In this study, an integrated solution methodology for a general discrete multi-criteria decision making problem is developed based on the well-known outranking method Promethee II. While the methodology handles the existence of interdependency between the criteria, it can also incorporate the prospect theory in order to correctly reflect the decision behavior of the decision maker. A software is also developed for the application of the methodology and some applications are performed and presented.

This article seeks to enhance multi criteria decision making by providing a scientic approach for decomposing and structuring decision problems. We propose a process, based on concept mapping, which integrates group creativity techniques, card sorting procedures, quantitative data analysis and algorithmic automatization to construct meaningful and complete hierarchies of criteria. The algorithmic aspect is covered by a newly proposed recursive cluster algorithm, which automatically generates hierarchies from card sorting data. Based on comparison with another basic algorithm and empirical engineered and real-case test data, we validate that our process efficiently produces reasonable hierarchies of descriptive elements like goal- or problem-criteria. (authors' abstract)

Products are priced in order to sell them and make a profit. Every firm, therefore, needs a pricing strategy. Such a strategy should be simple. It should ensure simplicity in tactics and decisions and minimize complications. It is difficult to set a price with the help of only one pricing model. The price of a product may vary due to factors like geographical area, different clients and time difference. Prices must always be cost-compatible. An essential step in deciding on a pricing strategy involves looking at the characteristics of pricing decisions. The classic economic theory is based upon demand and supply and attempts to balance these two concepts. In most cases it works on the basis of cost plus profit. This way of thinking about prices does not guarantee a profit, because costs and profit depend upon volume and volume is dependent on the correct price. Prices can be cut at first. In this way only a small profit will be ensured. If the price is too low it will not automatically ensure a profit. Usually little attention is paid to the market itself in deciding on a price. It is not an easy task to arrive at the 'envelope of acceptable prices". Not to fall into the standard trap of adding profit to cost, one has to have a broad overview of pricing strategies. Multiple approaches are followed in determining prices. Firstly, one can look at cost and its characteristics. By adding a profit margin to cost, one can determine a new price. It may be too low or it may be too high, resulting in the risk that customers will buy the competition's product. It is there for essential to look at strategic concepts like the competition's price as well. The way a buyer looks at certain prices and then decides whether to buy or not, also plays a very important role. All of these factors have to be taken into consideration and all aspects have to be balanced to arrive at a price. A framework for pricing decisions includes the recognition of the need for a pricing decision, determining a price, developing a model, identifying and anticipating pricing problems, developing feasible courses of action, forecasting the outcomes of each alternative and monitoring and reviewing the outcome of each action. Management's pricing decision is taken after studying all this information. Information can be given as a single answer or in detail. Costs can be divided into direct costs and absorption costs. Although prices can be determined in more ways than one, the ideal is to take more than one factor into consideration. Every aspect must carry a weight and these weights can be changed. That is why the multiple criteria decision method is so effective. With this method a few factors are taken into account. Each of these factors adds to the price definition in a certain manner with regard to each product. By changing the profit margin, the price can be adjusted until one is satisfied with the new price. A company's structure, location and nature will play a role in determining which technique is used to determine a price. The best technique is one that can be adjusted and where multiple criteria can be set. The choice of a technique is a personal choice. The multi-criteria method is flexible and prices can be determined uniformly for all products or for a single product. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2004.

The principles of parallel counting, the MPI program parcel, which was used in this work, allow to adapt them to solve tasks using computer net and peculiarities of implanting this parcel.

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Products are priced in order to sell them and make a profit. Every firm, therefore, needs a pricing strategy. Such a strategy should be simple. It should ensure simplicity in tactics and decisions and minimize complications. It is difficult to set a price with the help of only one pricing model. The price of a product may vary due to factors like geographical area, different clients and time difference. Prices must always be cost-compatible. An essential step in deciding on a pricing strategy involves looking at the characteristics of pricing decisions. The classic economic theory is based upon demand and supply and attempts to balance these two concepts. In most cases it works on the basis of cost plus profit. This way of thinking about prices does not guarantee a profit, because costs and profit depend upon volume and volume is dependent on the correct price. Prices can be cut at first. In this way only a small profit will be ensured. If the price is too low it will not automatically ensure a profit. Usually little attention is paid to the market itself in deciding on a price. It is not an easy task to arrive at the 'envelope of acceptable prices". Not to fall into the standard trap of adding profit to cost, one has to have a broad overview of pricing strategies. Multiple approaches are followed in determining prices. Firstly, one can look at cost and its characteristics. By adding a profit margin to cost, one can determine a new price. It may be too low or it may be too high, resulting in the risk that customers will buy the competition's product. It is there for essential to look at strategic concepts like the competition's price as well. The way a buyer looks at certain prices and then decides whether to buy or not, also plays a very important role. All of these factors have to be taken into consideration and all aspects have to be balanced to arrive at a price. A framework for pricing decisions includes the recognition of the need for a pricing decision, determining a price, developing a model, identifying and anticipating pricing problems, developing feasible courses of action, forecasting the outcomes of each alternative and monitoring and reviewing the outcome of each action. Management's pricing decision is taken after studying all this information. Information can be given as a single answer or in detail. Costs can be divided into direct costs and absorption costs. Although prices can be determined in more ways than one, the ideal is to take more than one factor into consideration. Every aspect must carry a weight and these weights can be changed. That is why the multiple criteria decision method is so effective. With this method a few factors are taken into account. Each of these factors adds to the price definition in a certain manner with regard to each product. By changing the profit margin, the price can be adjusted until one is satisfied with the new price. A company's structure, location and nature will play a role in determining which technique is used to determine a price. The best technique is one that can be adjusted and where multiple criteria can be set. The choice of a technique is a personal choice. The multi-criteria method is flexible and prices can be determined uniformly for all products or for a single product. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2004.

In this dissertation, we focus on the problem of self-management in distributed systems. In this context, we propose a new methodology for reactive self-management based on multiple criteria decision analysis (MCDA). The general structure of the proposed methodology is extracted from the commonalities of the former well-established approaches that are applied in other problem domains. The main novelty of this work, however, lies in the usage of MCDA during the reaction processes in the context of the two problems that the proposed methodology is applied to. In order to provide a detailed analysis and assessment of this new approach, we have used the proposed methodology to design distributed autonomous agents that can provide self-management in two outstanding problems. These two problems also represent the two distinct ways in which the methodology can be applied to self-management problems. These two cases are: 1) independent self management, and 2) coordinated self-management. In the simulation case study regarding independent self-management, the methodology is used to design and implement a distributed resource consolidation manager for clouds, called IMPROMPTU. In IMPROMPTU, each autonomous agent is attached to a unique physical machine in the cloud, where it manages resource consolidation independently from the rest of the autonomous agents. On the other hand, the simulation case study regarding coordinated self-management focuses on the problem of adaptive routing in mobile ad hoc networks (MANET). The resulting system carries out adaptation through autonomous agents that are attached to each MANET node in a coordinated manner. In this context, each autonomous node agent expresses its opinion in the form of a decision regarding which routing algorithm should be used given the perceived conditions. The opinions are aggregated through coordination in order to produce a final decision that is to be shared by every node in the MANET. Although MCDA has been previously considered within the context of artificial intelligence---particularly with respect to algorithms and frameworks that represent different requirements for MCDA problems, to the best of our knowledge, this dissertation outlines a work where MCDA is applied for the first time in the domain of these two problems that are represented as simulation case studies. / Graduate

This study is concerned with a sorting problem / the placement of alternatives into preference classes in the existence of multiple criteria. An interactive model is developed to address the problem, assuming that the decision maker has an underlying utility function which is linear. A recent methodology, Even-Swaps, which is based on value tradeoff is utilized in the model for both making an estimation of the underlying utility function and generating possible dominance among the alternatives on which it is performed. Convex combinations, dominance relations, weight space reduction, Even-Swaps and direct decision maker placements are utilized to place alternatives in preference classes. The proposed algorithm is experimented with randomly generated alternative sets having different characteristics.

At no point in history have we demanded so much from our agricultural land whilst simultaneously leaving so little room for management error. Of the many possible environmental impacts from agriculture, soil and water salinisation has some of the most long-lived and deleterious effects. Despite its importance, however, land managers are often unable to make informed decisions of how to manage the risk of salinisation due to a lack of data. Furthermore, there remains no universally agreed method for salinity risk mapping. This thesis addresses these issues by investigating new methods for producing high-resolution predictions of soil salinity, soil physical properties and groundwater depth using a variety of traditional and emerging ancillary data sources. The results show that the methodologies produce accurate predictions yielding natural resource information at a scale and resolution not previously possible. Further to this, a new methodology using fuzzy logic is developed that exploits this information to produce high-resolution salinity risk maps designed to aid both agricultural and natural resource management decisions. The methodology developed represents a new and effective way of presenting salinity risk and has numerous advantages over conventional risk models. The incorporation of fuzzy logic provides a meaningful continuum of salinity risk and allows for the incorporation of uncertainty. The method also allows salinity risk to be calculated relative to any vegetation community and shows where the risk is coming from (root-zone or groundwater) allowing more appropriate management decisions to be made. The development of this methodology takes us a step closer to closing what some have called our greatest gap in agricultural knowledge. That is, our ability to manage the salinity risk at the subcatchment scale.

Bi-level programming techniques are developed for decentralized decision problems with decision makers located in a two-level decision making system; the upper decision maker is termed the leader while the lower is the follower. Both the leader and the follower try to optimise their own objective functions and the corresponding decisions do not control but do affect those of the other level. This research aims at solving bi-level decision problems with five extensions, i.e. multiple leaders/followers/objectives, fuzzy coefficients and goals. By using particle swarm optimisation and/or cut set and/or goal programming and/or Nash equilibrium concept, related mathematical models and corresponding algorithms are developed to solve fuzzy linear bi-level decision problems, fuzzy linear multi-objective bi-level decision problems, fuzzy linear multi-follower multi-objective bi-level decision problems, fuzzy linear goal bi-level decision problems, multi-leader one-follower bi-level decision problems, one-leader multi-follower bi-level decision problems, and multileader multi-follower bi-level decision problems. A fuzzy bi-level decision support system is then developed which implements all the algorithms to support bi-level decision making with different features. Finally, by using these bi-level models and algorithms, we explore possible applications in the fields of railway train set organisation, railway wagon flow management, strategic bidding in the electricity market, and supply chains to solve real world bi-level decision problems. The results of experiments show that the models and algorithms are effective for solving real world bi-level decision problems.