A genetic algorithms simulation approach for the multi-attribute combinatorial dispatching decision problem

This paper presents a genetic algorithms (GA) simulation approach in solving a multi-attribute combinatorial dispatching (MACD) decision problem in a flow shop with multiple processors (FSMP) environment. The simulation is capable of modeling a non-linear and stochastic problem. GA are one of the commonly used metaheuristics and are a proven tool for solving complex optimization problems. The proposed GA simulation approach addresses a complex MACD problem. Its solution quality is illustrated by a case study from a multi-layer ceramic capacitor (MLCC) manufacturing plant. Because GA search results are often sensitive to the search parameters, this research optimized the GA parameters by using regression analysis. Empirical results showed that the GA simulation approach outperformed several commonly used dispatching rules. The improvements are ranging from 33% to 61%. On the other hand, the increased shop-floor-control complexity may hinder the implementation of the system. Finally, future research directions are discussed.

[1]  Robert T. Barrett,et al.  A SLAM II simulation study of a simplified flow shop , 1986, Simul..

[2]  H. Pierreval,et al.  Using evolutionary algorithms and simulation for the optimization of manufacturing systems , 1997 .

[3]  Yih-Long Chang,et al.  Ranking Dispatching Rules by Data Envelopment Analysis in a Job Shop Environment , 1996 .

[4]  Henri Pierreval,et al.  A distributed evolutionary simulation optimization approach for the configuration of multiproduct kanban systems , 2001, Int. J. Comput. Integr. Manuf..

[5]  Samir Barman.  Simple priority rule combinations: An approach to improve both flow time and tardiness , 1997 .

[6]  Chandrasekharan Rajendran,et al.  A comparative analysis of two different approaches to scheduling in flexible flow shops , 2000 .

[7]  Randall P. Sadowski,et al.  Simulation with Arena , 1998 .

[8]  Pupong Pongcharoen,et al.  Determining optimum Genetic Algorithm parameters for scheduling the manufacturing and assembly of complex products , 2002 .

[9]  H. Sarper,et al.  Combinatorial evaluation of six dispatching rules in a dynamic two-machine flow shop , 1996 .

[10]  Godfrey C. Onwubolu,et al.  Optimizing the multiple constrained resources product mix problem using genetic algorithms , 2001 .

[11]  Chandrasekharan Rajendran,et al.  A comparative study of dispatching rules in dynamic flowshops and jobshops , 1999, Eur. J. Oper. Res..

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

[13]  Yeong-Dae Kim,et al.  Due-date based scheduling and control policies in a multiproduct semiconductor wafer fabrication facility , 1998 .

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  Valerie Botta-Genoulaz,et al.  Hybrid flow shop scheduling with precedence constraints and time lags to minimize maximum lateness , 2000 .

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

[17]  Oliver Holthaus,et al.  Scheduling in job shops with machine breakdowns: an experimental study , 1999 .

[18]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[19]  Christian Hicks,et al.  Applying designed experiments to optimize the performance of genetic algorithms used for scheduling complex products in the capital goods industry , 2001 .

[20]  John L. Hunsucker,et al.  An evaluation of sequencing heuristics in flow shops with multiple processors , 1996 .

[21]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[22]  Antonio Rizzi,et al.  A fuzzy logic based methodology to rank shop floor dispatching rules , 2002 .