本文選題：Flow　切入點(diǎn)：Shop調(diào)度問(wèn)題　出處：《華東理工大學(xué)》2012年碩士論文

【摘要】：生產(chǎn)調(diào)度在計(jì)算機(jī)集成制造系統(tǒng)中是連接管理層與監(jiān)控層的樞紐,它通過(guò)傳遞決策層的經(jīng)營(yíng)管理決策,向監(jiān)督控制層下達(dá)指令,以保證企業(yè)生產(chǎn)有條不紊的進(jìn)行,是流程工業(yè)中能否成功實(shí)施CIMS的關(guān)鍵。調(diào)度問(wèn)題與企業(yè)的利益最大化是緊密相關(guān)的,對(duì)于我國(guó)現(xiàn)代化生產(chǎn)制造過(guò)程的發(fā)展起著至關(guān)重要的作用,其中Flow Shop調(diào)度問(wèn)題是一個(gè)非常典型的生產(chǎn)調(diào)度問(wèn)題。本文通過(guò)引入異步進(jìn)化策略設(shè)計(jì)與改進(jìn)現(xiàn)存的智能優(yōu)化算法用于解決Flow Shop調(diào)度問(wèn)題。根據(jù)大量的實(shí)驗(yàn)結(jié)果證明,異步智能算法對(duì)于解決生產(chǎn)調(diào)度問(wèn)題是非常有效的。 對(duì)于以總流程時(shí)間為目標(biāo)的置換Flow Shop問(wèn)題,提出一種改進(jìn)的粒子群算法。該算法通過(guò)引入最小位置值(SPV)規(guī)則,把粒子的各位置分量按照由小到大排序,將粒子位置映射到置換Flow Shop問(wèn)題的解空間。同時(shí)采用變鄰域局部搜索機(jī)制對(duì)父代個(gè)體執(zhí)行不同代數(shù)的搜索以實(shí)現(xiàn)異步行為來(lái)增加種群的多樣性,從而獲得更快的收斂速度及更好的解。仿真結(jié)果表明了該算法的有效性。 對(duì)于以Makespan為目標(biāo)函數(shù)的置換Flow Shop調(diào)度問(wèn)題,在改進(jìn)蛙跳算法NSFLA的基礎(chǔ)上,結(jié)合異步的理念,提出了一種全新的算法異步蛙跳算法(ASFLA)用于解決置換Flow Shop調(diào)度問(wèn)題。ASFLA全局通過(guò)采用洗牌策略加強(qiáng)全局信息交換,局部搜索則采用異步概念增強(qiáng)種群的多樣性。仿真實(shí)驗(yàn)表明蛙跳算法經(jīng)過(guò)異步化之后求解性能顯著提高。 對(duì)于一類加工時(shí)間不確定的以總流程時(shí)間為目標(biāo)的置換Flow Shop調(diào)度問(wèn)題,應(yīng)用模糊數(shù)學(xué)的方法來(lái)表示加工時(shí)間的不確定性,采用一種改進(jìn)的智能算法異步遺傳局部搜索算法(AGLA)。該算法的一個(gè)初始解是由構(gòu)造型啟發(fā)式算法產(chǎn)生,其他則是隨機(jī)產(chǎn)生,然后通過(guò)引入變鄰域搜索機(jī)制和簡(jiǎn)單交叉算子,對(duì)種群執(zhí)行異步進(jìn)化操作,算法最后加入重啟策略防止陷入局部極小。仿真實(shí)驗(yàn)結(jié)果驗(yàn)證了AGLA解決模糊Flow Shop問(wèn)題的有效性。
[Abstract]:Production scheduling is a key link between management and monitoring layer in the computer integrated manufacturing system. By transmitting the decision of management and management at the decision-making level, the production scheduling can give instructions to the supervision and control layer, so as to ensure that the production of the enterprise is carried out methodically. Scheduling problem is closely related to the profit maximization of enterprises and plays an important role in the development of modern manufacturing process in China. The Flow Shop scheduling problem is a typical production scheduling problem. This paper introduces asynchronous evolutionary strategy design and improves the existing intelligent optimization algorithm to solve the Flow Shop scheduling problem. Asynchronous intelligent algorithm is very effective to solve production scheduling problem. An improved particle swarm optimization (PSO) algorithm is proposed for the permutation Flow Shop problem with total flow time as the target. By introducing the minimum position value (Flow) rule, each position component of the particle is sorted from small to large. The particle position is mapped to the solution space of the permutation Flow Shop problem. At the same time, variable neighborhood local search mechanism is used to perform different algebraic searches on parent individuals to achieve asynchronous behavior to increase population diversity. In order to obtain faster convergence speed and better solution, the simulation results show the effectiveness of the algorithm. For the permutation Flow Shop scheduling problem with Makespan as the objective function, the idea of asynchronous is combined with the improved leapfrog algorithm NSFLA. In this paper, a novel asynchronous leapfrog algorithm (ASFLAA) is proposed to solve the replacement Flow Shop scheduling problem. The global shuffle strategy is used to enhance the global information exchange. The local search uses the asynchronous concept to enhance the diversity of the population, and the simulation results show that the performance of the leapfrog algorithm is improved significantly after the asynchronous algorithm. For a class of permutation Flow Shop scheduling problems with uncertain processing time, the uncertainty of processing time is expressed by fuzzy mathematics method. An improved intelligent algorithm named Asynchronous genetic Local search algorithm is used. One of the initial solutions of the algorithm is generated by the constructive heuristic algorithm, and the others are randomly generated, and then by introducing variable neighborhood search mechanism and simple crossover operator, The asynchronous evolution operation is performed on the population, and the restart strategy is added to prevent the population from falling into local minimization. The simulation results show that AGLA is effective in solving the fuzzy Flow Shop problem.
【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】：2012
【分類號(hào)】：TH186;TP18

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