Design of Forward/reverse Logistics with Environmental Consideration

Document Type: Research Paper


1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran


Growth of environmental issues has caused to consider various factors that influence on condition of environment. Green supply chain has absorbed care of researchers because of its considerable impacts on environment. In this regard, this study designs the forward/revers logistics network by putting emphasis on environmental aspects in its model such as quantity of CO2 emission. In this logistics network, three objective functions such as minimizing the total cost and quantity of CO2 emission as well as maximizing the satisfaction of customers have been considered, simultaneously. Because of considering of three objective functions in this model, multi objective optimization methods persuade the researchers to implement them. Non-dominated sorting genetic algorithms (NSGA-ӀӀ) and Multi-objective particle swarm optimization (MOPSO) are proposed to cope with this problem. The results acquired from experiments on several test problems are verified by GAMS software. Finally, the results obtained through experiments on different problems verify the superiority of NSGA-ӀӀ over MOPSO in terms of all comparison metrics.


Main Subjects

Choon Tan, K., Lyman, S. B., and Wisner, J. D. (2002). Supply chain management: a strategic perspective. International Journal of Operations & Production Management, Vol. 22(6), pp. 614-631.

Choudhary, A., Sarkar, S., Settur, S., and Tiwari, M. (2015). A carbon market sensitive optimization model for integrated forward–reverse logistics. International Journal of Production Economics, Vol. 164, pp. 433-444.

Coello, C. A. C., Pulido, G. T., and Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, Vol. 8(3), pp. 256-279.

Davis, P., and Ray, T. (1969). A branch‐bound algorithm for the capacitated facilities location problem. Naval Research Logistics (NRL), Vol. 16(3), pp. 331-344.

Diabat, A., Abdallah, T., Al-Refaie, A., Svetinovic, D., and Govindan, K. (2013). Strategic closed-loop facility location problem with carbon market trading. IEEE Transactions on engineering Management, Vol. 60(2), pp. 398-408.

El Saadany, A. M., and El-Kharbotly, A. K.(2004). Reverse logistics modeling. Paper presented at the 8th international conference on production engineering and design for development, Alexandria, Egypt.

Farrokhi-Asl, H., Tavakkoli-Moghaddam, R., Asgarian, B., and Sangari, E. (2017). Metaheuristics for a bi-objective location-routing-problem in waste collection management. Journal of Industrial and Production Engineering, Vol. 34(4), pp. 239-252.

Ghaderi, H., Pishvaee, M. S., and Moini, A. (2016). Biomass supply chain network design: An optimization-oriented review and analysis. Industrial Crops and Products, Vol. 94, pp. 972-1000.

Govindan, K., Soleimani, H., and Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, Vol. 240(3), pp. 603-626.

Graedel, T., Allenby, B., and CΟMRIΕ, P. (1995). Matrix approaches to abridged life cycle assessment. Environmental Science & Technology, Vol. 29(3), pp. 134A-139A.

Kalyanarengan, R. N., Zondervan, E. E., Fransoo, J. J., and Grievink, J. (2016). A Supply Chain Optimization Framework For CO2 Emission Reduction: Case Of The Netherlands.

Kumar, V., Kumar, V., Brady, M., Garza-Reyes, J. A., and Simpson, M. (2017). Resolving forward-reverse logistics multi-period model using evolutionary algorithms. International Journal of Production Economics, Vol. 183, pp. 458-469.

Lertworasirikul, S., Fang, S.-C., Joines, J. A., and Nuttle, H. L. (2003). Fuzzy data envelopment analysis (DEA): a possibility approach. Fuzzy Sets and Systems, Vol. 139(2), pp. 379-394.

Li, S., Ragu-Nathan, B., Ragu-Nathan, T., and Rao, S. S. (2006). The impact of supply chain management practices on competitive advantage and organizational performance. Omega, Vol. 34(2), pp. 107-124.

Lowe, E. (1993). Industrial ecology—an organizing framework for environmental management. Environmental Quality Management, Vol. 3(1), pp. 73-85.

Mousazadeh, M., Torabi, S. A., and Pishvaee, M. S. (2014). Green and reverse logistics management under fuzziness Supply Chain Management Under Fuzziness, pp. 607-637, Springer Berlin Heidelberg.

Nikoo, M. B., and Mahinpey, N. (2008). Simulation of biomass gasification in fluidized bed reactor using ASPEN PLUS. Biomass and Bioenergy, Vol. 32(12), pp. 1245-1254.

Pasandideh, S. H. R., Niaki, S. T. A., and Asadi, K. (2015). Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Sciences, Vol. 292, pp. 57-74.

Pishvaee, M. S., Farahani, R. Z., and Dullaert, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & operations research, Vol. 37(6), pp. 1100-1112.

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

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

Rabbani, M., Bajestani, M. A., and Khoshkhou, G. B. (2010). A multi-objective particle swarm optimization for project selection problem. Expert Systems with Applications, Vol. 37(1), pp. 315-321.

Rabbani, M., Mousavi, Z., and Farrokhi-Asl, H. (2016). Multi-objective metaheuristics for solving a type II robotic mixed-model assembly line balancing problem. Journal of Industrial and Production Engineering, Vol. 33(7), pp. 472-484.

Saffar, M., and Razmi, J. (2014). A new bi-objective mixed integer linear programming for designing a supply chain considering co2 emission. Uncertain Supply Chain Management, Vol. 2(4), pp. 275-292.

Saffari, H., Makui, A., Mahmoodian, V., and Pishvaee, M. S. (2015). Multi-objective robust optimization model for social responsible closed-loop supply chain solved by non-dominated sorting genetic algorithm. Journal of Industrial and Systems Engineering, Vol. 8(3), pp. 42-59.

Soleimani, H., Seyyed-Esfahani, M., and Shirazi, M. A. (2016). A new multi-criteria scenario-based solution approach for stochastic forward/reverse supply chain network design. Annals of Operations Research, Vol. 242(2), pp. 399-421.

Soysal, M., Bloemhof-Ruwaard, J. M., Haijema, R., and van der Vorst, J. G. (2015). Modeling an Inventory Routing Problem for perishable products with environmental considerations and demand uncertainty. International Journal of Production Economics, Vol. 164, pp. 118-133.

Wang, F., Lai, X., and Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, Vol. 51(2), pp. 262-269.