Many concrete problems may be cast in a multi-objective optimisation framework. The redundancy of existing methods for solving multi-objective programming problems susceptible to inconsistencies, coupled with the necessity for making in- herent assumptions before using a given method, make it hard for a nonspecialist to choose a method that ¯ts the situation at hand well. Moreover, using a method blindly, as suggested by the hammer principle (when you only have a hammer, you want everything in your hand to be a nail) is an awkward approach at best and a caricatural one at worst. This brings challenges to the design, development, implementation and deployment of a Decision Support System able to choose a method that is appropriate for a given problem and to apply the chosen method to solve the problem under consideration. The choice of method should be made according to the structure of the problem and the decision maker's opinion. The aim here is to embed a sample of methods representing the main multi-objective programming techniques and to help the decision maker find the most appropriate method for his problem. / Decisions Sciences / M. Sc. (Operations Research )

Many concrete problems may be cast in a multi-objective optimisation framework. The redundancy of existing methods for solving multi-objective programming problems susceptible to inconsistencies, coupled with the necessity for making in- herent assumptions before using a given method, make it hard for a nonspecialist to choose a method that ¯ts the situation at hand well. Moreover, using a method blindly, as suggested by the hammer principle (when you only have a hammer, you want everything in your hand to be a nail) is an awkward approach at best and a caricatural one at worst. This brings challenges to the design, development, implementation and deployment of a Decision Support System able to choose a method that is appropriate for a given problem and to apply the chosen method to solve the problem under consideration. The choice of method should be made according to the structure of the problem and the decision maker's opinion. The aim here is to embed a sample of methods representing the main multi-objective programming techniques and to help the decision maker find the most appropriate method for his problem. / Decisions Sciences / M. Sc. (Operations Research )

面對通訊與資訊科技的大幅進步，通訊網路正在進行一個巨大的變革，要將電信網路與數據網路整合成一個單一的All-IP網路以支援所有網路應用服務。欲達到整合型網路的理想，仍有許多困難尚待克服，而服務品質問題是其中最關鍵的問題之一。因為受限於封包交換網路之原有的特性，All-IP網路有影響服務品質的三項因素：過長的延遲時間、抖動以及封包遺失。首先，我們利用了達成度函數(achievement function)來處理單位的轉換，使得能夠同時考量此三項不同單位的因素。接著，本文中提出一套方法來解決All-IP網路上端對端(end-to-end)的資源配置及路徑規劃問題。在分配資源時，我們企圖提供一種成比例的公平性給各個不同等級。此公平性的精神是要使得所有網路使用者的滿足程度相當，而非各個不同等級的使用者分配到相同的資源。我們將以預算方式控制端對端品質管理以追求使用者之整體最大滿意程度。 本論文的規劃概念是將網路規劃分成兩個階段。第一階段是在一筆給定的總預算底下，以成比例的方式去分配資源給各個不同等級，並建置網路上的頻寬，使各等級能依其需求拿到適當的頻寬，確保滿足程度相當。 接下來第二階段則是在第一部份已完成的規劃基礎下，做路徑規劃，指派新進入的使用者到一條較好的路徑，在滿足此使用者的延遲時間要求下，使此系統的壅塞程度越小越好。路徑規劃的概念為如何挑選最佳網路路徑，以規劃具服務品質之端對端路徑，並可達到資源之最有效利用。網路營運者將可運用此套方法來調校自身所營運的網路以追求使用者最高滿意度。 / We present an approach for the fair resource allocation problem and QoS routing in All-IP networks that offer multiple services to users. The objective of the optimization problem is to determine the amount of required bandwidth for each link and each class to maximize the sum of the users' utility. In this work, we focus on approaches that, while allocating bandwidth, attempt to provide a proportionally fair treatment of all the competing classes. First, we will show that an achievement function can map different criteria subject to various utility onto a normalized scale. It may be interpreted as a measure of QoS (Quality of Service) on All-IP networks. Using the bandwidth allocation model, we can find a Pareto optimal allocation of bandwidth on the network under a limited available budget. This allocation can provide the so-called proportional fairness to every class, that is, this allocation can provide the similar satisfaction to each user. Next, we present a routing scheme under consideration of the delay. Such an optimal path provides the end-to-end QoS guarantees to each user. Finally, a numerical example is given to illustrate how to solve the fair resource allocation problem and how to modify the nonlinear parts.