in this paper we investigate multi-objective integer linear programming (moilp) problems with unbounded feasible region and introduce recession direction for moilp problems. then we present necessary and sufficient conditions to have unbounded feasible region and infinite optimal values for objective functions of moilp problems. finally we present some examples with unbounded feasible region and fi...

abstract. when a production facility is designed, there are various parameters affecting the number machines such as production capacity and reliability. it is often a tedious task to optimize different objectives simultaneously. the other issue is the uncertainty in many design parameters which makes it difficult to reach a desirable solution. in this paper, we present a new mathematical model...

The paper formalizes the bandwidth allocation process over space communication systems as a Multi – Objective Programming (MOP) problem and proposes an allocation scheme based on the GOAL programming called “Minimum Distance” (MD) algorithm. The method is aimed at following the performance obtained by each station when there is no conflict among the stations to access the channel. In short, the...

The design of supervised learning systems for classification require finding a trade-off among a number of competing performance criteria. A common approach is to optimize an overall objective derived from the individual performance objectives. Such a simplified approach promises only for the chosen performance metric when evaluated over the training samples, yet does not make any guarantee on ...

In this paper we examine the build order problem in real-time strategy (RTS) games in which the objective is to optimize execution of a strategy by scheduling actions with respect to a set of subgoals. We model the build order problem as a multi-objective problem (MOP), and solutions are generated utilizing a multiobjective evolutionary algorithm (MOEA). A three dimensional solution space is pr...

This paper considers three types of risk evaluation models within supply chains: chance constrained programming (CCP), data envelopment analysis (DEA), and multi-objective programming (MOP) models. Various risks are modeled in the form of probability and simulation of specific probability distribution in risk-embedded attributes is conducted in these three types of risk evaluation models. We mo...