Scheduling reentrant flexible job shops with sequence dependent setup times

Abstract

This study presents a new simulation-based local search approach for solving shop scheduling problems. Results for classical problems from the literature demonstrate the effectiveness and quality of the approach. Application is also shown for reentrant flexible job shop with sequence dependent setup times (RFJSSDS), a new, very general, class of problems. RFJSSDS is a generalization of the classical job shop, reentrant flow shop and flexible job shop problems. Multiple products (routes), sequence dependent setup times at the work centers and reentrancy of the jobs make RFJSSDS one of the more general and difficult shop scheduling problems. Examples of this type of problem include semiconductor wafer fabrication facilities and flexible machining systems. The solution methodology developed in this study features a new Simulation Based Local Improvement with Multi Start (SBLIMS) algorithm. The local search procedure modifies an initial feasible solution provided by the simulation module to generate promising neighbor solutions. A generated solution is considered to be better if there is a reduction in the total completion time or makespan. A unique filtering strategy is used to select and rank moves, using both task and resource views of a schedule. Multiple random starting points are generated in multistart fashion as part of the solution process. New theorems are presented that form the basis for SBLIMS. The SBLIMS algorithm was evaluated using test instances for several shop scheduling problems as well as RFJSSDS. A set of synthetic problems was generated to study RFJSSDS, because there were no RFJSSDS instances available from the literature. The SBLIMS algorithm was compared with various dispatch rules in the RFJSSDS domain and its performance was found to be better in most cases. SBLIMS was also tested with well known special cases of RFJSSDS: the classical job shop, reentrant flow shop and flexible job shop problems. The SBLIMS algorithm provided excellent results compared with those provided in the literature, establishing the generality of the approach for solving a broad class of shop scheduling problems.