SciELO - Scientific Electronic Library Online

 
vol.40MODELING A HYBRID FLOW SHOP PROBLEM APPLIED TO THE PRODUCTION LINE OF A HAIR COSMETICS FACTORYA CONSTRUCTIVE GLOBAL CONVERGENCE OF THE MIXED BARRIER-PENALTY METHOD FOR MATHEMATICAL OPTIMIZATION PROBLEMS author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

Share


Pesquisa Operacional

Print version ISSN 0101-7438On-line version ISSN 1678-5142

Abstract

BEOJONE, Caio Vitor  and  SOUZA, Regiane Máximo de. IMPROVING THE SHIFT-SCHEDULING PROBLEM USING NON-STATIONARY QUEUEING MODELS WITH LOCAL HEURISTIC AND GENETIC ALGORITHM. Pesqui. Oper. [online]. 2020, vol.40, e220764.  Epub May 18, 2020. ISSN 1678-5142.  https://doi.org/10.1590/0101-7438.2020.040.00220764.

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.

Keywords : nonstationary queues; genetic algorithm; local search heuristic.

        · text in English     · English ( pdf )