基于粒子群算法的多產(chǎn)品批處理生產(chǎn)調(diào)度問題研究
發(fā)布時間：2018-08-19 16:05
【摘要】：流程工業(yè)在世界經(jīng)濟中占很大比例,是許多國家的基礎產(chǎn)業(yè)和支柱產(chǎn)業(yè)。隨著市場需求的變化,其生產(chǎn)過程逐步向多產(chǎn)品批處理生產(chǎn)方式轉(zhuǎn)化。多產(chǎn)品批處理方式固有的靈活性使得其效率和效益很大程度上依賴生產(chǎn)計劃的編制和調(diào)度方案的確定。然而,其生產(chǎn)調(diào)度問題是復雜的優(yōu)化問題,現(xiàn)有的理論和方法不能很好的解決,迫切需要研究多產(chǎn)品批處理調(diào)度問題的建模和優(yōu)化技術,探索尋找更好的調(diào)度方法,以指導企業(yè)實踐、降低企業(yè)運營成本、提高企業(yè)管理水平。多產(chǎn)品批處理生產(chǎn)調(diào)度中各產(chǎn)品的物料按照相同的生產(chǎn)工藝依次連續(xù)通過各階段的設備進行加工,當各階段連續(xù)生產(chǎn)時類似于離散過程經(jīng)典Flow-Shop調(diào)度問題。本文在以往研究的基礎上對多產(chǎn)品批處理生產(chǎn)調(diào)度問題用PSO算法求解方面做了相關探索性研究并將研究應用于鋁錠生產(chǎn)制造過程當中,探求解決實際問題的方法,為企業(yè)生產(chǎn)管理系統(tǒng)提供理論基礎。首先,將各階段由單一設備連續(xù)加工的多產(chǎn)品批處理調(diào)度問題轉(zhuǎn)化為經(jīng)典一般Flow-shop調(diào)度問題,對其建模方法進行研究,然后運用三種方法求解問題。其中,前兩種方法是以往學者基于連續(xù)建模思想建立混合整數(shù)規(guī)劃模型求解的方法。第三種方法是提出一種改進粒子群算法求解,算法中引入雙向搜索策略改善了PSO易陷入局部最優(yōu)而喪失種群多樣性的缺陷。通過算例驗證,傳統(tǒng)的模型在求解小規(guī)模問題性能較好,而在大規(guī)模問題時,PSO算法是求解問題比較有效的方法。其次,在以上研究的基礎上將問題進一步向?qū)嶋H生產(chǎn)環(huán)境拓展,研究了各階段由并行設備協(xié)調(diào)生產(chǎn)的多產(chǎn)品批處理調(diào)度問題。由于加入了并行設備的選擇而使問題相對于單一設備問題更加復雜。將其轉(zhuǎn)化為經(jīng)典的混合Flow-shop調(diào)度問題,在一般Flow-shop調(diào)度問題建模的基礎上建立模型,并提出一種結(jié)合單純形搜索和粒子群算法優(yōu)勢提高算法求解能力和效率的改進粒子群算法。并用一類經(jīng)典實例測試驗證,將算法的計算結(jié)果與文獻中的模型計算結(jié)果比較,得出隨著問題規(guī)模的增大,傳統(tǒng)模型難以求解且解的質(zhì)量不高,而在處理大規(guī)模的問題中,PSO體現(xiàn)出了優(yōu)良性能。再次,基于以上研究對現(xiàn)代鋁工業(yè)生產(chǎn)進行了簡要描述和分析,根據(jù)實際生產(chǎn)流程對生產(chǎn)工藝過程簡化處理,提取某鋁廠由鋁土礦生產(chǎn)工業(yè)用鋁錠的生產(chǎn)過程,分析處理后將其抽象為一個各階段并行設備協(xié)調(diào)生產(chǎn)的多產(chǎn)品批處理生產(chǎn)模式,然后用批處理方法對產(chǎn)品各階段進行分批、計算確定對應批次的加工時間和能耗,最后將其轉(zhuǎn)化為經(jīng)典混合Flow-shop調(diào)度問題,從節(jié)能角度出發(fā)建立以最小能耗為調(diào)度目標的數(shù)學規(guī)劃模型,調(diào)用與單純形法混合的改進PSO進行求解,將求解結(jié)果與基本GA結(jié)果進行對比分析,進一步驗證了PSO的優(yōu)越性和其解決此類調(diào)度問題的能力。最后,總結(jié)了全文,展望了所研究問題未來的發(fā)展和應用。
[Abstract]:The process industry, which accounts for a large proportion of the world economy, is the basic and pillar industry in many countries. With the change of market demand, its production process is gradually transformed into multi-product batch production mode. However, the production scheduling problem is a complex optimization problem, and the existing theories and methods can not be well solved. It is urgent to study the modeling and optimization technology of multi-product batch scheduling problem, and explore a better scheduling method to guide enterprise practice, reduce enterprise operating costs and improve enterprise management level. In batch production scheduling, the material of each product is processed successively through the equipment of each stage according to the same production process. When each stage is continuous production, it is similar to the classical Flow-Shop scheduling problem of discrete process. Based on the previous research, this paper uses PSO algorithm to solve the multi-product batch production scheduling problem. The related exploratory research is carried out and applied to the aluminum ingot production and manufacturing process to explore the method to solve practical problems and provide a theoretical basis for the enterprise production management system. The first two methods are based on the idea of continuous modeling. The third method is to propose an improved particle swarm optimization algorithm to solve the problem. The introduction of two-way search strategy to improve PSO easy to fall into local optimum and loss of population diversity. A numerical example shows that the traditional model has better performance in solving small-scale problems, and the PSO algorithm is a more effective method for solving large-scale problems. Secondly, based on the above research, the problem is further extended to the actual production environment, and the multi-product batch production coordinated by parallel equipment in each stage is studied. The problem is more complicated than a single device problem because of the choice of parallel devices. It is transformed into a classical hybrid Flow-shop scheduling problem. Based on the modeling of the general Flow-shop scheduling problem, a model is established, and an improved algorithm is proposed which combines the advantages of simplex search and particle swarm optimization. An improved particle swarm optimization algorithm for force and efficiency is proposed. A class of classical examples are used to test and verify the proposed algorithm. The results are compared with those of the model in the literature. It is concluded that the traditional model is difficult to solve and the quality of the solution is not high with the increase of the scale of the problem. This paper briefly describes and analyzes the production of modern aluminum industry, simplifies the production process according to the actual production process, extracts the production process of industrial aluminum ingot produced by bauxite in an aluminum plant, and abstracts it into a multi-product batch production mode coordinated by concurrent equipment in each stage after analysis and treatment, and then uses batch production. Processing method is used to calculate and determine the processing time and energy consumption of corresponding batches in batches. Finally, it is transformed into a classical mixed Flow-shop scheduling problem. From the point of view of energy-saving, a mathematical programming model with minimum energy consumption as the scheduling objective is established. The improved PSO mixed with simplex method is invoked to solve the problem, and the solution results and basis are obtained. The GA results are compared and analyzed to further verify the superiority of PSO and its ability to solve such scheduling problems. Finally, the full text is summarized, and the future development and application of the research problems are prospected.
【學位授予單位】：江西理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP18;F273;F426.32
本文編號：2192153
[Abstract]:The process industry, which accounts for a large proportion of the world economy, is the basic and pillar industry in many countries. With the change of market demand, its production process is gradually transformed into multi-product batch production mode. However, the production scheduling problem is a complex optimization problem, and the existing theories and methods can not be well solved. It is urgent to study the modeling and optimization technology of multi-product batch scheduling problem, and explore a better scheduling method to guide enterprise practice, reduce enterprise operating costs and improve enterprise management level. In batch production scheduling, the material of each product is processed successively through the equipment of each stage according to the same production process. When each stage is continuous production, it is similar to the classical Flow-Shop scheduling problem of discrete process. Based on the previous research, this paper uses PSO algorithm to solve the multi-product batch production scheduling problem. The related exploratory research is carried out and applied to the aluminum ingot production and manufacturing process to explore the method to solve practical problems and provide a theoretical basis for the enterprise production management system. The first two methods are based on the idea of continuous modeling. The third method is to propose an improved particle swarm optimization algorithm to solve the problem. The introduction of two-way search strategy to improve PSO easy to fall into local optimum and loss of population diversity. A numerical example shows that the traditional model has better performance in solving small-scale problems, and the PSO algorithm is a more effective method for solving large-scale problems. Secondly, based on the above research, the problem is further extended to the actual production environment, and the multi-product batch production coordinated by parallel equipment in each stage is studied. The problem is more complicated than a single device problem because of the choice of parallel devices. It is transformed into a classical hybrid Flow-shop scheduling problem. Based on the modeling of the general Flow-shop scheduling problem, a model is established, and an improved algorithm is proposed which combines the advantages of simplex search and particle swarm optimization. An improved particle swarm optimization algorithm for force and efficiency is proposed. A class of classical examples are used to test and verify the proposed algorithm. The results are compared with those of the model in the literature. It is concluded that the traditional model is difficult to solve and the quality of the solution is not high with the increase of the scale of the problem. This paper briefly describes and analyzes the production of modern aluminum industry, simplifies the production process according to the actual production process, extracts the production process of industrial aluminum ingot produced by bauxite in an aluminum plant, and abstracts it into a multi-product batch production mode coordinated by concurrent equipment in each stage after analysis and treatment, and then uses batch production. Processing method is used to calculate and determine the processing time and energy consumption of corresponding batches in batches. Finally, it is transformed into a classical mixed Flow-shop scheduling problem. From the point of view of energy-saving, a mathematical programming model with minimum energy consumption as the scheduling objective is established. The improved PSO mixed with simplex method is invoked to solve the problem, and the solution results and basis are obtained. The GA results are compared and analyzed to further verify the superiority of PSO and its ability to solve such scheduling problems. Finally, the full text is summarized, and the future development and application of the research problems are prospected.
【學位授予單位】：江西理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP18;F273;F426.32
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