Purpose

This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks.

Design/methodology/approach

First, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales.

Findings

Numerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity.

Research limitations/implications

This study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery.

Originality/value

Both limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.

中文翻译：

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改进的具有新颖搜索策略的多目标布谷鸟搜索算法，用于汽车装配线点对点零件进给调度问题

目的

本文旨在研究用于汽车装配线的电动汽车（EV）的零件进给调度问题。制定了点对点零件进给模型，以通过指定EV和所有交付任务的顺序来最大程度地减少EV的数量和最长的处理时间。

设计/方法/方法

首先，提出了带有电动汽车的点对点零件进给调度问题（PTPPFSP）的数学编程模型。由于PTPPFSP具有NP难点性，因此开发了一种改进的多目标布谷鸟搜索（IMCS）算法，该算法具有新颖的搜索策略，具有自适应Levy飞行，高斯变异和精英选择策略，可增强算法的优化性能。此外，还为深度优化设计了两个本地搜索运算符。通过处理不同问题规模的PTPPFSP，验证了IMCS算法的有效性。

发现

数值实验用来证明IMCS算法如何作为一种有效的方法来解决带电动汽车的PTPPFSP。IMCS算法的有效性和可行性通过从不同问题规模的实例获得的近似Pareto前沿进行了验证。计算结果表明，在解决方案质量，收敛性和多样性方面，IMCS算法比其他高性能算法具有更好的性能。

研究局限/意义

这项研究适用于不考虑电动汽车故障的情况。当前的研究为汽车装配线的工厂内物流调度做出了贡献，可以对其进行修改以应对以准时（JIT）交货为特征的相似零件进给调度问题。

创意/价值

既考虑了有限的电力容量，又没有考虑过早和拖延的约束，并且为在工厂物流中使用电动汽车的高效JIT零件进料提供了令人满意和创新的调度问题。