【摘要】：車間調(diào)度問題由于其NP-Hard特性，傳統(tǒng)集中式的解決方案由于其求解消耗隨著問題規(guī)模的日漸增長指數(shù)級上漲而顯得臃腫不堪，而在更切合實際環(huán)境的柔性制造環(huán)境下更是無法滿足其復雜性，動態(tài)性，隨機性的需求。在這樣的背景下，很多學者都開始研究引進更適合問題模型的基于自治與協(xié)調(diào)的自適應系統(tǒng)。本文在結(jié)合分析近代國內(nèi)外相關(guān)研究成果的基礎上，以具有柔性生產(chǎn)加工的離散作業(yè)制造車間作為研究對象，建立了基于Agent單元和合同網(wǎng)協(xié)商機制的自治與協(xié)商模型，并為系統(tǒng)模型引入了Q-Learn算法和工序價值評估算法進行局部優(yōu)化，為柔性車間調(diào)度問題提供了有效實用的解決方案。 本文主要內(nèi)容可概括為： 1．首先針對柔性車間調(diào)度問題，對近年來的相關(guān)研究成果與發(fā)展現(xiàn)狀進行簡單概括與分析，進而提出本文研究的出發(fā)點與意義。 2．針對問題的復雜性、不確定性、多約束多資源互相協(xié)調(diào)的特點，提出以復雜適應系統(tǒng)（CAS）理論為基礎，Agent為基本單元，基于合同網(wǎng)協(xié)商機制的柔性車間調(diào)度問題的實時調(diào)度框架模型。對系統(tǒng)框架進行利弊分析，指出系統(tǒng)骨架的合同網(wǎng)缺乏優(yōu)化和動態(tài)學習的問題。 3．將Q-Learn強化學習算法引進系統(tǒng)模型中的標書評估決策，并根據(jù)算法的具體要求對Q-Learn算法的模型進行了詳細定義和描述，賦予合同網(wǎng)協(xié)議動態(tài)智能學習能力。 4．在標書評估過程中提出了工序價值的概念，將系統(tǒng)目標量化為“價值”的形式附加到子任務中，對系統(tǒng)進行局部優(yōu)化。 5．最后，使用Java+Swarm+Matlab工具，設計和開發(fā)了綜合的仿真系統(tǒng)，對文中的設計與觀點進行仿真測試，為該理論在問題的實際應用上做出可行性嘗試。本文對解決柔性制造環(huán)境下的車間調(diào)度問題提出了一個合理的通過仿真證明其可行性的基于Agent的自治與協(xié)商調(diào)度方案，在一定程度上推動了該方向?qū)栴}的求解研究。
[Abstract]:Because of its characteristic of NP-Hard, the traditional centralized solution of job shop scheduling problem appears to be bloated because of its solution consumption increasing exponentially with the increasing scale of the problem. And in the more practical environment of flexible manufacturing environment is not able to meet its complexity, dynamic, random needs. In this context, many scholars have begun to study the introduction of adaptive systems based on autonomy and coordination that are more suitable for problem models. Based on the analysis of relevant research results at home and abroad in modern times, this paper takes the discrete job shop with flexible production and processing as the research object, and establishes the autonomy and negotiation model based on Agent unit and contract net negotiation mechanism. The Q-Learn algorithm and the process value evaluation algorithm are introduced to the system model for local optimization, which provides an effective and practical solution for the flexible job shop scheduling problem. The main contents of this paper can be summarized as follows: 1. Firstly, this paper summarizes and analyzes the related research results and development status in recent years, and then puts forward the starting point and significance of this study. 2. 2. In view of the complexity, uncertainty, multi-constraint and multi-resource coordination of the problem, this paper proposes a complex adaptive system based on (CAS) theory and Agent as the basic unit. A real-time scheduling framework model for flexible job shop scheduling problem based on contract net negotiation mechanism. This paper analyzes the advantages and disadvantages of the system framework and points out the lack of optimization and dynamic learning of the contract network of the system skeleton. 3. The Q-Learn reinforcement learning algorithm is introduced into the bidding evaluation decision of the system model, and the model of the Q-Learn algorithm is defined and described in detail according to the specific requirements of the algorithm, which endows the contract net protocol with dynamic intelligent learning ability. 4. In the process of bid evaluation, the concept of process value is put forward, and the system objective is quantified as "value", which is attached to the sub-task, and the system is locally optimized. 5. Finally, a comprehensive simulation system is designed and developed by using Java Swarm Matlab tool. The design and viewpoint of this paper are simulated and tested, which makes a feasible attempt for the practical application of the theory. In this paper, a reasonable Agent based autonomy and negotiation scheduling scheme is proposed to solve the job shop scheduling problem in flexible manufacturing environment, which is proved to be feasible by simulation. To some extent, it promotes the research of solving the problem in this direction.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH186;N945.12

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