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A decoding-priority-based self-adaptive prairie dog optimizer for cyclic cross-period bidirectional milk-run vehicle scheduling problem
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DOI：10.1016/j.aei.2025.103311.png)
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Abstract
En 中文
In the context of mass customization, the milk-run model has emerged as a predominant logistics strategy. However, traditional milk-run models fail to exploit the potential of integrating transportation tasks across multiple periods. Additionally, growing environmental concerns necessitate the inclusion of reverse logistics. This paper investigates a Cyclic Cross-Period Bidirectional Milk-run Vehicle Scheduling Problem (CCBMVSP) that aims to minimize both economic costs (total operational cost) and service levels (total earliness and tardiness) simultaneously. To address this problem, we develop a mixed-integer programming model and apply the epsilonconstraint method to obtain exact solutions for small-scale instances. Given the NP-hard nature of the problem, we propose a multi-objective optimization algorithm, the Decoding-priority-based Self-adaptive Prairie Dog Optimizer (DSPDO). The encoding and decoding procedures are specifically designed with a constraint-oriented solution repair strategy. Chaotic mapping is introduced to enhance the diversity of the initial population. Moreover, we propose a multi-elite iteration strategy and a variable neighborhood search strategy to strengthen the algorithm's exploration and exploitation capabilities. Based on decoding priority, an adaptive exploration-exploitation balance strategy is also introduced. Finally, extensive numerical experiments demonstrate that the proposed algorithm outperforms benchmark methods when solving larger-scale instances.
Keywords:
Prairie dog optimizer
Epsilon Constraint
Milk-run
Bidirectional logistics
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