In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user’s intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this g...

Investment in landscapes to achieve outcomes that have multiple environmental benefits has become a major priority in many countries. This gives rise to opportunities for mathematical programming methods to provide solutions on where investments could be made on the landscape, to maximise multiple environmental benefits. The problem was formulated as a multi-objective integer programming model,...

When choosing a solution, decision makers are often required to account for multiple conflicting objectives. This is a situation that can result in a potentially huge number of candidate solutions. Despite the wide selection of multivariate visualizations that can potentially help decide between various candidates, no designated means exist to assess the effectiveness of these visualizations un...

The theory of constraints is an approach to production planning and control that emphasizes on the constraints to increase throughput by effectively managing constraint resources. One application in theory of constraints is product mix decision. Product mix influences the performance measures in multi-product manufacturing system. This paper presents an alternative approach by using of goal pro...

Abstract Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks problem involve interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving problems by transforming original into a series of small-scale subproblems. However, static...

Abstract Many multi-objective optimization problems in reality are dynamic, requiring the algorithm to quickly track moving optima after environment changes. Therefore, response strategies often used dynamic algorithms find Pareto optimal. In this paper, we propose a hybrid prediction strategy based on classification of decision variables, which consists three steps. After detecting change, fir...

Software Product Lines Engineering has created various tools that assist with the standardisation in design and implementation of clusters equivalent software systems an explicit representation variability choices form Feature Models, making selection most ideal product a Selection problem. With increase number properties, problem needs to be defined as multi-objective optimisation where object...

The use of computational methodologies for the optimization of aesthetic parameters is not frequent mainly due to the fact that these parameters are not quantifiable and are subjective. In this work an interactive methodology based on the use of multi-objective optimization algorithms is proposed. This strategy associates the results of different optimization runs considering the existent quant...