Hybrid Meta-Heuristics Based System for Dynamic Scheduling

2011 ◽  
pp. 853-859 ◽  
Author(s):  
Ana Maria Madureira
Keyword(s):  
Working Conditions ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Research Field ◽  
Scheduling Problems ◽  
Self Adaptation ◽  
Assembly Operations ◽  
Natural Dynamic

The complexity of current computer systems has led the software engineering, distributed systems and management communities to look for inspiration in diverse fields, e.g. robotics, artificial intelligence or biology, to find new ways of designing and managing systems. Hybridization and combination of different approaches seems to be a promising research field of computational intelligence focusing on the development of the next generation of intelligent systems. A manufacturing system has a natural dynamic nature observed through several kinds of random occurrences and perturbations on working conditions and requirements over time. For this kind of environment it is important the ability to efficient and effectively adapt, on a continuous basis, existing schedules according to the referred disturbances, keeping performance levels. The application of Meta-Heuristics to the resolution of this class of dynamic scheduling problems seems really promising. In this article, we propose a hybrid Meta-Heuristic based approach for complex scheduling with several manufacturing and assembly operations, in dynamic Extended Job-Shop environments. Some self-adaptation mechanisms are proposed.

Hybrid Meta-Heuristics Based System for Dynamic Scheduling

2012 ◽  
pp. 428-435
Author(s):  
Ana Maria Madureira
Keyword(s):  
Working Conditions ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Research Field ◽  
Scheduling Problems ◽  
Self Adaptation ◽  
Assembly Operations ◽  
Natural Dynamic

The complexity of current computer systems has led the software engineering, distributed systems and management communities to look for inspiration in diverse fields, e.g. robotics, artificial intelligence or biology, to find new ways of designing and managing systems. Hybridization and combination of different approaches seems to be a promising research field of computational intelligence focusing on the development of the next generation of intelligent systems. A manufacturing system has a natural dynamic nature observed through several kinds of random occurrences and perturbations on working conditions and requirements over time. For this kind of environment it is important the ability to efficient and effectively adapt, on a continuous basis, existing schedules according to the referred disturbances, keeping performance levels. The application of Meta-Heuristics to the resolution of this class of dynamic scheduling problems seems really promising. In this article, we propose a hybrid Meta-Heuristic based approach for complex scheduling with several manufacturing and assembly operations, in dynamic Extended Job-Shop environments. Some self-adaptation mechanisms are proposed.

Dynamic Scheduling Using Immune Genetic Algorithm for Agile MES

2012 ◽  
Vol 566 ◽  
pp. 494-497
Author(s):  
Shu Xia Li ◽  
Huan Cao ◽  
Hong Bo Shan
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Job Shop Scheduling ◽  
Planning System ◽  
Shop Floor ◽  
Manufacturing Execution System ◽  
Scheduling Problems ◽  
Immune Genetic Algorithm

As a bridge links the upper enterprise planning system and the lower shop floor control system, enormous real-time information interact in shop floor, which poses great difficulty for scheduling of manufacturing execution system(MES). To meet the requirement of MES agility in the volatile information environment, dynamic scheduling becomes one of most widely used methods. In this paper, a modified immune genetic algorithm which incorporates artificial immune mechanism into genetic algorithm is presented to solve dynamic job shop scheduling problems. Owing to its good solving capability and computing speed, the algorithm could utilize real-time production information to generate predictive and reactive scheduling solutions. At last, the algorithm is applied in a MT10×10 job shop proved to be effective in obtaining better solutions than traditional genetic algorithm.

Selected Papers from IWI 2009

2010 ◽  
Vol 14 (4) ◽  
pp. 383-383
Author(s):  
Seiji Yamada ◽  
Tsuyoshi Murata ◽  
Yasufumi Takama
Keyword(s):  
User Interfaces ◽  
Computational Intelligence ◽  
Interaction Design ◽  
Web Application ◽  
Intelligent Systems ◽  
Research Field ◽  
Acceptance Rate ◽  
Special Issue ◽  
Web Credibility ◽  
Web Systems

Various Web systems and services currently provide a great deal of benefits to users, with Web interaction becoming increasingly important in research and business. Such Web interaction has been realized through related technologies as interaction design, interactive information retrieval, interactive intelligent systems, personalization, user interfaces and interactive machine learning. However, each study and development in such different fields has been done independently, which might discourage us from studying Web interaction from an unified view of human-system interaction and making Web interaction more intelligent by applying AI and computational intelligence. Guest Editors (Seiji Yamada, Tsuyoshi Murata, and Yasufumi Takama) organized an Intelligent Web Interaction Workshop 2009 (IWIf09) in Milano, Italy, last year to bring together researchers in diversified fields including Web systems, AI, computational intelligence, humancomputer interaction and user interfaces. Held jointly with 2009 IEEE/WIC/ACM International Conference on Web Intelligence (WI-2009), IWIf09 produced 14 outstanding papers - an acceptance rate of 50%, and active discussions among speakers and participants. A subsequent workshop Intelligent Web Interaction Workshop 2010 (IWIf10) will be held in Toronto, Canada in this September. This special issue presents intelligent Web interaction as a new and promising research field. Speakers selected from among those at IWIf09 were encouraged to submit papers for this issue. The submissions were then reviewed for relevance, originality, significance and presentation based on JACIII review criteria. This special issue consists of five papers which describe excellent studies on Web interface, Web systems, Web credibility, constrained clustering for interactive Web application and graph analysis on the Web. The acceptance rate was 56%. All papers introduce promising approaches and interesting results that readers will find inspiring. We strongly believe intelligent Web interaction has tremendous potential as a new, active field of research, and we hope this issue will motivate researchers to expand studies on intelligent Web interaction.

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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