本文選題：集成式工藝規(guī)劃與車間調(diào)度問題 + 不確定問題　； 參考：《華中科技大學(xué)》2014年碩士論文

【摘要】：制造系統(tǒng)中，工藝規(guī)劃和車間調(diào)度分別承擔(dān)重要的功能，二者有著緊密的聯(lián)系。但是在以往的研究中，通常獨(dú)立對(duì)兩個(gè)系統(tǒng)進(jìn)行優(yōu)化研究。雖然兩個(gè)系統(tǒng)都可以通過技術(shù)手段進(jìn)行優(yōu)化，減少生產(chǎn)過程中的沖突與浪費(fèi)，但是將兩個(gè)系統(tǒng)進(jìn)行集成，將更大程度的提高系統(tǒng)的生產(chǎn)效率。 本文針對(duì)集成式工藝規(guī)劃與車間調(diào)度（Integrated Process Planning and Scheduling,IPPS）這一類經(jīng)典的車間調(diào)度問題開展研究，同時(shí)重點(diǎn)考慮實(shí)際生產(chǎn)過程中廣泛存在的不確定事件。這些事件主要是因外部因素而造成的加工環(huán)境的不確定，比如，運(yùn)輸時(shí)間的不確定、夾具和刀具裝卸時(shí)間的不確定、準(zhǔn)備時(shí)間的不確定、生產(chǎn)過程中的設(shè)備故障、能源短缺等，都會(huì)累積造成工序調(diào)度過程中的不確定性等。不確定IPPS問題能更好的處理這些事件，使得調(diào)度結(jié)果能更好地反映實(shí)際生產(chǎn)狀況。確定性的IPPS問題已被證明為NP-Complete問題，不確定IPPS問題由于考慮了更多的不確定事件，所以建模與求解更加復(fù)雜，這也導(dǎo)致國內(nèi)外鮮有針對(duì)不確定IPPS問題的研究。 本文重點(diǎn)針對(duì)不確定IPPS的建模與優(yōu)化方法開展研究。首先采用改進(jìn)的區(qū)間理論對(duì)問題進(jìn)行建模，主要內(nèi)容包括利用區(qū)間理論和區(qū)間數(shù)表征不確定加工時(shí)間。給出區(qū)間數(shù)的操作法則，并針對(duì)當(dāng)前區(qū)間數(shù)的比較方法進(jìn)行了研究，提出了一種新的基于可能度和偏序率的區(qū)間數(shù)比較方法以提高區(qū)間數(shù)比較的精度。最終將改進(jìn)的區(qū)間理論應(yīng)用在不確定IPPS問題的建模上，提出了基于區(qū)間數(shù)的不確定IPPS問題模型。 本文針對(duì)不確定IPPS問題的特性，首先提出了基于遺傳算法的不確定IPPS問題求解方法。在工藝規(guī)劃和車間調(diào)度部分，分別采用集成式編碼和基于工序的編碼方式，并設(shè)計(jì)了高效的遺傳操作方法�；跇�(biāo)準(zhǔn)IPPS算例將確定的加工時(shí)間進(jìn)行區(qū)間化形成不確定IPPS問題算例，，算例測(cè)試結(jié)果驗(yàn)證了提出算法的有效性。由于單一算法求解簡單問題比較有優(yōu)勢(shì)，而面對(duì)復(fù)雜的實(shí)際生產(chǎn)環(huán)境，需要擁有適應(yīng)大規(guī)模問題求解的算法，基于此，本文提出了一種基于粒子群優(yōu)化混合算法的不確定IPPS問題求解方法。在該方法中，對(duì)于基礎(chǔ)粒子群算法進(jìn)行了重新定義，以使其適用于非連續(xù)優(yōu)化問題。同時(shí)在該方法中引入了遺傳操作，提高了該方法處理組合優(yōu)化問題的能力。最后對(duì)測(cè)試實(shí)例進(jìn)行測(cè)試，驗(yàn)證了混合算法在求解大規(guī)模不確定IPPS問題上的卓越性能。 最后，對(duì)本文的研究工作進(jìn)行了總結(jié)，展望了下一步的研究工作。
[Abstract]:In manufacturing systems, process planning and job shop scheduling play important roles, which are closely related to each other. However, in previous studies, two systems are usually optimized independently. Although both systems can be optimized by technical means to reduce conflict and waste in the production process, the two systems are integrated. This paper focuses on the classical job shop scheduling problems such as integrated process planning and shop scheduling, such as integrated process planning and workshop scheduling. At the same time, it focuses on the uncertain events that exist widely in the actual production process. These events are mainly due to the uncertainty of the processing environment caused by external factors, such as the uncertainty of the transportation time, the uncertainty of the handling time of fixtures and cutters, the uncertainty of the preparation time, the failure of equipment in the production process, the shortage of energy, etc. Will accumulate to cause the process scheduling process uncertainty and so on. The uncertain IPPS problem can deal with these events better, so that the scheduling results can better reflect the actual production situation. The deterministic IPPS problem has been proved to be NP-Complete problem. This also leads to the lack of research on uncertain IPPS at home and abroad. This paper focuses on the modeling and optimization methods of uncertain IPPS. Firstly, the improved interval theory is used to model the problem. The main content includes the use of interval theory and interval number to characterize the uncertain processing time. The operation rule of interval number is given, and the comparison method of interval number is studied. A new method of interval number comparison based on probability and partial order rate is proposed to improve the accuracy of interval number comparison. Finally, the improved interval theory is applied to the modeling of uncertain IPPS problem, and a model of uncertain IPPS problem based on interval number is proposed. Firstly, an uncertain IPPS problem solving method based on genetic algorithm is proposed. In the part of process planning and job shop scheduling, integrated coding and process-based coding are adopted, and an efficient genetic operation method is designed. Based on the standard IPPS example, the fixed processing time is interlaced to form an example of uncertain IPPS problem. The experimental results show that the proposed algorithm is effective. Because a single algorithm has the advantage of solving simple problems, but in the face of complex actual production environment, it is necessary to have an algorithm suitable for large-scale problem solving. In this paper, a method for solving uncertain IPPS problems based on particle swarm optimization (PSO) hybrid algorithm is proposed. In this method, the basic particle swarm optimization algorithm is redefined to make it suitable for discontinuous optimization problems. At the same time, genetic operation is introduced into the method, which improves the ability of the method to deal with combinatorial optimization problems. Finally, a test example is tested to verify the excellent performance of the hybrid algorithm in solving large-scale uncertain IPPS problems. Finally, the research work of this paper is summarized, and the next research work is prospected.
【學(xué)位授予單位】：華中科技大學(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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