本文選題：作業(yè)車間調(diào)度 + 多工藝路線　； 參考：《華中科技大學》2011年碩士論文

【摘要】：當今市場競爭日益激烈,消費個性化、需求多樣化使得多品種小批量成為企業(yè)生產(chǎn)的新趨勢,因此高效的車間調(diào)度對生產(chǎn)制造企業(yè)顯得尤為重要,也是提高企業(yè)競爭力的關鍵因素。作業(yè)車間調(diào)度(Job Shop Scheduling, JSP)是實際調(diào)度問題的簡化,現(xiàn)實制造系統(tǒng)的調(diào)度問題通常還具有多工藝和多批量的特點。由于作業(yè)車間調(diào)度是NP-hard問題,多工藝路線作業(yè)車間批量調(diào)度問題大大增加了問題的復雜性,對該問題的研究具有重要的理論意義和實用價值。 首先,本文介紹了課題的來源、目的和意義,論述了作業(yè)車間調(diào)度問題、多工藝路線作業(yè)車間批量調(diào)度的研究現(xiàn)狀,以及車間調(diào)度問題的發(fā)展趨勢。 然后,研究了多工藝路線作業(yè)車間調(diào)度問題,構建了多工藝路線JSP問題的數(shù)學集成模型�；趶V義粒子群優(yōu)化模型,構造了一種求解多工藝路線JSP問題的廣義粒子群優(yōu)化算法(GPSO)。在該算法中,利用遺傳算法(GA)中的交叉操作作為粒子間的信息交換策略,遺傳算法中的變異操作則作為粒子的隨機搜索策略,而粒子的局部搜索策略則采用禁忌搜索(TS)來實現(xiàn)。實驗結果表明,該算法可有效地求解多工藝路線JSP問題。 接著,研究了多工藝路線作業(yè)車間批量調(diào)度問題。采用等量分批的策略,同時引入平行移動的方法,使其與等量分批結合起來,從而達到有效優(yōu)化生產(chǎn)周期的目的�；贕PSO算法,采用一種有效初始化方法,使得粒子編碼能產(chǎn)生較優(yōu)的初始解。針對多工藝路線批量調(diào)度問題,設計了一種新的交叉方法。通過對具體實例的仿真測試,研究批次變化對多工藝批量調(diào)度生產(chǎn)周期的影響,驗證了該算法的可行性和有效性。 隨后,通過研究多工藝路線批量調(diào)度問題,結合GPSO的優(yōu)化思想,開發(fā)了相應的調(diào)度原型系統(tǒng)。通過測試結果,再次驗證了算法的有效性。 最后,對全文進行總結,并對多工藝路線作業(yè)車間批量調(diào)度問題的研究做了進一步展望。
[Abstract]:Nowadays, the market competition is increasingly fierce, the consumption individuation, the demand diversification causes the multi-variety small batch to become the enterprise production new tendency, therefore the high efficiency workshop scheduling appears to the production manufacture enterprise to be particularly important. It is also the key factor to improve the competitiveness of enterprises. Job shop Shop scheduling (JSP) is the simplification of practical scheduling problem. The scheduling problem of real manufacturing system usually has the characteristics of multi-process and multi-batch. Because job shop scheduling is a NP-hard problem, the multi-process route job shop batch scheduling problem greatly increases the complexity of the problem, so the study of this problem has important theoretical significance and practical value. First of all, this paper introduces the source, purpose and significance of the subject, discusses the job shop scheduling problem, the research status of the multi-process route job shop batch scheduling, and the development trend of the job shop scheduling problem. Then, the multi-process route job shop scheduling problem is studied, and the mathematical integration model of multi-process route JSP problem is constructed. Based on the generalized particle swarm optimization (GPSO) model, a generalized particle swarm optimization (GPSO) algorithm for solving multi-process route JSP problem is proposed. In this algorithm, the crossover operation in GA) is used as the information exchange strategy between particles, the mutation operation in genetic algorithm is used as the random searching strategy of particles, and the local search strategy of particles is implemented by Tabu search (TS). The experimental results show that the algorithm can effectively solve the multi-process route JSP problem. Then, the batch scheduling problem of multi-process route job shop is studied. The strategy of equal quantity batch and the method of parallel movement are introduced to combine it with equal quantity batch, so that the production cycle can be optimized effectively. Based on the GPSO algorithm, an effective initialization method is adopted to make the particle coding produce better initial solution. A new crossover method is designed for batch scheduling of multi-process routes. The effect of batch change on the production cycle of multi-process batch scheduling is studied through the simulation test of a concrete example, and the feasibility and effectiveness of the algorithm are verified. Then, by studying the multi-process route batch scheduling problem and combining the optimization idea of GPSO, the corresponding scheduling prototype system is developed. The test results show that the algorithm is effective again. Finally, the paper summarizes the whole paper and makes a further prospect on batch scheduling problem of multi-process route job shop.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH186

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