A decision support methodology for stochastic multi-criteria linear programming using spreadsheets
Decision Support Systems
Solving efficiently the 0-1 multi-objective knapsack problem
Computers and Operations Research
Ant algorithms and simulated annealing for multicriteria dynamic programming
Computers and Operations Research
Dynamic programming with ordered structures: Theory, examples and applications
Fuzzy Sets and Systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Bio-inspired Learning and Intelligent Systems
A method for stochastic multiple criteria decision making based on dominance degrees
Information Sciences: an International Journal
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Multiple objectives and dynamics characterize many sequential decision problems. In the paper we consider returns in partially ordered criteria space as a way of generalization of single criterion dynamic programming models to multiobjective case. In our problem evaluations of alternatives with respect to criteria are represented by distribution functions. Thus, the overall comparison of two alternatives is equivalent to the comparison of two vectors of probability distributions. We assume that the decision maker tries to find a solution preferred to all other solutions (the most preferred solution). In the paper a new interactive procedure for stochastic, dynamic multiple criteria decision making problem is proposed. The procedure consists of two steps. First, the Bellman principle is used to identify the set of efficient solutions. Next interactive approach is employed to find the most preferred solution. A numerical example and a real-world application are presented to illustrate the applicability of the proposed technique.