Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty

Measuring and controlling emissions across the logistics network is an important challenge for today's firms according to increasing concern about the environmental impact of business activities. This paper proposes a bi-objective credibility-based fuzzy mathematical programming model for designing the strategic configuration of a green logistics network under uncertain conditions. The model aims to minimize the environmental impacts and the total costs of network establishment simultaneously for the sake of providing a sensible balance between them. A popular but credible environmental impact assessment index, i.e., CO"2 equivalent index is used to model the environmental impact across the concerned logistics network. Since transportation mode and production technology play important roles on the concerned objectives, the proposed model integrates their respective decisions with those of strategic network design ones. In addition, to solve the proposed bi-objective fuzzy optimization model, an interactive fuzzy solution approach based upon credibility measure is developed. An industrial case study is also provided to show the applicability of the proposed model as well as the usefulness of its solution method.

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

[2]  Mitsuo Gen,et al.  Integrated multistage logistics network design by using hybrid evolutionary algorithm , 2009, Comput. Ind. Eng..

[3]  Raul Poler Escoto,et al.  Fuzzy optimization for supply chain planning under supply, demand and process uncertainties , 2009, Fuzzy Sets Syst..

[4]  Xiaoyu Ji,et al.  Logistics network design for product recovery in fuzzy environment , 2010, Eur. J. Oper. Res..

[5]  Tseng-Hsing Hsu,et al.  Life cycle assessment on using recycled materials for rehabilitating asphalt pavements , 2008 .

[6]  Ismail Erol,et al.  A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain , 2011 .

[7]  Mir Saman Pishvaee,et al.  A possibilistic programming approach for closed-loop supply chain network design under uncertainty , 2010, Fuzzy Sets Syst..

[8]  Wen-Hsien Tsai,et al.  A fuzzy goal programming approach for green supply chain optimisation under activity-based costing and performance evaluation with a value-chain structure , 2009 .

[9]  Jacqueline M. Bloemhof,et al.  A Methodology for Assessing Eco-Efficiency in Logistics Networks , 2007, Eur. J. Oper. Res..

[10]  C. Hwang,et al.  Possibilistic linear programming for managing interest rate risk , 1993 .

[11]  Mir Saman Pishvaee,et al.  A stochastic optimization model for integrated forward/reverse logistics network design , 2009 .

[12]  C. Hwang.  Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[13]  Masatoshi Sakawa,et al.  An Interactive Fuzzy Satisficing Method for Multiobjective Linear-Programming Problems and Its Application , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Ching-Lai Hwang,et al.  Multiple Objective Decision Making , 1994 .

[15]  Ali Amiri,et al.  Production , Manufacturing and Logistics Designing a distribution network in a supply chain system : Formulation and efficient solution procedure , 2005 .

[16]  Xiaoxia Huang,et al.  Chance-constrained programming models for capital budgeting with NPV as fuzzy parameters , 2007 .

[17]  Mir Saman Pishvaee,et al.  A memetic algorithm for bi-objective integrated forward/reverse logistics network design , 2010, Comput. Oper. Res..

[18]  Mark S. Daskin,et al.  AN INTEGRATED MODEL OF FACILITY LOCATION AND TRANSPORTATION NETWORK DESIGN , 2001 .

[19]  Turan Paksoy,et al.  Operational and environmental performance measures in a multi-product closed-loop supply chain , 2011 .

[20]  Turan Paksoy,et al.  A genetic algorithm approach for multi-objective optimization of supply chain networks , 2006, Comput. Ind. Eng..

[21]  Hasan Pirkul,et al.  Planning and coordination of production and distribution facilities for multiple commodities , 2001, Eur. J. Oper. Res..

[22]  S.A. Torabi,et al.  An interactive possibilistic programming approach for multiple objective supply chain master planning , 2008, Fuzzy Sets Syst..

[23]  Samir K. Srivastava,et al.  Green Supply-Chain Management: A State-of-the-Art Literature Review , 2007 .

[24]  Yan-Kuen Wu,et al.  Two-phase approach for solving the fuzzy linear programming problems , 1999, Fuzzy Sets Syst..

[25]  J.A.E.E. van Nunen,et al.  Designing and Evaluating Sustainable Logistics Networks , 2006 .

[26]  Yian-Kui Liu,et al.  Expected value of fuzzy variable and fuzzy expected value models , 2002, IEEE Trans. Fuzzy Syst..

[27]  Z. Shen.  Integrated supply chain design models: a survey and future research directions , 2007 .

[28]  Gilbert Laporte,et al.  A comparative analysis of several vehicle emission models for road freight transportation , 2011 .

[29]  Efstratios N. Pistikopoulos,et al.  Environmentally conscious long-range planning and design of supply chain networks , 2005 .

[30]  Shabbir Ahmed,et al.  Supply chain design under uncertainty using sample average approximation and dual decomposition , 2009, Eur. J. Oper. Res..

[31]  Mir Saman Pishvaee,et al.  A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment , 2011, Adv. Eng. Softw..

[32]  P. Miranda,et al.  INCORPORATING INVENTORY CONTROL DECISIONS INTO A STRATEGIC DISTRIBUTION NETWORK DESIGN MODEL WITH STOCHASTIC DEMAND , 2004 .

[33]  Baoding Liu,et al.  Chance constrained programming with fuzzy parameters , 1998, Fuzzy Sets Syst..

[34]  M. El-Sayed,et al.  A stochastic model for forward-reverse logistics network design under risk , 2010, Comput. Ind. Eng..

[35]  Baoding Liu.  Uncertainty Theory: An Introduction to its Axiomatic Foundations , 2004 .

[36]  Haiping Zhu,et al.  A Credibility-Based Fuzzy Programming Model for APP Problem , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[37]  Gonzalo Guillén-Gosálbez,et al.  A global optimization strategy for the environmentally conscious design of chemical supply chains under uncertainty in the damage assessment model , 2010, Comput. Chem. Eng..

[38]  Xiang Li,et al.  A Sufficient and Necessary Condition for Credibility Measures , 2006, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[39]  Mir Saman Pishvaee,et al.  A robust optimization approach to closed-loop supply chain network design under uncertainty , 2011 .

[40]  Baoding Liu,et al.  Dependent-chance programming with fuzzy decisions , 1999, IEEE Trans. Fuzzy Syst..

[41]  M. Goedkoop,et al.  The Eco-indicator 99, A damage oriented method for Life Cycle Impact Assessment , 1999 .

[42]  Christine L. Mumford,et al.  Assessing the impact of cost optimization based on infrastructure modelling on CO2 emissions , 2011 .

[43]  Lixing Yang,et al.  Fuzzy fixed charge solid transportation problem and algorithm , 2007, Appl. Soft Comput..