A fuzzy optimization approach to the capacitated node-routing problem for municipal solid waste collection with multiple tours: A case study

Abstract

In many countries, municipal solid waste management is considered a very important challenge, and the most relevant costs in this field are dedicated to the collection process. Therefore, this study aimed to propose a mathematical model with multiple depots and multiple intermediate facilities to minimize fixed and variable costs of waste collection. Intermediate facilities are used in the developed countries in their waste collection network, because these facilities reduce the long-term costs of waste management and increase the quality of the waste collection process. Also, in reality, the amount of waste generated per day is not deterministic, so, to cope with the issue of uncertainty in the amount of waste, a fuzzy optimization approach was considered. Furthermore, a system where vehicles that could collect the wastes in multiple tours, with a maximum number of tours for each vehicle, was also considered. Due to the high complexity of this model, a genetic algorithm was elaborated. Further, the efficiency of the proposed algorithm was confirmed by comparison with the exact solution in small dimensions. It should be noted that the initial solution of this algorithm was obtained by a proposed heuristic algorithm. Finally, a case study on the vehicle routing of municipal solid waste was conducted in a district of Tehran, Iran. Moreover, the solutions of the model were validated by comparing the results of the proposed model and the current real-life situation. The contractors could improve vehicle routes and reduce costs by implementing the results of the proposed model, without any additional cost.

Designing a sustainable supply chain network for producing high-value products from waste glass

“…Tirkolaee et al (2019) presented a multi-trip arc routing problem for the urban solid waste management under uncertainty and introduced a chance-constrained programming approach based on the fuzzy credibility theory to cope with uncertain parameters. Aliahmadi et al (2019) presented a mathematical model for the waste collection problem with multiple depots and intermediate facilities under uncertainty. They developed a fuzzy optimization approach to deal with the uncertainty and a genetic algorithm to solve the proposed model.…”

Optimization Model for Integrated Municipal Solid Waste System Using Stochastic Chance-Constraint Programming under Uncertainty: A Case Study in Qazvin, Iran

“…e achievement of their research was a 7.16% reduction in carbon emissions compared to the traditional collection model. More studies performed on the application of routing problems for MSW collection application can be found in Babaee Tirkolaee et al [27]; Babaee Tirkolaee et al [28]; Babaee Tirkolaee et al [29] Aliahmadi et al [30]. Goli et al [31] applied a robust optimization approach for a multiobjective product portfolio problem.…”

Indicators and ICTs application for municipal waste management

“…The “European Union regulation” recommends a hierarchical system based on four resulting levels (European Union, 2006; Madadian et al, 2013), namely reducing “solid waste production, recovery of material, recovery of energy, and landfill disposal.” In most cases, each waste management project has several options based on diverse procedural solutions, objectives, or spot selection (Karmperis et al, 2012). The share of waste collection in the total waste management budget is the largest (Aliahmadi et al, 2020; Hoke and Yalcinkaya, 2021; Jacobsen et al, 2013). And it usually corresponds to over 80% in low-income countries and over 60% in middle-income nations (Aremu, 2013).…”

“…In most cases, each waste management project has several options based on diverse procedural solutions, objectives, or spot selection (Karmperis et al, 2012). The share of waste collection in the total waste management budget is the largest (Aliahmadi et al, 2020;Hoke and Yalcinkaya, 2021;Jacobsen et al, 2013). And it usually corresponds to over 80% in low-income countries and over 60% in middle-income nations (Aremu, 2013).…”