Performance comparison of search-based simulation optimisation algorithms for operations scheduling

This paper discusses the use of meta-heuristics coupled with discrete event simulations of various manufacturing systems to find the optimal operation schedules. Two search-based heuristic algorithms, OptQuest® (based on scatter search, tabu search and neural networks) and SimRunner® (based on genetic algorithm), are compared with respect to the quality of results and the computational time for a family of manufacturing system problems. The set of manufacturing systems configurations have been defined using the factors "type of shop" (flow shop and job shop), "number of part types" and "number of machines". This family of problems is analysed based on the stochasticity of data, which is, using either deterministic or stochastic data for part inter-arrival times and processing times. A structured experiment has been conducted to test the responses of the two algorithms in optimising two different objective functions, maximising throughput rate and minimising percentage of tardy jobs. Arena® embedding OptQuest® and ProModel® embedding SimRunner® have been used in this research. Significant validation efforts have been made to ensure that simulation models built in Arena® and ProModel® are identical so that the performance difference only accrues from the heuristics. Evidences have been found to indicate that SimRunner® produced better results when the computation time is limited; however, OptQuest® produced comparable, sometimes superior results, when allowed infinite computation time.

[1]  Jeffrey D. Tew,et al.  Simulation optimization by genetic search , 1994 .

[2]  Hyunbo Cho,et al.  A robust adaptive scheduler for an intelligent workstation controller , 1993 .

[3]  Reha Uzsoy,et al.  Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial , 2001, J. Heuristics.

[4]  Richard A. Wysk,et al.  A multi-pass simulation-based, real-time scheduling and shop floor control system , 1999 .

[5]  Paul A. Rubin,et al.  A comparison of four methods for minimizing total tardiness on a single processor with sequence dependent setup times , 2000 .

[6]  Gerald W. Evans,et al.  Comparison of global search methods for design optimization using simulation , 1991, 1991 Winter Simulation Conference Proceedings..

[7]  Averill M. Law,et al.  Simulation-based optimization , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[8]  T. Lacksonen.  Empirical comparison of search algorithms for discrete event simulation , 2001 .

[9]  L. N. Van Wassenhove,et al.  Analysis of Scheduling Rules for an FMS , 1990 .

[10]  Jeffrey S. Smith,et al.  Job shop scheduling considering material handling , 1999 .

[11]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[12]  Ihsan Sabuncuoglu,et al.  Simulation metamodelling with neural networks: An experimental investigation , 2002 .

[13]  Purushothaman Damodaran,et al.  Comparison of push and pull systems with transporters: A metamodelling approach , 2002 .

[14]  Carl Napoletano,et al.  ProModel Corporation , 2005, WSC '05.

[15]  Don T. Phillips,et al.  A state-of-the-art survey of dispatching rules for manufacturing job shop operations , 1982 .

[16]  Michael C. Fu,et al.  Optimization for Simulation: Theory vs. Practice , 2002 .

[17]  George S. Fishman,et al.  Discrete-event simulation , 2001 .

[18]  Fulya Altiparmak,et al.  A comparison of the performance of artificial intelligence techniques for optimizing the number of kanbans , 2002, J. Oper. Res. Soc..

[19]  S. S. Panwalkar,et al.  A Survey of Scheduling Rules , 1977, Oper. Res..

[20]  Mauricio G. C. Resende,et al.  Designing and reporting on computational experiments with heuristic methods , 1995, J. Heuristics.

[21]  Hsin-Pin Fu,et al.  A comparison of search techniques for minimizing assembly time in printed wiring assembly , 2000 .

[22]  Lee W. Schruben,et al.  A survey of simulation optimization techniques and procedures , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[23]  John M. Usher,et al.  Using evolution strategies and simulation to optimize a pull production system , 1996 .

[24]  Richard A. Wysk,et al.  Multi-pass expert control system - a control/scheduling structure for flexible manufacturing cells , 1988 .

[25]  B. K. Ghosh,et al.  Simulation Using Promodel , 2000 .

[26]  Rafael Martí,et al.  The OptQuest Callable Library , 2003 .

[27]  Farhad Azadivar,et al.  Simulation optimization with qualitative variables and structural model changes: A genetic algorithm approach , 1999, Eur. J. Oper. Res..

[28]  Albert Jones,et al.  A real-time production scheduler for a stochastic manufacturing environment , 1988 .