A heuristic procedure for the automobile assembly-line sequencing problem considering multiple product options

• Published in: International journal of production research Vol. 46; no. 20; pp. 5827 - 5847
• Main Authors: Ding, F.-Y., He, J.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 01.10.2008; Washington, DC Taylor & Francis LLC
• Subjects: Applied sciences; Assembly line sequencing; Assembly lines; Automobile industry; Automobile production; Automobiles; Exact sciences and technology; Heuristic; Inventory control, production control. Distribution; Operational research and scientific management; Operational research. Management science; Sequencing considering multiple options; Sequential scheduling; Simulated annealing; Studies; Assembly line; Motor car; Assembly plant; Multiobjective programming; Blocking; Spacing; Multiple production; Assembly line sequencing; Automobile production; Smoothing; Mixed model; Automobile industry; Heuristic method; Simulated annealing; Sequencing considering multiple options; Sequencing; Assembly
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207540701381291

Mixed-model assembly nowadays is a common practice in the automobile industry. In an automobile assembly plant, many car options often need to be considered in sequencing an assembly line, for example, the multiple sequencing objectives that consider a pattern, blocking, spacing, and smoothing of options. A general heuristic procedure is developed in this paper for sequencing automobile assembly lines considering multiple options. The procedure obtains an initial sequence by an enhanced constructive procedure, swaps orders for the most deteriorating category of objectives, and performs re-sequencing attempting to improve the swapped sequence. The heuristic procedure was shown to frequently improve the initial sequences by swapping and re-sequencing when swapping opportunities exist. A further improvement step is also proposed to perform a limited search based on the swapped solution. The limited-search improvement step was shown to be effective in further improving solutions from the heuristic procedure in the computational experimentation. Solutions from the heuristic procedure in conjunction with the limited-search improvement step were compared to those from the simulated annealing procedure for large-size problems and showed relatively positive results.