Using MILP to plan holidays and working hours under an annualised hours agreement
Annualising work hours (AH) is a means of achievement flexibility in the use of human resources to face the seasonal nature of demand. In Corominas et al. (1) two MILP models are used to solve the problem of planning staff working hours with annual horizon. The costs due to overtime and to the emplo...
| Autores: | , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2006 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/306 |
| Acceso en línea: | https://hdl.handle.net/2117/306 |
| Access Level: | acceso abierto |
| Palabra clave: | Manpower planning Integer programming Hours of labor Service industries Annualised hours Service industry Hores de treball anual Horas de trabajo anual Programació entera Programación entera Mà d'obra -- Planificació Indústries de serveis -- Direcció i administració Classificació AMS::90 Operations research, mathematical programming Àrees temàtiques de la UPC::Economia i organització d'empreses::Gestió i direcció |
| Sumario: | Annualising work hours (AH) is a means of achievement flexibility in the use of human resources to face the seasonal nature of demand. In Corominas et al. (1) two MILP models are used to solve the problem of planning staff working hours with annual horizon. The costs due to overtime and to the employment of temporary workers are minimised, and the distribution of working time over the course of the year for each worker and the distribution of working time provided by temporary workers are regularised. In the aforementioned paper, the following is assumed: (i) the holiday weeks are fixed a priori and (ii) the workers are from different categories who are able to perform specific type of task have se same efficiency; moreover, the values of the binary variables (and others) in the second model are fixed to those in the first model (thus, in the second model these will intervene as constants and not as variables, resulting in an LP model). In the present paper, these assumptions are relaxed and a more general problem is solved. The computational experiment leads to the conclusion that MILP is a technique suited to dealing with the problem. |
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