Decision making under incompleteness based on soft set theory

[EN]Decision making with complete and accurate information is ideal but infrequent. Unfortunately, in most cases the available infor- mation is vague, imprecise, uncertain or unknown. The theory of soft sets provides an appropriate framework for decision making that may be used to deal with uncertain decisions. The aim of this paper is to propose and analyze an effective algorithm for multiple attribute decision-making based on soft set theory in an incomplete information environment, when the distribution of incomplete data is unknown. This procedure provides an accurate solution through a combinatorial study of possible cases in the unknown data. Our theoretical development is complemented by practical examples that show the feasibility and implementability of this algorithm. Moreover, we review recent research on decision making from the standpoint of the theory of soft sets under incomplete information

Collaborative dynamic decision making: a case study from B2B supplier selection

The problem of supplier selection can be easily modeled as a multiple-criteria decision making (MCDM) problem: businesses express their preferences with respect to suppliers, which can then be ranked and selected. This approach has two major pitfalls: first, it does not consider a dynamic scenario, in which suppliers and their ratings are constantly changing; second, it only addressed the problem from the point of view of a single business, and cannot be easily applied when considering more than one business. To overcome these problems, we introduce a method for supplier selection that builds upon the dynamic MCDM framework of Campanella and Ribeiro [1] and, by means of a linear programming model, can be used in the case of multiple collaborating businesses plan- ning their next batch of orders together.Fundação para a Ciência e a Tecnologia, Portugal, under contract CONT DOUT/49/UNINOVA/0/5902/1/200

Online Performance Tracking

This paper describes the conceptual framework, development process, and theoretical structure for an online performance tracking system. The principle factors influencing online performance tracking are described using the weighted sum model as computational method on measures of performance. Input data for the computational model were obtained directly from a real-time system in an actual organization that directly measured staff performance. In this multicriteria decision-making approach, the criteria weights are computed using the entropy information method and ranking of 15 alternatives (employees) is computed using the weighted sum model. Computational results obtained using the online performance appraisal system are evaluated and discussed relative to the weighted sum model

Deliberation, Representation, Equity

"What can we learn about the development of public interaction in e-democracy from a drama delivered by mobile headphones to an audience standing around a shopping center in a Stockholm suburb? In democratic societies there is widespread acknowledgment of the need to incorporate citizens’ input in decision-making processes in more or less structured ways. But participatory decision making is balancing on the borders of inclusion, structure, precision and accuracy. To simply enable more participation will not yield enhanced democracy, and there is a clear need for more elaborated elicitation and decision analytical tools. This rigorous and thought-provoking volume draws on a stimulating variety of international case studies, from flood risk management in the Red River Delta of Vietnam, to the consideration of alternatives to gold mining in Roșia Montană in Transylvania, to the application of multi-criteria decision analysis in evaluating the impact of e-learning opportunities at Uganda's Makerere University. Editors Love Ekenberg (senior research scholar, International Institute for Applied Systems Analysis [IIASA], Laxenburg, professor of Computer and Systems Sciences, Stockholm University), Karin Hansson (artist and research fellow, Department of Computer and Systems Sciences, Stockholm University), Mats Danielson (vice president and professor of Computer and Systems Sciences, Stockholm University, affiliate researcher, IIASA) and Göran Cars (professor of Societal Planning and Environment, Royal Institute of Technology, Stockholm) draw innovative collaborations between mathematics, social science, and the arts. They develop new problem formulations and solutions, with the aim of carrying decisions from agenda setting and problem awareness through to feasible courses of action by setting objectives, alternative generation, consequence assessments, and trade-off clarifications. As a result, this book is important new reading for decision makers in government, public administration and urban planning, as well as students and researchers in the fields of participatory democracy, urban planning, social policy, communication design, participatory art, decision theory, risk analysis and computer and systems sciences.

Performance evaluation of petrochemical firms accepted in Tehran stock exchange using DEA (window analysis)

In the past two decades, organizational performance management has become one of the most attractive topics of study. Each organization is needed to evaluate its system to determine the appropriateness and quality of your work in dynamic environments. Data envelopment analysis provides a theoretical framework for performance analysis and performance measurement. The data envelopment analysis (DEA) is a linear programming technique, whose main purpose is to compare and evaluate a number of similar decision making units which have different amounts of used inputs and produced outputs. Dynamic method of data envelopment analysis (window analysis), is a method that enables the calculation of performance over time and can result in improved outcomes.We use the model described in this paper, the performance of listed companies in the petrochemical industry review. The evaluation results are indicated with different companies. The results showed that the six companies are 80% more efficient