IMPROVING THE SHIFT-SCHEDULING PROBLEM USING NON-STATIONARY QUEUEING MODELS WITH LOCAL HEURISTIC AND GENETIC ALGORITHM

• Autor(a) principal: Beojone,Caio Vitor
• Data de Publicação: 2020
• Outros Autores: Souza,Regiane Máximo de
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Pesquisa operacional (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382020000100201
• topic: nonstationary queues; genetic algorithm; local search heuristic

ABSTRACT We improve the shift-scheduling process by using nonstationary queueing models to evaluate schedules and two heuristics to generate schedules. Firstly, we improved the fitness function and the initial population generation method for a benchmark genetic algorithm in the literature. We also proposed a simple local search heuristic. The improved genetic algorithm found solutions that obey the delay probability constraint more often. The proposed local search heuristic also finds feasible solutions with a much lower computational expense, especially under low arrival rates. Differently from a genetic algorithm, the local search heuristic does not rely on random choices. Furthermore, it finds one final solution from one initial solution, rather than from a population of solutions. The developed local search heuristic works with only one well-defined goal, making it simple and straightforward to implement. Nevertheless, the code for the heuristic is simple enough to accept changes and cope with multiple objectives.