Applied Mathematics & Information Sciences
Multi-Objective Criteria in Hybrid Flow Shop Scheduling Using Improved Genetic Algorithm
Flow shop scheduling problem consists of scheduling n jobs on m machines. As an attempt for meeting this objective, all jobs are allotted with the same sequence of operations, where the problem is analysed in terms of make-span, total tardiness and ﬂow time. In order to solve this scheduling problem, an approach based on Improved Genetic Algorithm (IGA) is developed. In this regard, job data Four Drawer Furniture Component (4dfc) has been collected from the company, from where the time sequence for each operation has been calculated manually. By using genetic algorithm, various sequences have been generated and the make-span time has also been calculated. Two factors namely, crossover and mutation are employed, thus to improve the genetic algorithm used, which in turn reduces the make-span time. Various sequences have been developed by using C where both the manual and program results are correlated. At the end of process, best sequence is found using IGA and the results are validated.
Digital Object Identifier (DOI)
L. Brabin Nivas, M. and Prabaharan, T.
"Multi-Objective Criteria in Hybrid Flow Shop Scheduling Using Improved Genetic Algorithm,"
Applied Mathematics & Information Sciences: Vol. 11:
2, Article 25.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol11/iss2/25