| Summary: | 博士 === 輔仁大學 === 商學研究所博士班 === 102 === This study investigates production scheduling problems with due windows using flow shop and job shop cases. In practice, due windows have become an important issue for real time production in order to facilitate the interests of related industries. Reentrant flow shop scheduling, two-stage flexible flow shop, and job shop scheduling problems are the most common production activities in the real world. Therefore, by applying the due windows constraint, this study attempts to minimize weighted early and tardy costs, based on the just-in-time concept. For solving scheduling problems with time windows, this study develops three effective methods that prevent unexpected delays and associated large losses during production.
With related studies demonstrating that ant colony optimization algorithm (ACO), and particle swarm algorithm (PSO) are both effective and efficient means of solving scheduling problems, this study; develops a farness particle swarm optimization algorithm (FPSO) to solve reentrant two-stage multiprocessor flow shop scheduling problems in order to minimize earliness and tardiness, a novel effective ant colony optimization (EACO) algorithm to solve two-stage flexible flow shop scheduling problems and thereby minimize earliness, tardiness and makespan, and a wise select ant colony optimization (WSACO) utilizing due window and sequence dependent setup time for constraints, and solves the job shop scheduling problem in real world.
Computational results indicate that either small or large scale problems are involved in which FPSO algorithm, EACO algorithm, and WSACO algorithm outperform original PSO algorithm and ACO algorithm with respect to effectiveness and robustness. Importantly, this study demonstrates that FPSO algorithm can solve such a complex reentrant flow shop scheduling, EACO algorithm can solve the two-stage flexible flow shop, and WSACO algorithm can solve the job shop scheduling problems with due window and sequence dependent setup time for constraints efficiently. This study offers IP solutions for the best solutions within a satisfactory time and the three proposed algorithms provides solutions for quick response to market within a short time. Computational results prove that the three proposed algorithms have a higher solving capacity than the common ACO and PSO algorithms. The proposed methods reduce waiting and tardiness costs, helping enterprises simultaneously shorten makespan, increasing profits, and lower overhead costs, and the results of this study can also be used as the basis for further study about other management issues of related companies.
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