A superimposition based approach for maintenance and quality plan optimization with production schedule, availability, repair time and detection time constraints for a single machine. (October 2015)

• Record Type: Journal Article
• Authors: Tambe, Pravin P.; Kulkarni, Makarand S.
• Is Part Of: Journal of manufacturing systems. Volume 37:Part 1(2015)
• Page Start: 17
• Page End: 32
• Publication Date: 2015-10
• Subjects: Selective maintenance -- Quality control -- Scheduling -- Integrated approach -- Simulated annealing -- Genetic algorithm; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2015.09.009
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract : Highlights: An integrated approach for maintenance, quality control and production scheduling for a multi-component system. Approach result in simultaneously determination of maintenance actions for each component, values of parameters of sampling procedure, considering optimal production schedule. Makes use of selective maintenance, sampling procedure based on economic design principle and production scheduling. Simulated Annealing (SA) and Genetic Algorithm (GA) are used for optimization. Abstract: Performance of a production system significantly depends upon the effective planning of its shop floor level operations like, production scheduling, maintenance and quality control. These three functions have an interaction effect on each other; hence a combined operational policy of these functions can improve the system performance. The objective of this paper is to develop a methodology for optimizing maintenance and quality plan with the constraint on schedule, availability, repair time and detection time, for a single machine. The approach includes the selective maintenance and a sampling procedure based on the economic design principle. Cost models developed for the problem at hand are presented along with their detailed explanation. Two solution methodologies namely, Simulated Annealing (SA) and Genetic Algorithm (GA) are used for obtaining the near optimal solution. A computational experiment to compare the performance of SA and GA is presented. Results indicate a better performance of the proposed approach over the conventional approach of independent planning.