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Manuscript ID : IMJ-2101-1532 (R3) Visit : 156 Page: 139 - 160

10.30495/imj.2021.684245

Article Type: Original Research

Robust Optimization model for forward/revers logistics network using Experiment Design

Subject Areas : Industrial Management

hamid saffari 1 , karim atashgar 2 , Morteza Abbasi 3

1 - Malek Ashtar University of Technology
2 - Malek Ashtar University of Technology
3 - مدیرگروه علمی مهندسی صنایع

Received: 2021-01-09 Accepted : 2021-05-18 Published : 2021-08-23

Keywords: Design of Experime, Fractional design of experiment, Robust Optimization, Supply chain network design,

Abstract :

Increasing attention to the closed-loop supply chain and considering forward /reverse logistics has led to the presentation of various mathematical models in this field. This paper first presents a robust model for a supply chain network with forward /reverse flow approach, and then uses the experiment design method to determine the effect of each of the robust modeling parameters and the parameters of cost, production rate, return of products in the top and bottom level. this paper presents a new robust optimized model for the supply chain network.design The use of robust mathematical design and experimental modeling at the same time, which is done for the first time in this paper, has caused: 1) to increase the speed of achieving optimal answers, 2) to help the decision maker in the appropriate choice of model parameters in a structured way. The report and the presented results, according to the information of iron and steel industry, show the efficiency of using experimental design in order to reduce the time of solving the mathematical model and providing guidelines to the decision maker in the strategic area of supply chain.

References:


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_||_

  1. Assarzadegan, P., & Rasti-Barzoki, M. (2016). Minimizing sum of the due date assignment costs, maximum tardiness and distribution costs in a supply chain scheduling problem. Applied Soft Computing, 47, 343-356.
    2.
  2. Atashgar, K. (2019). Design Of Experiment And Taguchi Method (DOE). Malek Ashtar University of Technology. (in persian)
    3.
  3. Bashiri, M., Badri, H., & Talebi, J. (2012). A new approach to tactical and strategic planning in production–distribution networks. Applied Mathematical Modelling, 36(4), 1703-1717.
    4.
  4. Biehl, M., Prater, E., & Realff, M. J. (2007). Assessing performance and uncertainty in developing carpet reverse logistics systems. Computers & Operations Research, 34(2), 443-463.
    5.
  5. Bozorgi-Amiri, A., Mahmoodian, V., Fahimnia, E., & Saffari, H. (2015). A new memetic algorithm for solving split delivery vehicle routing problem. Management Science Letters, 5(11), 1017-1022.
    6.
  6. Blumberg, D., Zanjirani Farahani, R., Asgari, N.,  Hafezi, M.(2009). Revers Logestics. Institute for Trade studies and Research.
    7.
  7. Cedillo-Campos, M., & Sánchez-Ramírez, C. (2013). Dynamic self-assessment of supply chains performance: an emerging market approach. Journal of applied research and technology, 11(3), 338-347.
    8.
  8. Chompu-inwai, R., Jaimjit, B., & Premsuriyanunt, P. (2015). A combination of Material Flow Cost Accounting and design of experiments techniques in an SME: the case of a wood products manufacturing company in northern Thailand. Journal of Cleaner Production, 108, 1352-1364.
    9.
  9. Fercoq, A., Lamouri, S., & Carbone, V. (2016). Lean/Green integration focused on waste reduction techniques. Journal of Cleaner production, 137, 567-578.
    10.
  10. Fleischmann, M., Beullens, P., BLOEMHOF‐RUWAARD, J. M., & Van Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and operations management, 10(2), 156-173.
    11.
  11. Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design. New York [ua].
    12.
  12. Gholipoor, A., Paydar, M. M., & Safaei, A. S. (2019). A faucet closed-loop supply chain network design considering used faucet exchange plan. Journal of Cleaner Production, 235, 503-518.
    13.
  13. Grobler, F., & Minnitt, R. C. A. (1999). The increasing role of direct reduced iron in global steelmaking.
    14.
  14. http://www.steelonthenet.com
    15.
  15. Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & Industrial Engineering, 116, 178-191.
    16.
  16. Jerbia, R., Boujelben, M. K., Sehli, M. A., & Jemai, Z. (2018). A stochastic closed-loop supply chain network design problem with multiple recovery options. Computers & Industrial Engineering, 118, 23-32.
    17.
  17. Ko, H. J., & Evans, G. W. (2007). A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computers & Operations Research, 34(2), 346-366.
    18.
  18. Lee, D. H., & Dong, M. (2008). A heuristic approach to logistics network design for end-of-lease computer products recovery. Transportation Research Part E: Logistics and Transportation Review, 44(3), 455-474.
    19.
  19. Sherafati, M., Bashiri, M., Tavakkoli-Moghaddam, R., & Pishvaee, M. S. (2019). Supply chain network design considering sustainable development paradigm: A case study in cable industry. Journal of Cleaner Production, 234, 366-380.
    20.
  20. Mousavi, S. M., Pardalos, P. M., Niaki, S. T. A., Fügenschuh, A., & Fathi, M. (2019). Solving a continuous periodic review inventory-location allocation problem in vendor-buyer supply chain under uncertainty. Computers & Industrial Engineering, 128, 541-552.
    21.
  21. Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281.
    22.
  22. Özkır, V., & Başlıgil, H. (2013). Multi-objective optimization of closed-loop supply chains in uncertain environment. Journal of Cleaner Production, 41, 114-125.
    23.
  23. Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.
    24.
  24. Pishvaee, M. S., Jolai, F., & Razmi, J. (2009). A stochastic optimization model for integrated forward/reverse logistics network design. Journal of Manufacturing Systems, 28(4), 107-114.
    25.
  25. 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.
    26.
  26. Ramezani, M., Bashiri, M., & 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(1-2), 328-344.
    27.
  27. Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A robust design for a closed-loop supply chain network under an uncertain environment. The International Journal of Advanced Manufacturing Technology, 66(5-8), 825-843.
    28.
  28. De Rosa, V., Gebhard, M., Hartmann, E., & Wollenweber, J. (2013). Robust sustainable bi-directional logistics network design under uncertainty. International Journal of Production Economics, 145(1), 184-198.
    29.
  29. Saffari, H., Makui, A., Mahmoodian, V., & 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, 8(3), 42-58.
    30.
  30. Salema, M. I. G., Póvoa, A. P. B., & Novais, A. Q. (2005). Design and planning of supply chains with reverse flows. In Computer Aided Chemical Engineering (Vol. 20, pp. 1075-1080.
    31.
  31. Salema M. I. G., Póvoa A. P. B., Novais A. Q., (2007), An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty, European Journal of Operational Research,  179, 1063-1077.
    32.
  32. Salema, M. I. G., Barbosa-Povoa, A. P., & Novais, A. Q. (2007). An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. European journal of operational research, 179(3), 1063-1077.
    33.
  33. Subramanian, P., Ramkumar, N., Narendran, T. T., & Ganesh, K. (2013). PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem. Applied soft computing, 13(2), 1121-1135.
    34.
  34. Sudarto, S., Takahashi, K., Morikawa, K., & Nagasawa, K. (2016). The impact of capacity planning on product lifecycle for performance on sustainability dimensions in Reverse Logistics Social Responsibility. Journal of Cleaner Production, 133, 28-42.
    35.
  35. Narayana, S. A., Pati, R. K., & Padhi, S. S. (2019). Market dynamics and reverse logistics for sustainability in the Indian Pharmaceuticals industry. Journal of cleaner production, 208, 968-987.
    36.
  36. Turrisi, M., Bruccoleri, M., & Cannella, S. (2013). Impact of reverse logistics on supply chain performance. International Journal of Physical Distribution & Logistics Management..

  37. Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., & Baboli, A. (2012). Reliable design of a forward/reverse logistics network under uncertainty: a robust-M/M/c queuing model. Transportation Research Part E: Logistics and Transportation Review, 48(6), 1152-1168.
    38.
  38. Olivares Vera, D. A., Olivares-Benitez, E., Puente Rivera, E., López-Campos, M., & Miranda, P. A. (2018). Combined use of mathematical optimization and design of experiments for the maximization of profit in a four-echelon supply chain. Complexity, 2018.
    39.
  39. Wang, H. F., & Hsu, H. W. (2010). A closed-loop logistic model with a spanning-tree based genetic algorithm. Computers & operations research, 37(2), 376-389.
    40.
  40. Yu, C. S., & Li, H. L. (2000). A robust optimization model for stochastic logistic problems. International journal of production economics, 64(1-3).
    41.

 

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