Computers and Industrial Engineering
Simulation system for real-time planning, scheduling, and control
WSC '96 Proceedings of the 28th conference on Winter simulation
Simulation with visual SLAM and AweSim
Simulation with visual SLAM and AweSim
Generic simulation model for hybrid flow-shop
Computers and Industrial Engineering
Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling
Computers and Industrial Engineering
Computer simulation of due-date setting in multi-machine job shops
Computers and Industrial Engineering
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Application of a multi-criteria simulation optimization based DSS
Proceedings of the 2007 Summer Computer Simulation Conference
Application of a multi-criteria simulation optimization based DSS
SpringSim '07 Proceedings of the 2007 spring simulation multiconference - Volume 3
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One of the basic and significant problems, that a shop or a factory manager is encountered, is a suitable scheduling and sequencing of jobs on machines. One type of scheduling problem is job shop scheduling. There are different machines in a shop of which a job may require some or all these machines in some specific sequence. For solving this problem, the objective may be to minimize the makespan. After optimizing the makespan, the jobs sequencing must be carried out for each machine. The above problem can be solved by a number of different methods such as branch and bound, cutting plane, heuristic methods, etc. In recent years, researches have used genetic algorithms, simulated annealing, and machine learning methods for solving such problems. In this paper, a simulation model is presented to work out job shop scheduling problems with the objective of minimizing makespan. The model has been coded by Visual SLAM which is a special simulation language. The structure of this language is based on the network modeling. After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented and compared with other results reported in the literature. Finally, the model output is analyzed.