Procedia Computer Science
Complex Adaptive Systems, Conference Organized by Missouri University of Science and Technology 2016 - Los Angeles, CA
The mixed job shop scheduling problem is one in which some jobs have fixed machine orders and other jobs may be processed in arbitrary orders. In past literature, optimal solutions have been proposed based on adaptations of classical solutions such as by Johnson, Thompson and Giffler among many others, by pseudopolynomial algorithms, by simulation, and by Genetic Algorithms (GA). GA based solutions have been proposed for flexible Job shops. This paper proposes a GA algorithm for the mixed job shop scheduling problem. The paper starts with an analysis of the characteristics of the so-called mixed shop problem. Based on those properties, a modified GA is proposed to minimize the makespan of the mixed shop schedule. In this approach, sample instances used as test data are generated under the constraints of shop scheduling problems. A comparison of our results based on benchmark data indicate that our modified GA provides an efficient solution for the mixed shop scheduling problem.
Original Publication Citation
Nguyen, V., & Bao, H. P. (2016). An efficient solution to the mixed shop scheduling problem using a modified genetic algorithm. Procedia Computer Science, 95, 475-482. doi:https://doi.org/10.1016/j.procs.2016.09.324
Nguyen, V. and Bao, H. P., "An Efficient Solution to the Mixed Shop Scheduling Problem Using a Modified Genetic Algorithm" (2016). Mechanical & Aerospace Engineering Faculty Publications. 47.