Decision-making tasks sometimes involve soft objectives. They are soft in the sense that they contain uncertainty, imprecision or vagueness. For example, decisions on built environment aim to maximize comfort or other experiential qualities. Pareto-optimal solutions to such problems can be found using multi-objective evolutionary search together with other soft computing methods. Beyond optimal...

Clustering is an important problem in knowledge discovery and decision making. In this study, a multi-objective genetic algorithm (MOGA) is used to search for well separated clusters and each evolved solution is evaluated by both data-driven and human-driven metrics developed for this study. The proposed system in this paper also allows the decision maker to navigate non-dominated solutions and...

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user’s intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this g...

Investment in landscapes to achieve outcomes that have multiple environmental benefits has become a major priority in many countries. This gives rise to opportunities for mathematical programming methods to provide solutions on where investments could be made on the landscape, to maximise multiple environmental benefits. The problem was formulated as a multi-objective integer programming model,...

An untraditional multi-objective programming approach, called as fuzzy game multi-objective optimization model (FGMOM), is illustrated to discuss suitability and significances in the problems of sustainable watershed management. FGMOM is developed based on the combination of fuzzy set theory, game theory and traditional multi-objective programming. The model is implemented to support decision m...

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-...

In this paper, a multi-objective method is used to optimize a heat recovery steam generator (HRSG). Two objective functions have been used in the optimization, which are irreversibility and HRSG equivalent volume. The former expresses the exergetic efficiency and the latter demonstrates the cost of the HRSG. Decision variables are geometric and operational parameters of the HRSG. The result...

In this paper, a multi-objective method is used to optimize a heat recovery steam generator (HRSG). Two objective functions have been used in the optimization, which are irreversibility and HRSG equivalent volume. The former expresses the exergetic efficiency and the latter demonstrates the cost of the HRSG. Decision variables are geometric and operational parameters of the HRSG. The results of...

The increased competition in the domestic and international market has posed excessive pressure to the manufacturing organizations to successfully implement the ISO 9001 quality standards. The implementation process becomes more challenging when it is surrounded by numerous barriers. Though the presence of barriers makes it more difficult but certain factors (hereby known as enablers) makes it ...

Outsourcing is a good strategy for firms that need to reduce operating costs and improve competitiveness, but it remains important for firms to scientifically select the appropriate outsourcing providers. Some efforts have been made regarding outsourcing problems, but these efforts incorrectly assumed that the criteria in the decision process are independent, which is not true in the real world...