A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design

2019 ◽  
Author(s):  
Zhiguo Wang ◽  
Lufei Huang ◽  
Cici Xiao He
Keyword(s):  
Network Design ◽  
Reverse Logistics ◽  
Optimization Model ◽  
Logistics Network ◽  
Multi Objective ◽  
Reverse Logistics Network

A novel multi-objective model for green forward and reverse logistics network design

2019 ◽  
Vol 208 ◽  
pp. 1304-1316 ◽  
Author(s):  
Navid Zarbakhshnia ◽  
Hamed Soleimani ◽  
Mark Goh ◽  
Seyyedeh Sara Razavi
Keyword(s):  
Network Design ◽  
Reverse Logistics ◽  
Logistics Network ◽  
Multi Objective ◽  
Reverse Logistics Network ◽  
Objective Model

An Integrated Bi-Objective Reverse Logistics Network Design for Remanufacturing

2014 ◽  
pp. 281-316
Author(s):  
Ali Zolghadr Shojai ◽  
Jamal Shahrabi ◽  
Masoud Jenabi
Keyword(s):  
Network Design ◽  
Reverse Logistics ◽  
Network Design Problem ◽  
Evolutionary Strategy ◽  
Test Problems ◽  
Logistics Network ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Multi Stage ◽  
Reverse Logistics Network

Growing environmental and economical concern has led to increasing attention towards management of product return flows. An effective and efficient reverse logistics network enables companies to gain more profit and customer satisfaction. Consequently, the reverse logistics network design problem has become a critical issue. After a brief introduction to the basic concepts of reverse logistics, the authors formulate a new integrated multi-stage, multi-period, multi-product reverse logistics model for a remanufacturing system where the inventory is considered. Two objectives, minimization of the costs and maximization of coverage, are addressed. Since such network design problems belong to a class of NP-hard problems, a multi-objective genetic algorithm and a multi-objective evolutionary strategy algorithm are developed in order to find the set of non-dominated solutions. Finally, the model is tested on test problems with different sizes, and the proposed algorithms are compared based on the number, quality, and distribution of non-dominated solutions that belong to the Pareto front.

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