A homogeneous resource coming from several suppliers is divided among agents with singlepeaked preferences. Each agent has access to an arbitrary fixed subset of supliers. Examples include balancing the workload of machines, sharing earmarked funds between different projects, and distributing utilities under geographic constraints. Unlike in the one supplier model (Sprumont [25]), in a Pareto o...

This paper addresses a multi-objective order scheduling problem in production planning under a complicated production environment with the consideration of multiple plants, multiple production departments and multiple production processes. A Pareto optimization model, combining a NSGA-II-based optimization process with an effective production process simulator, is developed to handle this probl...

The trade-off between obtaining a wellconverged and well-distributed set of Pareto optimal solutions, and obtaining them efficiently and automatically is an important issue in multi-objective evolutionary algorithms (MOEAs). Many studies have depicted different approaches that evolutionary algorithms can progress towards the Pareto optimal set with a wide-spread distribution of solutions. Howev...

In this chapter, we present a new method for interactive multiobjective optimization, which is based on application of a logical preference model built using the Dominance-based Rough Set Approach (DRSA). The method is composed of two main stages that alternate in an interactive procedure. In the first stage, a sample of solutions from the Pareto optimal set (or from its approximation) is gener...

Design of an optimal controller requires the optimization of differential evolution performance measures that are often no commensurable and competing with each other. Being a population based approach; Differential Evolution (DE) is well suited to solve designing problem of TCSC – based controller. This paper investigates the application of DE-based multi-objective optimization technique for t...

Evolutionary optimization algorithms work with a population of solutions, instead of a single solution. Since multi-objective optimization problems give rise to a set of Pareto-optimal solutions, evolutionary optimization algorithms are ideal for handling multi-objective optimization problems. Over many years of research and application studies have produced a number of efficient multi-objectiv...

Requirements prioritisation is a key decision making activity of the software development process, which relies on the capability of different decision-makers to identify the optimal candidate rankings of the requirements, in order to be able to perform a strategic choice among them. In this paper, we formulate such multi-decision-maker requirements prioritisation as a multi-objective optimisat...

A multi-objective chance-constrained programming integrated with Genetic Algorithm and robustness evaluation methods was proposed to weigh the conflict between system investment against risk for watershed load reduction, which was firstly applied to nutrient load reduction in the Lake Qilu watershed of the Yunnan Plateau, China. Eight sets of Pareto solutions were acceptable for both system inv...

The problems presented and analyzed in this paper are interesting testproblems for multi-objective optimization. The aim is to find the distance to two points in a d-dimensional space. Closed analytical expressions of all pareto-optimal fronts and pareto-optimal sets will be derived in this paper. Moreover, a discussion of the characteristics of the properties of these sets is given. Depending ...

We consider the problem of learning a probabilistic model from the viewpoint of an expected utility maximizing decision maker/investor who would use the model to make decisions (bets), which result in well defined payoffs. In our new approach, we seek good out-of-sample model performance by considering a one-parameter family of Pareto optimal models, which we define in terms of consistency with...