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Multi-objective evolutionary algorithm based flexible assembly job-shop rescheduling with component sharing for order insertion
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DOI：10.1016/j.cor.2024.106744.png)
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Abstract
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This paper addresses a rescheduling problem in an order -driven flexible assembly job -shop, which encompasses a complete production line from machining to assembly. Some products have a multi -level assembly structure, and certain components can be shared among different products. When an order with tight delivery date is inserted, rescheduling is necessary for adjusting original production scheme and tradeoffs among makespan, total tardiness and count of machine changes. To formulate the flexible assembly job -shop rescheduling problem with component sharing (FAJRP-CS), a mixed -integer linear programming model is established. The model involves three categories of decision variables that determine the machine assignment, shared component allocation, and operation sequencing. Meanwhile, solution of FAJRP-CS is analyzed by using graph model, and a neighborhood structure containing two -type move for critical operations is proposed for local optimizing. Then, a hybrid nondominated sorting genetic algorithm with tabu search (NSGATS) is developed to maintain diversity while improving convergence. The approach is first compared with 8 algorithms on nine test instances, and the effectiveness of the operators, framework and neighborhood structure are discussed separately. Subsequently, NSGATS is applied to solve the multi -stage rescheduling problem in a medical device workshop with 6 products and 56 components. The experimental results demonstrate that NSGATS is effective for solving the FAJRP-CS.
Keywords:
Flexible assembly job -shop
Rescheduling optimization
Non -dominated sorting genetic algorithm
Tabu search
Graph model
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4.3
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6.5K
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