Web-based Technologies Integration for Distributed Manufacturing Scheduling in a Virtual Enterprise

2012 ◽  
Vol 4 (2) ◽  
pp. 19-34 ◽  
Author(s):  
Maria Leonilde R. Varela ◽  
Goran D. Putnik ◽  
Maria Manuela Cruz-Cunha

Today’s manufacturing enterprises face enormous competitive pressures stemming from the current dynamic and open business context. Global competition and market demand for customized products and services, delivered ‘just in time’, exert real stress on businesses. Recently, new production paradigms, such as the extended enterprise, as well as agile, virtual and networked manufacturing, have appeared in response to the increasingly dynamic conditions of the marketplace. These new concepts prompt geographically dispersed manufacturers to build alliances with their suppliers and customers in order to work more closely with them. They need to work to build manufacturing networks which bridge large sections of the supply chain. In this context distributed scheduling problems are challenging tasks to researchers and practitioners that have been gaining increasing popularity over the years. This is partly attributed to the fact that multi-site production and networked manufacturing environments are increasing as a consequence of globalization. In this paper a web based system for technologies integration for supporting distributed scheduling in a Virtual Enterprise, by combining a simulation-based approach, with the Hungarian algorithm, for solving job-shop scheduling problems is presented, in order to show how the authors can benefit from this technologies integration for supporting collaborative distributed manufacturing scheduling.

2019 ◽  
Vol 24 (3) ◽  
pp. 80 ◽  
Author(s):  
Prasert Sriboonchandr ◽  
Nuchsara Kriengkorakot ◽  
Preecha Kriengkorakot

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.


Author(s):  
Karim Tamssaouet ◽  
Stéphane Dauzère-Pérès ◽  
Sebastian Knopp ◽  
Abdel Bitar ◽  
Claude Yugma

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