發(fā)布時間：2019-01-14 15:22

【摘要】：近年來,煤炭供應(yīng)鏈出現(xiàn)了庫存增加、供求不平衡、對顧客需求反應(yīng)遲鈍等問題。面對動態(tài)開放的市場競爭環(huán)境,若想了解并快速滿足消費(fèi)者的需求,煤炭供應(yīng)鏈上的各主體需要緊密聯(lián)系彼此,在整條供應(yīng)鏈上實(shí)現(xiàn)對煤炭資源的調(diào)運(yùn),即實(shí)現(xiàn)企業(yè)主體之間的良好協(xié)商。煤炭供應(yīng)鏈中各個主體的行為變化影響著煤炭供應(yīng)鏈的演化發(fā)展,整個系統(tǒng)形成了一個復(fù)雜適應(yīng)系統(tǒng)。目前,研究復(fù)雜適應(yīng)系統(tǒng)的一種有效途徑是結(jié)合多Agent的理論對其進(jìn)行建模和仿真。在該理論指導(dǎo)下,煤炭供應(yīng)鏈中的各企業(yè)以及企業(yè)內(nèi)部的各生產(chǎn)單元可看作動態(tài)變化的Agent智能主體,企業(yè)之間以及企業(yè)內(nèi)部各生產(chǎn)單元之間的協(xié)商實(shí)際為Agent智能主體之間的動態(tài)協(xié)商。 Agent之間的協(xié)商機(jī)制主要通過Agent行為及資源的動態(tài)選擇實(shí)現(xiàn)。合同網(wǎng)是實(shí)現(xiàn)Agent之間資源分配及復(fù)雜任務(wù)求解的一種經(jīng)典協(xié)商策略。而對于MAS中主體行為協(xié)商過程的規(guī)范約束則依賴于一種有效的語義描述工具。組織符號學(xué)中的規(guī)范擴(kuò)充了規(guī)則與慣例的語義表述范圍,可以很好的描述Agent的行為特性,實(shí)現(xiàn)對系統(tǒng)中Agent行為的規(guī)范約束。Norm作為Agent掌握的一種知識,需要適應(yīng)動態(tài)變化的復(fù)雜環(huán)境。而對煤炭供應(yīng)鏈中企業(yè)Agent知識的學(xué)習(xí)和發(fā)現(xiàn)機(jī)制則依賴于一種有效的學(xué)習(xí)算法。GALCS是一種并行的、基于規(guī)則的、可自動更新的智能系統(tǒng),可以實(shí)現(xiàn)對企業(yè)Norm的學(xué)習(xí)。 論文的研究課題來源于國家自然科學(xué)基金項(xiàng)目—基于規(guī)范和多Agent的企業(yè)演化建模與仿真。本文在其基礎(chǔ)上,分析其相關(guān)領(lǐng)域的國內(nèi)外研究現(xiàn)狀,結(jié)合規(guī)范和多Agent技術(shù)對企業(yè)Agent的協(xié)商機(jī)制進(jìn)行了改進(jìn),主要內(nèi)容如下： (1)結(jié)合基于遺傳算法的學(xué)習(xí)分類器系統(tǒng)設(shè)計(jì)一種基于Norm學(xué)習(xí)的企業(yè)決策框架,通過對供應(yīng)鏈采購環(huán)節(jié)Norm的相關(guān)舉例,證明在該框架下設(shè)計(jì)的企業(yè)模型能夠?qū)ζ髽I(yè)Norm進(jìn)行有效的學(xué)習(xí)和監(jiān)督。 (2)結(jié)合對遺傳算法和合同網(wǎng)的改進(jìn)提出基于Norm的全局信任協(xié)商模型。通過對煤炭供應(yīng)鏈內(nèi)外部環(huán)境下協(xié)商模型的舉例,證明模型可以從局部到全局對企業(yè)涉及到的各類協(xié)商議題進(jìn)行任務(wù)求解。 (3)在仿真實(shí)驗(yàn)中利用了之前的研究。通過仿真實(shí)驗(yàn),證明本文研究的Agent協(xié)商模型的有效性。
[Abstract]:In recent years, the coal supply chain has some problems, such as increasing inventory, unbalanced supply and demand, slow response to customer demand and so on. In the face of dynamic open market competition environment, if we want to understand and quickly meet the needs of consumers, the main bodies in the coal supply chain need to connect closely with each other to realize the transfer of coal resources in the whole supply chain. That is, to achieve good consultation between the main body of the enterprise. The behavior change of each main body in the coal supply chain affects the evolution and development of the coal supply chain, and the whole system forms a complex adaptive system. At present, an effective way to study complex adaptive systems is to model and simulate them based on the theory of multiple Agent. Under the guidance of this theory, the enterprises in the coal supply chain and the production units within the enterprises can be regarded as dynamic Agent intelligent agents. The negotiation between enterprises and production units is actually a dynamic negotiation between Agent agents. The negotiation mechanism between Agent is mainly realized by Agent behavior and dynamic selection of resources. Contract net is a classical negotiation strategy for resource allocation and complex task solving between Agent. The canonical constraints for the negotiation process of agent behavior in MAS depend on an effective semantic description tool. The specification in organizational semiotics expands the scope of semantic representation of rules and conventions, and can well describe the behavior characteristics of Agent and realize the canonical constraint on Agent behavior in the system. Norm is a kind of knowledge that Agent grasps. Complex environments that need to adapt to dynamic changes. However, the learning and discovery mechanism of Agent knowledge in coal supply chain depends on an effective learning algorithm. GALCS is a parallel, rule-based and automatic updating intelligent system, which can realize the learning of enterprise Norm. The research topic of this paper comes from the project of National Natural Science Foundation-Enterprise Evolution Modeling and Simulation based on Specification and multiple Agent. Based on it, this paper analyzes the current situation of domestic and foreign research in related fields, and improves the negotiation mechanism of enterprise Agent with the combination of specification and multiple Agent technology. The main contents are as follows: (1) combined with the learning classifier system based on genetic algorithm, an enterprise decision-making framework based on Norm learning is designed, and an example of Norm in supply chain procurement is given. It is proved that the enterprise model designed under this framework can effectively learn and supervise the enterprise Norm. (2) combined with the improvement of genetic algorithm and contract net, a global trust negotiation model based on Norm is proposed. Through the example of negotiation model in coal supply chain, it is proved that the model can solve all kinds of negotiation issues involved in enterprises from local to global. (3) the previous research is used in the simulation experiment. The effectiveness of the Agent negotiation model studied in this paper is proved by simulation experiments.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F407.21;F274;TP18

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