Purpose

The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks.

Design/methodology/approach

This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time.

Findings

Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window.

Research limitations/implications

The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies.

Originality/value

For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

中文翻译：

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具有产品时间窗的跨站台卡车调度的自适应记忆马鹿算法

目的

越库策略对供应链和物流效率有显着影响。本文旨在研究最合适和最有效的物流活动转移调度方法，并提出一种跨站台物流卡车调度问题的元启发式方法。研究了具有产品时间窗口的卡车调度问题，其目标是最小化总产品转运时间以及出境卡车的提前和延误成本。

设计/方法/方法

本研究提出了一种具有产品时间窗的卡车调度问题的元启发式方法。为了解决这个问题，通过一个新的两阶段拉格朗日松弛问题建立了问题的下界，并考虑到卡车调度问题的 NP-hard 性质，新的具有启发式振荡局部机制的马鹿算法为了克服原red deer算法在局部搜索和运行时间方面能力较差的问题，提出了搜索算法以及自适应内存编程。

发现

本文以具有产品时间窗的交叉配送中心的工业案例进行了理论分析和仿真实验。令人满意的结果表明，由于启发式振荡局部搜索算法和自适应内存编程的机制，红鹿算法的性能得到了提高，并且所提出的方法有效地解决了卡车调度问题与产品时间交叉对接的实际大小情况窗户。

研究限制/影响

产品时间窗口的考虑在冷链物流、医药供应链等不同物流应用中具有非常现实的意义。此外，新颖的自适应记忆马鹿算法可以修改并应用于其他复杂的优化调度问题，例如考虑能源效率或其他物流策略的调度问题。

原创性/价值

在具有越库策略的卡车调度问题中，首次考虑了产品的时间窗口。此外，开发了一个数学模型，其目标是最小化总产品转运时间以及出境卡车的提前和延误成本。针对该问题，提出了一种具有启发式振荡局部搜索算法机制的自适应记忆马鹿算法，以克服遗传算法在局部搜索和运行时间方面能力较差的问题。