Abstract Multi-stage resource leveling problem in sharing logistics network is a multi-objective optimization problem, which is strongly non-deterministic polynomial hard in open loop environment. In this paper, we attempt to develop a logistics task-resource allocation model for this problem, which not only considers the total cost and duration for sharing logistics network, but also refers to the resource efficiency intra-stage and stability inter-stage for resource providers. As the defects of slow convergence, weak local search and easy-to-precocious in traditional algorithms, an improved multi-objective artificial bee colony algorithm is developed with adaptive neighborhood rules. The process of algorithm improvement involves: (I) an adaptive moving step size in population update strategy instead of random step size and (II) an adaptive weigh updating method with multiple neighborhood search rules in local optimum. The results show that the improved algorithm can effectively solve multi-stage resource leveling problem proposed in this paper, compared with traditional artificial bee colony algorithm, non-dominated sorting genetic algorithm-II and multi-objective particle swarm optimization, and can obtain a better non-dominated solution set with multiple metrics for algorithm.

中文翻译：

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共享物流网络多阶段资源均衡问题的多目标人工蜂群算法

摘要 共享物流网络中的多阶段资源均衡问题是一个多目标优化问题，在开环环境下是强非确定性多项式。在本文中，我们尝试针对该问题建立一个物流任务-资源分配模型，该模型不仅考虑了共享物流网络的总成本和持续时间，还考虑了资源阶段内的资源效率和阶段间的稳定性。提供者。针对传统算法收敛速度慢、局部搜索弱、易早熟等缺陷，提出一种改进的自适应邻域规则的多目标人工蜂群算法。算法改进的过程包括：(I) 种群更新策略中的自适应移动步长而不是随机步长和 (II) 局部最优中具有多个邻域搜索规则的自适应权重更新方法。结果表明,与传统人工蜂群算法、非支配排序遗传算法-II和多目标粒子群优化相比,改进算法能够有效解决本文提出的多阶段资源均衡问题,并能获得更好的结果。具有多个算法指标的非支配解决方案集。