Integrated multi-item packaging and vehicle routing with split delivery problem for fresh agri-product emergency supply at large-scale epidemic disease context

被引:29
作者
Jiang, Yiping [1 ]
Bian, Bei [1 ]
Liu, Yang [2 ]
机构
[1] Nanjing Agr Univ, Coll Informat Management, Nanjing 210095, Peoples R China
[2] Nanjing Agr Univ, Coll Artificial Intelligence, Nanjing 210095, Peoples R China
基金
中国国家自然科学基金;
关键词
Transportation engineering; Fresh agri-product emergency supply; Multi-item packaging; Vehicle routing with split delivery; Corona virus disease 2019 (COVID-19); RESOURCE-ALLOCATION; OPTIMIZATION MODEL; CONSOLIDATION; ALGORITHMS; CHALLENGES; LOGISTICS;
D O I
10.1016/j.jtte.2020.08.003
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Fresh agri-product emergency supply is crucial to secure the basic livelihood of residents at large-scale epidemic disease context. Considering the massive demand and limited transportation resources, this study integrates multi-item packaging and vehicle routing with split delivery to improve the emergency supply capacity. Firstly, three specific objectives of fresh agri-product emergency supply at large-scale epidemic disease context are formulated, i.e., average response time, infectious risk possibility and transportation resource utilization. Then, a multi-item packaging strategy is proposed to consolidate different categories of fresh agri-products according to the food cold chain temperatures. An optimization model integrating multi-item packaging and vehicle routing with split delivery is developed to jointly decide the optimal packaging scheduling, vehicle assignment and delivery routing. Next, an improved genetic algorithm based on solution features (IGA-SF) is designed to solve the integrated model with multiple decision variables. Finally, a case on fresh agri-product emergency supply of Huangpi District, Wuhan in the context of the Corona Virus Disease 2019 (COVID-19) is carried out to illustrate the efficiency and feasibility of the proposed model. The numerical results of medium-to-largescale cases demonstrate that the proposed IGA-SF could save 23.91% CPU time and 37.80% iteration number on average than genetic algorithm. This study could satisfy different emergency scenario requirements flexibly, and provide scientific decision support for provincial and national governments on fresh agri-product emergency supply. (C) 2020 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
引用
收藏
页码:196 / 208
页数:13
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