Journal
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
Volume 233, Issue 14, Pages 5113-5130Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0954406219839083
Keywords
Automobile assembly line; part replenishment; just-in-time; unrelated parallel machines; backtracking; teaching-learning-based optimization
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Funding
- National Natural Science Foundation of China [71471135]
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With increasing product customization, just-in-time part replenishment has become a significant scheduling problem in the automobile assembly system. This paper investigates a new unrelated parallel machine scheduling problem of an assembly line, where machines are employed to deliver material boxes from an in-house warehouse to workstations. The schedule is to appropriately specify the assignment and sequence of material boxes on each machine for minimizing line-side inventories under no stock-out constraints. By taking advantages of domain properties, an exact algorithm is developed to cope up with small-scale instances. In terms of real-world scale instances, a hybrid teaching-learning-based optimization metaheuristic is established by integrating teaching-learning-based optimization with a beam search technique. Experimental results indicate that the scheduling algorithms are effective and efficient in solving the proposed unrelated parallel machine scheduling.
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