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(xué)》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(yīng)快速變化的市場(chǎng)需求,提高企業(yè)的競(jìng)爭(zhēng)優(yōu)勢(shì)。多產(chǎn)品廠調(diào)度是一個(gè)典型的調(diào)度問(wèn)題,本文著重研究通過(guò)設(shè)計(jì)和改進(jìn)粒子群優(yōu)化算法來(lái)解決多產(chǎn)品廠調(diào)度問(wèn)題,并通過(guò)大量的仿真實(shí)驗(yàn),驗(yàn)證了所提算法的可行性和有效性。 針對(duì)零等待多產(chǎn)品廠調(diào)度問(wèn)題的總流程時(shí)間最小化問(wèn)題,提出了一種改進(jìn)粒子群算法。提出了一種帶有“創(chuàng)新因子”的改進(jìn)粒子群算法,提高了粒子的隨機(jī)性,使粒子不再單純跟蹤個(gè)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(shí)擴(kuò)大了搜索范圍,使粒子獲得了更好的“探索”能力,增強(qiáng)了種群在進(jìn)化過(guò)程中的多樣性,提高了算法的全局搜索能力。 針對(duì)多產(chǎn)品廠生產(chǎn)過(guò)程中,同時(shí)以最小化最大完成時(shí)間和最大拖延時(shí)間為多目標(biāo)的生產(chǎn)調(diào)度問(wèn)題,以量子行為粒子群算法(Quantum-behaved Particle Swarm Optimization, QPSO)為基礎(chǔ),通過(guò)對(duì)QPSO算法中的位置和距離進(jìn)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(lái)提高算法的局部搜索能力；算法基于Pareto支配的概念,對(duì)不同的解進(jìn)行優(yōu)劣評(píng)價(jià)。 針對(duì)模糊加工時(shí)間下的多產(chǎn)品廠調(diào)度問(wèn)題進(jìn)行了研究,提出了一種改進(jìn)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(gè)全局最優(yōu)值,這就會(huì)降低種群的多樣性,使算法容易陷入局部極值,通過(guò)引入“選擇池”的思想,使得算法不只是跟蹤全局最優(yōu)的個(gè)體,而是跟蹤種群中的一個(gè)最優(yōu)區(qū)域,以提高算法的全局搜索能力；同時(shí)通過(guò)增加SWAP搜索算子中交換項(xiàng)的個(gè)數(shù),以提高算法的局部搜索能力。 最后,通過(guò)對(duì)不同算例進(jìn)行仿真,驗(yàn)證了改進(jìn)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(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TB497

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