本文選題：多產(chǎn)品廠調(diào)度 + 粒子群算法��； 參考：《華東理工大學》2014年碩士論文

【摘要】：生產(chǎn)調(diào)度在企業(yè)的生產(chǎn)管理處于核心的地位,好的調(diào)度不僅可以使企業(yè)提高設(shè)備的利用率,降低生產(chǎn)成本,也可以使企業(yè)適應快速變化的市場需求,提高企業(yè)的競爭優(yōu)勢。多產(chǎn)品廠調(diào)度是一個典型的調(diào)度問題,本文著重研究通過設(shè)計和改進粒子群優(yōu)化算法來解決多產(chǎn)品廠調(diào)度問題,并通過大量的仿真實驗,驗證了所提算法的可行性和有效性。 針對零等待多產(chǎn)品廠調(diào)度問題的總流程時間最小化問題,提出了一種改進粒子群算法。提出了一種帶有“創(chuàng)新因子”的改進粒子群算法,提高了粒子的隨機性,使粒子不再單純跟蹤個體極值和群體極值,避免了粒子快速聚集到群體極值周圍,同時擴大了搜索范圍,使粒子獲得了更好的“探索”能力,增強了種群在進化過程中的多樣性,提高了算法的全局搜索能力。 針對多產(chǎn)品廠生產(chǎn)過程中,同時以最小化最大完成時間和最大拖延時間為多目標的生產(chǎn)調(diào)度問題,以量子行為粒子群算法(Quantum-behaved Particle Swarm Optimization, QPSO)為基礎(chǔ),通過對QPSO算法中的位置和距離進行重新定義,形成了離散量子行為粒子群算法(Discrete Quantum-behaved Particle Swarm Optimization, DQPSO),并引入鄰域搜索算法,來提高算法的局部搜索能力；算法基于Pareto支配的概念,對不同的解進行優(yōu)劣評價。 針對模糊加工時間下的多產(chǎn)品廠調(diào)度問題進行了研究,提出了一種改進的離散量子行為粒子群算法(Improved Discrete Quantum-behaved Particle Swarm Optimization, IDQPSO)。原有的離散量子行為粒子群算法(Discrete Quantum-behaved Particle Swarm Optimization, DQPSO)中,所有粒子都跟蹤同一個全局最優(yōu)值,這就會降低種群的多樣性,使算法容易陷入局部極值,通過引入“選擇池”的思想,使得算法不只是跟蹤全局最優(yōu)的個體,而是跟蹤種群中的一個最優(yōu)區(qū)域,以提高算法的全局搜索能力；同時通過增加SWAP搜索算子中交換項的個數(shù),以提高算法的局部搜索能力。 最后,通過對不同算例進行仿真,驗證了改進算法的有效性和優(yōu)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.
【學位授予單位】：華東理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TB497

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本文編號：2111471