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Multi-task differential evolution algorithm with dynamic resource allocation: A study on e-waste recycling vehicle routing problem☆
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DOI：10.1016/j.swevo.2024.101806.png)
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Abstract
En 中文
The quantity of e-waste is increasing year by year, and there are several recycling companies in the same region conducting the recycling business. It is urgent to obtain the effective overall recycling scheduling schemes considering the business of different recycling companies. Multi-task differential evolutionary algorithm with dynamic resource allocation (MTDE-DRA) is proposed to realize the rational allocation of resources and vehicle routing optimization. First, a task complexity evaluation metric is designed to evaluate the complexity of each task quantitatively. The saturation of customer distribution volume, the dispersion of customer geographical location, and the spaciousness of customer time windows are considered in the complexity metric. Second, a twostage resource allocation strategy is proposed to allocate the appropriate resources for each task. The complexity and evolutionary state of each task are used as key factors for resource allocation in the two stages, respectively. Finally, bidirectional evolutionary resource adjustment strategy is proposed to improve the optimization efficiency of difficult recycling tasks. The similarity matrix with the source task not only constructs the additional population for the current task, but also realizes the positive transfer of knowledge. In addition, experimental studies conducted on Solomon's dataset validate the proposed MTDE-DRA algorithm is promising in solving multiple e-waste recycling vehicle routing problems with different volumes of business.
Keywords:
Multi-task optimization
Differential evolution
Dynamic resource allocation
E -waste recycling
Vehicle routing problem
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