Abstract
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.
TopIntroduction
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.
Key Terms in this Chapter
Hybrid Intelligent Systems: Denotes a software system which employs, a combination of Artificial Intelligence models, methods and techniques, such Evolutionary Computation, Meta-Heuristics, Multi-Agent Systems, Expert Systems and others.
Tabu Search: A approximation method, belonging to the class of local search techniques, that enhances the performance of a local search method by using memory structures (Tabu List).
Cooperation: The practice of individuals or entities working together with common goals, instead of working separately in competition, and in which the success of one is dependent and contingent upon the success of the other.
Meta-Heuristics: Form a class of powerful and practical solution techniques for tackling complex, large-scale combinatorial problems producing efficiently high-quality solutions.
Genetic Algorithms: Particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.
Evolutionary Computation: A subfield of artificial intelligence that involve techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest.
Dynamic Scheduling Systems: Are frequently subject to several kinds of random occurrences and perturbations, such as new job arrivals, machine breakdowns, employee’s sickness, jobs cancellation and due date and time processing changes, causing prepared schedules becoming easily outdated and unsuitable.
Multi-Agent Systems: A system composed of several agents, collectively capable of solve complex problems in a distributed fashion without the need for each agent to know about the whole problem being solved.
Scheduling: Can be seen as a decision making process for operations starting and resources to be used. A variety of characteristics and constraints related with jobs and machine environments (Single Machine, Parallel machines, Flow-Shop and Job-Shop) can affect scheduling decisions.