Group Scheduling With Nonperiodical Maintenance and Deteriorating Effects
[EN] In this paper, we consider single-machine group scheduling with nonperiodical maintenance and deteriorating effects. Nonperiodical maintenance, which has unfixed maintaining interval or the number of jobs in each group is unfixed, results in a variable number of groups. Deteriorating effects le...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2021 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositório: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglês |
| OAI Identifier: | oai:riunet.upv.es:10251/184693 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/184693 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Maintenance engineering Single machine schedulin Job shop scheduling Analytical models Time series analysis Indexes Deteriorating effects Group scheduling Nonperiodical maintenance Single machine ESTADISTICA E INVESTIGACION OPERATIVA |
| Resumo: | [EN] In this paper, we consider single-machine group scheduling with nonperiodical maintenance and deteriorating effects. Nonperiodical maintenance, which has unfixed maintaining interval or the number of jobs in each group is unfixed, results in a variable number of groups. Deteriorating effects lead to longer processing times of which the deterioration index depends on job grouping. This problem is of significance in different production settings and is much more difficult than and general that other simpler single-machine group scheduling problems. Making use of historical processing times, we construct the actual processing time model for jobs. We prove that the problem under study is NP-hard. By transforming the optimization objective, properties are discovered and two batch-based heuristics are presented for small size problems. To further improve the effectiveness for large size problems, an iterated greedy algorithm is proposed being its main advantages simplicity and effectiveness. The proposed methods are evaluated over a large number of random instances with calibrated parameters and components. Comprehensive computational and statistical analyses demonstrate the superiority of the methods proposed over adapted existing approaches |
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