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CHAPTER 1
INTRODUCTION
1.1 MANUFACTURING SYSTEMS
Manufacturing is the economic term for making goods and services
available to satisfy human wants. Manufacturing implies creating value by applying
useful mental or physical labour. The manufacturing processes are collected
together to form a Manufacturing system. The manufacturing system takes inputs
and produces products for the customer. A manufacturing system can be defined as
“a collection of operations and processes used to obtain a desired product(s) or
component(s)”. A manufacturing system is therefore the design or arrangement of
the manufacturing processes (Paul Degarmo et al 2003).
1.2 CLASSIFICATION OF MANUFACTURING SYSTEMS
The manufacturing systems themselves differ in structure or physical
arrangement. According to the physical arrangement, there are four kinds of
classical manufacturing systems and two modern manufacturing systems that gain
acceptance in industries. The classical systems are
a. Job shop
b. Flow shop
c. Project shop
d. Continuous process.
Modern manufacturing systems are
a. Linked cell system or Cellular manufacturing system
b. Flexible manufacturing system
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1.2.1 Job Shop
Job shops are the most common manufacturing system. In general, job
shops are designed to achieve maximum flexibility so that a wide variety of
products with small lot sizes can be manufactured. Products manufactured in job
shops usually require different operations and have different operation sequences.
Operating time for each operation could vary significantly. Products are released to
the shops in batches (jobs). The requirements of the job shop - a variety of products
and small lot sizes - dictate what types of machines are needed and how they are
grouped and arranged. General-purpose machines are utilized in job shops because
they are capable of performing many different types of operations. Machines are
functionally grouped according to the general type of manufacturing process: lathes
in one department, drill presses in another, and so forth. Figure 1.1 illustrates a job
shop. A job shop layout can also be called a functional layout.
In job shops, jobs spend only 5% of their time in machines and the rest
of the time waiting or being moved from one functional area to the next (Paul
Degarmo et al 2003). When the processing of a part in the job shop has been
completed, it usually must be moved a relatively larger distance to reach the next
stage. It may have to travel the entire facility to complete all of the required
processes, as shown in Figure 1.1. Therefore, to make processing more economical,
parts are moved in batches. Each part in a batch must wait for the remaining parts in
its batch to complete processing before it is moved to the next stage. This leads to
longer production times, high levels of in-process inventory, high production costs
and low production rates.
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Fig 1.1 Job Shop Manufacturing
1.2.2 Flow Shop
The flow shop has a product oriented layout composed mainly of flow
lines. In contrast to job shops, flow lines are designed to manufacture high volumes
of products with high production rates and low costs. A flow line is organized
according to the sequence of operations required for a product. Specialized
machines, dedicated to the manufacture of the product, are utilized to achieve high
production rates. These machines are usually expensive; to justify the investment
cost of such machines, a large volume of the product must be produced. A major
limitation of flow lines is the lack of flexibility to produce products for which they
are not designed. This is because specialized machines are setup to perform limited
operations and are not allowed to be reconfigured. Figure 1.2 shows an example of
a flow line.
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Figure 1.2 Flow Line Manufacturing
1.2.3 Project Shop
In this, a product must remain in a fixed position or location because of
its size and weight. The materials, machines and people used in fabrication are
brought to the site. The layout is fixed position layout. Figure 1.3 a & b shows an
example of the project manufacturing.
a) Air craft assembly b) Ship building
Figure 1.3 Project manufacturing
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1.2.4 Continuous Process
In this continuous process, the project primarily deals with liquids, gases
(such as oil refineries, chemical process plants and food process industries) rather
than solids or discrete parts. It is the efficient but least flexible of the manufacturing
systems. This system is earliest to control having the least work in progress. Figure
1.4 a & b is an example for this type.
a) Oil refinery b) Food processing industry
Figure 1.4 a & b Continuous process manufacturing.
1.2.5 Linked Cell or Cellular Manufacturing System
Within the manufacturing system context, Group Technology (GT) is
defined as a manufacturing philosophy identifying similar parts and grouping them
together into families to take advantage of their similarities in design and
manufacturing. Cellular manufacturing is an application of group technology in
which dissimilar machines (or) processes have been aggregated into cells each of
which is dedicated to the production of a part (or) product family (or) a limited
group of families. Cellular Manufacturing (CM) involves the formation of part
families based upon their similar processing requirements and the grouping of
machines into manufacturing cells to produce the formed part families. A part
family is a collection of parts which are similar either because of geometric shape
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and size or similar processing steps required in their manufacture (Groover 1987). A
manufacturing cell consists of several functionally dissimilar machines, which are
placed in close proximity to one another and dedicated to the manufacture of a part
family.
The tenet of CM is to break up a complex manufacturing facility into
several groups of machines (cells), each being dedicated to the processing of a part
family. Therefore, each part type is ideally produced in a single cell. Thus, material
flow is simplified and the scheduling task is made much easier. As reported in the
survey by Wemmerlov and Johnson (1989), production planning and control
procedures have been simplified with the use of CM. The job shop in Figure 1.1 is
converted into a cellular manufacturing system (CMS) as shown in Figure 1.5.
Obvious benefits gained from the conversion of the shop are less travel distance for
parts, less space requirement, and need for fewer machines. Since similar part types
are grouped, this could lead to a reduction in setup time and allow a quicker
response to changing conditions. On the other hand, in the job shop, each part type
may have to travel through the entire shop; hence scheduling and materials control
are difficult. In addition, job priorities are complex to set and hence large
inventories are needed.
CM is a hybrid system linking the advantages of both job shops
(flexibility in producing a wide variety of products) and flow lines (efficient flow
and high production rate). In CM, machines are located in close proximity to one
another and dedicated to a part family. This provides the efficient flow and high
production rate similar to a flow line. The use of general-purpose machines and
equipments in CM allows machines to be changed in order to handle new product
designs and product demand with little efforts in terms of cost and time. So it
provides great flexibility in producing a variety of products.
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Figure 1.5 Cellular Manufacturing
In conclusion, CM is a manufacturing system that can produce medium-
volume / medium-variety part types more economically than other types of
manufacturing systems (Black 1983). If volumes are very large, pure item flow
lines are preferred; if volumes are small and part types are varied to the point of
only slight similarities between jobs, there is less to be gained by CM. The survey
by Wemmerlov and Johnson (1989) affirms that the greatest reported benefits from
CM appear along the dimension of time (manufacturing lead time and customer
response time). Thus, CM represents a logical choice for firms whose strategy is
time-based competitive manufacturing (Stalk and Hout 1990).
1.2.6 Flexible Manufacturing System
The FMS has established itself among the conventional production
systems as an efficient way to produce work pieces in medium lot sizes and medium
variety. It combines the merits of the job shop production and flow shop production.
The high level of automation previously reserved for mass production is now
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achievable for the medium sized production. The flexibility in manufacturing
enables companies to react fast on changing demands of the customers. FMS is
distinguished by the use of computer control in place of the hard automation usually
found in transfer lines. This enables FMS to reconfigure very rapidly to produce
multiple part types. Use of fixtures and tool magazines practically eliminates setup
time. These features permit economic production of large variety of parts in low
volumes.
A flexible manufacturing system integrates all major elements of
manufacturing into a highly automated system. The flexibility of FMS is such that it
can handle a variety of part configuration and produce them in any order. The basic
elements of FMS are a) works station b) automated material handling and AS/RS
systems c) control systems. Because of major capital investment efficient machine
utilization is essential. Consequently proper scheduling and process planning are
very complex. Because of the flexibility in FMS no setup time is wasted in between
manufacturing operations, the system is capable of different operations in different
orders and on different machines.
Figure 1.6 shows typical model of an FMS setup. Existing FMS
implementations have a number of benefits in terms of cost reductions, increased
utilizations, reduced work-in-process levels etc. However, there are a number of
problems faced during the life cycle of an FMS. These problems are classified into:
Design, Planning, and Scheduling and Control problems. In particular, the
scheduling task is of importance owing to the dynamic nature of the FMS such as
flexible parts, tools, AGV routings and Automated Storage and Retrieval System
(AS/RS) operation and control.
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Figure 1.6 Model of a Flexible Manufacturing System
1.3 SCHEDULING
Scheduling is considered to be a major task for shop floor productivity
improvement. Scheduling is the allocation of resources to apply the limiting factors
of time and cost to perform a collection of tasks. Scheduling theory is concerned
primarily with mathematical models that relate to the scheduling function and the
development of useful models and techniques. The objective function generally
consists of all costs in the system depending upon the type of scheduling decisions.
Frequently, an important cost-related measure of system performances (such as
makespan time, machine idle time, job waiting time, flow time of jobs and lateness
or tardiness or combination of these measures) can be used as a substitute for total
system cost.
Two kinds of feasibility constraints are commonly found in scheduling
problems. The first set of constraints is related to the amount of resource available
(like number of machines available in each type). The second set of constraints is
based on technological restrictions on the sequence in which tasks can be
performed.
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The objectives of the scheduling problem are listed below:
Determining the sequence in which tasks are to be performed.
Determining the start time and finish time of each task.
It is evident that the essence of scheduling is to make allocation
decisions pertaining to start and finish times for tasks. Scheduling can be classified
as given below:
Single Machine Scheduling (SMS)
Flow Shop Scheduling (FSS)
Job Shop Scheduling (JSS)
Cellular Manufacturing System Scheduling (CMSS)
Flexible Manufacturing System Scheduling (FMSS)
1.3.1 Single machine scheduling
In SMS, it is generally considered that n jobs (n = 1,2,3….n) are to be
processed on a single machine. The basic SMS problem is characterized by the
following conditions.
A set of independent, single-operation jobs are available for
processing at time zero.
Set-uptime of each job is independent of its position in jobs sequence
and hence this can be included in its processing time.
Job descriptors are known in advance.
One machine is continuously available and is never kept idle when
work is waiting.
Each job is processed till its completion without break.
The total number of sequences in the basic SMS problem is (n!), which
is the number of permutation of n elements. Due date (DD) is the time allowable
for completion of a job. If a job is not able to meet the DD, it is said to be late job
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or tardy job. Obviously the objective will be minimizing the tardiness or lateness.
Different time rules like shortest processing time, weighed mean flow time rule and
earliest due date rule are applied based on the objective function of the problem.
1.3.2 Flow shop scheduling
In FSS problems, there are n jobs; each requires processing of m
different machines. The order in which the machines are required to process a job
is called process sequence of that job. The process sequences of all the jobs are the
same. But the processing times for various jobs on a machine may differ. If an
operation is absent in job, the processing time of the operation of that job is
assumed as zero. The selection of an appropriate order of a set of jobs is known as
sequencing. In a flow shop, the machines are set up in series as the same processing
order of jobs. Every sequence of a set of jobs will have different performance
measures such as makespan time (total time required to complete processing of all
the jobs), total flow time (sum of actual time spent by all the jobs in the processing
environment), idle time of machines & jobs and tardiness (the positive lateness of
jobs beyond its due date) etc. It is difficult to suggest a sequence, which optimizes
all these performances together; rather these performances are purely independent
among themselves.
The FSS problem can be characterized as given below.
a) A set of multiple – operation jobs is available for processing at time
zero (Each job requires m operations and each operation requires a
different machine).
b) Set-up times for the operations are sequence independent, and are
included in processing times.
c) Job descriptors are known in advance.
d) M different machines are continuously available.
e) Each individual operation of jobs is processed till its completion
without break.
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The selection of an appropriate order of a set of jobs is known as
sequencing. In a flow shop, the machines are set up in series as the same processing
order of jobs. A sequence of a set of jobs will have different performance measures
such as makespan time, total flow time, idle time of machines & jobs and tardiness,
etc. It is difficult to suggest a sequence, which optimizes all these performances
together; rather these performances are purely independent among themselves.
1.3.2.1 General Flow shop
The general flow shop scheduling is one in which passing is allowed i.e.
a job can overtake another job while waiting in a queue to be processed by a
machine.
1.3.2.2 Permutation Flow shop
The permutation flow shop scheduling problem consists of scheduling n
jobs with given processing times on m machines, where the sequence of processing
a job on all machines is identical and unidirectional for each job. In studying flow
shop scheduling problems, it is a common assumption that the sequence in which
each machine processes all jobs is identical on all machines (permutation flow
shop). A schedule of this type is called a permutation schedule and is defined by a
complete sequence of all jobs.
1.3.3 Job shop Scheduling
In Job Shop Scheduling (JSS) problem, it is assumed that each job has m
different operations. If some of the jobs are having less than m operations, required
number of dummy operations with zero process times is assumed. By this
assumption, the condition of equal number of operations for all the jobs is ensured.
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In job shop scheduling problem, the process sequences of the jobs are not the same.
Hence, the flow of each job in JSS is not unidirectional. The time complexity
function of the JSS problem is combinatorial in nature. Hence, heuristic approaches
are popular in this area. Unlike the flow shop model, there is no initial machine that
neither performs only the first operation of a job, nor there a terminal machine that
performs only the last operation of a job. In job shop different jobs will have
different operations sequences. Hence, there is no straight flow of jobs in JSS.
Each operation j in the operation sequence of the job i in the JSS will be described
with triplet (i, j, k) where k is the required machine for processing the jth operation
on the ith job.
1.3.4 FMS Scheduling
The field of application for FMS concerning the product variety and lot
size fills the gap between the traditional production systems. Job shops and the
conventional equipment as shown in Figure 1.7 are characterized by a high variety,
low volume production and a random part flow. The random part flow results in a
relatively high risk of machine tools becoming idle due to a lack of parts to be
machined. This risk can be reduced by a high level of work-in-process. However,
the consequence of this measure is that, the lead times for a batch are usually high.
Flow lines (transfer line and special systems) on the other hand, are characterized
by a low variety, high volume production and a linear part flow. Linear part flow
results in a very low amount of work-in-process. Consequently, the machine
utilization is high and the lead time for work pieces is short. The purpose of the
FMS is to combine the flexibility of the job shop with the high work station
utilization. Scheduling in FMS is more complex than in classical machine shops. In
FMS, each operation of a part may be performed by any one of the several
machines. In other words the number of decision points where scheduling or
operation can be varied is greater in FMS than in job shops.
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Figure 1.7 Application Field of FMS
An FMS is a complex system which consists of a set of work stations,
Material Handling System (MHS) that connects these work stations by Automated
Guided Vehicle (AGV) and service centers (e.g., material warehouse, tool room,
repair equipment). The workstation is an autonomous unit that performs certain
manufacturing functions (e.g., a machining centre, inspection machine and a load-
unload robot). The MHS is used to distribute the appropriate input to the work
stations, so that the work station can perform its tasks and remove the output from
the work stations. To reduce the cost and increase production, the planning and
decision are made, such as balancing the workload of the workstations, scheduling
and dispatching, automated tool and material management.
1.3.5 CMS Scheduling
Due to the similarities in the design, shape, function, etc. parts in a part
family generally visit machines in the same sequence with minor differences in
setup requirements (Schaller 2001). Therefore, a part family can be divided into
several groups so that each group needs similar setup requirements. In other words,
a group is a sub set of a part family and all parts in the same group need similar set
up requirements. In group scheduling, it is assumed that each part family can be
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processed in one cell by duplicating bottleneck machines or subcontracting
exceptional parts (Logendran et al 1995). An exceptional part can be also be called
an exceptional element or a bottleneck part. A small example is given next in order
to introduce some of the terminology to be used in this dissertation. An example of
five part types and four machine types are used in order to form cells. A machine-
part matrix is one way to represent the processing requirements of part types on
machine types as shown in Table 1.1. A 1 entry on row i and column j indicates
that part type j has one or more operations on machine type i. For instance, part type
1 has operations on machine types 1 and 3. Manufacturing cells are formed with the
objective of minimizing inter cell moves. Two cells (clusters) are formed as shown
in Table 1.2. Cell 1 consists of machine type 2 and 4, and produces part type 5 and
2. Cell 2 consists of machine type 1 and 3, and produces part type 3, 1 and 4. Part
type 3 needs to be processed on machine type 1 and 3 in cell 2; however, it also
needs to be processed on machine type 2, which is assigned in cell 1. Therefore an
inter cell move is required: the symbol “*” represents an inter cell move of part type
3. Part type 3 is an exceptional part, so these two cells (clusters) are called partially
separable.
Analogous to an exceptional part, a bottleneck machine is one that
processes parts belonging to more than one cell. Two possible approaches to
eliminate exceptional parts are by considering alternative process plans for parts or
additional machines. In Table 1.2, 0 represents a void in cell 2. A void indicates that
a machine assigned to a cell is not required for the processing of a particular part in
the cell. In this example, machine type 3 is not necessary for part type 4. The
presence of voids leads to inefficient large cells, which in turn could lead to
additional intra cell material handling costs and complex control requirements.
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Table 1.1 Machine-Part Matrix Table 1.2 Cell Formation
Machine Type
Part Type Machine Type
Part Type 1 2 3 4 5 1 2 3 4 5
1 1 1 1 2 1 1 * 2 1 1 1 4 1 1 3 1 1 1 1 1 1 4 1 1 3 1 1 0
However, subcontracting exceptional parts may not be practical or
duplicating bottleneck machines may not be possible in every CMS environment
due to production economic, budget and manufacturing space limitations, etc. thus,
in typical CMS environment, it is difficult to form independent manufacturing cells
and mostly there are some exception parts that create inter-cellular moves (Shankar
and Vrat 1998). These constraints limit the applicability of group scheduling
method in real life.
1.4 TERMINOLOGIES
Processing time (tj): It is the time required to process job (j) on any machine. The
processing time (tj) will normally include both actual processing time and set-up
time.
Due date (dj): It is the time at which job (j) is to be completed.
Completion Time (Cj): It is the time at which the job (j) is completed in a
sequence. Performance measures like flow time, lateness and tardiness for
evaluating schedules are usually function of job completion time.
Flow time (Fj): It is the amount of time job (j) spends in the system. Flow time is a
measure of actual time spent by a job in the system. This in turn gives some idea
about in-process inventory of the shop floor.
Lateness time (Lj): It is the amount of time by which the completion time of job (j)
differs from the due date (Lj = Cj – dj). Lateness is a measure of non-conformity to
the due date. It may either be positive lateness or negative lateness. Positive
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lateness of the job is said to be lateness or tardiness and the negative lateness is
called as earliness. Therefore, it is often desirable to minimize the tardiness.
Tardiness (Tj): Tardiness is the lateness of job (j) if it fails to meet its due date and
it may either be zero or otherwise
Ti = max {Cj – dj}
1.5 MEASURE OF PERFORMANCES
Schedules are generally evaluated by aggregate quantities that involve
information about all jobs, resulting in one-dimensional performance measures.
Measures of a schedule performance are usually functions of the set of completing
times in a schedule. For example, suppose that n jobs are scheduled. The different
performance measures usually considered are listed below (Baker 1974).
Mean flow time : F =
n
jjFn
1/1
Mean Tardiness : T =
n
jjTn
1/1
Maximum flow time : }{max max jF
njlF
Maximum Tardiness : }{max max jT
njlT
Number of Tardy jobs :
n
jjT NfN
1)(
1.6 GENERAL STRUCTURE
1.6.1 FSS Problems
Let us consider the general structure of FSS problems of n jobs (n =
1,2,3…n), m machines (m = 1,2,3….m) with processing time tij represents the
processing time of ith machine. The general structure is shown in the Table 1.3.
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Table 1.3 General structures of FSS with n jobs and m machines.
Machines Jobs M1 M2 M3 . Mj. . Mm
J1 J2 .
Ji. .
Jn
t11 t12 t13 . t1j . t1m t21 t22 t23 . t2j . t2m . . . . . . . ti1 .ti2 .ti3 . tij . tim . . . . . . . tn1 tn2 tn3 . tnj. . tnm
The processing times of all the jobs are known and assumed that they are
processed in the same order on various machines.
1.6.2 Job shop scheduling problem
Each operation j in the operation sequence of the job i in the JSS
problem will be described with triplet (i,j,k) where k is the required machine for
processing the j th operation of the i th job. Let us consider the general structure of
JSS problems of 3 jobs, 3 operations and 3 machines. The general structure is
shown in the Table 1.4. The Table1.4 consists of machine sequence for each job
with processing time is given within the parenthesis.
Table 1.4 General structure of JSS.
Machine sequence (processing time in minutes)
Job 1 2 (6) 3(2) 1(7) Job 2 1(4) 3(9) 2(1) Job 3 3(5) 2(3) 1(4)
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1.7 OBJECTIVES
1.7.1 Makespan and Total flow time
Makespan is the time required to complete all the jobs. Let us consider
two machines and five-job problem with the assumption that the processing times
are known as shown in Table 1.5.
Table 1.5 2 Machines and 5 Jobs problems
Jobs
Machines j1 j2 j3 j4 j5
m1
m2
3 5 1 6 7
6 2 2 6 5
By applying Johnson’s rule (Johnson 1954) the sequence that optimizes
the makespan is 3 1 4 5 2. Computation of the entering and leaving time of jobs
with machines m1 and m2 with idle time of machines, m1 and m2 are shown in
Table 1.6.
Table 1.6 Computation of entering and leaving time of jobs on machine.
Jobs Machine (m1) Machine (m2) Idle
time of m1
Idle time of m2 in out in out
3 0 1 1 3 0 1 1 1 4 4 10 0 1 4 4 10 10 16 0 0 5 10 17 17 22 0 1 2 17 22 22 24 2 0 Total flow time 75
The total time required to complete all the five jobs is 24 time units,
known as makespan. Any other sequence other than this optimal sequence will
yield either equal or higher makespan. The total flow time of all the jobs will be the
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sum of all the flow time of jobs arrived in this example is 75. The mean flow time
is the mean of the total flow time.
1.7.2 Tardiness
In scheduling problems, when the jobs are considered with due dates, the
job which is completed beyond its due date is considered to be a tardy job. This
tardiness or the positive lateness of the jobs affect the production, by delayed
delivery time to the customer, the organization suffers with loss of reputation and
leads to customer dissatisfaction. Hence, tardiness of the jobs is to be minimized.
It is also one of the performance measures of the sequence. Many authors have
chosen this as an objective in FSS problems recently.
1.7.3 Multiple Objectives
The performance measures of FSS problems are mutually independent in
nature. In recent past, very few works have been reported with multiple criterions
of these performance measures. Different authors have tried different combinations
of these measures. However, the result proposed by multicriterion objectives of
FSS problems may not yield good result in any one particular criterion, instead it
minimizes more than one criterion so as to the minimize the total cost of
production.
1.8 NP-HARDNESS OF SCHEDULING PROBLEMS
Many authors have worked on optimizing for scheduling with various
objectives. In scheduling problems, considering n jobs and m machines, to suggest
a best sequence, (n!)m different sequences are to be examined with respect to any
performance measure. When the processing order is same for all jobs, the possible
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sequences will be reduced to (n!). This implies that for problems of larger size, the
searching region is increased exponentially, which results the scheduling problems
as NP hard problems or in other words the optimal solution will be in the region,
which is exponentially increasing. Scheduling problems come under combinatorial
category and the time taken to obtain optimum solution will be exponential in
nature. Hence, heuristic approaches are resorted to find acceptable solution in
reasonable time. In recent time many researchers in this area have developed their
heuristics by employing some of the exhaustive searching tools like, Genetic
Algorithm (GA), Ant System (AS), Simulate Annealing (SA), etc.
1.9 SCHEDULING OBJECTIVES
The scheduling is made to meet specific objectives. The objectives are
decided upon the situation, market demands, company demands and the customer’s
satisfaction. There are two broad categories for the scheduling objectives:
(i) Minimizing the makespan (ii) Due date based cost minimization.
The objectives considered under minimizing the makespan are,
(a) Minimize machine idle time
(b) Minimize the in process inventory costs
(c) Finish each job as soon as possible
(d) Finish the last job as soon as possible
The objectives considered under the due date based cost minimization are,
(a) Minimize the costs due to not meeting the due dates
(b) Minimize the maximum lateness of any job
(c) Minimize the total tardiness
(d) Minimize the number of late jobs
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1.10 SCHEDULING TECHNIQUES
There are a number of optimization techniques used for the scheduling.
The techniques are generally,
(i) Traditional Techniques
Mathematical Programming (Linear Programming, Integer
Programming, Goal Programming, Dynamic Programming, Transportation,
Network, Branch-and-Bound and Cutting Plane / Column Generation Method),
Heuristics Procedures (Priority Dispatching Rules, Composite Dispatching Rules),
Beam-Search, Enumerative Procedure, Decomposition (Lagrangian Relaxation),
McNaughton’s algorithm, Palmer’s heuristic, CDS algorithm, NEH algorithm,
Johnson algorithm etc.,
(ii) Non-Traditional Techniques
Evolutionary Programs (Genetic Algorithm, Particle Swarm
Optimization), Local Search Techniques (Ants Colony Optimization, Simulated
Annealing, Adaptive Search), scatter search and Artificial Intelligence Techniques
(Expert System, Artificial Neural Network) and Hybrid Techniques.
1.11 OBJECTIVE OF THE RESEARCH
The present research is focused on the scheduling problems on various
manufacturing systems with single and combined objective functions using non-
traditional techniques. The software codes have been developed using ‘C++’
language. The research scheme is depicted as shown in the Figure 1.8.
The research carried out encompasses the following objectives:
Non-traditional techniques are implemented for the problems of Scheduling
for various manufacturing systems
Evaluate the potential of Scatter search method for scheduling of various
manufacturing systems.
Comparison and analysis of the results.
Among the non-traditional techniques the superior one is identified.
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Figure 1.8 Research Scheme
Cellular Manufacturing
Minimizing the makespan
Minimizing Penalty Cost
Minimization of Weighted sum of makespan & total flow time
Single Machine
Flow Shop Job Shop FMS
Scheduling
Scheduling with CDD
Scheduling with CDW
General Scheduling
Permutation Scheduling
Minimizing the makespan
Minimizing the sum of the Earliness and Tardiness Penalties
Minimization of weighted sum of makespan & total flow time
Minimizing the makespan
Minimizing the machine idle time and total penalty
Implementation of Meta – heuristic methods
Results & Discussions
Conclusion
Scatter Search algorithms for scheduling of various manufacturing Systems
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1.12 ORGANIZATION OF THE THESIS
The present, chapter 1 gives bird’s eye view of various manufacturing
systems used and scheduling in general, single machine scheduling, flow shop
scheduling, job shop scheduling and scheduling on group technology concepts. Also
gives the research scheme presented in this research.
Chapter 2 presents the literature review done during the course of this research
work. It describes the research works reported by different authors in the field of
scheduling on various manufacturing systems with various objectives.
Chapter 3 entitled ‘Heuristics and metaheuristcs’ explain the details of heuristic and
metaheuristic methods used for solving scheduling problems. It also deals with the
metaheuristics used in this research.
Chapter 4 entitled ‘Single Machine Scheduling’ deals with the scheduling for Single
Machine Scheduling with Common Due Window and Common Due Date. The
compared results are presented with conclusion remarks.
Chapter 5 entitled ‘Flow Shop Scheduling’ describes the application of non-
traditional optimization techniques for solving the problem of flow shop scheduling
problems i.e. General and permutation flow shop with makespan objectives and
permutation flow shop with combined objectives. The results are compared with
results available in the literature and other methods used.
Chapter 6 entitled ‘Job Shop Scheduling’ details the scheduling of job shop problem
using Scatter search method and the results obtained is compared with various
metaheuristics results in the literature.
Chapter 7 entitled ‘Scheduling for Flexible Manufacturing Systems’ explains the
scheduling for FMS problems and the results obtained by Scatter search and ant
colony algorithm methods are compared with the results available in the literature.
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Chapter 8 entitled ‘Scheduling for Cellular Manufacturing Systems’ presents the
comparison and analysis of the results obtained by using non-traditional
optimization techniques for the cellular manufacturing scheduling problems with
objectives of penalty cost, makespan objective by considering inter cell and intra
cell movements and bi-criteria objectives.
Chapter 9 describes the conclusion of the research, which contains the outcome of
this research, limitations of this research work, and future research scope.
Appendix, references, list of publications and curriculum vitae are given
at the end.