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A cluster-based optimization framework for vehicle routing problem with workload balance
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AI
DOI：10.1016/j.cie.2022.108221.png)
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Abstract
En 中文
Reducing transport costs and guaranteeing the fairness of the workload are major concerns for logistic companies. This paper addresses a vehicle routing problem with workload balance (VRPWB) and a microclusterbased VRPWB(MVRPWB) to minimize the total traveling costs and balance the workload. Different from existing works, we propose an optimization framework that includes three components: clustering, microcluster fusion, and route search. Various proposed algorithms, such as a clustering algorithm, a cluster fusion scheme, an enhanced ant colony algorithm, and noise processing algorithms, are embedded in these components. We can flexibly combine different components to solve VRPWB and MVRPWB. The experiment results on the traditional and revised instances show that the framework can get satisfactory solutions in most instances. Furthermore, a real case study illustrates that the framework can solve the first mile and last mile problems in practice.
Keywords:
Multi-objective optimization
Vehicle routing problem
Workload balance
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