A robust green fuzzy multi-objective model to location - allocation - routing – inventory problem in optimizing the urban waste management process using meta-heuristic algorithms

Document Type : Scientific - Research

Authors

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

2 Faculty of Architecture and Urban Planning, Persian Gulf University, Bushehr, Iran

Abstract

This study presents a new form of the problem of location, allocation, routing and inventory in the municipal waste management system by considering three economic, social and environmental issues. In the economic sector, various types of costs related to collection Municipal waste is examined in such a way that at the same time the total system costs and the number of machines required for waste collection in the whole network are minimized. Also in the social and environmental sector, it seeks to provide a model for finding the shortest path between waste collection tanks in the city to the final collection and recycling site. The research problem is formulated in the form of a multi-objective nonlinear programming model of complex integer. Also, to control the degree of uncertainty of some parameters in the research model, the fuzzy robust method has been used. Since the problem of this research is np-hard type and also the optimal amount of variables in these problems interact with each other, the multi-objective colonial competition algorithm and multi-objective genetic algorithm have been used for the final solution. The results of solving different scenarios as well as validating the results of the proposed algorithms show that the average error of the multi-objective metaphorical competition algorithm is lower than the multi-objective genetic algorithm in solving different scenarios, which indicates the high efficiency of the algorithm for a wide range of problems. It comes in different sizes.

Keywords

References

- Enayatifar, R., Yousefi, M., Abdullah, A., & Darus, A. (2013). MOICA: Anovel multi-objective approach based onimperialist competitive algorithm. Applied Mathematics and Computation 219 (2013)8829–8841.
 
- Safari, H., & Faghih, A. (2014). Solving the Resource-Constrained Project Scheduling Problems (RCPSP) Using Developed Imperialistic Competition Algorithm (DICA).
 
- Arena, U., & Di Gregorio, F. (2014). A waste management planning based on substance flow analysis. Resources, Conservation and Recycling, 85, 54-66.
 
- Atanassov, K. (2012). On Intuitionistic Fuzzy Sets Theory.
 
- Chauhan, A., & Singh, A. (2016). A hybrid multi-criteria decision making method approach for selecting a sustainable location of healthcare waste disposal facility . J. Cleaner Prod. 139, 1001–1010.
- Eiselt, H., & Marianov, V. (2015). Location modeling for municipal solid waste facilities . Comput. Oper. Res. 62, 305–315.
 
- Enayatifar, R. (2013). MOICA: Anovel multi-objective approach based onimperialist competitive algorithm . Applied Mathematics and Computation 219 (2013)8829–8841.
 
- Galante, G., Aiello, G., Enea, M., & Panascia, E. (2017). A multi-objective approach to solid waste management . Waste Management, 30(8), 1720-1728.
 
- Gambella, C., Maggioni, F., & Vigo, D. (2019). A stochastic programming model for a tactical solid waste management problem. Eur. J. Oper. Res. 273 (2), 684–694.
 
- Ghiani, G., Laganà, D., Manni, E., & Triki, C. (2012). Capacitated location of collection sites in an urban waste management system. Waste Manage. 32 (7), 1291–1296.
 
- Ghiani, G., Manni, A., Manni, E., & Toraldo, M. (2014). The impact of an efficient collection sites location on the zoning phase in municipal solid waste management. Waste Manage. 34 (11), 1949–1956.
 
- Habib, M., Sarkar, B., Tayyab, M., Saleem, M., Hussain, A., Ullah, M., et al. (2019). Large-scale disaster waste management under uncertain environment. J.Cleaner Prod. 212, 200–222.
 
- Hadi nezahd, F., Ramzi, A., & Khaje, M. (2018). Evaluation and comparison of multi-objective meta-heuristic algorithms to solve the optimization problem. No. 8.
 
- Khadivi, M., & Ghomi, S. (2012). Solid waste facilities location using of analytical network process and data envelopment analysis approaches. Waste Manage. 32(6), 1258–1265.
 
- Li, Y., & Huang, G. (2012). Robust interval quadratic programming and its application to waste management under uncertainty. Environ. Syst. Res. 1 (1), 1. .
 
- Lu, H. W., Huang, G. H., He, L., & Zeng, G. M. (2018). An inexact dynamic optimization model for municipal solid waste management in association with greenhouse gas emission control. Journal of environmental management,90(1), 396-409.
 
- Minciardi, R., Paolucci, M., Robba, M., & Sacile, R. (2017). Multi-objective optimization of solid waste flows: environmentally sustainable strategies for municipalities. Waste Management, 28(11), 2202-2212.
 
- Muneeb, S., Adhami, A., Jalil, S., & Asim, Z. (2018). Decentralized bi-level decision planning model for municipal solid waste recycling and management with cost reliability under uncertain environment. Sustain. Prod. Consumpt. 16, 33–44.
 
- Rathore, P., & Sarmah, S. (2019). Modeling transfer station locations considering source separation of solid waste in urban centers: A case study of Bilaspur city India. J. Cleaner Prod. 211, 44–60.
 
- Sadat Hosseini, M., & Shahbandarzadeh, H. (2019). Designing a multi-objective mathematical model with intuitive fuzzy approach for selecting investment projects (Study of investment opportunities in Bushehr province. Journal of Business Strategies / Shahed University / 26th Year/ No. 14.
 
- Wang, K., & Yang, Q. (2014). . Hierarchical facility location for the reverse logistics network design under uncertainty. J. Uncertain Syst. 8 (4), 255–270.
 
- Yadav, V., Bhurjee, A., Karmakar, S., & Dikshit, A. (2017). A facility location model for municipal solid waste management system under uncertain environment. Sci. Total Environ. 603, 760–771.
 
- Yadav, V., Karmakar, S., Dikshit, A., & Bhurjee, A. (2018). Interval-valued facility location model: An appraisal of municipal solid waste management system. J.Cleaner Prod. 171, 250–263.

Files

History

  • Receive Date: 22 April 2021
  • Revise Date: 12 June 2021
  • Accept Date: 03 July 2021

Share

How to cite

Statistics