Goal Programming Model for Production-Distribution Planning by Considering Carbon Emission

Year 2020, , 135 - 150, 01.03.2020

Ahmet Aktas , İzzettin Temiz

https://doi.org/10.35378/gujs.471083

Cited By: 7

Abstract

Companies must manage their supply chains effectively under changing conditions in marketplace in order to be successful against their competitors. As a result of some regulations in recent years, companies are forced to consider the damage they cause to the environment by their supply chain activities. In this paper, a production-distribution problem, which concerns economic and environmental effects, is considered. A multi-product, multi-stage production-distribution network with different transportation alternatives is modelled in the problem. A goal programming model is proposed to support planning decisions of this production-distribution network by considering the profit of network activities and the carbon emission value caused by material and product transportation. A randomly generated set of test data was used to evaluate the effectiveness of the proposed model. The results show that the proposed model can be used as an effective tool for environmentally friendly production-distribution planning

Keywords

Production – distribution planning, Goal programming, Lead times, Bill-of-materials, Carbon emission

References

• [1] Singh, A., 2014. Supplier evaluation and demand allocation among suppliers in a supply chain. Journal of Purchasing & Supply Management, 20, 167–176.
• [2] Sadrnia, A., Ismail, N., Zulkifli, N., Ariffin, M.K.A., Nezamabadi-pour, H., Mirabi, H., 2013. A Multiobjective Optimization Model in Automotive Supply Chain Networks. Mathematical Problems in Engineering, 2013, 1-10.
• [3] Plambeck, E.L., 2012. Reducing greenhouse gas emissions through operations and supply chain management. Energy Economics, 34, S64-S74.
• [4] IPCC, 2007. Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, in: B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (Eds.), Cambridge, United Kingdom and New York, NY, USA.
• [5] Altiparmak, F.,Gen, M., Lin, L., Paksoy, T., 2006. A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & Industrial Engineering, 51, 196-215.
• [6] Torabi, S.A., Hassini, E., 2009. Multi-site production planning integrating procurement and distribution plans in multi-echelon supply chains: an interactive fuzzy goal programming approach. International Journal of Production Research, 47, 5475-5499.
• [7] Franca, R.B., Jones, E.C., Richards, C.N., Carlson, J.P., 2010. Multi-objective stochastic supply chain modeling to evaluate tradeoffs between profit and quality. International Journal of Production Economics, 127, 292-299.
• [8] Bashiri, M., Badri, H., Talebi, J., 2012. A new approach to tactical and strategic planning in production–distribution networks. Applied Mathematical Modelling, 36, 1703-1717.
• [9] Torabi, S.A., Moghaddam, M., 2012. Multi-site integrated production-distribution planning with trans-shipment: a fuzzy goal programming approach. International Journal of Production Research, 50, 1726-1748.
• [10] Badri, H., Bashiri, M. Hejazi, T.H., 2013. Integrated strategic and tactical planning in a supply chain network design with a heuristic solution method. Computers & Operations Research, 40, 1143-1154.
• [11] Paksoy, T., Pehlivan, N.Y., Özceylan, E., 2013. A New Tradeoff Model for Fuzzy Supply Chain Network Design and Optimization. Human and Ecological Risk Assessment: An International Journal, 19, 492-514.
• [12] Peidro, D., Mula, J., Alemany, M.M.E., Lario, F.-C., 2012. Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. International Journal of Production Research, 50, 3011-3020.
• [13] Shaw, K., Shankar, R., Yadav, S.S., Thakur, L.S., 2013. Modeling a low-carbon garment supply chain. Production Planning & Control, 24, 851-865.
• [14] Ahmadizar, F., Zeynivand, M., 2014. Bi-objective supply chain planning in a fuzzy environment. Journal of Intelligent & Fuzzy Systems, 26, 153–164.
• [15] Paydar, M. M., Saidi-Mehrabad, M., 2015. Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters. International Journal of Computer Integrated Manufacturing, 28:3, 251-265.
• [16] Pan, F., Nagi, R., 2013. Multi-echelon supply chain network design in agile manufacturing. Omega, 41, 969-983.
• [17] Su, W., Huang, S.X., Fan, Y.S., Mak, K.L., 2015. Integrated partner selection and production–distribution planning for manufacturing chains. Computers & Industrial Engineering, 84, 32–42.