A distributed approximation approach for solving the sustainable supply chain network design problem

This paper introduces a comprehensive Mixed Integer Linear Programming (MILP) model for a sustainable supply chain network design problem, and an efficient Distributed Approximation Approach (DAA) to solve it approximately. We study a multi-echelon, multi-product and multi-modal supply chain with di...

Full description

Saved in:
Bibliographic Details
Published in:International journal of production research 2019-06, Vol.57 (11), p.3695-3718
Main Authors: Guo, Yuhan, Hu, Fangxia, Allaoui, Hamid, Boulaksil, Youssef
Format: Article
Language:English
Subjects:
Algorithms
Approximation
approximation approach
Branch and bound methods
Computer networks
Computer Science
distributed computing
Distributed processing
Distribution costs
Environmental impact
Integer programming
Linear programming
Mixed integer
Network design
Procurement
supply chain design
Supply chains
Sustainable supply chain
Transportation planning
Water consumption
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper introduces a comprehensive Mixed Integer Linear Programming (MILP) model for a sustainable supply chain network design problem, and an efficient Distributed Approximation Approach (DAA) to solve it approximately. We study a multi-echelon, multi-product and multi-modal supply chain with different transportation modes. Besides relevant costs in the supply chain such as procurement, production and distribution cost, we also explicitly consider the environmental footprint, represented by carbon emissions and water consumption from production and transportation. The approximation approach is a decomposition-based method. First, the original problem is divided into a partner selection sub-problem and a transportation planning sub-problem. Then multiple filter mechanisms are used to remove potentially infeasible solutions, and an approximate value of the objective function is calculated for each of the remaining solutions to perform a further selection. The one with the lowest approximation is chosen to be applied with a branch-and-bound method. Finally, the algorithm is paralleled and implemented in Apache Spark distributed computing framework to further improve efficiency. Experimental results show that the proposed DAA can provide high quality solutions compared to the optimal solutions of the MILP model with mostly a negligible relative gap and solve large instances in much shorter time than CPLEX. Moreover, in our numerical study, we also compare the results of our model with another version of the model that does not take the environmental footprint into consideration. The results show that explicitly incorporating environmental footprint results in a substantial decrease of CO 2 emissions and water consumption at a negligible cost increase. This insight may be of interest to managers and other decision makers and policy makers.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2018.1556412