傅立成臺灣大學:資訊工程學研究所劉廣平Liu, Kuang-PingKuang-PingLiu2010-06-022018-07-052010-06-022018-07-052008U0001-3001200821482600http://ntur.lib.ntu.edu.tw//handle/246246/184840資訊科技的發達使得電子商務發展日益蓬勃，同時B2B電子商務的應用也因計算科技的進步而趨於多元。隨著電子商務快速成長的腳步，在電子平台上自動化協商的角色顯得越來越重要。因此在本篇論文裡，我們提出一個電子商務多邊協商模型，其中的自動化協商機制(ANM)可以處理現實生活中最複雜的多人多議題協商，並以達到總體效益最大化、供需平衡及買賣雙方之雙贏等目標。自動化協商機制的關鍵核心技術是基因演算法與整數規劃模型，其在供需平衡的條件下預測買賣雙方最佳的協商條件。除了實作電子商務網站界面之外，並且在模擬實驗中以三個賣家與四個買家之協商為例，以實驗之結果分析驗證本論文所提之理論模型。In the near future, semantic web technologies such as XML, RDF, OWL will enable more B2B e-commerce applications. In light of the intractability of negotiation by multiple parties, we propose a novel approach for the many-to-many negotiation problem in which n sellers negotiate with m buyers for one kind of merchandise that has multiple issues. An automated negotiation mechanism (ANM) is introduced to facilitate many-to-many negotiation with the goal of optimizing interests of both parties. Genetic algorithm (GA) and integer programming are employed as the core technique. Other than implementation of the e-commerce website, experimental results analysis of simulation in negotiations of three sellers and four buyers verified the proposed theoretical model and the GA approach in this thesis.Chapter 1 Introduction 1.1 Motivation 2.2 Related Work 3.3 Contributions 5.4 Organization 6hapter 2 Preliminaries 7.1 Negotiation 7.2 Genetic Algorithm 8hapter 3 Modeling of Many-to-Many Negotiation 11.1 Problem Descriptions 11.2 Overview of the Negotiation Model 15.3 Automated Negotiation Mechanism (ANM) 17hapter 4 Automated Negotiation Mechanism (ANM) 20.1 Problem Formulation 20.1.1 Measures of Both Sides’ Utility 20.1.2 Basic Idea 22.1.3 Metrics of the Many-to-Many Negotiation 25.1.4 The Problem 27.2 Genetic Algorithm 29.2.1 Chromosome Representation 29.2.2 The Fitness Function 30.2.3 Initial Population 30.2.4 GA Operators 31.2.5 Local Search 32hapter 5 Implementation, Experiments, and Results 33.1 Implementation 33.2 Experimental Design and Results in IP 37.3 Experimental Design and Results in GA 44hapter 6 Conclusions 53uture Work 55eferences 57ppendix A 61application/pdf1502778 bytesapplication/pdfen-US多對多協商基因演算法自動化協商合作式賽局多議題協商電子商務Many-to-Many NegotiationGenetic AlgorithmAutomated NegotiationCooperative GameMulti-issue NegotiationE-Commercethesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/184840/1/ntu-97-J94922016-1.pdf