Design of Mathematical Modeling in a Green Supply Chain Network by Collection Centers in the Environment

Document Type: Research Article

Authors

1 Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran

2 Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran

Abstract

Nowadays, Economic systems play an important role in environment's field. Along with the rapid change in global manufacturing scenario, environmental and social issues are becoming more important in managing any business. Increasing pressures and challenges to improve economic and environmental performance have been caused developing countries in generally in particular to consider and to start implementing green supply chain management. Green Supply Chain Network Design and Management are an approach to improve performance of the process and products according to the requirements of the environmental regulations. It is emerging as an important approach which not only reduces environmental issues but also brings economic benefit to manufacturers. Green Supply Chain Management (GSCM) has a significant influence to reduce environment's risks. Choosing the suitable supplier is a key strategic decision for productions and logistics management on the supply chain management. The purpose of this study is to describe the GSCM, to determine the allocation of products between plants, collection centers as well as effect of GSCM to the system's cost is investigated. In this paper, GSCM with multiple and conflicting objectives such as reducing costs, increasing customer's level of service and increased flexibility (accountability), respectively by providing mathematical model for optimal allocation of manufacturing products to market demand. In the event of a problem return them to factory pays the collection centers. Also, Green Supply Chain Network Design that includes several manufacturing plants, collection centers, and production with the aim of minimizing the total cost of the chain to be considered.

Keywords


Ramezani M., Bashiri M. and Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling, 37, 328-344. 

Cohen, M.A. and Lee, H.L. (1988). Strategic analysis of integrated production-distribution systems: Models and methods, Operations Research, 36 (2), 216–228.

Martin, C.H., Dent, D.C. and Eckhart. J.C. (1993). Integrated production, distribution and inventory planning at Libbey-Owens-Ford, Interfaces, 23 (3) 68–78.

Alumur, S. A., Nickel, S., Saldanha-da-Gama F. & Verter, V. (2012). Multi-period reverse logistics network design. European Journal of Operational Research, 220, 67-78.

Salema, M.I.G., Barbosa-Povoa, A.P. & Novais, A.Q. (2010). Simultaneous design and planning of supply chains with reverse flows: A generic modelling framework. European Journal of Operational Research, 203, 336-349.

Mutha, A. and Pokharel, S. (2009). Strategic network design for reverse logistics and remanufacturing using new and old product modules. Computers & Industrial Engineering, 56, 334-346.

Saffar, M.M., Shakouri, H. and Razmi, J. (2014). A new multi objective optimization model for designing a green supply chain network under uncertainty. International Journal of Industrial Engineering Computations, 6, 15–32. 

Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649.

Pishvaee, M. S., and Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433-3446.

Pishvaee, M. S., and Torabi, S. A. (2010). A possibility programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets and Systems, 161(20), 2668-2683.

Aydinel, M., Sowlati, T., Cerda, X., Cope, E. and Gerschman, M. (2008). Optimization of production allocation and transportation of customer orders for a leading forest products company. Mathematical and Computer Modelling, 48, 1158–1169.

Hassanzadeh Amin, S. and Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37, 4165–4176. 

Wu, K., Tseng, M. and Vy, T. (2011). Evaluation the drivers of green supply chain management practices in uncertainty. International Conference on Asia Pacific Business Innovation & Technology Management, 25, 384 – 397.

Rajabzadeh Ghatari, A., Khodadad Hosseini S.H. and Shekari, H. (2012). Developing Factors of GSCM (Green SCM With) With Considering the Impact on Voice of Customers (Case Study Cable Industry), International Conference on Education, Applied Sciences and Management, Dubai (UAE).

Kumar, R. and Chandrakar R. (2012). Overview of Green Supply Chain Management: Operation and Environmental Impact at Different Stages of the Supply Chain, International Journal of Engineering and Advanced Technology (IJEAT) 1(3), 2249 – 8958.  

Gilbert, S. (2000). Greening supply chain: Enhancing competitiveness through green productivity. Report of the Top Forum on Enhancing Competitiveness through Green Productivity held in the Republic of China, 25-27 May, 2000. ISBN: 92-833-2290-8.

Torres, B., Nones, S., Morques, S. and Evgenio, R. (2004). A Theoretical Approach for Green Supply Chain Management. Federal University DO RIO GRANDE, Industrial Engineering Program, Natal-Brazil.

Olugu, E.U., Wong, K.Y. and Shaharoun, A.M. (2010). A Comprehensive Approach in Assessing the Performance of an Automobile closed loop Supply Chain Sustainability. 2, 871-879

Large, R.O. & Thomsen, C.G. (2011). Drivers of Green Supply Chain Management Performance: Evidence from Germany. Journal of Purchasing and Supply Management. 17, 176-184.

Chiou, T.Y., Chan, H.K., Lettice, F. and Chung, S.H. (2011). The Influence of Greening the Suppliers and Green Innovation on Environmental Performance and Competitive Advantage in Taiwan. Transportation Research Part E. 47, 822-836.