A utility function is an important tool for representing a DM’s preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utili...

We consider multi-attribute Bayesian optimization, where each design in an optimization problem’s feasible space is associated with a vector of attributes that can be evaluated via a time-consuming computer code, and each vector of attributes is assigned a utility according to a decision-maker’s utility function. A standard Bayesian optimization approach could be applied if the utility function...

We develop efficient algorithms to construct approximately utility maximizing mechanisms for a risk averse seller in the presence of potentially risk-averse buyers in Bayesian single parameter and multiparameter settings. We model risk aversion by concave utility function. Bayesian mechanism design has usually focused on maximizing expected revenue in a risk-neutral environment, i.e. where all ...

In the last decade, theory of constraint application in project management lead to make a new approach for project scheduling and control as a critical chain. In this paper, a multi-objective optimization model for multi-project scheduling on critical chain is investigated. The objectives include time, cost and quality. In order to solve the problem, a Simulated Annealing algorithm is developed...

A multi-sensory service is to induce customer’s purchases through emotional stimuli. The system for this service needs to predict emotion and selects relevant stimuli depending on customer’s emotion. In this paper, we propose a stimuli selection system using modular dynamic Bayesian networks and utility function. The emotion prediction module uses dynamic Bayesian network to predict customer’s ...

In this report we continue development of a Bayesian CBIR system by considering the issue of an image display strategy. This is a decision problem and so within the Bayesian paradigm is to be solved by decision theory. We show how different display strategies may be quantified by an appropriate utility function, and compare different optimisation strategies. Examples are given.