A Simulated Annealing Algorithm for Solving a Routing Problem in the Context of Municipal Solid Waste Collection

Fermani, Matías; Rossit, Diego Gabriel; Toncovich, Adrián

doi:10.1007/978-3-030-76310-7_5

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…For the fairness of the experiment, this work uses the same evaluation method in this paper to evaluate the performance indicators of method 1. A simulated annealing (SA) algorithm for planning the collecting path of garbage vehicles in the context of MSW (denoted as method 2) proposed by M. Fermani et al [ 35 ]. A simulated annealing algorithm is a classical stochastic optimization algorithm for solving the CVRP, and it is representative.…”

Section: Simulation Experiments and Discussionmentioning

confidence: 99%

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

Wei

Liang

Tang

2022

Computational Intelligence and Neuroscience

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The disposal of municipal solid waste (MSW) is based on the divide-regional operation model at present, which brings management convenience. However, due to the constraints of the division between the operating regions, there are problems such as inflexible vehicle scheduling and low efficiency. Changing this mode will put pressure on management. It is necessary to break the constraint of the region and form a fully automatic operating scheme without too much manual management. First, the initial clusters are formed by considering the distance in the initial allocation module. Secondly, through the type labeling and reallocation module, the single big data set is transformed into multiple small data sets by considering the allocated amount of garbage and the carrying capacity of garbage vehicles. Then, this work proposes the improved hierarchical agglomerative clustering (IHAC) algorithm and the garbage collecting path planning (GCPP) algorithm to realize the intelligent allocation of waste and scheduling route planning of garbage vehicles. Finally, through simulation by real example and comparative analysis, the advantages of the proposed scheme are discussed. The results show that the proposed scheme is more effective than the original scheme and other advanced methods, which can provide decision support for the scientific and intelligent collection and transportation of MSW.

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Section: Simulation Experiments and Discussionmentioning

confidence: 99%

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

Wei

Liang

Tang

2022

Computational Intelligence and Neuroscience

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“…Te intense brightness of the glowworms is found in the location of their objective function and utility value. Te position is better if the glow is brighter [17]. Te glowworms will search in their neighbour set for a new local range of decisions.…”

Section: Glowworm Swarm Optimization (Gwo) Algorithmmentioning

confidence: 99%

Slow Heat‐Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

Pagadala

Kumari

Thakur

et al. 2023

Computational Intelligence and Neuroscience

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Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well-suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat-based SA-GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature-based SA-GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA-GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.

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“…Moreover, some works explicitly consider the location of garbage accumulation points taking into account a proxy to posterior routing costs among the different objectives (Rossit et al, 2018(Rossit et al, , 2020. Other works addressed the waste collection problem considering the previous location of waste bins (Fermani et al, 2020;Rossit et al, 2021), while others considered both problems simultaneously, that is, optimizing the sizing of waste bins and designing the collection routes (Mahéo et al, 2020(Mahéo et al, , 2023. Finally, in Vazquez et al (2020), an analysis of the composition and amount of waste in Bahía Blanca was performed based on a thorough field work.…”

Section: Related Workmentioning

confidence: 99%

Optimization of waste collection through the sequencing of micro-routes and transfer station convenience analysis: An Argentinian case study

et al. 2023

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Municipal solid waste management is a paramount activity in modern cities due to environmental, social and economic problems that can arise when mishandled. In this work, the sequencing of micro-routes in the Argentine city of Bahía Blanca is addressed, which is modelled as a vehicle routing problem with travel time limit and the vehicle’s capacity. Particularly, we propose two mathematical formulations based on mixed integer programming and we solve a set of instances of the city of Bahía Blanca based on real data. Moreover, with this model, we estimate the total distance and travel time of the waste collection and use this data to analyse the possibility of installing a transfer station. The results demonstrate the competitiveness of the approach to resolve realistic instances of the target problem and suggest the convenience of installing a transfer station in the city considering the reduction of the travelled distance.

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A Simulated Annealing Algorithm for Solving a Routing Problem in the Context of Municipal Solid Waste Collection

Cited by 8 publications

References 15 publications

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

Slow Heat‐Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

Optimization of waste collection through the sequencing of micro-routes and transfer station convenience analysis: An Argentinian case study

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