A very fast TS/SA algorithm for the job shop scheduling problem

• Published in: Computers & operations research Vol. 35; no. 1; pp. 282 - 294
• Main Authors: Zhang, Chao Yong, Li, PeiGen, Rao, YunQing, Guan, ZaiLin
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 2008; Elsevier Science; Pergamon Press Inc
• Subjects: Algorithms; Applied sciences; Exact sciences and technology; Heuristic; Inventory control, production control. Distribution; Job shop scheduling problem; Job shops; Makespan; Operational research and scientific management; Operational research. Management science; Optimization algorithms; Production scheduling; Scheduling, sequencing; Simulated annealing; Studies; Tabu search; Job shop scheduling problem; Makespan; Tabu search; Simulated annealing; Job shop; Algorithmics; Task scheduling; Test standard; Scheduling; Optimization; Workload; Upper bound; Standard test; Heuristic method; NP complete problem; Fast algorithm
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2006.02.024

The job shop scheduling problem (JSP) is one of the most notoriously intractable NP-complete optimization problems. Over the last 10–15 years, tabu search (TS) has emerged as an effective algorithmic approach for the JSP. However, the quality of solutions found by tabu search approach depends on the initial solution. To overcome this problem and provide a robust and efficient methodology for the JSP, the heuristics search approach combining simulated annealing (SA) and TS strategy is developed. The main principle of this approach is that SA is used to find the elite solutions inside big valley (BV) so that TS can re-intensify search from the promising solutions. This hybrid algorithm is tested on the standard benchmark sets and compared with the other approaches. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times. For example, 17 new upper bounds among the unsolved problems are found in a short time.