Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem
The solid waste collection problem refers to truck route optimisation to collect waste from containers across various locations. Recent concerns exist over the impact of solid waste management on the environment. Hence, it is necessary to find feasible routes while minimising operational costs and f...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2023/5398400 |
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| author | Haneen Algethami |
| author_facet | Haneen Algethami |
| author_sort | Haneen Algethami |
| collection | DOAJ |
| description | The solid waste collection problem refers to truck route optimisation to collect waste from containers across various locations. Recent concerns exist over the impact of solid waste management on the environment. Hence, it is necessary to find feasible routes while minimising operational costs and fuel consumption. In this paper, in order to reduce fuel consumption, the number of trucks used is considered in the objective function along with the waste load and the travelling time. With the current computational capabilities, finding an optimal solution is challenging. Thus, this study aims to investigate the effect of well-known metaheuristic methods on this problem’s objective function and computational times. The routing solver in the Google OR-tools solver is utilised with three well-known metaheuristic methods for neighbourhood exploration: a guided local search (GLS), a tabu search (TS), and simulated annealing (SA), with two initialisation strategies, Clarke and Wright’s algorithm and the nearest neighbour algorithm. Results showed that optimal solutions are found in faster computational times than using only an IP solver, especially for large instances. Local search methods, notably GLS, have significantly improved the route construction process. The nearest neighbour algorithm has often outperformed the Clarke and Wright's methods. The findings here can be applied to improve operations in Saudi Arabia’s waste management sector. |
| format | Article |
| id | doaj-art-0cb4b3fd27f2436bb103cbe7d80a2674 |
| institution | OA Journals |
| issn | 1687-9732 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-0cb4b3fd27f2436bb103cbe7d80a26742025-08-20T02:03:05ZengWileyApplied Computational Intelligence and Soft Computing1687-97322023-01-01202310.1155/2023/5398400Local Search-Based Metaheuristic Methods for the Solid Waste Collection ProblemHaneen Algethami0Department of Computer ScienceThe solid waste collection problem refers to truck route optimisation to collect waste from containers across various locations. Recent concerns exist over the impact of solid waste management on the environment. Hence, it is necessary to find feasible routes while minimising operational costs and fuel consumption. In this paper, in order to reduce fuel consumption, the number of trucks used is considered in the objective function along with the waste load and the travelling time. With the current computational capabilities, finding an optimal solution is challenging. Thus, this study aims to investigate the effect of well-known metaheuristic methods on this problem’s objective function and computational times. The routing solver in the Google OR-tools solver is utilised with three well-known metaheuristic methods for neighbourhood exploration: a guided local search (GLS), a tabu search (TS), and simulated annealing (SA), with two initialisation strategies, Clarke and Wright’s algorithm and the nearest neighbour algorithm. Results showed that optimal solutions are found in faster computational times than using only an IP solver, especially for large instances. Local search methods, notably GLS, have significantly improved the route construction process. The nearest neighbour algorithm has often outperformed the Clarke and Wright's methods. The findings here can be applied to improve operations in Saudi Arabia’s waste management sector.http://dx.doi.org/10.1155/2023/5398400 |
| spellingShingle | Haneen Algethami Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem Applied Computational Intelligence and Soft Computing |
| title | Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem |
| title_full | Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem |
| title_fullStr | Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem |
| title_full_unstemmed | Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem |
| title_short | Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem |
| title_sort | local search based metaheuristic methods for the solid waste collection problem |
| url | http://dx.doi.org/10.1155/2023/5398400 |
| work_keys_str_mv | AT haneenalgethami localsearchbasedmetaheuristicmethodsforthesolidwastecollectionproblem |