Search results for "decision support"
showing 10 items of 377 documents
Intelligent purchasing : How artificial intelligence can redefine the purchasing function
2021
Abstract Artificial intelligence (AI) can affect all of a company’s functions, not least the purchasing department. In addition to automating and optimizing existing processes, AI opens up new opportunities for purchasers to undertake new, strategic, collaborative, enduring missions. AI enables complex, strategic decision-making in an unpredictable, hostile environment. This article analyzes to what extent AI can improve the performance of the purchasing department. First, a review is undertaken of how AI is used in purchasing. Thereafter, the research follows an exploratory, inductive, and qualitative approach based on a multiple case study of the following technologies: (1) the Synertrade…
Bayesian methods in cost-effectiveness studies: objectivity, computation and other relevant aspects.
2009
In a probabilistic sensitivity analysis (PSA) of a cost-effectiveness (CE) study, the unknown parameters are considered as random variables. A crucial question is what probabilistic distribution is suitable for synthesizing the available information (mainly data from clinical trials) about these parameters. In this context, the important role of Bayesian methodology has been recognized, where the parameters are of a random nature. We explore, in the context of CE analyses, how formal objective Bayesian methods can be implemented. We fully illustrate the methodology using two CE problems that frequently appear in the CE literature. The results are compared with those obtained with other popu…
SMAA - Stochastic multiobjective acceptability analysis
1998
Stochastic multiobjective acceptability analysis (SMAA) is a multicriteria decision support technique for multiple decision makers based on exploring the weight space. Inaccurate or uncertain input data can be represented as probability distributions. In SMAA the decision makers need not express their preferences explicitly or implicitly; instead the technique analyses what kind of valuations would make each alternative the preferred one. The method produces for each alternative an acceptability index measuring the variety of different valuations that support that alternative, a central weight vector representing the typical valuations resulting in that decision, and a confidence factor mea…
Incorporating preference information in interactive reference point methods for multiobjective optimization
2009
In this paper, we introduce new ways of utilizing preference information specified by the decision maker in interactive reference point based methods. A reference point consists of desirable values for each objective function. The idea is to take the desires of the decision maker into account more closely when projecting the reference point onto the set of nondominated solutions. In this way we can support the decision maker in finding the most satisfactory solutions faster. In practice, we adjust the weights in the achievement scalarizing function that projects the reference point. We identify different cases depending on the amount of additional information available and demonstrate the c…
Prospect theory and stochastic multicriteria acceptability analysis (SMAA)
2009
Abstract We consider problems where multiple decision makers (DMs) want to choose their most preferred alternative from a finite set based on multiple criteria. Several approaches to support DMs in such problems have been suggested. Prospect theory has appealed to researchers through its descriptive power, but rare attempts have been made to apply it to support multicriteria decision making. The basic idea of prospect theory is that alternatives are evaluated by a difference function in terms of gains and losses with respect to a reference point. The function is suggested to be concave for gains and convex for losses and steeper for losses than for gains. Stochastic multicriteria acceptabil…
Fully Polynomial Time Approximation Scheme for the Two-Parallel Capacitated Machines Scheduling Problem Under Unavailability Constraint
2010
Abstract Decision Support Systems (DSS) ensure the computer-based support for the conscientious decision-making in solving problems that require a large amount of information processing and complex scenarios. DSS for Transportation (DSST) are intelligent systems that are used at operational and organizational management levels. Operating a DSST in a public transportation web-based monitoring system is presented in this paper.
Fast Nash Hybridized Evolutionary Algorithms for Single and Multi-objective Design Optimization in Engineering
2014
Evolutionary Algorithms (EAs) are one of advanced intelligent systems and they occupied an important position in the class of optimizers for solving single-objective/reverse/inverse design and multi-objective/multi physics design problems in engineering. The chapter hybridizes the Genetic Algorithms (GAs) based computational intelligent system (CIS) with the concept of Nash-Equilibrium as an optimization pre-conditioner to accelerate the optimization procedure. Hybridized GAs and simple GAs are validated through solving five complex single-objective and multi-objective mathematical design problems. For real-world design problems, the hybridized GAs (Hybrid Intelligent System) and the origin…
Decision making in multiobjective optimization problems under uncertainty: balancing between robustness and quality
2018
As an emerging research field, multiobjective robust optimization employs minmax robustness as the most commonly used concept. Light robustness is a concept in which a parameter, tolerable degradations, can be used to control the loss in the objective function values in the most typical scenario for gaining in robustness. In this paper, we develop a lightly robust interactive multiobjective optimization method, LiRoMo, to support a decision maker to find a most preferred lightly robust efficient solution with a good balance between robustness and the objective function values in the most typical scenario. In LiRoMo, we formulate a lightly robust subproblem utilizing an achievement scalarizi…
Classifying efficient alternatives in SMAA using cross confidence factors
2006
Abstract Stochastic multicriteria acceptability analysis (SMAA) is a family of methods for aiding multicriteria group decision making. These methods are based on exploring the weight space in order to describe the preferences that make each alternative the most preferred one. The main results of the analysis are rank acceptability indices, central weight vectors and confidence factors for different alternatives. The rank acceptability indices describe the variety of different preferences resulting in a certain rank for an alternative; the central weight vectors represent the typical preferences favouring each alternative; and the confidence factors measure whether the criteria data are suff…
Stochastic Multicriteria Acceptability Analysis (SMAA)
2010
Stochastic multicriteria acceptability analysis (SMAA) is a family of methods for aiding multicriteria group decision making in problems with uncertain, imprecise or partially missing information. These methods are based on exploring the weight space in order to describe the preferences that make each alternative the most preferred one, or that would give a certain rank for a specific alternative. The main results of the analysis are rank acceptability indices, central weight vectors and confidence factors for different alternatives. The rank acceptability indices describe the variety of different preferences resulting in a certain rank for an alternative, the central weight vectors represe…