A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

Abstract

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters

“…Other interesting avenues of future research are the following: i) the use of the proposed approaches to handle preferences combined with state-of the art many-objective evolutionary algorithms such as [11] and [40]; and ii) the combination of our results with the proposals of Fernández et al [24] and [2] in the context of group evolutionary multi-objective optimization.…”

“…In this sense, the aggregation of diverse parameter settings as interval numbers could be a reasonable model of collective preferences that could be used to identify solutions that are close to the so-called "social RoI." From a different perspective, in [2] and [24], consensus search in group evolutionary multi-objective optimization has been addressed by using the interval outranking approach. In such papers, consensus is identified through the optimization of a measure of group satisfaction/dissatisfaction.…”

Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques

“…Many factors can lead to differences in the desired type of decision-making representation utilized within a model, including: differences in the identified relevant model context [5][6][7]; differences in stakeholder perspectives [8][9][10]; differing perspectives on the importance of rare events versus likely outcomes [11]. Keeney and Raiffa [12] describe decision analysis as a "prescriptive approach...to think hard and systematically about some important real problems" [12].…”

Integrated Artificial Intelligence in Data Science

“…In [19], a variant of the data particle geometrical divide was proposed to tackle classification problems in the presence of imbalanced data. The article [20] deals with group multi-objective optimization, and the specific case study is related to portfolio optimization. In [21], a DL-based approach combined with natural language description was discussed and incorporated into a smart surveillance system.…”