Scheduling flexible manufacturing cells using Tabu Search

• Published in: International journal of production research Vol. 47; no. 24; pp. 6907 - 6928
• Main Authors: Pitts, R.A., Ventura, J.A.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 15.12.2009; Taylor & Francis; Taylor & Francis LLC
• Subjects: Algorithms; Applied sciences; Exact sciences and technology; flexible manufacturing; Flexible manufacturing systems; Integer programming; Inventory control, production control. Distribution; Job shops; Linear programming; Mathematical models; Mathematical programming; meta-heuristics; mixed-integer linear programming; Operational research and scientific management; Operational research. Management science; Production scheduling; Scheduling; Scheduling, sequencing; Studies; Tabu Search; Mixed programming; Task scheduling; Mixed integer programming; Routing; Scheduling; Makespan; Modeling; Linear model; Search algorithm; Workload; Production management; Zero one programming; Tabu search; Flexible manufacturing system; mixed-integer linear programming; Heuristic method; flexible manufacturing; Cellular manufacturing; Metamodel; meta-heuristics; Large scale system
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207540802400628

Scheduling is an important aspect in the overall control of a flexible manufacturing system. The research presented focuses on production scheduling of jobs within a flexible manufacturing cell (FMC)-one type of flexible manufacturing system. Due to the complexity of the FMC scheduling problem, a 0-1 mixed-integer linear programming (MILP) model is formulated for M machines and N jobs with alternative routings. Although small instances of the problem can be solved optimally with MILP models, a two-stage Tabu Search (TS 2 ) algorithm that minimises the manufacturing makespan (MS) is proposed to solve medium-to-large-scale problems more efficiently. During Stage I (construction phase), two heuristics are utilised to generate an initial feasible sequence and an initial MS solution. In Stage II (improvement phase), the acquired initial solutions from Stage I are combined with a Tabu Search meta-heuristic procedure that provides improved MS solutions. The TS 2 algorithm provides tremendous savings in computational time for medium/large-sized multi-machine FMC problems.