Optimizing the integrated production and maintenance planning using genetic algorithm. (4th May 2018)

• Record Type: Journal Article
• Authors: Ettaye, Ghita; El Barkany, Abdellah; Jabri, Abdelouahhab; El Khalfi, Ahmed
• Is Part Of: International journal of engineering business management. Volume 10(2018)
• Publication Date: 2018-05-04
• Subjects: Integrated planning -- production -- preventive replacement -- minimal repair -- optimization -- genetic algorithm; Engineering -- Periodicals; Industrial management -- Periodicals; Engineering; Industrial management; Periodicals; 620.0068
• DOI: 10.1177/1847979018773260
• Languages: English
• ISSNs: 1847-9790

Abstract

In spite of the interdependence between them, production and maintenance planning decisions are generally studied and used independently in the majority of the manufacturing systems. Our contribution is summarized to obtain a maintenance policy including preventive replacement in each maintenance cycle and minimal repair in case of unplanned failure, and on the other side, for a set of products and in each period, specify the quantity to be produced and when is the production set up, also the stock and the breaking on demand level, so that to minimize the total cost. The purpose of the research was aimed at achieving the optimization of an integrated planning of preventive maintenance and production in a multi-period, multiproduct, and single-line production system. To achieve this purpose, our model is configured as a mixed integer linear programming and solved by IBM ILOG CPLEX OPL studio 12.6 (USA), and we propose our own genetic algorithms (GAs) using Python solver with respect to resolution time and the quality of results. Then, to find the performance of the model and the usefulness of the proposed resolution method, a numerical example is considered to produce two products for a finite horizon with 11 periods. The results of the analysis show that this GA provides a new tool for the integrated planning in the industrial sector. These results reflect the experiences of single-line system and further studies are needed for generalizability in the multiline cases, also we will compare the proposed GA with other evolutionary algorithms.