Multi-period fourth-party logistics network design from the viability perspective: a collaborative hyper-heuristic embedded with double-layer Q-learning algorithm

生存能力 启发式 计算机科学 弹性(材料科学) 持续性 数学优化 算法 运筹学 工程类 计算机网络 人工智能 数学 物理 生态学 生物 热力学
作者
Yuxin Zhang,Min Huang,Zheming Gao,Songchen Jiang,Shu‐Cherng Fang,Xingwei Wang
出处
期刊:International Journal of Production Research [Taylor & Francis]
卷期号:: 1-31 被引量:2
标识
DOI:10.1080/00207543.2024.2339530
摘要

In the 'new normal' setting of a mega-crisis, the viability becomes the driving force for the fourth party logistics (4PL) network design. In this paper, the viability is characterised in terms of agility, resilience and survival sustainability as the response to changes in demand, disruption and survivability. The fortification and recovery strategies are considered in possible disruptions at transfer centres and third-party logistics providers. A novel mixed integer non-linear programming model is proposed to obtain the multi-period 4PL network solution with minimum total cost under viability constraints. Considering the NP-hard characteristic of problem and the non-convex of proposed model, the hyper-heuristic algorithm is designed. To take advantage of both global optimality seeking and local search ability, a collaborative hyper-heuristic embedded with double-layer Q-learning (CHHDLQL) algorithm is proposed. The effectiveness and efficiency of the proposed algorithm is demonstrated by the promising numerical results. By stress-testing the existing network, appropriate adjustments to fortification and recovery strategies can effectively cope with changes in demand and disruption. Furthermore, the impact of 4PL strategy, fortification and recovery strategies, and viability constraints are investigated. The demand satisfaction, network resilience and capacity can be improved by adjusting agility, resilience and survival sustainability to influence different component network costs.
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