T‌H‌E R‌O‌B‌U‌S‌T O‌P‌T‌I‌M‌I‌Z‌A‌T‌I‌O‌N O‌F M‌U‌L‌T‌I-O‌B‌J‌E‌C‌T‌I‌V‌E G‌R‌E‌E‌N C‌L‌O‌S‌E‌D L‌O‌O‌P S‌U‌P‌P‌L‌Y C‌H‌A‌I‌N U‌N‌D‌E‌R U‌N‌C‌E‌R‌T‌A‌I‌N‌T‌Y (C‌A‌S‌E S‌T‌U‌D‌Y: I‌N‌D‌U‌S‌T‌R‌I‌A‌L B‌R‌E‌A‌D

Document Type : Article

Authors

1 F‌a‌c‌u‌l‌t‌y o‌f M‌a‌n‌a‌g‌e‌m‌e‌n‌t a‌n‌d A‌c‌c‌o‌u‌n‌t‌i‌n‌g F‌a‌r‌a‌b‌i U‌n‌i‌v‌e‌r‌s‌i‌t‌y o‌f T‌e‌h‌r‌a‌n

2 F‌a‌c‌u‌l‌t‌y o‌f I‌n‌d‌u‌s‌t‌r‌i‌a‌l E‌n‌g‌i‌n‌e‌e‌r‌i‌n‌g F‌a‌r‌a‌b‌i U‌n‌i‌v‌e‌r‌s‌i‌t‌y o‌f T‌e‌h‌r‌a‌n

Abstract

Lack of resources, ecosystems at risk, and climate change have a great impact on the living environment. One of the most important issues and challenges that countries, including our country, face is the waste of resources. In the past decade, due to the increasing importance of economic competitiveness, legal pressures, environmental concerns in the context of old products, and social impacts, the issue of a closed-loop supply chain has attracted many researchers. Increasing the efficiency and effectiveness of supply chain activities is one of the sustainable competitive advantages for companies. Also, companies and organizations are not only aware of environmental factors but are also aware of the revenues of collecting and recycling their used products. The closed-loop supply chain is a way of considering the recycling of products to control environmental barriers. This research aims to develop a robust optimization approach for designing a supply chain network in industrial bread. A multi-objective integrated integer linear programming model is presented as a single-product, single-cycle, and multi-capacity. In this model, two economic and environmental objectives are examined under certainty and uncertainty conditions. A balance between the goals of the equilibrium is established and the model is tested in different sizes and uncertainties by using the Torabi and Hassini (TH) method. The results demonstrate that in all cases, the robust approach overcomes the deterministic approach based on the mean value of the objective function. Regarding the standard deviation, the robust approach in the problem of 6*10*10*6 with uncertainties ρ=0.2 and ρ=1, the problem of 9*15*15*9 with uncertainties ρ=0.2 and ρ=0.5, and the problem of 12*20*20*12 with uncertainties ρ=0.2, ρ=0.5 and ρ=1, extremely overcome but in other cases, the deterministic approach has better outcomes. A robust strategy model achieves higher quality and better performance than the deterministic strategy in large problems and higher uncertainty. Due to the size of the problem and the uncertainty, in most cases, the gap between these two approaches increases with respect to both coefficients of performance.

Keywords

References

FAO. (2016). Mediterra 2016: Zero waste in the Mediterranean. Natural Ressources, Food and Knowledge. Chapter 13: Consumer behaviour with respect to food losses and waste. Albisu, L. M. [1] Azadbakht, N., Khosravinezhad, K., & Trahir, M.J. (2008). Evaluation of aflatoxin contamination of bread waste in Lorestan province. Yafte, 10, 87-96. [In Persian] [2] Soleymani, M. & Omidi, H. (2013). Comparative study of the bread industry of Malaysia and Iran (with emphasis on the educational system). Business reviews. 11(59), 47-58. [In Persian] [3] Javanbakht, H., Javanbakht, K., & Ansari, F. (2017). Effects of dry bread waste on environmental pollution. International Conference on New Ideas in Agriculture, Environment and Tourism, Ardabil. [In Persian] [4] Pashaee, V., & Haghnazari, S. (2019). Effect of fermentation and infrared and traditional baking methods on reducing aflatoxin levels in Lavash bread. Food industry research, 29(1), 43-52. [In Persian] [5] Karami, F., Omrani, Gh.A., Shoeybi, Sh., Tabaraei, B., Rahimifard, N., & Arjmandi, R. (2012). Investigation of fungal contamination of recycled bread waste in areas 6 and 7 of Tehran Municipality. Iranian Journal of Medical Microbiology, 6(3), 52-58. [In Persian] [6] Azizi, M. H. (2002). Investigating strategies to reduce waste and improve bread quality. Department of Food Science and Technology, Faculty of Agriculture, Tarbiat Modares University, Institute of Nutritional Research and Food Industry. [In Persian] [7] Linton, J. D., Klassen, R., & Jayaraman, V. (2007). Sustainable supply chains: An introduction. Journal of operations management, 25(6), 1075-1082. https://doi.org/10.1016/j.jom.2007.01.012 [8] Izadpanah, N., Mohamadi, V., & Aghayarmakouee, N. (2018). A review of bread waste (the need to reduce bread waste to increase productivity. Eleventh International Conference on Accounting and Management and Eighth Conference on Entrepreneurship and Open Innovation. [In Persian] [9] Winkler, H. (2011). Closed-loop production systems—A sustainable supply chain approach. CIRP Journal of Manufacturing Science and Technology, 4(3), 243-246. https://doi.org/10.1016/j.cirpj.2011.05.001 [10] Vahdani, B., Razmi, J., & Tavakkoli-Moghaddam, R. (2012). Fuzzy possibilistic modeling for closed loop recycling collection networks. Environmental Modeling & Assessment, 17(6), 623-637. https://doi.org/10.1007/s10666-012-9313-7 [11] Willows, R., Reynard, N., Meadowcroft, I., & Connell, R. (2003). Climate adaptation: Risk, uncertainty and decision-making. UKCIP Technical Report. UK Climate Impacts Programme. [12] Mula, J., Poler, R., & Garcia-Sabater, J. P. (2007). Material Requirement Planning with fuzzy constraints and fuzzy coefficients. Fuzzy Sets and Systems, 158(7), 783-793. https://doi.org/10.1016/j.fss.2006.11.003 [13] Uster [14] Chouinard, M., D’Amours, S., & Aït-Kadi, D. (2008). A stochastic programming approach for designing supply loops. International Journal of Production Economics, 113(2), 657-677. https://doi.org/10.1016/j.ijpe.2007.10.023 [15] Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581-598 https://doi.org/10.1016/j.apm.2010.07.013 [16] Snyder, L. V. (2006). Facility location under uncertainty: a review. IIE Transactions, 38(7), 547-564. https://doi.org/10.1080/07408170500216480 [17] Neto, J. Q. F., Bloemhof-Ruwaard, J. M., van Nunen, J. A., & van Heck, E. (2008). Designing and evaluating sustainable logistics networks. International Journal of Production Economics, 111(2), 195-208. https://doi.org/10.1016/j.ijpe.2006.10.014 [18] Dehghanian, F., & Mansour, S. (2009). Designing sustainable recovery network of end-of-life products using genetic algorithm. Resources, Conservation and Recycling, 53(10), 559-570. https://doi.org/10.1016/j.resconrec.2009.04.007 [19] Amin, S. H., Zhang, G., & Akhtar, P. (2017). Effects of uncertainty on a tire closed-loop supply chain network. Expert Systems with Applications, 73, 82-91. https://doi.org/10.1016/j.eswa.2016.12.024 [20] Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281. https://doi.org/10.1287/opre.43.2.264 [21] 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. https://doi.org/10.1016/j.apm.2010.07.013 [22] Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182-214. https://doi.org/10.1016/j.tre.2016.02.011 [23] Lieckens, K., & Vandaele, N. (2007). Reverse logistics network design with stochastic lead times. Computers & Operations Research, 34(2), 395-416. https://doi.org/10.1016/j.cor.2005.03.006 [24] d’Amore, F., & Bezzo, F. (2016). Strategic optimisation of biomass-based energy supply chains for sustainable mobility. Computers & Chemical Engineering, 87, 68-81. https://doi.org/10.1016/j.compchemeng.2016.01.003 [25] Santibañez-Aguilar, J. E., Morales-Rodriguez, R., González-Campos, J. B., & Ponce-Ortega, J. M. (2016). Stochastic design of biorefinery supply chains considering economic and environmental objectives. Journal of cleaner production, 136, 224-245. https://doi.org/10.1016/j.jclepro.2016.03.168 [26] Safaei, A. S., Roozbeh, A., & Paydar, M. M. (2017). A robust optimization model for the design of a cardboard closed-loop supply chain. Journal of Cleaner Production, 166, 1154-1168. https://doi.org/10.1016/j.jclepro.2017.08.085 [27] Shahparvari, S., Chhetri, P., Chan, C., & Asefi, H. (2018). Modular recycling supply chain under uncertainty: a robust optimisation approach. The International Journal of Advanced Manufacturing Technology, 96(1-4), 915-934. https://doi.org/10.1007/s00170-017-1530-4 [28] Shi, J., Liu, Z., Tang, L., & Xiong, J. (2017). Multi-objective optimization for a closed-loop network design problem using an improved genetic algorithm. Applied Mathematical Modelling, 45, 14-30. https://doi.org/10.1016/j.apm.2016.11.004 [29] Hamidieh, A., Naderi, B., Mohammadi, M., & Fazli-Khalaf, M. (2017). A robust possibilistic programming model for a responsive closed loop supply chain network design. Cogent Mathematics, 4(1), 1329886. https://doi.org/10.1080/23311835.2017.1329886 [30] Polo, A., Pena, ˜ N., Munoz, ˜ D., Can˜on, ´ A., & Escobar, J. W. (2019). Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria. Omega, 88, 110–132. https://doi.org/10.1016/j.omega.2018.09.003 [31] Pant, K., Yadav, V. S., & Singh, A. R. (2021). Design of multi-tier multi-time horizon closed-loop supply chain network with sustainability under uncertain environment for Indian paper industry. International Journal of Sustainable Engineering, 14(2), 107-122. https://doi.org/10.1080/19397038.2020.1774817 [32] Jouzdani, J., & Govindan, K. (2021). On the sustainable perishable food supply chain network design: A dairy products case to achieve sustainable development goals. Journal of Cleaner Production, 278, Article 123060. https://doi.org/10.1016/j.jclepro.2020.123060 [33] Ben-Tal, A., & Nemirovski, A. (1998). Robust convex optimization. Mathematics of operations research, 23(4), 769-805. https://doi.org/10.1287/moor.23.4.769 [34] Soyster, A. (1973).” Convex programming with set-inclusive constraints and applications to inexact linear programming”, Operations Research. 21(5):1154-1157. https://doi.org/10.1287/opre.21.5.1154 [35] Rahimi, E., Paydar, M. M., Mahdavi, I., Jouzdani, J., & Arabsheybani, A. (2018). A robust optimization model for multi-objective multi-period supply chain planning under uncertainty considering quantity discounts. Journal of Industrial and Production Engineering. https://doi.org/10.1080/21681015.2018.1441195 [36] Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy sets and systems, 1(1), 45-55. https://doi.org/10.1016/0165-0114(78)90031-3 [37] Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 1(1), 3-28. https://doi.org/10.1016/0165-0114(78)90029-5 [38] Werners, B. (1987). An interactive fuzzy programming system. Fuzzy sets and systems, 23(1), 131-147. https://doi.org/10.1016/0165-0114(87)90105-9 [39] Selim, H., & Ozkarahan, I. (2008). A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3), 401-418. https://doi.org/10.1007/s00170-006-0842-6 [40] Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems, 159(2), 193-214. https://doi.org/10.1016/j.fss.2007.08.010 [41] Mirakhorli, A. (2014). Fuzzy multi-objective optimization for closed loop logistics network design in bread-producing industries. The International Journal of Advanced Manufacturing Technology, 70(1-4), 349-362. https://doi.org/10.1007/s00170-013-5264-7 [42] Ho, J. C., & Wijeysundra, N. E., & Chou, S. K. (1986). Energy analysis applied to food processing. Pergamon Journals Ltd, 887-892. https://doi.org/10.1016/0360-5442(86)90008-3 [43]
Sharif Journal of Industrial Engineering & Management
Volume 39, Issue 2
March 2024
Pages 51-64

Files

Share

How to cite

Statistics