A simheuristic algorithm for time-dependent waste collection management with stochastic travel times
A major operational task in city logistics is related to waste collection. Due to large problem sizes and numerous constraints, the optimization of real-life waste collection problems on a daily basis requires the use of metaheuristic solving frameworks to generate near-optimal collection routes in...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/127049 |
| Acceso en línea: | https://hdl.handle.net/10609/127049 http://doi.org/10.2436/20.8080.02.103 |
| Access Level: | acceso abierto |
| Palabra clave: | waste collection management vehicle routing problem stochastic optimization simheuristics biased randomization case study gestión de recolección de residuos problema de generación de rutas de vehículos optimización estocástica simheurística aleatorización sesgada estudio de caso gestió de recollida de residus problema d&apos encaminament de vehicles optimització estocàstica aleatorització esbiaixada estudi de casos Algorithms Algorismes Algoritmos |
| Sumario: | A major operational task in city logistics is related to waste collection. Due to large problem sizes and numerous constraints, the optimization of real-life waste collection problems on a daily basis requires the use of metaheuristic solving frameworks to generate near-optimal collection routes in low computation times. This paper presents a simheuristic algorithm for the time-dependent waste collection problem with stochastic travel times. By combining Monte Carlo simulation with a biased randomized iterated local search metaheuristic, time-varying and stochastic travel speeds between different network nodes are accounted for. The algorithm is tested using real instances in a medium-sized city in Spain. |
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