TY - JOUR
T1 - A distributed approximation approach for solving the sustainable supply chain network design problem
AU - Guo, Yuhan
AU - Hu, Fangxia
AU - Allaoui, Hamid
AU - Boulaksil, Youssef
N1 - Funding Information:
This work was supported by Department of Education of Liaoning Province [grant number LJYL051].
Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2019/6/3
Y1 - 2019/6/3
N2 - 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 CO2 emissions and water consumption at a negligible cost increase. This insight may be of interest to managers and other decision makers and policy makers.
AB - 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 CO2 emissions and water consumption at a negligible cost increase. This insight may be of interest to managers and other decision makers and policy makers.
KW - Sustainable supply chain
KW - approximation approach
KW - distributed computing
KW - supply chain design
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U2 - 10.1080/00207543.2018.1556412
DO - 10.1080/00207543.2018.1556412
M3 - Article
AN - SCOPUS:85058679176
SN - 0020-7543
VL - 57
SP - 3695
EP - 3718
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 11
ER -