Several Heuristic Scheduling Algorithms for Solving Flexible Flow-Shop Problems

• Main Authors: Chan-Lon Wang, 王乾隆
• Other Authors: Tzung-Pei Hong
• Format: Others
• Language: zh-TW
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/05064275625500016449

碩士 === 義守大學 === 資訊工程學系 === 89 === Scheduling is an important process widely used in manufacturing, production, management, computer science, and so on. It mainly concerns allocating resources to tasks over time, under necessary constrains. In real-world applications, appropriate scheduling cannot only reduce manufacturing costs but also reduce possibilities for violating due dates. Finding good schedules for given sets of jobs can thus help factory supervisors effectively control job flows and provide solutions for job sequencing. In simple flow-shop problems, each machine operation center includes just one machine. If at least one machine center includes more than one machine, the scheduling problem becomes a flexible flow-shop problem. Flexible flow shops can thus be thought of as generalizations of simple flow shops. In the past, Sriskandarajah and Sethi proposed a heuristic algorithm for solving flexible flow-shop problems of two machine centers. In this thesis, we extend their algorithm to solve flexible flow-shop problems of more than two machine centers. We propose several heuristic scheduling algorithms. The first one is based on the A-star algorithm to find an optimal schedule. Since the problem is NP-hard, it is still intractable even when the job number is small. We thus propose the second heuristic algorithm to reduce the computation time needed in solving the flexible flow-shop problems. This heuristic assumes that all the jobs are executed in each machine center by the same schedule. The corresponding machines in the machine centers then comprise a flow shop. The second heuristic algorithm still has to find the combinations in each flow shop to get the final schedule. We thus propose another two heuristic algorithms, respectively based on the Gupta algorithm and the CDS algorithm, to further reduce the computational time to polynomial time complexity. Since the flexible flow-shop problems are NP-hard, optimal solutions seem unnecessary especially when the number of jobs is large. Finally, experiments are made to compare the performance of the four proposed algorithms.