A new multi-objective mathematical model for hazardous waste management considering social and environmental issues

Document Type: Research Paper

Authors

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

3 Institute for Manufacturing, University of Cambridge, Cambridge, United Kingdom & W.P. Carey School of Business, Arizona State University, Tempe, United States

Abstract

Hazardous waste management incorporates collection, separation, treatment, recycling and disposal of hazardous wastes. In this paper, a new multi-objective mixed integer model is presented for hazardous waste collection problem. The model aims to minimize transportation and construction costs, and environmental and population risks in hazardous waste management systems. This model is applied in a case study of Iran in order to help decision makers to decide on the location of separation, treatment, recycle, disposal centers, and established technology in treatment center. Moreover, this paper specifies routes between different facilities in collection network. For addressing population and environmental impacts and economical costs, three objective functions including total costs, total population exposure risk, and environmental risks are considered. An augmented ε-constraint method is used to generate Pareto optimal solution for these conflicting objectives. Finally, proposed model is utilized in our case study and numerical results and some managerial insights are provided.

Keywords

Main Subjects

Article Title [Persian]

ارائه مدل ریاضی چندهدفه برای مدیریت زباله‌های خطرناک با در نظرگرفتن معیارهای اجتماعی و محیط‌زیستی

Authors [Persian]

  • مسعود ربانی 1
  • سعید دانش شهرکی 1
  • حامد فرخی اصل 2
  • استنلی فردریک دبلیو تی لیم 3
1 دانشکده مهندسی صنایع، پردیس دانشکده‌های فنی دانشگاه تهران، تهران، ایران
2 دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران
3 دانشکده تولید، دانشگاه کمبریج، کمبریج، بریتانیا و دانشکده کسب و کار کری، دانشگاه ایالتی آریزونا، تمپ، ایالات متحده آمریکا
Abstract [Persian]

مدیریت زباله‌های خطرناک شامل جمع‌آوری، تفکیک، اصلاح، بازیافت و دفن زباله‌های خطرناک می‌شود. در این مقاله، یک مدل ترکیبی چند هدفه برای مسأله زباله‌های خطرناک تهیه شده است. هدف از این مدل، کمینه کردن هزینه‌های ساخت تسهیلات و جابجایی زباله‌های خطرناک، کمینه کردن ریسک خطرات اجتماعی و محیط‌زیستی مدیریت زباله‌های خطرناک می‌باشد. این مدل دارای مطالعه موردی ایران است تا به مدیران تصمیم‌گیران این حوزه برای تصمیم‌گیری درباره مکان تأسیس مراکز تفکیک، اصلاح (با تکنولوژی‌های گوناگون)، بازیافت و دفن زباله‌های خطرناک کمک کند. علاوه بر این، این مقاله مسیرهای ارتباطی بین این مراکز مختلف را نیز مشخص می‌کند. برای بررسی هدف‌های فوق، سه تابع هدف برای هزینه‌ها، تأثیرات اجتماعی و محیط‌زیستی تعریف شده است. برای تولید جواب‌های بهینه پارتویی در سایه وجود توابع هدف تناقض‌دار از روش محدودیت اپسیلون تکامل یافته استفاده شده است. و در نهایت، این مدل پیشنهادی ما در مطالعه موردی و نتایج عددی و ارائه بینش‌های مدیریتی مورد استفاده و اثبات قرار گرفته است.

Keywords [Persian]

  • مدیریت زباله‌های خطرناک
  • برنامه‌ریزی عدد صحیح ترکیبی
  • محدودیت اپسیلون تکامل‌یافته
  • جواب‌های پارتویی و بهینه‌سازی چند هدفه

References

  1. Achillas, C., Moussiopoulos, N., Karagiannidis, A., Banias, G., and Perkoulidis, G. (2013). The use of multi-criteria decision analysis to tackle waste management problems: A literature review. Waste Management and Research, 31(2), 115-129. doi:doi:10.1177/0734242X12470203
  2. Alçada-Almeida, L., Coutinho-Rodrigues, J., and Current, J. (2009). A multiobjective modeling approach to locating incinerators. Socio-Economic Planning Sciences, 43(2), 111-120. doi:http://dx.doi.org/10.1016/j.seps.2008.02.008
  3. Alumur, S., and Kara, B. Y. (2007). A new model for the hazardous waste location-routing problem. Computers and Operations Research, 34(5), 1406-1423. doi:http://dx.doi.org/10.1016/j.cor.2005.06.012
  4. Ardjmand, E., Young, W. A., Weckman, G. R., Bajgiran, O. S., Aminipour, B., and Park, N. (2016). Applying genetic algorithm to a new bi-objective stochastic model for transportation, location, and allocation of hazardous materials. Expert Systems with Applications, 51, 49-58.
  5. Caballero, R., González, M., Guerrero, F. M., Molina, J., and Paralera, C. (2007). Solving a multiobjective location routing problem with a metaheuristic based on tabu search. Application to a real case in Andalusia. European Journal of Operational Research, 177(3), 1751-1763. doi:http://dx.doi.org/10.1016/j.ejor.2005.10.017
  6. Carotenuto, P., Giordani, S., and Ricciardelli, S. (2007). Finding minimum and equitable risk routes for hazmat shipments. Computers and Operations Research, 34(5), 1304-1327. doi:http://dx.doi.org/10.1016/j.cor.2005.06.003
  7. Chiou, S.-W. (2017). A risk-averse signal setting policy for regulating hazardous material transportation under uncertain travel demand. Transportation Research Part D: Transport and Environment, 50, 446-472.
  8. Dadkar, Y., Jones, D., and Nozick, L. (2008). Identifying geographically diverse routes for the transportation of hazardous materials. Transportation Research Part E: Logistics and Transportation Review, 44(3), 333-349. doi:http://dx.doi.org/10.1016/j.tre.2006.10.010
  9. Diebold, F., and Bichler, M. (2017). Matching with indifferences: A comparison of algorithms in the context of course allocation. European Journal of Operational Research, 260(1), 268-282. doi:http://dx.doi.org/10.1016/j.ejor.2016.12.011
  10. Dinler, E., and Güngör, Z. (2017). Planning decisions for recycling products containing hazardous and explosive substances: A fuzzy multi-objective model. Resources, Conservation and Recycling, 117, 93-101.
  11. Drexl, M., and Schneider, M. (2015). A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, 241(2), 283-308.
  12. Emek, E., and Kara, B. Y. (2007). Hazardous waste management problem: The case for incineration. Computers and Operations Research, 34(5), 1424-1441. doi:http://dx.doi.org/10.1016/j.cor.2005.06.011
  13.  Erkut, E., and Ingolfsson, A. (2005). Transport risk models for hazardous materials: Revisited. Operations Research Letters, 33(1), 81-89. doi:http://dx.doi.org/10.1016/j.orl.2004.02.006
  14. Erkut, E., and Verter, V. (1998). Modeling of Transport Risk for Hazardous Materials. Operations Research, 46(5), 625-642. doi:10.1287/opre.46.5.625
  15. Fabiano, B., Currò, F., Palazzi, E., and Pastorino, R. (2002). A framework for risk assessment and decision-making strategies in dangerous good transportation. Journal of Hazardous Materials, 93(1), 1-15. doi:http://dx.doi.org/10.1016/S0304-3894(02)00034-1
  16. 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, 34(4), 239-252.
  17. Govindan, K., Jafarian, A., Khodaverdi, R., and Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28.
  18. Hicks, C., Heidrich, O., McGovern, T., and Donnelly, T. (2004). A functional model of supply chains and waste. International Journal of Production Economics, 89(2), 165-174. doi:http://dx.doi.org/10.1016/S0925-5273(03)00045-8
  19. Hong, J., Han, X., Chen, Y., Wang, M., Ye, L., Qi, C., and Li, X. (2017). Life cycle environmental assessment of industrial hazardous waste incineration and landfilling in China. The International Journal of Life Cycle Assessment, 22(7), 1054-1064.
  20. Hu, T.-L., Sheu, J.-B., and 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. doi:http://dx.doi.org/10.1016/S1366-5545(02)00020-0
  21. Kara, B. Y., Erkut, E., and Verter, V. (2003). Accurate calculation of hazardous materials transport risks. Operations Research Letters, 31(4), 285-292. doi:http://dx.doi.org/10.1016/S0167-6377(02)00238-9
  22. Mavrotas, G. (2009). Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213(2), 455-465. doi:http://dx.doi.org/10.1016/j.amc.2009.03.037
  23. Killmer, K. A., Anandalingam, G., and Malcolm, S. A. (2001). Siting noxious facilities under uncertainty. European Journal of Operational Research, 133(3), 596-607. doi:http://dx.doi.org/10.1016/S0377-2217(00)00206-X
  24. Mehrjerdi, Y. Z., and Nadizadeh, A. (2013). Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands. European Journal of Operational Research, 229(1), 75-84.
  25. Nagy, G., and Salhi, S. (2007). Location-routing: Issues, models and methods. European Journal of Operational Research, 177(2), 649-672. doi:http://dx.doi.org/10.1016/j.ejor.2006.04.004
  26. Nema, A. K., and Gupta, S. K. (1999). Optimization of regional hazardous waste management systems: An improved formulation. Waste Management, 19(7–8), 441-451. doi:http://dx.doi.org/10.1016/S0956-053X(99)00241-X
  27. Pamučar, D., Ljubojević, S., Kostadinović, D., and Đorović, B. (2016). Cost and risk aggregation in multi-objective route planning for hazardous materials transportation—a neuro-fuzzy and artificial bee colony approach. Expert Systems with Applications, 65, 1-15. doi:http://dx.doi.org/10.1016/j.eswa.2016.08.024
  28. Pradhananga, R., Taniguchi, E., Yamada, T., and Qureshi, A. G. (2014). Environmental Analysis of Pareto Optimal Routes in Hazardous Material Transportation. Procedia - Social and Behavioral Sciences, 125, 506-517. doi:http://dx.doi.org/10.1016/j.sbspro.2014.01.1492
  29. Rabbani, M., Farrokhi-Asl, H., and Asgarian, B. (2016). Solving a bi-objective location routing problem by a NSGA-II combined with clustering approach: Application in waste collection problem. Journal of Industrial Engineering International, 13(1), 13-27.
  30. Rakas, J., Teodorović, D., and Kim, T. (2004). Multi-objective modeling for determining location of undesirable facilities. Transportation Research Part D: Transport and Environment, 9(2), 125-138. doi:http://dx.doi.org/10.1016/j.trd.2003.09.002
  31. Rath, S., and Gutjahr, W. J. (2014). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers and Operations Research, 42, 25-39.
  32. ReVelle, C., Cohon, J., and Shobrys, D. (1991). Simultaneous siting and routing in the disposal of hazardous wastes. Transportation Science, 25(2), 138-145.
  33. Saat, M. R., Werth, C. J., Schaeffer, D., Yoon, H., and Barkan, C. P. L. (2014). Environmental risk analysis of hazardous material rail transportation. Journal of Hazardous Materials, 264, 560-569. doi:http://dx.doi.org/10.1016/j.jhazmat.2013.10.051
  34. 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. doi:http://dx.doi.org/10.1016/j.ejor.2012.11.019
  35. Saxena, G., Chandra, R., and Bharagava, R. N. (2016). Environmental Pollution, Toxicity Profile and Treatment Approaches for Tannery Wastewater and Its Chemical Pollutants.
  36. Vidović, M., Ratković, B., Bjelić, N., and Popović, D. (2016). A two-echelon location-routing model for designing recycling logistics networks with profit: MILP and heuristic approach. Expert Systems with Applications, 51, 34-48.
  37. Wang, Z., Yin, J., and Ma, W. (2008, 1-6 June 2008). A reverse logistics optimization model for hazardous waste in the perspective of fuzzy multi-objective programming theory. Paper presented at the 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).
  38. Xie, Y., Lu, W., Wang, W., and Quadrifoglio, L. (2012). A multimodal location and routing model for hazardous materials transportation. Journal of hazardous materials, 227, 135-141.
  39. Xu, T., Yang, F., Li, J., and Yuan, W. (2013). A bi-objective mathematical model for hazmat vehicle routing problem with path-based risk estimation. Paper presented at the Business Intelligence and Financial Engineering (BIFE), 2013 Sixth International Conference on.
  40. Yilmaz, O., Kara, B. Y., and Yetis, U. (2017). Hazardous waste management system design under population and environmental impact considerations. Journal of Environmental Management. 203, 720-731.
  41. Zhang, D., Cai, S., Ye, F., Si, Y. W., and Nguyen, T. T. (2017). A hybrid algorithm for a vehicle routing problem with realistic constraints. Information Sciences, 394, 167-182.
  42. Zhang, J., Hodgson, J., and Erkut, E. (2000). Using GIS to assess the risks of hazardous materials transport in networks. European Journal of Operational Research, 121(2), 316-329. doi:http://dx.doi.org/10.1016/S0377-2217(99)00220-9
Iranian Journal of Management Studies

Volume 11, Issue 4
Autumn 2018
Pages 831-865

Files

Share

How to cite

Statistics