferior solutions. They were accrued out of the generation cycles
with different combinations of feeding of solutions from the above said
two secondary populations. Seven different implementation schemes are
designed with the aim of intensifying the convergence and diversification
capabilities of the genetic process of Multi-objective Evolutionary
Algorithm.
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R. Rajesh et al [35] dealt with the balanced allocation of
customers to multiple third party logistics warehouses. They focused on
clustering of customers so as to achieve minimum total resource viz.,
cost or time. Babak Sohrabi et al [37], investigated the performance of
simulated annealing (SA) and genetic algorithm (GA) in preventive part
replacement for the minimum downtime maintenance planning.
Michael W. Trosset et al [38], critically reviewed the Simulated
Annealing found that it involves in rigorous mathematics. They provided
an elementary, self-contained introduction to simulated annealing in
terms of ‘Markov chains’. Krishnan et al [39], proposed a novel hybrid
algorithm based on Scatter Search Algorithm (SSA) and Simulated
Annealing Technique (SAT) which is the first of its kind for solving this
Non-deterministic Polynomial (NP) complete problem. They focused on
the problem of optimal layout in FMS with the objectives of minimizing
the total distance travelled by the AGV and distance of backtrackings
occurred in the AGV scheduling.
S. M. Homayouni et al [40], focused on an integrated scheduling
of quay cranes and automated guided vehicle is formulated as a mixed
integer linear programming model, which minimizes the make span of
all the loading and unloading tasks for a set of cranes in scheduling
problem.
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S. V. Kamble et al [41] focused on the problem of simultaneous
scheduling of machine and automated guided vehicle (AGV) in a flexible
manufacturing system (FMS) so as to minimize the make span.
Zhigang Lian et al [42] used multiple objective decision-making
method, a global criterion approach, to develop a multi-objective
scheduling problem model with different due-dates on parallel machines
processes. They considered three performance measures, namely
minimum run time of every machine, earliness time (with no tardiness)
and process time of every job, simultaneously.
Xinyu Li et al [43], proposed a new active learning genetic
algorithm based method which has been developed to facilitate
integration and optimization of process. Planning and scheduling are
carried out sequentially, where scheduling is done separately after the
process plan has been generated.
HongGuang et al [44], focused on a discrete particle swarm
optimization (DPSO) algorithm and is proposed to solve the assembly
sequence planning (ASP) problem.
Y.W. Guo et al [46] developed combinatorial optimization model
for solving the IPPS problem, and focused on a modern evolutionary
algorithm, i.e., the particle swarm optimization (PSO) algorithm which
was applied to solve it effectively. They made a comparison between
modified PSO algorithm and the previous results generated by the
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genetic algorithm (GA) and the simulated annealing (SA) algorithm,
respectively.
Wei-Bo Zhang et al [47], proposed four different versions of
particle swarm optimization to solve discrete problems. They have
proposed two models for the operators of PSO. One is based on value
exchange and the other on order exchange. Based on these two models,
they formulated two different versions of PSO.
K. Suresh et al [48], presented a model for maintenance
scheduling (MS) of generators using hybrid improved binary particle
swarm optimization (IBPSO) based on coordinated deterministic and
stochastic approach. The main objective of this paper is to reduce the
Loss Of Load Probability (LOLP) and minimizing the annual supply
reserve ratio deviation for a power system which are considered as a
measure of power system reliability.
Tamer F. Abdelmaguid et al [54] focused on the traditional job
shop scheduling problem by incorporating the scheduling of the material
handling tasks with objective of minimizing the maximum completion
time of all manufacturing and material handling tasks.
J. Rezaeian et al [55] discussed an important issue regarding the
implementation of cellular manufacturing systems. They proposed two
heuristic methods based on multi-stage programming and genetic
algorithm for incremental cell formation.
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Kalyanmoy Deb et al [56] presented Multi-objective evolutionary
algorithms (EAs) that use non-dominated sorting which suggests a non-
dominated sorting-based multi-objective EA (MOEA), called non-
dominated sorting genetic algorithm II (NSGA-II), which alleviates some
difficulties related to computational complexity, non-elitism approach
and need for specifying a sharing parameter.
Chichang Jou et al [58], they focused on suboptimal scheduling
solutions for parallel flow shop machines where jobs are queued in a
bottleneck stage. A Genetic Algorithm with Sub-indexed Partitioning
genes (GASP) is proposed to allow more flexible job assignments to
machines. Their fitness function is related to tardiness, earliness, and
utilization rate is related to variable costs which reflect real
requirements.
S. Saravana Sankar et al [59], discussed on difficulty which can
be overcome by scheduling the variety of incoming parts into the system
efficiently. They designed an appropriate scheduling mechanism to
generate a nearer to optimum schedule using Genetic Algorithm (GA)
with two different GA Coding Schemes.
Felix T.S. et al [60] proposed an adaptive genetic algorithm for
distributed scheduling problems in multi-factory and multi-product
environment. They introduced a new crossover mechanism named
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dominated gene crossover to enhance the performance of genetic search
and eliminate the problem of determining optimal crossover rate.
Guohui Zhang et al [61] proposed an effective genetic algorithm
for solving the flexible job-shop scheduling problem (FJSP) to minimize
make span time. They designed the Global Selection (GS) and Local
Selection (LS) to generate high-quality initial population in the
initialization stage.
Xiaodan Wu et al [62], proposed a new approach to concurrently
make the CF, GL and GS decisions. A conceptual framework and
mathematical model is proposed, which integrates these decisions in
Cellular manufacturing (CM).
R. Yang et al [63], they focused on the uses of genetic diversity
measurements to avoid premature convergence and a hybridizing genetic
algorithm with simplex downhill method to speed up convergence. They
outlined the concepts of genetic algorithms. Based on the concepts of the
traditional GA and the simple downhill method, a new algorithm (GOD)
has been made and verified by case problems.
Fardin Ahmadizar et al [64], proposed a hybrid genetic algorithm
for the open shop scheduling problem with the objective of minimizing
the make span. In the proposed algorithm, a specialized crossover
operator is used that preserves the relative order of jobs on machines
and a strategy is applied to prevent from searching redundant solutions
in the mutation operator.
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K. Sivakumar et al [65] focused on tolerance design in product
components is to produce a product with least machining cost possible.
They introduced Multi-objective non-linear, constrained optimization
model for solving test problems with the help of Simple Genetic Algorithm
(SGA) and Particle Swarm Optimization (PSO).
David He et al [66] addressed the scheduling issues related to
assembly-driven product differentiation strategies in agile
manufacturing. They framed, and solved the scheduling problems
associated with the assembly-driven product differentiation strategy in a
general flexible manufacturing system.
Abdulziz M et al [67], proposed a concept and implementation of
the Petri nets for measuring and analysis of performance measures of
FMS is applied. Further they modeled a system in Visual Slam
software.(AweSim). They found that the simulation techniques are easy
to analyze the complex flexible manufacturing system. Rekha Bhowmik
et al [68], presented an Iterative Heuristic Algorithm and Branch and
Bound Algorithm for optimal location of clusters on different levels. The
author proposed the use of cluster analysis for grouping highly related
departments for both the methods.
Dario Pacino et al [69], considered a constraint-based scheduling
approach to the flexible job shop, a generalization of the traditional job
shop scheduling where activities have a choice of machines. They
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presented large neighbourhood and adaptive randomized decomposition
approaches to the flexible job shop problem.
Hamesh babu Nanvala et al [70] reviewed the literature on
machine loading problem of FMS and classified the articles according to
the approaches used to solve the machine loading problem in FMS. Then
the approaches were categorized as 1).The Mathematical approach. 2).
The heuristic approach. 3).The artificial intelligence-based approaches
and presented the list of the reviewed papers in these categories.
Tilak Raj et al [71] studied the work of various researchers and
found that it is really a very difficult task for any organization to
transform into FMS on the basis of existing research results. They
revealed that there is a wide gap exists between the proposed approaches
algorithms for the design of different components of FMS and the real-life
complexities.
George Jiri Mejtsky et al [72], presented an improved sweep
meta-heuristic for discrete event simulation optimization. They discussed
new additions, such as backtracking and local search, to the basic sweep
algorithm. They concluded that additions, along with the new search
framework, increased diversification and intensification of our
hierarchical search process.
Kasin Oey et al [73], considered a complex job shop problem
with reentrant flow and batch processing machines and also considered
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a Modified Shifting Bottleneck heuristic (MSB) for generating machine
schedules to minimize the total weighted tardiness.
L. Siva Rama Krishna et al [99], dealt with the real time
implementation of a web integrated scheduling support system for a
multiproduct manufacturing industry. They considered a priority based
scheduling system based on the precedence of the customer.
Summary
Most of the work on FMS optimization have focused either on
optimizing the layout or flexible manufacturing system scheduling. The
author has observed that no research work is made on optimization of
flexible manufacturing system layout with integrated scheduling.
Discrete-event simulation is another area which has the
potential to make major contribution to FMS operation. Simulation can
be used to model FMS quite comprehensively, and may be used to
evaluate control policies, either heuristic or as per rules. Distributed
processing makes the use of simulation feasible. There are some
published papers Deb S.K et al [16] using a simulation approach, but
usually these do not provide comprehensive modeling of FMS.
This prompted the author in selecting the problem of
optimization of flexible manufacturing system layout using scheduling as
constraint, by discrete event simulation.
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Literature survey reveals that the problem related to the layout
design in FMS is one of the foremost important issues and this should be
resolved suitably at the beginning of the system design. Of late, research
works have focused on the FMS layout design and FMS scheduling which
have been taken as two separate streams. However, only a few
researchers have highlighted the importance of FMS layout design with
integrated scheduling. Since the hardware used in FMS is rather
expensive, the FMS layout designer should select suitable layouts duly
examining the various alternative layouts. The machine layout problem
implies the arrangement of machines on a factory floor so that the total
time required to transfer material between each pair of machines is
minimized. Apart from time and distance factors, factors such as
handling carrier path, clearance between machines, etc., are to be taken
into account while evaluating the alternative layouts.