Article: A novel scheduling framework: integrating genetic algorithms and discrete event simulation Journal: International Journal of Management and Decision Making (IJMDM) 2018 Vol.17 No.4 pp.371 - 395 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. Inderscience Publishers - linking academia, business and industry through research

Title: A novel scheduling framework: integrating genetic algorithms and discrete event simulation

Authors: Luca Fumagalli; Elisa Negri; Edoardo Sottoriva; Adalberto Polenghi; Marco Macchi

Addresses: Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milan, Italy ' Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milan, Italy ' Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milan, Italy ' Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milan, Italy ' Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milan, Italy

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.

Keywords: scheduling framework; job-shop; job-shop scheduling problem; JSSP; genetic algorithm; discrete event simulation; DES.

DOI: 10.1504/IJMDM.2018.095738

International Journal of Management and Decision Making, 2018 Vol.17 No.4, pp.371 - 395

Received: 22 Dec 2017
Accepted: 28 May 2018
Published online: 18 Oct 2018 *

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Title: A novel scheduling framework: integrating genetic algorithms and discrete event simulation

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