Most of the research works on methods and techniques for solving the job-shop scheduling problem (JSSP) propose theoretically powerful optimisation algorithms that indeed are practically difficult to apply in real industrial scenarios due to the complexity of these production systems. This paper aims at filling the gap between research and industrial worlds by creating a framework of general applicability for solving the JSSP. Through a literature analysis, the constituent elements of the framework have been identified: an optimisation method that can solve NP-hard JSSP problem in a reasonable time, i.e., the genetic algorithm (GA), and a tool that allows precisely modelling the production system and evaluating the goodness of the schedules, i.e., a simulation model. A case study of a company that bases its business in the manufacturing of aerospace components proved the applicability of the proposed framework.

A novel scheduling framework: Integrating genetic algorithms and discrete event simulation

Fumagalli, Luca;Negri, Elisa;Sottoriva, Edoardo;Polenghi, Adalberto;Macchi, Marco
2018

Abstract

Most of the research works on methods and techniques for solving the job-shop scheduling problem (JSSP) propose theoretically powerful optimisation algorithms that indeed are practically difficult to apply in real industrial scenarios due to the complexity of these production systems. This paper aims at filling the gap between research and industrial worlds by creating a framework of general applicability for solving the JSSP. Through a literature analysis, the constituent elements of the framework have been identified: an optimisation method that can solve NP-hard JSSP problem in a reasonable time, i.e., the genetic algorithm (GA), and a tool that allows precisely modelling the production system and evaluating the goodness of the schedules, i.e., a simulation model. A case study of a company that bases its business in the manufacturing of aerospace components proved the applicability of the proposed framework.
INTERNATIONAL JOURNAL OF MANAGEMENT AND DECISION MAKING
DES; Discrete event simulation; Genetic algorithm; Job-shop; Job-shop scheduling problem; JSSP; Scheduling framework; Decision Sciences (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1079494
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