Hybrid simulation and ga for a flexible flow shop problem with variable processors and re-entrant flow

The problem of FFSP (Flexible Flow Shop Problem) has been sufficiently investigated due to its importance for production programming and control, although many of the solution methods have been based on GA (Genetic Algorithm) and simulation, these techniques have been used in deterministic environments and under specific conditions of the problem, that is, complying with restrictions given in the Graham notation. In this paper we describe an application of these techniques to solve a very particular case where manual work stations and equipment with different degrees of efficiency, technological restrictions, recirculation process are used. The nesting of the GA is used within a simulation process. It is showed that the method proposed in adjustment and efficiency is better compared with other heuristics, in addition to the benefits of using different techniques in series to solve problems of real manufacturing environments. © 2018, Springer Nature Switzerland AG.

• Fecha de publicación: 2018
• Institución: Universidad del Rosario
• Repositorio: Repositorio EdocUR - U. Rosario
• Idioma: eng
• OAI Identifier: oai:repository.urosario.edu.co:10336/23750
• Acceso en línea: https://doi.org/10.1007/978-3-030-00350-0_21; https://repository.urosario.edu.co/handle/10336/23750
• Palabra clave: Efficiency; Genetic algorithms; Heuristic methods; Machine shop practice; Flexible flow-shop problems; GA (genetic algorithm); Hybrid simulation; Manufacturing environments; Parallel machine; Recirculation process; Simulation; Simulation process; Problem solving; Flexible workshop programming; Genetic algorithm; Parallel machines; Process re-entry; Simulation
• License: http://purl.org/coar/access_right/c_abf2