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Hybrid ant colony algorithm - based job-shop scheduling problem solving

Author: ChenCheng
Tutor: WangZhongRen
School: University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: Job Shop Scheduling Problem Ant Colony Algorithm Neighborhood search algorithm Dynamic adjustment strategy
CLC: TP301.6
Type: Master's thesis
Year: 2008
Downloads: 230
Quote: 2
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Abstract


With the increasingly fierce market competition, the ordering customer-oriented, multi-species, small batch production has become the dominant mode of production of the 21st century. Accordingly, manufacturing companies are toward the development of lean production and agile manufacturing direction. In this production environment, and how to arrange the production planning, scheduling enterprise effective production of key issues. Effective scheduling optimization plays an important role in the research and application of technology for manufacturing enterprises to improve production efficiency, reduce production costs, and thus more and more attention of scholars. Production and Operations Management is the core of the Job Shop Scheduling Problem (JSSP) can efficiently obtain the optimal solution, therefore, the the JSSP scheduling strategy has been one of the priorities of manufacturing research, the JSSP research has important theoretical and practical significance . The ant colony optimization algorithm (Ant Colony Algorithms, ACA) is a recent development, novel bionic intelligent optimization algorithm, with positive feedback, distributed computing and heuristic search. As one of the important branch of computational intelligence and swarm intelligence, ant colony optimization algorithm in the ascendant, and a lot of attention. The ant colony optimization algorithm of thinking comes from the intelligence of ants in our real world groups. This article describes the the JSSP research purposes, meaning and significance of analysis of the domestic and international of JSSP study, with the development of the status quo; research the the JSSP characteristics and its classification, discussed JSSP scheduling policy, recalled solving JSSP the course of its methods; ant colony optimization algorithm development background, content, methods are described in detail, and conducted in-depth research of the algorithm itself, proposed improvements program. This paper has the following aspects of innovation: This paper presents a new the JSSP neighborhood structure, compared with the traditional neighborhood structure, effectively reduced the scale of the neighborhood space; ant colony optimization algorithm pheromone strength between the ant key role in starting the communication, collaboration, global and local optimal solution to enhance the intensity of pheromone on a path quality individual programs; simplify the ant colony optimization parameters set some parameters to implement dynamic adjustment strategy; proposed a new hybrid algorithm for solving JSSP a variable neighborhood in stages. Finally, the object-oriented thinking system core scheduling algorithm, presents a system design methodology based on the MVC pattern, lay the foundation for further work. Some representative benchmark simulation, mixed ant colony algorithm for faster searching better solution to speed up the convergence rate of the algorithm to improve the search capabilities of the algorithm can effectively solve the JSSP problem. The results of this study certain reference value for the study of the ant colony algorithm, as well as on the establishment of modern optimization scheduling system realistic theoretical significance and application value.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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