Abstract
Greening of supply chain operations is best to be addressed at the network design phase where strategic facility location, technology and transport mode decisions are made. This has been an important area of research focus for almost a decade now. Given the increasing frequency and intensity of disruptive events facing today’s organizations, the greening analyses of supply chains need to take into consideration how the economic and environmental performance of the supply chain can be affected in the face of unanticipated disruptions. Thus, static greening analysis is simplistic and achieving a truly green supply chain requires a dynamic analysis to develop robust supply chains whose sustainability performance remains unaffected or only lightly affected by disruptions of various types. This chapter presents a framework and optimization model for dynamic sustainability analysis. A numerical example is presented to illustrate the application of the approach in performing tradeoff analysis in business-as-usual and disruption circumstances.
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References
Aköz, O., & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European Journal of Operational Research, 181, 1427–1433.
Altiparmak, F., Gen, M., Lin, L., & Paksoy, T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers and Industrial Engineering, 51, 196–215.
Amid, A., Ghodsypour, S. H., & O’Brien, C. (2006). Fuzzy multiobjective linear model for supplier selection in a supply chain. International Journal of Production Economics, 104, 394–407.
Aouni, B., Kettani, O. (2001). Goal programming model: A glorious history and a promising future. European Journal of Operational Research, 133, 225–231.
Arntzen, B. C., Brown, G. G., Harrison, T. P., & Trafton, L. L. (1995). Global supply chain management at digital equipment corporation. Interfaces, 25, 69–93.
Aryanezhad, M. B., Jalali, S. G., & Jabbarzadeh, A. (2010). An integrated supply chain design model with random disruptions consideration. African Journal of Business Management, 4, 2393–2401.
Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227, 199–215.
Berman, O., Krass, D., & Menezes, M. B. (2007). Facility reliability issues in network p-median problems: Strategic centralization and co-location effects. Operations Research, 55, 332–350.
Boukherroub, T., Ruiz, A., Guinet, A., & Fondrevelle, J. (2015). An integrated approach for sustainable supply chain planning. Computers and Operations Research, 54, 180–194.
Chen, L.-H., & Tsai, F.-C. (2001). Fuzzy goal programming with different importance and priorities. European Journal of Operational Research, 133, 548–556.
Chen, C.-T., Lin, C.-T., & Huang, S.-F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102, 289–301.
Chen, Q., Li, X., & Ouyang, Y. (2011). Joint inventory-location problem under the risk of probabilistic facility disruptions. Transportation Research Part B: Methodological, 45, 991–1003.
Cui, T., Ouyang, Y., & Shen, Z. J. M. (2010). Reliable facility location design under the risk of disruptions. Operations Research, 58, 998–1011.
Fahimnia, B., Sarkis, J., Choudhary, A., Eshragh, A. (2014a). Tactical supply chain planning under a carbon tax policy scheme: A case study. International Journal of Production Economics. doi:10.1016/j.ijpe.2014.12.015
Fahimnia, B., Sarkis, J., Eshragh, A. (2014b). A Tradeoff model for green supply chain planning: A leanness-versus-greenness analysis. OMEGA. doi:10.1016/j.omega.2015.01.014
Fahimnia, B., Sarkis, J., Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101–104.
Goedkoop, M., Heijungs, R., Huijbregts, M., Schryver, A. D., Struijs, J., Zelm, R. (2009). Report I: Characterisation, ReCiPe 2008: A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level. Ministry of Housing, Spatial planning and the Environment (VROM), The Netherlands.
Guinèe, J. B., Gorrée, M., Heijungs, R., Huppes, G., Kleijn, R., de Koning, A., et al. (2001). Life cycle assessment; an operational guide to the ISO standards. In J. B. Guinèe (Ed.), Ministry of housing. The Netherlands: Spatial Planning and Environment (VROM) and Centre of Environmental Science (CML)—Leiden University.
Jabbarzadeh, A., Jalali Naini, S. G., Davoudpour, H., Azad, N. (2012). Designing a supply chain network under the risk of disruptions. Mathematical Problems in Engineering.
Jamalnia, A., & Soukhakian, M. A. (2009). A hybrid fuzzy goal programming approach with different goal priorities to aggregate production planning. Computers and Industrial Engineering, 56, 1474–1486.
Jolliet, O., Margni, M., Charles, R., Humbert, S., Payet, J., Rebitzer, G., & Rosenbaum, R. (2003). IMPACT 2002+: A new life cycle impact assessment methodology. The International Journal of Life Cycle Assessment, 8, 324–330.
Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply chain. Computers and Industrial Engineering, 46, 69–85.
Li, X., & Ouyang, Y. (2010). A continuum approximation approach to reliable facility location design under correlated probabilistic disruptions. Transportation Research Part B: Methodological, 44, 535–548.
Li, Q., Zeng, B., & Savachkin, A. (2013). Reliable facility location design under disruptions. Computers and Operations Research, 40, 901–909.
Liang, T. F. (2007). Integrating production-transportation planning decision with fuzzy multiple goals in supply chains. International Journal of Production Research, 46, 1477–1494.
Lim, M., Daskin, M. S., Bassamboo, A., & Chopra, S. (2010). A facility reliability problem: Formulation, properties, and algorithm. Naval Research Logistics (NRL), 57, 58–70.
Nagurney, A., & Nagurney, L. S. (2010). Sustainable supply chain network design: a multicriteria perspective. International Journal of Sustainable Engineering, 3, 189–197.
Narasimhan, R. (1980). Goal programming in a fuzzy environment. Decision Sciences, 11, 325–336.
O’Hanley, J. R., Scaparra, M. P., & García, S. (2013). Probability chains: A general linearization technique for modeling reliability in facility location and related problems. European Journal of Operational Research, 230, 63–75.
Özceylan, E., & Paksoy, T. (2012). Fuzzy multi-objective linear programming approach for optimising a closed-loop supply chain network. International Journal of Production Research, 51, 2443–2461.
Peng, P., Snyder, L. V., Lim, A., & Liu, Z. (2011). Reliable logistics networks design with facility disruptions. Transportation Research Part B: Methodological, 45, 1190–1211.
Pinto-Varela, T., Barbosa-Póvoa, A. P. F. D., Novais, A. Q. (2011). Bi-objective optimization approach to the design and planning of supply chains: Economic versus environmental performances. Computers and Chemical Engineering, 35, 1454–1468.
Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36, 3433–3446.
Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28, 581–598.
Selim, H., & Ozkarahan, I. (2008). A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. International Journal of Advanced Manufacturing Technology, 36, 401–418.
Selim, H., Araz, C., & Ozkarahan, I. (2008). Collaborative production–distribution planning in supply chain: A fuzzy goal programming approach. Transportation Research Part E: Logistics and Transportation Review, 44, 396–419.
Shen, Z.-J. M., Zhan, R. L., & Zhang, J. (2011). The reliable facility location problem: formulations, heuristics, and approximation algorithms. INFORMS Journal on Computing, 23, 470–482.
Snyder, L. V., & Daskin, M. S. (2005). Reliability models for facility location: The expected failure cost case. Transportation Science, 39, 400–416.
Tiwari, R. N., Dharmar, S., & Rao, J. R. (1987). Fuzzy goal programming—An additive model. Fuzzy Sets and Systems, 24, 27–34.
Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159, 193–214.
Wang, R.-C., & Liang, T.-F. (2004). Application of fuzzy multi-objective linear programming to aggregate production planning. Computers and Industrial Engineering, 46, 17–41.
Zakeri, A., Dehghanian, F., Fahimnia, B., Sarkis, J. (2015). Carbon pricing versus emissions trading: A supply chain planning perspective. International Journal of Production Economics.
Zolli, A., & Healy, A. M. (2012). Resilience. Why things bounce back. New York: Simon & Schuster.
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Jabbarzadeh, A., Fahimnia, B. (2015). Dynamic Supply Chain Greening Analysis. In: Fahimnia, B., Bell, M., Hensher, D., Sarkis, J. (eds) Green Logistics and Transportation. Greening of Industry Networks Studies, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-17181-4_3
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DOI: https://doi.org/10.1007/978-3-319-17181-4_3
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