Research on Job Scheduling Problems with DMS for Cell Controller Based Bogie

2013 ◽  
Vol 791-793 ◽  
pp. 595-598
Author(s):  
Xian Hong Wang ◽  
Wen Xu ◽  
Wei Dong Feng
Keyword(s):  
Machine Tools ◽  
Job Scheduling ◽  
Job Shop ◽  
Object Oriented ◽  
Control Model ◽  
Scheduling Problems ◽  
Control Logic ◽  
Cell Control ◽  
Discrete Manufacturing System ◽  
Cell Controller

For tackling job scheduling problems encountered with discrete manufacturing system for 380km/h motor bogie, Object-oriented Petri Net based Job-Shop Cell Controller System (OPNCC) was brought forward identifying categories of all objects within cell control model, describing relationship between categories, analyzing and recognizing cell control logic through the dynamic behavior of physical objects and introducing control decision and strategy into the control logic of as designed OPN model, thereby constituting a complete object-oriented cell control model. With improved genetic algorithm, it can offer dynamic optimization to balanced production of multiple work pieces on several machine tools so that the machine tools can be utilized to a maximum extent.

Solution of Large-Scale Scheduling Problems for Job-Shop Type Machining Systems with Alternative Machine Tools

CIRP Annals ◽  
1980 ◽  
Vol 29 (1) ◽  
pp. 335-338 ◽  
Author(s):  
K. Iwata ◽  
Y. Murotsu ◽  
F. Oba ◽  
K. Okamura
Keyword(s):  
Machine Tools ◽  
Large Scale ◽  
Job Shop ◽  
Scheduling Problems

A Case Study on Object-oriented Modelling and Simulation of Machine Tools

2013 ◽  
Vol 23 (3-4) ◽  
Author(s):  
Bernhard Heinzl ◽  
Michael Landsiedl ◽  
Fabian Duer ◽  
Alexandros-Athanassios Dimitriou ◽  
Wolfgang Kastner ◽  
...  
Keyword(s):  
Machine Tools ◽  
Object Oriented ◽  
Modelling And Simulation ◽  
Object Oriented Modelling

scholarly journals Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

2019 ◽  
Vol 24 (3) ◽  
pp. 80 ◽  
Author(s):  
Prasert Sriboonchandr ◽  
Nuchsara Kriengkorakot ◽  
Preecha Kriengkorakot
Keyword(s):  
Differential Evolution ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Differential Evolution Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Relative Errors ◽  
Improved Differential Evolution Algorithm

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

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