基于改进粒子群算法的多产品厂调度问题研究

发布时间：2018-07-10 01:22

本文选题：多产品厂调度 + 粒子群算法　；参考：《华东理工大学》2014年硕士论文

【摘要】：生产调度在企业的生产管理处于核心的地位,好的调度不仅可以使企业提高设备的利用率,降低生产成本,也可以使企业适应快速变化的市场需求,提高企业的竞争优势。多产品厂调度是一个典型的调度问题,本文着重研究通过设计和改进粒子群优化算法来解决多产品厂调度问题,并通过大量的仿真实验,验证了所提算法的可行性和有效性。针对零等待多产品厂调度问题的总流程时间最小化问题,提出了一种改进粒子群算法。提出了一种带有“创新因子”的改进粒子群算法,提高了粒子的随机性,使粒子不再单纯跟踪个体极值和群体极值,避免了粒子快速聚集到群体极值周围,同时扩大了搜索范围,使粒子获得了更好的“探索”能力,增强了种群在进化过程中的多样性,提高了算法的全局搜索能力。针对多产品厂生产过程中,同时以最小化最大完成时间和最大拖延时间为多目标的生产调度问题,以量子行为粒子群算法(Quantum-behaved Particle Swarm Optimization, QPSO)为基础,通过对QPSO算法中的位置和距离进行重新定义,形成了离散量子行为粒子群算法(Discrete Quantum-behaved Particle Swarm Optimization, DQPSO),并引入邻域搜索算法,来提高算法的局部搜索能力；算法基于Pareto支配的概念,对不同的解进行优劣评价。针对模糊加工时间下的多产品厂调度问题进行了研究,提出了一种改进的离散量子行为粒子群算法(Improved Discrete Quantum-behaved Particle Swarm Optimization, IDQPSO)。原有的离散量子行为粒子群算法(Discrete Quantum-behaved Particle Swarm Optimization, DQPSO)中,所有粒子都跟踪同一个全局最优值,这就会降低种群的多样性,使算法容易陷入局部极值,通过引入“选择池”的思想,使得算法不只是跟踪全局最优的个体,而是跟踪种群中的一个最优区域,以提高算法的全局搜索能力；同时通过增加SWAP搜索算子中交换项的个数,以提高算法的局部搜索能力。最后,通过对不同算例进行仿真,验证了改进算法的有效性和优越性。
[Abstract]:Production scheduling plays a key role in the production management of enterprises. Good scheduling can not only improve the utilization rate of equipment, reduce production costs, but also make enterprises adapt to the rapidly changing market demand and improve their competitive advantage. Multi-product plant scheduling is a typical scheduling problem. This paper focuses on the design and improvement of particle swarm optimization algorithm to solve the multi-product plant scheduling problem, and through a large number of simulation experiments, verify the feasibility and effectiveness of the proposed algorithm. An improved particle swarm optimization (PSO) algorithm is proposed to minimize the total flow time of zero wait multi-product plant scheduling problem. In this paper, an improved particle swarm optimization algorithm with "innovation factor" is proposed, which improves the randomness of particles, makes the particles no longer track the individual extremum and population extremum, and avoids the particles rapidly gathering around the population extremum. At the same time, the search scope is expanded, the particle can obtain better "exploration" ability, the diversity of population in the evolution process is enhanced, and the global search ability of the algorithm is improved. In order to solve the multi-objective scheduling problem in the process of multi-product production, the Quantum-beamed Particle Swarm Optimization (QPSO) algorithm is used to minimize the maximum completion time and the maximum delay time, and the quantum behavior particle swarm optimization (QPSO) algorithm is used to solve the scheduling problem, which is based on the Quantum-behaving Particle Swarm Optimization (QPSO). By redefining the position and distance in QPSO algorithm, a discrete Quantum-behaving Particle Swarm Optimization (DQPSO) algorithm is formed, and a neighborhood search algorithm is introduced to improve the local search ability of the algorithm, which is based on the concept of Pareto domination. The merits and demerits of different solutions are evaluated. In this paper, the problem of multi-product plant scheduling under fuzzy processing time is studied, and an improved discrete Quantum-beared Particle Swarm Optimization (IDQPSO) is proposed. In the original discrete Quantum-Behaved Particle Swarm Optimization (DQPSO) algorithm, all particles track the same global optimal value, which reduces the diversity of population and makes the algorithm easily fall into local extremum. The algorithm not only tracks the globally optimal individuals, but also tracks an optimal region in the population to improve the global search ability of the algorithm. At the same time, by increasing the number of swap items in the swap search operator, the local search ability of the algorithm is improved. Finally, the effectiveness and superiority of the improved algorithm are verified by simulation of different examples.
【学位授予单位】：华东理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TB497

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