Multiobjective decision-making and combinatorial optimization have been studied extensively over the past few decades (cf. [16], and [4] for bibliographies). Both fields play a decisive role in multiobjective combinatorial optimization, for which the class of (multiobjective) portfolio selection is of particularly high practical relevance (cf. [10] for a survey). Research and development (R&D) ...

A participatory integrated (social, economic, environmental) approach based on causal loop diagram, Bayesian belief networks and evolutionary multiobjective optimisation is proposed for efficient water resources management. The proposed methodology incorporates all the conflicting objectives in the decision making process. Causal loop diagram allows a range of different factors to be considered...

Building maintenance management involves decision-making under multiple objectives and uncertainty, in addition to budgetary constraints. This paper presents the development of a multiobjective and stochastic optimization system for maintenance management of roofing systems that integrates stochastic condition assessment and performance prediction models with a multiobjective optimization appro...

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics that are desirable for this type of problem, this class of search strategies has been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdi...

This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria De...

Due to increasing the number of decision-making criteria in today's ever complicated geometrical optimization problems, the traditional multiobjective optimization approaches, whether a priori, a posteriori or interactive's, found to be insufficient and ineffective. In this paper the drawbacks of the current algorithms are reviewed and the urgent need for inserting a learning componen...

This paper presents a web-based general decision support system for solving multi-objective decision problems. This is referred to WMODSS. The system provides three decision-making methods in its method base. They deal with a wide range of linear multiobjective decision-making problems and different user requirements for the solution process. In particular, the WMODSS provides an intelligent gu...

Decision making is hard. It often requires reasoning about uncertain environments, partial observability and action spaces that are too large to enumerate. In such tasks decisiontheoretic agents can often assist us. In most research on decision-theoretic agents, the desirability of actions and their effects is codified in a scalar reward function. However, many real-world decision problems have...

Most of the real-life decision-making problems have more than one conflicting and incommensurable objective functions. In this paper, we present a multiobjective two-stage stochastic linear programming problem considering some parameters of the linear constraints as interval type discrete random variables with known probability distribution. Randomness of the discrete intervals are considered f...