A hybrid MCDM-fuzzy multi-objective programming approach for a G-resilient supply chain network design

Stakeholders are being increasingly encouraged to improve their supply chain risk management in order to cope efficiently and successfully with disruption risks due to unexpected events. Notwithstanding, supply chain managers lagged behind this target overlooking green development in considering environmental impact which has become a main criterion in supply chain management. Where the era of greenness threatens current supply chain partners with the need to either cope with the new green regulations or leave the field for new players. Thus, an approach to design supply chains that are simultaneously resilient, and green is needed. This study satisfies this need by developing a green and resilient (G-resilient, here after) fuzzy multi-objective programming model (GR-FMOPM) to present a G-resilient supply chain network design in determining the optimal number of facilities that should be established. The objectives are minimization of total cost and environmental impact and maximization of Value of resilience pillars where Redundancy, Agility, Leanness and Flexibility (V-RALF) can be seen four of main pillars required for supply chain resilience. Fuzzy AHP is used for determining the importance weight for each pillar followed by a usage of a Fuzzy technique for assigning the importance weight for each potential facility with respect to RALF. The importance weights obtained by Fuzzy AHP and the Fuzzy technique are then integrated in the third objective (maximization of V-RALF) to maximize the value of resilience pillars. Based on the fuzzy multi-objective model, the e-constraint method is used to reveal Pareto optimal solutions and TOPSIS was then used to select the final Pareto solution. A case study is used to validate the applicability of the developed GR-FMOPM in obtaining a G-resilient supply chain network design and a trade-off among economic, green and resilience objectives. Finally, a sensitivity analysis is performed on the importance weight for facilities Pareto solutions with respect to the importance weight of RALF. Research findings proved that the developed GR-FMOPM could be used as a tool in evaluating and ranking related facilities with respect to their resilience performance. It can also be used to obtain a G-resilient supply chain network design in terms of facilities that should be established towards a trade-off among the three aforementioned objectives.

[1]  M. C. Holcomb,et al.  Understanding the concept of supply chain resilience , 2009 .

[2]  Ali H. Diabat,et al.  Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain , 2013 .

[3]  Amy Hsin-I Lee,et al.  A fuzzy supplier selection model with the consideration of benefits, opportunities, costs and risks , 2009, Expert Syst. Appl..

[4]  Anthony J. Jakeman,et al.  Selecting among five common modelling approaches for integrated environmental assessment and management , 2013, Environ. Model. Softw..

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

[6]  Pierre Dejax,et al.  Sustainable supply chain network design: An optimization-oriented review☆ , 2015 .

[7]  Kannan Govindan,et al.  Fuzzy multi-objective sustainable and green closed-loop supply chain network design , 2017, Comput. Ind. Eng..

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

[9]  Mbarek Elbounjimi,et al.  Green Closed-loop Supply Chain Network Design: A Literature Review , 2014 .

[10]  Tadeusz Sawik,et al.  Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing , 2014, Comput. Oper. Res..

[11]  Armin Jabbarzadeh,et al.  An integrated supply chain design model with random disruptions consideration , 2010 .

[12]  Lawrence V. Snyder,et al.  The stochastic location model with risk pooling , 2007, Eur. J. Oper. Res..

[13]  R. Rajesh,et al.  Modeling enablers of supply chain risk mitigation in electronic supply chains: A Grey-DEMATEL approach , 2015, Comput. Ind. Eng..

[14]  Manoj Kumar Tiwari,et al.  A Hybrid Territory Defined evolutionary algorithm approach for closed loop green supply chain network design , 2016, Comput. Ind. Eng..

[15]  Mostafa Zandieh,et al.  Robust bi-level optimization for green opportunistic supply chain network design problem against uncertainty and environmental risk , 2017, Comput. Ind. Eng..

[16]  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..

[17]  A. Hu,et al.  Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management , 2013 .

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

[19]  C. Redman.  Should sustainability and resilience be combined or remain distinct pursuits , 2014 .

[20]  Cevriye Gencer,et al.  Analytic network process in supplier selection: A case study in an electronic firm , 2007 .

[21]  Hossam A. Gabbar.  Engineering design of green hybrid energy production and supply chains , 2009, Environ. Model. Softw..

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

[23]  Jun Zhuang,et al.  Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study , 2017 .

[24]  N. Viswanadham,et al.  Supplier selection based on supply chain ecosystem, performance and risk criteria , 2013 .

[25]  Zhongsheng Hua,et al.  On the extent analysis method for fuzzy AHP and its applications , 2008, Eur. J. Oper. Res..

[26]  R. J. Kuo,et al.  Integration of artificial neural network and MADA methods for green supplier selection , 2010 .

[27]  Andrew Lim,et al.  Reliable logistics networks design with facility disruptions , 2011 .

[28]  F. Caniato,et al.  BUILDING A SECURE AND RESILIENT SUPPLY NETWORK. , 2003 .

[29]  Chandra Prakash Garg,et al.  An integrated framework for sustainable supplier selection and evaluation in supply chains , 2017 .

[30]  Arezoo Entezaminia,et al.  A multi-objective model for multi-product multi-site aggregate production planning in a green supply chain: Considering collection and recycling centers , 2016 .

[31]  Reza Zanjirani Farahani,et al.  Resilient supply chain network design under competition: A case study , 2017, Eur. J. Oper. Res..

[32]  Kash Barker,et al.  A Bayesian network model for resilience-based supplier selection , 2016 .

[33]  Qian Wang,et al.  Optimisation of a sustainable manufacturing system design using the multi-objective approach , 2018 .

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

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

[36]  Mir Saman Pishvaee,et al.  An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain , 2014 .

[37]  Adam Rose,et al.  Resilience and sustainability in the face of disasters , 2011 .

[38]  Qian Wang,et al.  Multi-objective optimization for an RFID-enabled automated warehousing system , 2016, 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[39]  P. C. Jha,et al.  A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design , 2015 .

[40]  Mohamed Darwish,et al.  Design and optimization of an RFID-enabled passport tracking system , 2018, J. Comput. Des. Eng..

[41]  T. Saaty.  Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process , 2000 .

[42]  Muhammad Saad Memon,et al.  Sustainable and Resilient Supply Chain Network Design under Disruption Risks , 2014 .

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

[44]  Chase Rainwater,et al.  Robust facility location: Hedging against failures , 2014, Reliab. Eng. Syst. Saf..

[45]  M. Parast,et al.  A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research , 2016 .

[46]  M. Christopher,et al.  Building the Resilient Supply Chain , 2004 .

[47]  Rossitza Setchi,et al.  An integrated methodology for a sustainable two-stage supplier selection and order allocation problem , 2018, Journal of Cleaner Production.

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

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

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

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

[52]  Lakshman S. Thakur,et al.  Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain , 2012, Expert Syst. Appl..

[53]  Matthias Ehrgott,et al.  Multicriteria Optimization , 2005 .

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

[55]  Kannan Govindan,et al.  Fuzzy Axiomatic Design approach based green supplier selection: A case study from Singapore , 2015 .

[56]  Anthony Soroka,et al.  Evaluating Green and Resilient Supplier Performance: AHP-Fuzzy Topsis Decision-Making Approach , 2018, ICORES.

[57]  Keely L. Croxton,et al.  Ensuring Supply Chain Resilience: Development and Implementation of an Assessment Tool , 2013 .

[58]  Krishnendu Shaw,et al.  Low carbon chance constrained supply chain network design problem: a Benders decomposition based approach , 2016, Comput. Ind. Eng..

[59]  AliReza Madadi,et al.  Supply network design: Risk-averse or risk-neutral? , 2014, Comput. Ind. Eng..

[60]  Evi Hartmann,et al.  Robust sustainable bi-directional logistics network design under uncertainty , 2013 .

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

[62]  Jian Li,et al.  Production and transportation outsourcing decisions in the supply chain under single and multiple carbon policies , 2017 .

[63]  Ue-Pyng Wen,et al.  A Hybrid Multiple Criteria Group Decision-Making Approach for Green Supplier Selection in the TFT-LCD Industry , 2014 .

[64]  H.-J. Zimmermann.  Fuzzy set theory , 2010 .

[65]  Dariush Khezrimotlagh,et al.  An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach , 2015, Neural Computing and Applications.

[66]  Armin Jabbarzadeh,et al.  Marrying supply chain sustainability and resilience: A match made in heaven , 2016 .

[67]  Kash Barker,et al.  Inherent Costs and Interdependent Impacts of Infrastructure Network Resilience , 2015, Risk analysis : an official publication of the Society for Risk Analysis.

[68]  Mary E. Helander,et al.  Supply Chain Risk Management: A Delicate Balancing Act , 2008 .

[69]  Amelia Bilbao-Terol,et al.  Linear programming with fuzzy parameters: An interactive method resolution , 2007, Eur. J. Oper. Res..

[70]  Fariborz Jolai,et al.  Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions , 2014 .

[71]  M. Talaei,et al.  A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry , 2016 .

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

[73]  Olcay Polat,et al.  A model proposal for green supply chain network design based on consumer segmentation , 2016 .

[74]  Keely L. Croxton,et al.  ENSURING SUPPLY CHAIN RESILIENCE: DEVELOPMENT OF A CONCEPTUAL FRAMEWORK , 2010 .

[75]  Qian Wang,et al.  A sustainable manufacturing system design: A fuzzy multi-objective optimization model , 2017, Environmental Science and Pollution Research.

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

[77]  Yanfeng Ouyang,et al.  Joint Inventory-Location Problem Under Risk of Probabilistic Facility Disruptions , 2011 .

[78]  Jesse R. O'Hanley,et al.  Optimizing system resilience: A facility protection model with recovery time , 2012, Eur. J. Oper. Res..

[79]  Wing-Keung Wong,et al.  An Integrated Model of Material Supplier Selection and Order Allocation Using Fuzzy Extended AHP and Multiobjective Programming , 2013 .

[80]  Anjali Awasthi,et al.  Green supplier development program selection using NGT and VIKOR under fuzzy environment , 2016, Comput. Ind. Eng..

[81]  Tadeusz Sawik,et al.  On the risk-averse optimization of service level in a supply chain under disruption risks , 2016 .

[82]  Rommert Dekker,et al.  Design and planning for green global supply chains under periodic review replenishment policies , 2014 .

[83]  Francisco Rodrigues Lima Junior,et al.  A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection , 2014, Appl. Soft Comput..

[84]  Virgilio Cruz-Machado,et al.  Supply chain redesign for resilience using simulation , 2012, Comput. Ind. Eng..

[85]  S. Ramesh,et al.  Application of modified NSGA-II algorithm to multi-objective reactive power planning , 2012, Appl. Soft Comput..