A Bi-Objective Reverse Logistics Network Design Under the Emission Trading Scheme

In general, reverse logistics network design has been driven by a need to reduce costs and to improve customer service without considering its environmental impact. In this paper, we address a reverse logistics network design problem regarding carbon emission. The problem is formulated as a bi-objective, mixed-integer, and nonlinear programming model under various operation technologies and transport modes in the truck tire remanufacturing industry. An improved non-dominated sorting genetic algorithm II (NSGA-II) solves this NP-hard problem with bi-objectives. The numerical cases demonstrate the validation of the proposed model and the advantage of improved NSGA-II over the basic NSGA-II. Furthermore, we conducted extensive sensitivity analysis, and several managerial insights are derived.

[1]  Hokey Min,et al.  A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns , 2006 .

[2]  Yong Wang,et al.  Profit distribution in collaborative multiple centers vehicle routing problem , 2017 .

[3]  Reynaldo Cruz-Rivera,et al.  Production , Manufacturing and Logistics Reverse logistics network design for the collection of End-of-Life Vehicles in Mexico , 2009 .

[4]  S. Baskar,et al.  Application of NSGA-II Algorithm to Generation Expansion Planning , 2009, IEEE Transactions on Power Systems.

[5]  Kannan Govindan,et al.  Application of fuzzy analytic network process for barrier evaluation in automotive parts remanufacturing towards cleaner production – a study in an Indian scenario , 2016 .

[6]  Qiang Feng,et al.  Availability-based engineering resilience metric and its corresponding evaluation methodology , 2018, Reliab. Eng. Syst. Saf..

[7]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[8]  Ahmad Jafarian,et al.  Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic , 2015, Comput. Oper. Res..

[9]  Lei Huang,et al.  Bayesian Networks in Fault Diagnosis , 2017, IEEE Transactions on Industrial Informatics.

[10]  Ali H. Diabat,et al.  A carbon-capped supply chain network problem , 2009, 2009 IEEE International Conference on Industrial Engineering and Engineering Management.

[11]  Peter J. Byrne,et al.  A case analysis of a sustainable food supply chain distribution system—A multi-objective approach , 2014 .

[12]  A. Ramudhin,et al.  Design of sustainable supply chains under the emission trading scheme , 2012 .

[13]  A. Noorul Haq,et al.  A multi-echelon reverse logistics network design for product recovery—a case of truck tire remanufacturing , 2010 .

[14]  Marc Lambrecht,et al.  Network and contract optimization for maintenance services with remanufacturing , 2015, Comput. Oper. Res..

[15]  Walter J. Gutjahr,et al.  Multi-objective decision analysis for competence-oriented project portfolio selection , 2010, Eur. J. Oper. Res..

[16]  A. Ramudhin,et al.  Carbon market sensitive sustainable supply chain network design , 2010 .

[17]  Liping Fang,et al.  Process planning for closed-loop aerospace manufacturing supply chain and environmental impact reduction , 2014, Comput. Ind. Eng..

[18]  Xiaofan Lai,et al.  A multi-objective optimization for green supply chain network design , 2011, Decis. Support Syst..

[19]  Yong Liu,et al.  Two-echelon location-routing optimization with time windows based on customer clustering , 2018, Expert Syst. Appl..

[20]  Kalyanmoy Deb,et al.  Multi‐objective optimisation and multi‐criteria decision making in SLS using evolutionary approaches , 2011 .

[21]  Mohammad Saadatseresht,et al.  Evacuation planning using multiobjective evolutionary optimization approach , 2009, Eur. J. Oper. Res..

[22]  Stephan M. Wagner,et al.  Modeling carbon footprints across the supply chain , 2010 .

[23]  Sajan T. John,et al.  Multi-period reverse logistics network design with emission cost , 2017 .

[24]  Der-Horng Lee,et al.  A HEURISTIC APPROACH TO LOGISTICS NETWORK DESIGN FOR END-OF-LEASE COMPUTER PRODUCTS RECOVERY , 2008 .

[25]  T. Cheng,et al.  Managing carbon footprints in inventory management , 2011 .

[26]  Ahmad Jafarian,et al.  Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques , 2014, Eur. J. Oper. Res..

[27]  Joseph Sarkis,et al.  The impact of carbon pricing on a closed-loop supply chain: an Australian case study , 2013 .

[28]  Mahmoud H. Alrefaei,et al.  A carbon footprint based reverse logistics network design model , 2012 .

[29]  Samir Elhedhli,et al.  Green supply chain network design to reduce carbon emissions , 2012 .