Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/42452
metadata.artigo.dc.title: A multiperiod workforce scheduling and routing problem with dependent tasks
metadata.artigo.dc.creator: Pereira, Dilson Lucas
Alves, Júlio César
Moreira, Mayron César de Oliveira
metadata.artigo.dc.subject: Workforce scheduling and routing
Scheduling
Vehicle routing
Escalonamento de veículos
Escalonamento da força de trabalho
Roteamento de veículos
metadata.artigo.dc.publisher: Elsevier
metadata.artigo.dc.date.issued: Jun-2020
metadata.artigo.dc.identifier.citation: PEREIRA, D. L.; ALVES, J. C.; MOREIRA, M. C. de O. A multiperiod workforce scheduling and routing problem with dependent tasks. Computers & Operations Research, [S I.], v. 118, June 2020. DOI: https://doi.org/10.1016/j.cor.2020.104930.
metadata.artigo.dc.description.abstract: In this paper, we study a new Workforce Scheduling and Routing Problem, denoted Multiperiod Workforce Scheduling and Routing Problem with Dependent Tasks. In this problem, customers request services from a company. Each service is composed of dependent tasks, which are executed by teams of varying skills along one or more days. Tasks belonging to a service may be executed by different teams, and customers may be visited more than once a day, as long as precedences are not violated. The objective is to schedule and route teams so that the makespan is minimized, i.e., all services are completed in the minimum number of days. In order to solve this problem, we propose a Mixed-Integer Programming model, a constructive algorithm and heuristic algorithms based on the Ant Colony Optimization (ACO) metaheuristic. The presence of precedence constraints makes it difficult to develop efficient local search algorithms. This motivates the choice of the ACO metaheuristic, which is effective in guiding the construction process towards good solutions. Computational results show that the model is capable of consistently solving problems with up to about 20 customers and 60 tasks. In most cases, the best performing ACO algorithm was able to match the best solution provided by the model in a fraction of its computational time.
metadata.artigo.dc.identifier.uri: https://doi.org/10.1016/j.cor.2020.104930
http://repositorio.ufla.br/jspui/handle/1/42452
metadata.artigo.dc.language: en
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