15

Chapter-2

LITERATURE SURVEY

16

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

17

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

18

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

19

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

20

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.

21

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

22

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

23

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

24

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.

26

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

27

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.

28

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.

29

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.

30

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.

32

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.

34

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

35

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

36

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.

37

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.