In multi-objective optimization the objective is to reach a point which is Pareto efficient. However we usually encounter many such points and choosing a point amongst them possesses another problem. In many applications we are required to choose a point having a good spread over all objective functions which is a direct consequence of the notion of fairness. In this paper we propose two things...

This paper describes the application of Kriging metamodeling in multiple-objective simulation optimization. An Arenabased simulation model of an (s, S) inventory system is utilized to demonstrate the capabilities of Kriging metamodeling as a simulation tool. Response surface methodology and Kriging metamodeling are compared to determine the situations in which one approach might be preferred ov...

In practical applications of mathematical programming it is frequently observed that the decision maker prefers apparently suboptimal solutions. A natural explanation for this phenomenon is that the applied mathematical model was not sufficiently realistic and did not fully represent all the decision makers criteria and constraints. Since multicriteria optimization approaches are specifically d...

This paper presents an optimization methodology that includes three important components necessary for a systematic approach to naval ship concept design. These are: • An efficient and effective search of design space for non-dominated designs • Well-defined and quantitative measures of objective attributes • An effective format to describe the design space and to present non-dominated concepts...

‎A common approach to determine efficient solutions of a multiple objective optimization problem‎ ‎is reformulating it to a parameter dependent scalar optimization problem‎. ‎This reformulation is called scalarization approach‎. Here, a well-known scalarization approach named Pascoletti-Serafini scalarization is considered‎. First, some difficulties of this scalarization are discussed and then ...