A Genetic Algorithm for Split Delivery Open Vehicle Routing Problem with Physical Workload Consideration

2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics (RI2C) Pub Date : 2021-09-01 DOI:10.1109/RI2C51727.2021.9559769

Tarit Rattanamanee

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Abstract

Last-mile delivery is an important part of a logistics activity in the city. Usually, delivery workers are required to manually unload goods at customer locations. These manual tasks induce physiological fatigue in the workers and increase delivery time. This paper discusses a genetic algorithm (GA) approach to the open vehicle routing problem with split delivery (SDOVRP), where manual unloading is addressed. The workers are pre-assigned to vehicle and split delivery is allowed. Its objective is to minimize the total cost of total fixed cost of vehicles and delivery workers and total transportation cost. For safety, the total physical workload imposed on each worker must not exceed the daily limit. Since an optimization approach cannot find the optimal solution within reasonable computation time especially when solving large size problem. A GA with heuristic for pre-determine split delivery is developed to solve the problem. The computational experiment results show that the GA approach is efficient and can obtain near-optimal SDOVRP solutions.

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考虑物理工作负荷的分割配送开放车辆路径问题的遗传算法

最后一英里配送是城市物流活动的重要组成部分。通常，送货员需要在客户所在地手动卸货。这些体力劳动会引起工人的生理疲劳，并增加交货时间。本文讨论了一种遗传算法(GA)来解决带有分离配送的开放式车辆路径问题(SDOVRP)，其中解决了人工卸载问题。工人被预先分配到车辆上，并且允许分批交货。其目标是尽量减少车辆和运送工人的总固定成本和总运输成本的总成本。为了安全起见，每个工人的总体力负荷不得超过每日限额。由于优化方法无法在合理的计算时间内找到最优解，特别是在求解大型问题时。为了解决这一问题，提出了一种启发式的遗传算法。计算实验结果表明，该方法是有效的，可以得到接近最优的SDOVRP解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics (RI2C)

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