Multi-objective optimization for a closed-loop network design problem using an improved genetic algorithm

被引：48

作者：

Shi, Jianmai ^{[1
]}

Liu, Zhong ^{[1
]}

Tang, Luohao ^{[1
]}

Xiong, Jian ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha 410073, Hunan, Peoples R China

来源：

APPLIED MATHEMATICAL MODELLING | 2017年 / 45卷

基金：

中国国家自然科学基金;

关键词：

Closed loop supply chain; Carbon emission; Multi-objective programming; Facility location; Evaluation algorithm; SUPPLY-CHAIN NETWORK; REVERSE LOGISTICS NETWORK; FACILITY LOCATION MODEL; COLLECTION CENTERS; PRODUCT RETURNS; INVENTORY MODEL; IMPACT;

D O I：

10.1016/j.apm.2016.11.004

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper develops a multi-objective Mixed Integer Programming model for a closed-loop network design problem. In addition to the overall costs, the model optimizes overall carbon emissions and the responsiveness of the network. An improved genetic algorithm based on the framework of NSGA II is developed to solve the problem and obtain Pareto-optimal solutions. An example with 95 cities in China is presented to illustrate the approach. Through randomly generated examples with different sizes; the computational performance of the proposed algorithm is also compared with former genetic algorithms in the literature employing the weight-sum technique as a fitness evaluation strategy. Computational results indicate that the proposed algorithm can obtain superior Pareto-optimal solutions. (C) 2016 Elsevier Inc. All rights reserved.

引用

页码：14 / 30

页数：17

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