Document Type : Research Paper


Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran.


Selecting appropriate locations for Municipal Solid Waste (MSW) management facilities, such as landfills, is an important issue in rapidly developing regions. Multiple alternatives and evaluation attributes need to be analyzed to finalize the locations of these facilities. The selection of a landfill site in an urban area is a critical issue due to the involvement of many parameters. The decisive parameters are environmental, economic, and social, some of them conflicting, making landfill site selection a tedious and complex process. Multi Attribute Decision Making (MADM) approaches are found to be very effective for ranking several potential locations and, hence, selecting the best among them based on the identified attributes. Therefore, this study presents a two-stage MADM model that also accounts for all possible combinations of locations. This study evaluates economic, environmental, social, and technical attributes based on realistic conditions. Based on the results, 15 attributes are first identified through a comprehensive literature review and with the help of municipal officials during field surveys. These attributes are categorized into four types, i.e., economic, technical, environmental, and social, based on their respective propensity.
In the second step, a statistical analysis questionnaire was distributed among the study population, and Cronbach's alpha was explained for all four main factors of the study. Therefore, in the last step, the rank of all research variables was calculated using the Nonlinear analysis method. Based on the results of this study, the technical variable was ranked first, the economic variable was ranked second, and the environmental and social variable was ranked third. This article has three theoretical, practical, and technical contributions. Also, this article provides a clear explanation of the theoretical contribution related to the accumulated knowledge, both in the introduction and theoretical background sections of the article. Therefore, studying the past research describes a relatively complete background of the planned theoretical contributions of this article compared to the previous research. Therefore, the theoretical contribution of this article solves the scientific gap about effective indicators for determining the location of waste disposal. From the point of view of practical contribution, this article presents practical concepts related to managers and experts and has practical suggestions presented in the conclusion section. Also, the technical contribution of this article is presented by combining fuzzy logic and Nonlinear mathematical programming.


Main Subjects

[1]     Cao, L. W., Cheng, Y. H., Zhang, J., Zhou, X. Z., & Lian, C. X. (2006). Application of grey situation decision-making theory in site selection of a waste sanitary landfill. Journal of china university of mining and technology, 16(4), 393–398. DOI:10.1016/S1006-1266(07)60033-9
[2]     Bahrani, S., Ebadi, T., Ehsani, H., Yousefi, H., & Maknoon, R. (2016). Modeling landfill site selection by multi-criteria decision making and fuzzy functions in GIS, case study: Shabestar, Iran. Environmental earth sciences, 75(4), 1–14. DOI:10.1007/s12665-015-5146-4
[3]     Eskandari, M., Homaee, M., & Falamaki, A. (2016). Landfill site selection for municipal solid wastes in mountainous areas with landslide susceptibility. Environmental science and pollution research, 23(12), 12423–12434. DOI:10.1007/s11356-016-6459-x
[4]     Beskese, A., Demir, H. H., Ozcan, H. K., & Okten, H. E. (2015). Landfill site selection using fuzzy AHP and fuzzy TOPSIS: a case study for Istanbul. Environmental Earth sciences73, 3513-3521.
[5]     Hanine, M., Boutkhoum, O., Tikniouine, A., & Agouti, T. (2016). Comparison of fuzzy AHP and fuzzy TODIM methods for landfill location selection. Springer plus, 5(1), 1–30. DOI:10.1186/s40064-016-2131-7
[6]     Reed, M., Yiannakou, A., & Evering, R. (2014). An ant colony algorithm for the multi-compartment vehicle routing problem. Applied soft computing journal, 15, 169–176. DOI:10.1016/j.asoc.2013.10.017
[7]     Hemmelmayr, V., Doerner, K. F., Hartl, R. F., & Rath, S. (2013). A heuristic solution method for node routing based solid waste collection problems. Journal of heuristics, 19(2), 129–156. DOI:10.1007/s10732-011-9188-9
[8]     Banditvilai, S., & Niraso, M. (2017). Simulation of the Night Shift Solid Waste Collection System of Phuket Municipality. In Soft Methods for data science (pp. 17-24). Springer International Publishing.
[9]     Sarra, A., Mazzocchitti, M., & Rapposelli, A. (2017). Evaluating joint environmental and cost performance in municipal waste management systems through data envelopment analysis: Scale effects and policy implications. Ecological indicators, 73, 756–771.
[10]   Rabbani, M., Amirhossein Sadati, S., & Farrokhi-Asl, H. (2020). Incorporating location routing model and decision making techniques in industrial waste management: Application in the automotive industry. Computers and industrial engineering, 148, 106692. DOI:10.1016/j.cie.2020.106692
[11]   Das, A. K., Islam, M. N., Billah, M. M., & Sarker, A. (2021). COVID-19 pandemic and healthcare solid waste management strategy – a mini-review. Science of the total environment, 778, 146220. DOI:10.1016/j.scitotenv.2021.146220
[12]   Hantoko, D., Li, X., Pariatamby, A., Yoshikawa, K., Horttanainen, M., & Yan, M. (2021). Challenges and practices on waste management and disposal during COVID-19 pandemic. Journal of environmental management, 286, 112140. DOI:10.1016/j.jenvman.2021.112140
[13]   Goel, S., Ranjan, V. P., Bardhan, B., & Hazra, T. (2017). Forecasting solid waste generation rates. Modelling trends in solid and hazardous waste management, 35–64. DOI:10.1007/978-981-10-2410-8_3
[14]   Mostafayi Darmian, S., Moazzeni, S., & Hvattum, L. M. (2020). Multi-objective sustainable location-districting for the collection of municipal solid waste: Two case studies. Computers and industrial engineering, 150, 106965. DOI:10.1016/j.cie.2020.106965
[15]   Lin, Z., Xie, Q., Feng, Y., Zhang, P., & Yao, P. (2020). Towards a robust facility location model for construction and demolition waste transfer stations under uncertain environment: The case of Chongqing. Waste management, 105, 73–83. DOI:10.1016/j.wasman.2020.01.037
[16]   Pérez-López, G., Prior, D., Zafra-Gómez, J. L., & Plata-Díaz, A. M. (2016). Cost efficiency in municipal solid waste service delivery. Alternative management forms in relation to local population size. European journal of operational research, 255(2), 583–592.
[17]   Schoeman, Y., Oberholster, P., & Somerset, V. (2021). A decision-support framework for industrial waste management in the iron and steel industry: A case study in Southern Africa. Case studies in chemical and environmental engineering, 3, 100097. DOI:10.1016/j.cscee.2021.100097
[18]   Monzambe, G. M., Mpofu, K., & Daniyan, I. A. (2021). Optimal location of landfills and transfer stations for municipal solid waste in developing countries using non-linear programming. Sustainable futures, 3, 100046. DOI:10.1016/j.sftr.2021.100046
[19]   Yannis, G., Kopsacheili, A., Dragomanovits, A., & Petraki, V. (2020). State-of-the-art review on multi-criteria decision-making in the transport sector. Journal of traffic and transportation engineering, 7(4), 413–431. DOI:10.1016/j.jtte.2020.05.005
[20]   Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-based systems, 121, 23–31.
 [21] Ali, S. A., Parvin, F., Al-Ansari, N., Pham, Q. B., Ahmad, A., Raj, M. S., … Thai, V. N. (2021). Sanitary landfill site selection by integrating AHP and FTOPSIS with GIS: a case study of Memari Municipality, India. Environmental science and pollution research, 28(6), 7528–7550.
[22]   Tirkolaee, E. B., Mahdavi, I., Esfahani, M. M. S., & Weber, G. W. (2020). A robust green location-allocation-inventory problem to design an urban waste management system under uncertainty. Waste management, 102, 340–350. DOI:10.1016/j.wasman.2019.10.038
[23]   Yadav, V., Kalbar, P. P., Karmakar, S., & Dikshit, A. K. (2020). A two-stage multi-attribute decision-making model for selecting appropriate locations of waste transfer stations in urban centers. Waste management, 114, 80–88. DOI:10.1016/j.wasman.2020.05.024
[24]   Ibáñez-Forés, V., Bovea, M. D., Coutinho-Nóbrega, C., & de Medeiros, H. R. (2019). Assessing the social performance of municipal solid waste management systems in developing countries: Proposal of indicators and a case study. Ecological indicators, 98, 164–178. DOI:10.1016/j.ecolind.2018.10.031
[25]   Archetti, C., Coelho, L. C., & Grazia Speranza, M. (2019). An exact algorithm for the inventory routing problem with logistic ratio. Transportation research part e: logistics and transportation review, 131, 96–107. DOI:10.1016/j.tre.2019.09.016
[26]   Jatinkumar Shah, P., Anagnostopoulos, T., Zaslavsky, A., & Behdad, S. (2018). A stochastic optimization framework for planning of waste collection and value recovery operations in smart and sustainable cities. Waste management, 78, 104–114. DOI:10.1016/j.wasman.2018.05.019
[27]   Boonmee, C., Arimura, M., & Asada, T. (2018). Location and allocation optimization for integrated decisions on post-disaster waste supply chain management: On-site and off-site separation for recyclable materials. International journal of disaster risk reduction, 31, 902–917.
[28]   Toro, E. M., Franco, J. F., Echeverri, M. G., & Guimarães, F. G. (2017). A multi-objective model for the green capacitated location-routing problem considering environmental impact. Computers & industrial engineering, 110, 114–125.
[29]   Yıldız-Geyhan, E., Altun-Çiftçiouglu, G. A., & Kadirgan, M. A. N. (2017). Social life cycle assessment of different packaging waste collection system. Resources, conservation and recycling, 124, 1–12.
[30]   Mirdar Harijani, A., Mansour, S., Karimi, B., & Lee, C. G. (2017). Multi-period sustainable and integrated recycling network for municipal solid waste – A case study in Tehran. Journal of cleaner production, 151, 96–108. DOI:10.1016/j.jclepro.2017.03.030