.png){kind=icon}
返回
Optimizing routing and delivery patterns with multi-compartment vehicles
.png){kind=icon}
OA
AI
DOI：10.1016/j.ejor.2020.12.033.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Retailers usually apply repetitive weekly delivery patterns when scheduling the workforce for shelf replenishment, defining cyclic transportation routes and managing warehouse capacities. In doing so, all logistics subsystems are jointly scheduled. Grocery products require different temperature zones. As long as transport was in separated vehicles due to temperature requirements, it was not possible to coordinate deliveries across different temperature zones. The recent introduction of multi-compartment trucks has changed this and allows joint deliveries. This simultaneous delivery of multiple product segments impacts repetitive weekly delivery patterns as, for example, low volume segments can be delivered more frequently if they are transported together with high volume segments. We address the problem of defining delivery patterns for delivery with multi-compartment vehicles. After deriving decision-relevant costs, we propose a novel model that defines the Periodic Multi-Compartment Vehicle Routing Problem. The model is solved by an integrated framework that determines delivery patterns within an Adaptive Large Neighborhood Search in combination with a Large Neighborhood Search for solving the routing problem. We analyze the impact of selecting delivery patterns across product segments and show the efficiency of our integrated planning approach using numerical studies. Joint planning generates cost savings of up to 15%. Furthermore, we show that the algorithm provided can also improve single-segment problems by 3% compared to a state-of-the art benchmark. Beyond that we demonstrate the applicability and advantage of our approach in a case study with a large German grocery retailer. (C) 2020 The Author(s). Published by Elsevier B.V.
Keyword:
Supply chain management
Multi-temperature logistics
Transportation
Retailing
Adaptive large neighborhood search
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
机构
引用论文
Organocatalyzed atom transfer radical polymerization driven by visible light可见光驱动的有机催化原子转移自由基聚合.png)
Science
OAAI
ScienceIF0
OAAI分享收藏Recovery of uranium by polyurethane foam impregnated with 5,8-diethyl-7-hydroxy-6-dodecanone oxime
PREAI
分享收藏分享收藏分享收藏分享收藏
查看更多内容