A two-stage methodology for short-term batch plant scheduling

Abstract In this paper, a two-stage methodology for solving jobshop scheduling problems is proposed. The first step involves the development of a discrete-event simulation (DES) model to represent dynamically the production system behavior, taking into account the main features inherent to the application field. Since most scheduling problems in batch processing belong to the family of problems classified as NP-complete, probabilistic optimization algorithms (such as simulated annealing, evolutionary algorithms) represent a good alternative for solving large-scale combinatorial problems (for instance, the traveling salesman problem). In the second step of our approach, we thus investigate genetic algorithms (GAs) for solving batch process scheduling problems: a GA has been developed for minimizing the average residence time to produce a set of batches in function of batch order in a multipurpose-multiobjective plant with unlimited storage. The evaluation of the objective function is provided by its coupling with the DES model embedded in the optimization loop. Computational results show that the use of this approach can significantly help to improve the efficiency of the production system. This paper is focused on semiconductor application, which is the first example treated in our laboratory, although the general approach adopted in this study is now extended to other fields of applications (e.g. fine chemistry with finite intermediate storage and unstable intermediates).

[1]  David W.T. Rippin,et al.  Simulation of single- and multiproduct batch chemical plants for optimal design and operation☆ , 1983 .

[2]  David W.T. Rippin,et al.  Batch process systems engineering: A retrospective and prospective review , 1993 .

[3]  R. Sargent,et al.  Optimum Design of Multipurpose Chemical Plants , 1979 .

[4]  Jan Karel Lenstra,et al.  Job Shop Scheduling by Simulated Annealing , 1992, Oper. Res..

[5]  H. Das,et al.  Scheduling of serial multiproduct batch processes via simulated annealing , 1990 .

[6]  G. Reklaitis,et al.  Scheduling of multipurpose batch chemical plants. 2. Multiple-product campaign formation and production planning , 1991 .

[7]  M.-C. Portmann,et al.  Les algorithmes génétiques et leur application aux problèmes d'ordonnancement , 1995 .

[8]  D.W.T. Rippin,et al.  Short-term scheduling for multiproduct batch chemical plants , 1986 .

[9]  H. Ku,et al.  Scheduling in serial multiproduct batch processes with finite interstage storage: mixed integer linear program formulation , 1988 .

[10]  L. Puigjaner,et al.  A comprehensive approach to production planning in multipurpose batch plants , 1989 .

[11]  L. B. Evans,et al.  Batch process management , 1990 .

[12]  D. Rippin.  Design and operation of multiproduct and multipurpose batch chemical plants. — An analysis of problem structure , 1983 .

[13]  Luis Puigjaner,et al.  On the solution of the retrofitting problem for multiproduct batch/simicontinuous chemical plants , 1989 .

[14]  G. Reklaitis,et al.  Scheduling of multipurpose batch chemical plants. 1. Formation of single-product campaigns , 1991 .

[15]  Iftekhar A. Karimi,et al.  Completion times in serial mixed-storage multiproduct processes with transfer and set-up times , 1989 .

[16]  H. Ku,et al.  An evaluation of simulated annealing for batch process scheduling , 1991 .

[17]  Gintaras V. Reklaitis,et al.  Intermediate storage in noncontinuous processes involving stages of parallel units , 1985 .

[18]  Eric Peyrol.  Gestion d'un atelier de fabrication de composants électroniques , 1992 .

[19]  E. Ignall,et al.  Application of the Branch and Bound Technique to Some Flow-Shop Scheduling Problems , 1965 .

[20]  Ignacio E. Grossmann,et al.  Multiperiod LP models for simultaneous production planning and scheduling in multiproduct batch plants , 1990 .

[21]  Jeffrey C. Kantor,et al.  Modeling discrete-event dynamical systems for chemical process control—a survey of several new techniques , 1990 .

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

[23]  A. E. Eiben,et al.  Global Convergence of Genetic Algorithms: A Markov Chain Analysis , 1990, PPSN.

[24]  Peter T. Cummings,et al.  Scheduling of multiple products on parallel units with tardiness penalties using simulated annealing , 1995 .

[25]  D. Rippin,et al.  Production planning and scheduling for multi-purpose batch chemical plants , 1979 .

[26]  Hugh M. Cartwright,et al.  Simultaneous optimization of chemical flowshop sequencing and topology using genetic algorithms , 1993 .

[27]  P. Floquet,et al.  Scheduling and simulated annealing application to a semiconductor circuit fabrication plant , 1993 .

[28]  Serge Domenech,et al.  A discrete-event simulation approach for scheduling batch processes , 1995 .

[29]  E. Peyrol,et al.  SEMICONDUCTOR CIRCUIT FABRICATION PLANT MANAGEMENT BY DISCRETE SIMULATION , 1991 .

[30]  H. Ku,et al.  Scheduling in serial multiproduct batch processes with due-date penalties , 1990 .

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

[32]  A. Alan B. Pritsker,et al.  Introduction to simulation and SLAM II , 1979 .