本文選題：車間調(diào)度 + 免疫算法��； 參考：《大連交通大學(xué)》2014年碩士論文

【摘要】：隨著全球經(jīng)濟(jì)競(jìng)爭(zhēng)的不斷加劇,企業(yè)的生產(chǎn)方式也在不斷變化,車間生產(chǎn)調(diào)度在制造業(yè)中占有愈來(lái)愈重要的地位�；旌狭魉囬g是在基本流水車間的基礎(chǔ)上推廣而來(lái),在調(diào)度的各階段增加并行設(shè)備數(shù),達(dá)到工件的并行生產(chǎn)。它普遍的存在于石油化工、制藥、鋼鐵等流程制造業(yè)中。在柔性作業(yè)車間中,需要考慮更多的約束限制,各種設(shè)備的加工狀況不確定,需要制定更加靈活的調(diào)度方案。而解決生產(chǎn)車間調(diào)度問題,就是根據(jù)工件的加工要求,滿足前提約束條件的同時(shí),將資源進(jìn)行合理配置,使企業(yè)獲得更多的效益。因此,對(duì)車間調(diào)度問題的中的混合流水和柔性作業(yè)車間問題進(jìn)行研究,具有很好的代表性,順應(yīng)生產(chǎn)發(fā)展的要求,具有重要的應(yīng)用價(jià)值。 車間調(diào)度問題是一類組合優(yōu)化問題,目前求解該類問題的算法很多,但是應(yīng)用傳統(tǒng)算法求解大規(guī)模復(fù)雜問題往往存在各種限制。智能優(yōu)化算法在解決復(fù)雜的NP-hard問題時(shí),呈現(xiàn)出無(wú)可比擬的優(yōu)越性。免疫算法是受生物免疫系統(tǒng)的啟發(fā),建立在免疫學(xué)理論基礎(chǔ)之上的一種新的智能優(yōu)化算法。但是免疫算法的研究還處于起步階段,在具體優(yōu)化問題中應(yīng)用時(shí),還存在一定的缺陷。算法在進(jìn)行一定的迭代次數(shù)之后,會(huì)出現(xiàn)搜索退化的現(xiàn)象,容易陷入局部最優(yōu)。模擬退火算法具有完整的理論基礎(chǔ),在進(jìn)行問題的全局優(yōu)化時(shí)表現(xiàn)出強(qiáng)有力的競(jìng)爭(zhēng)優(yōu)勢(shì)。通過(guò)對(duì)免疫算法和模擬退火算法的性能分析,明確了算法進(jìn)行結(jié)合的優(yōu)勢(shì)。針對(duì)實(shí)際生產(chǎn)中存在較多的混合流水車間調(diào)度問題和柔性作業(yè)車間調(diào)度問題設(shè)計(jì)了改進(jìn)的混合免疫算法,并用該算法解決車間調(diào)度問題。 本文在將免疫算法和模擬退火算法進(jìn)行融合時(shí),設(shè)計(jì)了混合算法的整體結(jié)構(gòu),制定了合理的編碼規(guī)則,對(duì)免疫算子、多樣性評(píng)價(jià)等進(jìn)行了研究,結(jié)合自適應(yīng)的退火操作共同完成了混合算法的設(shè)計(jì)。為了檢驗(yàn)改進(jìn)算法的有效性,分別選擇了流水車間和作業(yè)車間兩種類型問題的實(shí)例進(jìn)行測(cè)試,將對(duì)應(yīng)改進(jìn)算法的性能進(jìn)行了評(píng)估。最后,將算法應(yīng)用到了車間調(diào)度問題的模擬系統(tǒng)中,結(jié)果顯示改進(jìn)的算法能夠很好的收斂到可行解,說(shuō)明本文的改進(jìn)算法相比傳統(tǒng)的調(diào)度算法而言‘,在解決實(shí)際問題時(shí)更有優(yōu)越性
[Abstract]:With the aggravation of the global economic competition, the production mode of the enterprise is also changing constantly, the workshop production scheduling plays an increasingly important role in the manufacturing industry. Hybrid flow shop is extended on the basis of basic flow shop to increase the number of parallel equipment in each stage of scheduling to achieve the parallel production of workpieces. It is common in petrochemical, pharmaceutical, steel and other process manufacturing. In the flexible job shop, more constraints need to be considered, and the processing conditions of various equipments are uncertain, and a more flexible scheduling scheme is needed. To solve the production shop scheduling problem is to meet the requirements of workpiece processing and meet the prerequisite constraints, at the same time, the reasonable allocation of resources, so that enterprises can get more benefits. Therefore, the study of hybrid flow and flexible job shop in the job shop scheduling problem has good representativeness, conforms to the requirement of production development, and has important application value. Job shop scheduling problem is a kind of combinatorial optimization problem. At present, there are many algorithms to solve this kind of problem. However, there are many limitations in solving large-scale complex problems with traditional algorithms. The intelligent optimization algorithm has unparalleled superiority in solving the complex NP-hard problem. Immune algorithm is a new intelligent optimization algorithm inspired by biological immune system and based on immunology theory. However, the study of immune algorithm is still in its infancy, and there are still some defects when it is applied to specific optimization problems. After a certain number of iterations, the algorithm will appear the phenomenon of search degradation, which is easy to fall into local optimum. The simulated annealing algorithm has a complete theoretical foundation and shows a strong competitive advantage in the global optimization of the problem. By analyzing the performance of immune algorithm and simulated annealing algorithm, the advantages of combining the algorithm are clarified. An improved hybrid immune algorithm is designed to solve the problem of hybrid flow shop scheduling problem and flexible job shop scheduling problem in practical production, and the algorithm is used to solve the job shop scheduling problem. In this paper, when the immune algorithm and simulated annealing algorithm are fused, the whole structure of the hybrid algorithm is designed, and the reasonable coding rules are worked out. The immune operator, the diversity evaluation and so on are studied. Combined with adaptive annealing operation, the hybrid algorithm is designed. In order to test the effectiveness of the improved algorithm, the performance of the improved algorithm is evaluated by choosing two kinds of problems, the flow shop and the job shop, respectively. Finally, the algorithm is applied to the simulation system of job shop scheduling problem. The results show that the improved algorithm can converge to the feasible solution well, which shows that the improved algorithm in this paper is better than the traditional scheduling algorithm. Have more advantages in solving practical problems
【學(xué)位授予單位】：大連交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP18;TB497

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