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Scenario-Based Robust Remanufacturing Scheduling Problem Using Improved Biogeography-Based Optimization Algorithm
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DOI：10.1109/TSMC.2022.3225443.png)
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Abstract
En 中文
As a promising method for organizing remanufacturing production activities, remanufacturing scheduling has attracted increasing attention in recent years. However, extant studies have primarily focused on solving remanufacturing scheduling problems in a deterministic environment, while neglecting the impact of uncertainties on remanufacturing. Therefore, a new scenario-based robust remanufacturing scheduling problem was investigated in this study, and a robust optimization model for this problem was established. In the proposed model, a discrete scenario set is used to describe the uncertain arrival time and uncertain processing time of end-of-life products, and the variable start-up batch size constraint is considered to improve the practicality and flexibility of the model. To solve this model, an improved biogeography-based optimization algorithm with a new three-dimensional unequal-length representation scheme is proposed, in which, new migration and mutation operators, a local search strategy, and a new batch promotion mechanism are designed to improve the algorithmic performance. The results of the experiments demonstrate the feasibility of the proposed model and the superiority of the presented algorithm in solving the proposed model.
Keywords:
Uncertainty
Optimization methods
Job shop scheduling
Optimal scheduling
Stochastic processes
Heuristic algorithms
Biological system modeling
Biogeography-based optimization (BBO) algorithm
discrete scenario set
remanufacturing scheduling
robust scheduling
uncertainty
variable start-up batch size
Journal
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10.5
Papers:
1.1W
Citations:
5.0W
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