A bi-objective model for sustainable logistics and operations planning of WEEE recovery
DOI:
https://doi.org/10.11121/ijocta.01.2019.00718Keywords:
waste recovery, bi-objective model, sustainable operations planningAbstract
The Triple-bottom-line concept suggests that firms must consider the environmental and social impacts of their decisions, beside the economic aspects. Hence, the sustainability of the firms’ operations can be reached. The purpose of this study is to develop a bi-objective, multi-product and multi-period mixed-integer model for the operations planning of electrical-electronic waste (WEEE) recovery facilities, by considering social (workforce) constraints. Main objective is the minimization of net recycling and logistics costs offset by the profit earned by recovered material sales, and second objective is the maximization of hazardous materials recovery. Collection of used products from the specified regions is decided and the required machine-hours, inventory and workforce decisions are made. Besides, both weight-based and unit-based WEEE recovery targets are separately considered, as a unique aspect. A sensitivity analysis is conducted with various scrap prices to understand operations planning in changing conditions. Results show that weight-based targets enhance recovery amounts.
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References
Kumar, S. & Putnam, V. (2008). Cradle to cradle: Reverse logistics strategies and opportunities across three industry sectors. International Journal of Production Economics, 115, 305–315.
Elkington, J. (1994). Towards the sustainable corporation: Win-win-win business strategies for sustainable development. California Management Review, 36(2), 90-100.
Nikolaou, I.E., Evangelinos, K.I. & Allan, S., (2013). A reverse logistics social responsibility evaluation framework based on the triple bottom line approach. Journal of Cleaner Production, 56, 173–184.
Fleischmann, M., Bloemhof-Ruwaard, J. M., Dekker, R., Van der Laan, E., Van Nunen, J. A., & Van Wassenhove, L. N. (1997). Quantitative models for reverse logistics: A review. European Journal of Operational Research, 103(1), 1-17.
Presley, A., Meade, L., & Sarkis, J. (2007). A strategic sustainability justification methodology for organizational decisions: a reverse logistics illustration. International Journal of Production Research, 45(18-19), 4595-4620.
Url-1 https://eur-lex.europa.eu/resource.html?uri=cellar:ac89e64f-a4a5-4c13-8d96-1fd1d6bcaa49.0004.02/DOC_1&format=PDF Accessed 15.11.2017.
Url-2 https://www.initial.co.uk/waste-legislation/ Accessed 15.11.2018.
Url-3 http://www.environmentlaw.org.uk/rte.asp?id=82 Accessed 15.11.2018.
Url-4 https://www.environment.gov.za/sites/default/files/legislations/nema_amendment_act59.pdf Accessed 15.11.2018.
Url-5 http://www.resmigazete.gov.tr/eskiler/2012/05/20120522-5.htm Accessed 15.11.2018.
Url-6 http://ec.europa.eu/environment/waste/weee/pdf/final_rep_unu.pdf Accessed 25.06.2017.
Fleischmann, M., Bloemhof-Ruwaard, J. M., Dekker, R., Van der Laan, E., Van Nunen, J. A., & Van Wassenhove, L. N. (1997). Quantitative models for reverse logistics: A review. European Journal of Operational Research, 103(1), 1-17.
Ilgin, M. A., & Gupta, S. M. (2010). Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. Journal of Environmental Management, 91(3), 563-591.
Agrawal, S., Singh, R. K., & Murtaza, Q. (2015). A literature review and perspectives in reverse logistics. Resources, Conservation and Recycling, 97, 76-92.
Govindan, K., & Soleimani, H. (2017). A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus. Journal of Cleaner Production, 142, 371-384.
Hu, T.-L., Sheu, J.-B. & Huang, K.-H., (2002) . A reverse logistics cost minimization model for the treatment of hazardous wastes. Transportation Research Part E: Logistics and Transportation Review, 38(6), 457–473.
Widmer, R., Oswald-Krapf, H., Sinha-Khetriwal, D., Schnellmann, M., Böni, H. (2005), Global perspectives on e-waste. Environmental Impact Assessment Review, 25(5), 436-458.
Bal, A., Sarvari, P. A., & Satoglu, S. I. (2018). Analyzing the Recycling Operations Data of the White Appliances Industry in the Turkish Market. In Industrial Engineering in the Industry 4.0 Era (pp. 147-157). Springer.
Kahhat, R., Kim, J., Xu, M., Allenby, B., Williams, E., & Zhang, P. (2008). Exploring e-waste management systems in the United States. Resources, Conservation and Recycling, 52(7), 955-964.
Dekker, R., Bloemhof, J. & Mallidis, I. (2012). Operations Research for green logistics - An overview of aspects, issues, contributions and challenges. European Journal of Operational Research, 219(3), 671–679.
Shih, Li-Hsing. (2001). Reverse logistics system planning for recycling electrical appliances and computers in Taiwan. Resources, Conservation and Recycling, 32(1), 55-72.
Listes, O. & Dekker, R. (2005). A stochastic approach to a case study for product recovery network design. European Journal of Operational Research, 160(1), 268–287.
Listes, O. (2007). A generic stochastic model for supply-and-return network design. Computers & Operations Research, 34(2), 417–442.
El-Sayed, M., Afia, N. & El-Kharbotly, A. (2010). A stochastic model for forward-reverse logistics network design under risk. Computers & Industrial Engineering, 58(3), 423–431.
Achillas, C., Vlachokostas, C., Aidonis, D., Moussiopoulos, Ν., Iakovou, E., & Banias, G. (2010). Optimizing reverse logistics network to support policy-making in the case of electrical and electronic equipment. Waste Management, 30(12), 2592-2600.
Dondo, R. G., & Méndez, C. A. (2016). Operational planning of forward and reverse logistic activities on multi-echelon supply-chain networks. Computers & Chemical Engineering, 88, 170-184.
Pedram, A., Yusoff, N. B., Udoncy, O. E., Mahat, A. B., Pedram, P., & Babalola, A. (2017). Integrated forward and reverse supply chain: A tire case study. Waste Management, 60, 460-470.
Tuzkaya, G., Gülsün, B. & Önsel, Ş. (2011). A methodology for the strategic design of reverse logistics networks and its application in the Turkish white goods industry. International Journal of Production Research, 49(15), 4543–4571.
Ahluwalia, P. K., Nema, A. K. (2007). A life cycle based multi-objective optimization model for the management of computer waste. Resources, Conservation and Recycling, 51(4), 792-826.
Ahluwalia, P. K., & Nema, A. K. (2011). Capacity planning for electronic waste management facilities under uncertainty: multi-objective multi-time-step model development. Waste Management & Research, 29(7), 694-709.
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.
Wang, F., Lai, X. & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support System, 51(2), 262–269.
Türkay, M., Saraçoğlu, Ö., & Arslan, M. C. (2016). Sustainability in Supply Chain Management: Aggregate Planning from Sustainability Perspective. PloS One, 11(1), e0147502.
Amin, S.H. & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165–4176.
Samanlioglu, F. (2013), A multi-objective mathematical model for the industrial hazardous waste location–routing problem. European Journal of Operational Research, 226(2), 332–340.
Ene, S., & Öztürk, N. (2015). Network modeling for reverse flows of end-of-life vehicles. Waste Management, 38, 284-296.
Url-7 http://ec.europa.eu/eurostat/statisticsexplained/index.php/File:Domestically_treated_waste_excluding_major_mineral_wastes_by_country_and_by_type_of_treatment,_2014.png#file Accessed 05.06.2017.
Url-8 http://homeguides.sfgate.com/average-life-sidebyside-fridge-82650.html Accessed 25.09.2017
Url-9 http://www.aeha.or.jp/assessment/en/english_flame_rp.html#Refrigerator Accessed 11.06.2018.
Url-10 http://eco3e.eu/en/base/refrigerator/ Accessed 05.06.2017.
Url-11 http://hurdafiyatlarim.com/ Accessed 25.06.2017.
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation. 213(2), 455–465.
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