Simulation-based multi-objective model for supply chains with disruptions in transportation

被引：15

作者：

Chavez, Hernan ^{[1
]}

Castillo-Villar, Krystel K. ^{[1
]}

Herrera, Luis ^{[2
]}

Bustos, Agustin ^{[3
]}

机构：

[1] Univ Texas San Antonio, Dept Mech Engn, San Antonio, TX 78249 USA

[2] Tecnol Monterrey, Dept Ind & Syst Engn, Mexico City 01389, DF, Mexico

[3] Inst Mexicano Transporte, Integrated Transport Res Ctr, Sanfandila 76700, Queretaro, Mexico

来源：

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING | 2017年 / 43卷

基金：

美国食品与农业研究所;

关键词：

Minimum cost flow; Simulated annealing; Simulation optimization; Stochastic multi-objective optimization; Resilient supply chains; COST FLOW PROBLEM; OPTIMIZATION; ALGORITHM; NETWORKS;

D O I：

10.1016/j.rcim.2015.12.008

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Unpredictable disruptions (e.g., accidents, traffic conditions, among others) in supply chains (SCs) motivate the development of decision tools that allow designing resilient routing strategies. The transportation problem, for which a model is proposed in this paper, consists of minimizing the stochastic transportation time and the deterministic freight rate. This paper extends a stochastic multi-objective minimum cost flow (SMMCF) model by proposing a novel simulation-based multi-objective optimization (SimMOpt) solution procedure. A real case study, consisting of the road transportation of perishable agricultural products from Mexico to the United States, is presented and solved using the proposed SMMCF-Continuous/SimMOpt solution framework. In this case study, time variability is caused by the inspection of products at the U.S.-Mexico border ports of entry. The results demonstrate that this framework is effective and overcomes the limitations of the multi-objective stochastic minimum cost flow problem (which becomes intractable for large-scale instances). (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：39 / 49

页数：11