Eco-Gresilient: Coalescing Ingredient of Economic, Green and Resilience in Supply Chain Network Design

This research presents a new approach that considers green and resilience dimensions in addition to economic (eco-gresilient, henceforth) aspects to design an eco-gresilient supply chain network. Thus, fuzzy AHP (analytical hierarchy process) is used to determine the relative weight of evaluation criteria for each resilience pillars (robustness, agility, leanness and flexibility (RALF)), and then it is used for assigning the importance weight for each potential facility with respect to RALF. The determined weights revealed via fuzzy AHP are then integrated into a multi-objective optimization model to identify the number of facilities that should be established in the meat supply chain. Three objective functions were formulated and include minimization of total cost and environmental impact and maximization of value of resilience (V-RALF). The ε-constraint approach is used to obtain a set of Pareto solutions. The effectiveness of the developed eco-gresilient multiobjective model is presented on a case study in the meat sector.

[1]  Kannan Govindan,et al.  Supply chain network design under uncertainty: A comprehensive review and future research directions , 2017, Eur. J. Oper. Res..

[2]  Maolin Tang A Hybrid , 2010 .

[3]  A. Chaabane,et al.  Global reverse supply chain redesign for household plastic waste under the emission trading scheme , 2015 .

[4]  Qian Wang,et al.  Developing a meat supply chain network design using a multi-objective possibilistic programming approach , 2017 .

[5]  Vipul Jain,et al.  Measuring supply chain resilience using a deterministic modeling approach , 2014, Comput. Ind. Eng..

[6]  Qian Wang,et al.  Design and optimization of an RFID-enabled automated warehousing system under uncertainties: a multi-criterion fuzzy programming approach , 2017 .

[7]  Thomas L. Saaty Fundamentals of decision making and priority theory , 2000 .

[8]  C. R. Pereira,et al.  Key Organisational Factors to Building Supply Chain Resilience: a Multiple Case Study of Buyers and Suppliers , 2015 .

[9]  Matthias Ehrgott,et al.  Multicriteria Optimization (2. ed.) , 2005 .

[10]  Manoj Kumar Tiwari,et al.  Performance measures based optimization of supply chain network resilience: A NSGA-II + Co-Kriging approach , 2016, Comput. Ind. Eng..

[11]  Phanarut Srichetta,et al.  Applying Fuzzy Analytic Hierarchy Process to Evaluate and Select Product of Notebook Computers , 2012 .

[12]  Mahour Mellat Parast,et al.  A multi-objective approach to supply chain risk management: Integrating visibility with supply and demand risk , 2015 .

[13]  Catherine Azzaro-Pantel,et al.  A green supply chain network design framework for the processed food industry: Application to the orange juice agrofood cluster , 2017, Comput. Ind. Eng..

[14]  Christine L. Mumford,et al.  A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling , 2014 .

[15]  Virginia L. M. Spiegler,et al.  Developing a resilient supply chain strategy during ‘boom’ and ‘bust’ , 2016 .

[16]  Qian Wang,et al.  Integrity of RFID-enable HMSC Networks , 2015, Digital Enterprise and Information Systems.

[17]  C. Perrings Resilience and sustainable development , 2006, Environment and Development Economics.

[18]  Rodrigo Reyes Levalle,et al.  A resilience by teaming framework for collaborative supply networks , 2015, Comput. Ind. Eng..

[19]  Qian Wang,et al.  Multi-criteria optimization for a cost-effective design of an RFID-based meat supply chain , 2017 .

[20]  Qian Wang,et al.  The fuzzy multi-objective distribution planner for a green meat supply chain , 2017 .