An elitist seasonal artificial bee colony algorithm for the interval job shop
In this paper, a novel Artificial Bee Colony algorithm is proposed to solve a variant of the Job Shop Scheduling Problem where only an interval of possible processing times is known for each operation. The solving method incorporates a diversification strategy based on the seasonal behaviour of bees...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:ucreareposit::fcc46ec94a85f4c8ce96f69ee98a0c55 |
| Acceso en línea: | https://hdl.handle.net/10902/39910 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial bee colony Job shop scheduling Makespan Interval uncertainty Robustness |
| Sumario: | In this paper, a novel Artificial Bee Colony algorithm is proposed to solve a variant of the Job Shop Scheduling Problem where only an interval of possible processing times is known for each operation. The solving method incorporates a diversification strategy based on the seasonal behaviour of bees. That is, the bees tend to explore more at the beginning of the search (spring) and be more conservative towards the end (summer to winter). This new strategy helps the algorithm avoid premature convergence, which appeared to be an issue in previous papers tackling the same problem. A thorough parametric analysis is conducted and a comparison of different seasonal models is performed on a set of benchmark instances from the literature. The results illustrate the benefit of using the new strategy, improving the performance of previous ABC-based methods for the same problem. An additional study is conducted to assess the robustness of the solutions obtained under different ranking operators, together with a sensitivity analysis to compare the effect that different levels of uncertainty have on the solutions' robustness. |
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