Determining the Sequence and Schedule of Job-shop Production Systems using Genetic Algorithm by considering Possible Values
محورهای موضوعی : Operations Management
seyed ahmad shayan nia
1
(Department of Industrial Management, Islamic Azad University, Firoozkooh Branch, Firoozkooh, Iran.)
mostafa mohammadi
2
(Department of Industrial Management ,Islamic Azad University, Firoozkooh Branch, , Firoozkooh, Iran)
Mohammad Reza lotfi
3
(Department of Industrial engineering ,Islamic Azad University, Firoozkooh Branch, , Firoozkooh, Iran)
Javad Rezaeian
4
(Department of Industrial Engineering, Faculty of Management and Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran)
کلید واژه: Genetic Algorithm, system, jobshop production schedule,
چکیده مقاله :
Sequencing and scheduling of production in jobshop production systems was investigated in this article. Each operation specific to a job has a random duration with a mean and variance, taking into account this uncertainty in the assumptions of the model, the adaptation of the model to the real conditions of the production environment. Each job has the operating costs of the machine during processing, the cost of equipment idle for each unit of time delay in receiving the work. In this study, the optimal scheduling is Reduction of costs. The algorithm used to solve the problem is a genetic algorithm. The efficiency of the proposed algorithms has been tested and analyzed with a number of selected problems from the literature. This study was performed in a situation where the time of operation is uncertain and follows a specific statistical distribution (normal, exponential and uniform). The performance of the genetic algorithm was evaluated based on time criteria and objective function. The results obtained from the genetic algorithm were compared with the results obtained from the combined algorithm (neural network and SA algorithm) and the results obtained from the optimal solving procedures using Lingo software version 6 for 5 sample production scheduling problems. The results show that the integrated algorithm performed better than the genetic algorithm
