In the first part of this study we have introduced six different multiobjective dual problems to a general multiobjective optimization problem, for which we presented weak as well as strong duality assertions. Afterwards, we derived some inclusion results for the image sets of three of these problems. The aim of this second part is to complete our investigations by studying the relations betwee...

In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in ¯exible manufacturing systems. The PSGA is used to generate approximately ef®cient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the ®rst implementation of PSGA to solve a multiobjective optimization problem (MOP)....

The seminar "Hybrid and Robust Approaches to Multiobjective Optimization" was a sequel to two previous Dagstuhl seminars (04461 in 2004 and 06501 in 2006). The main idea of this seminar series has been to bring together two contemporary elds related to multiobjective optimization Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) to discuss critical rese...

Within the past several years, important advances have been made in conditional value-at-risk (CVaR) model in finance engineering. CVaR has superior properties in many respects. But, some practical risk problems, such as lending plan of bank, are multiobjective decision making. So, it is key question to study multiobjective CVaR model. This paper proposes a general multiobjective CVaR model in ...

This paper presents an optimization strategy for a broke system design and operation in papermaking process. A multiobjective stochastic optimization model is presented featuring (i) a stochastic two-state Markov process based submodel for the broke tower, (ii) an operational submodel for the optimization of the broke dosage, and (iii) a multiobjective design problem. An efficient optimization ...

A nondifferentiable multiobjective optimization problem with nonempty set constraints is considered, and the equivalence of weakly efficient solutions, the critical points for the nondifferentiable multiobjective optimization problems, and solutions for vector variational-like inequalities is established under some suitable conditions. Nonemptiness and compactness of the solutions set for the n...

In this paper, we consider solving a telecommunication problem called a transmission link selection problem. The problem is a multiobjective scheduling problem where a set of data components is to be transferred using distinct wireless communication links. The objective of the problem is to minimize both the time used for the transfers and the costs of the transfers. First, we discuss dealing w...

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cann...

Abstract In the real world, we often encounter cases where the information / data items can’t be determined with certainty. Hence the value of the datum in the data is assessed using an interval. Meanwhile multiobjective linear programming model is more adequate to describe the problem in the real world. Thus, the multiobjective linear programming problem will be developed into a multiobjective...

Recognising the multiobjective nature of the decision process for rehabilitation of water supply distribution systems, this paper presents a comparative study of two multiobjective evolutionary methods, namely, multiobjective genetic algorithm (MOGA) and strength Pareto evolutionary algorithm (SPEA). The analyses were conducted on a simple hypothetical network for cost minimisation and minimum ...