Design of a supply chain network considering environmental factors under uncertainty and solving the model by multi- objective differential evolutionary algorithm (MODE)

Subject Areas : environmental management

Mohammad Mahdi  Saffar ¹ (MSc of Industrial Engineering, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.* (Corresponding Author))
Hamed  Shakouri Ganjavi ² (Associate Professor of Industrial Engineering, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.)
Jafar  Razmi ³ (Professor of Industrial Engineering, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.)

Received: 2014-05-11 Accepted : 2014-09-17 Published : 2017-03-21

Keywords: Reverse Supply Chain, CO2 emission, Uncertainty, Jimenez Method, Multi Objective Differential E,

Abstract :

Background and Objective: Today, design of a supply chain network which balances economic and environmental issues is attractive not only for researchers and practitioners, but also for managers and industrial decision makers. Method: This study introduces a bi-objective supply chain network which uses a fuzzy approach in order to include uncertainty in parameters of the model and apply it to a real case. Moreover, this model takes environmental and economic issues into account simultaneously. This consideration occurs in production and recovery technologies. Furthermore, the aim of the represented model is to choose the optimal production technology at all centers, the optimal production planning, facilities and location and the optimal number of those technologies which must be purchased. The fuzzy model is converted to an auxiliary crisp model by Jimenez approach and then solved with є-constraint. For the large sized problems, the Multi Objective Differential Evolutionary algorithm (MODE) is applied. Findings: It was shown that the cost objective functions and CO2 emission objective function are in conflict with each other, implying that any increase in one of them leads to decrease of another one and vice versa. Totally, it can be concluded that the -constraint method and the MODE method are appropriate and qualified methods for solving the auxiliary crisp model of supply chain network design problems.

References:

 

1- رزمی. جعفر و پیشوایی. میر سامان، 1389، روش‌های کمی برای مدیریت لجستیک معکوس، انتشارات موسسه مطالعات و پژوهش‌های بازرگانی.

2- Melkote, S., Daskin, M.S., 2001. Capacitated facility location/network design problem. European Journal of Operation Research, Vol 129, pp. 481-495.

3- Nga Thanh, P., Bostel, N., Peton, O., 2008. A dynamic model for facility location in the design of complex supply chains. International Journal of Production Economics, Vol 113, pp. 678-693.

4- Drezner, Z., wesolowsky, G.O., 2003. Network design: selection and design of links and facility location. Transportation Research, Vol 37, pp. 241-256.

5- Ambrosino, D., Scutella, M.G., 2005. Distribution Network Design: New Problems and Related Models. European Journal of Operational Research, Vol 165, pp. 610-624.

6- Ozdemir, D., Yucesan, E., Herer, Y.T., 2006. Multi-location transshipment problem with capacitated transportation. European Journal of Operational Research, Vol 175, pp. 602-621.

7- Pirkul, H., Jayaraman, V., 1998. A multi-commodity, Multi-Plant, Capacitated Facility Location Problem: Formulation and Efficient Heuristic Solution. Computers and Operational Research, Vol 25, pp. 869-878.

8- Hinojosa, Y., Kalcsics, J., Nickel, S., Puerto, J., Velten, S., 2008. Dynamic supply chain design with inventory. Computers & Operations Research, Vol 35, pp. 373-391.

9- Lu, Z., Bostel, N., 2007. A facility location model for logistics systems including reverse flows: the case of remanufacturing activities. Computers & Operations Research, Vol 34, pp. 299–323.

10- Pishvaee, M.S., Zanjirani Farahani, R., Dullaert, W., 2010. A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & Operations Research, Vol 37, pp. 1100-1112.

11- Pishvaee, M.S., Rabbani, M., Torabi, S.A., 2010. A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, Vol 35, pp. 637-649.

12- Lian Qi., Zuo-Jun Max Shen., 2007. A supply chain design model with unreliable supply. Naval Research Logistics, Vol 54, pp. 829-844.

13- Sayed, M., Afia, N., Kharbotly, A., 2008. A stochastic model for forward–reverse logistics network design under risk. Computers & Industrial Engineering, Vol 58, pp. 423-431.

14- Qin, Y., Jin, M., 2009. Optimal Model and Algorithm for Multi-Commodity Logistics Network Design Considering Stochastic Demand and Inventory Control  Original Research Article. Systems Engineering-Theory & Practice, Vol 29, pp. 176-183.

15- Jabal Ameli, M.S., Azad, N., Rastpour, A., 2009. Designing a Supply Chain Network Model with Uncertain Demand and Lead Times. Journal of Uncertain Systems, Vol 3, pp. 123-130.

16- Syam, S.S., 2002. A model and methodologies for the location problem with logistical components. Computers and Operations Research, Vol 29, pp. 1173-1193.

17- Baghalian, A., Rezapour, Sh., Zanjirani Farahani, R., 2013. Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, Vol 227, pp. 199-215.

18- Pishvaee, M.S., Razmi, J., 2012. Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, Vol 36, pp. 3433-3446.

19- Pishvaee, M.S., Torabi, S.A., 2010. A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets and Systems, Vol 161, pp. 2668-2683.

20- Sawik,T., 2011. Supplier selection in make-to-order environment with risks. Mathematical and Computer Modelling, Vol 53, pp. 1670-1679.

21- Shaw, K., Shankar, R., Yadav, S.S., Thakur, L.S., 2012. Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications, Vol 39,pp. 8182-8192.

22- Jiménez, M., 2007. Linear programming with fuzzy parameters: an interactive method resolution. European Journal ofOperational Research, Vol 177, pp. 1599-1609.

23- Jiménez, M., 1996. Ranking fuzzy numbers through the comparison of its expected intervals. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol 4, pp. 379-388.

24- Yager, R. R., 1981. A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, Vol 24, pp. 143-161.

25- Hwang, C. L., Masud, A. S. M., 1979. Multiple objective decision making-methodsand applications. Lecture Notes in Economics and Mathematical Systems, Vol 164, Springer-Verlag, Berlin.

26- Pishvaee, M.S., Shakouri, H., 2009. A System Dynamics Approach for Capacity Planning and Price Adjustment in a Closed-Loop Supply Chain. IEEE, Computer Modeling and Simulation  EMS` 09, pp. 435-439.