A cluster-based optimization framework for vehicle routing problem with workload balance. (July 2022)

• Record Type: Journal Article
• Authors: LI, Jingjing; Fang, Yaohuiqiong; Tang, Na
• Is Part Of: Computers & industrial engineering. Volume 169(2022)
• Publication Date: 2022-07
• Subjects: Multi-objective optimization -- Vehicle routing problem -- Workload balance; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108221
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Two VRPs with balanced workload are formulated. An optimization framework for VRPs at two stages of the logistics chain is proposed. A clustering algorithm is proposed for grouping the customers into clusters. A fusion scheme is developed to balance the workload. Route search based on ant colony optimization is executed in the clusters. Abstract: 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 microcluster-based 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.