Performance Comparison of
Search Algorithms for
Manufacturing Systems using
Discrete-event Simulation
Mohammed Yaseen Kalachikan Jafferali
09/13/02
Agenda
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Background
Literature Review
Motivation
Problem Definition and Scope
Experiments and Analysis
Summary and Conclusion
1. Background
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Search Algorithms
Simulation Optimization
OptQuest Algorithm
SimRunner Optimization Algorithm
1.1.Search Algorithms
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Helps to guide the model to a near-optimal
solution.
Local and Global Search Technique.
Local techniques are myopic and get
trapped in local optimum.
Global Search techniques use special
mechanisms to avoid local optima.
Contd…
1.2. Simulation Optimization
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The process of finding the best input variable
values from among all possibilities without
explicitly evaluating each possibility (Carson
et al, 1997).
A Simulation Optimization Model
Feedback
Input
Simulation
Model
Output
Optimization
Algorithm
1.3. OptQuest Algorithm
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The software combines scatter search, tabu
search, integer programming and neural
networks into a single, composite search
algorithm (Fu, 2001).
Scatter search is a population based
approach like genetic algorithm which
produces off springs more intelligently by
incorporating search history of past
evaluations (Glover et al, 1999).
1.4. SimRunner Optimization
Algorithm
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The software combines evolutionary and
genetic algorithms (Fu, 2001).
Evolutionary algorithms operate on a
population of potential solutions applying
the principle of survival of the fittest to
produce better and better approximations to
a solution.
Structure of an Evolutionary
Algorithm (GEA Toolbox)
Contd…
2. Literature Review
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Yunker et al, 1994 made a quantitative
comparison between pattern search,
response surface method and a genetic
algorithm using simulation optimization on
a university time-shared computer system.
Lacksonen, 2001 compared pattern search,
simplex, simulated annealing and genetic
algorithm optimization algorithms on
Contd…
variations of four industrial case study
problems.
 Fu et al, 1997 investigated the use of
stochastic approximation for the
optimization of a variety of discrete-event
systems: a single-server queue, a queueing
network, and a transportation network via
simulation.
3. Motivation
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Performance Comparison of popular search
techniques have not been done explicitly for
a more general configuration of a
manufacturing system.
Simulation optimization has been an active
area of research.
Genetic Algorithms, Tabu Search, Scatter
Search and Neural Network are popular
search techniques reported in literature.
Contd…
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So commercial simulation optimization
softwares (OptQuest for Arena and
SimRunner for ProModel) implementing
these techniques were used.
Time constraints and difficulties involved in
integrating simulation packages with search
algorithms through computer codes.
4. Problem Definition and Scope
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This research compares the performance of
OptQuest and SimRunner optimization algorithms
to find the best set of queue dispatching rules, on
different manufacturing system performances.
Machine1
Buffer1
Machine 2
Buffer2
Machine n
Buffer n
Contd…
The following performance measures were
considered: i) Proportion of Tardy Jobs; ii)
Total Throughput.
 The method used to assign due dates is the
Total Work content method (TWK): Di = Ri +
Ai, where, Ai = k x Pi
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Queue disciplines considered: FIFO, LIFO,
SPT, LPT, EDD
Assumptions
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Model has a run time of 7200 min (8 hr
production shift, 3 shifts everyday and 5
days a week).
Material handling time between two
machines is assumed to be negligible and
the part routing time is assumed to be zero.
Machine breakdown and maintenance
activities are not considered.
Batch size is assumed to be 1.
Assumptions (contd…)
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In the stochastic case, the interarrival times
and the processing times are sampled from
an exponential distribution.
In simulations that involve stochastic
variables, the number of replications was
assumed to be 5.
The processing times of each job type is
randomly generated from U[10,50].
5. Experiments and Analysis
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Factors Affecting the Performance of a
Search Algorithm
Factors Considered in the Research
Design of Experiments
Analysis of Results
5.1. Factors Affecting the
Performance of a Search Algorithm
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System Complexity
Nature of the variables (deterministic or
stochastic)
Computation Time
Type of Variables (integer or real)
5.2. Factors Considered in the
Research
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# of part types (2, 10, 20)
# of machines (5, 12, 20)
Capacity of queues (Finite, Infinite)
Job Inter-arrival time (Deterministic,
Stochastic)
Processing time (Deterministic, Stochastic)
Type of shop (Flowshop, Jobshop)
5.3. Design of Experiments
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Five factors, two of the factors at 3 levels,
and the other factors at 2 levels each,
constituting a total of 144 experimental runs
to analyze the behavior of the system under
full factorial design with 5 replicates each.
6. Conclusion
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Performance comparison of search
algorithms was carried out for a more
general manufacturing system rather than an
adhoc case.
Analysis of results will be carried out by
running the experiments and by comparing
the performance measures, the best search
algorithm among the two will be reported.
Scope for Future Research
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More factors affecting the performance of
the search algorithms like computation
time, and the effect of different factors in
manufacturing systems like material
handling, batch size, machine breakdown
etc. could be studied.
More search algorithms could be compared.