An outranking-based general approach to solving group multi-objective optimization problems

Fernández, Eduardo; Olmedo, Rafael

doi:10.1016/j.ejor.2012.10.023

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…Thus, the best consensus can be found in the current stage of the preferences and judgments of the group members. According to Fernandez & Olmedo (2013), interactive approaches often assume that collective preferences are transitive and comparable when the former solution is replaced by other solutions that appear to be better. For example, when a financing system policy is acceptable to most participants, additional participants tend to adjust their value systems to the new environment.…”

Section: Coordination Mechanismmentioning

confidence: 99%

How Do Tech Companies Finance in the Context of Sci-Tech Finance?

Li,

Yusoff

2024

ChnStd

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The sci-tech finance aims to cultivate high-value-added industries and enhance the economy's overall competitiveness. A good match of science, technology, and finance helps to accelerate the growth of tech companies and regional economies. This article focuses on the operation mechanism of the financing system of tech companies. The driving mechanism, coordination mechanism, and balancing mechanism are conducive to technology enterprises to obtain more funds and improve the utilization rate of funds. Three models reveal how the financing system operates depending on the specific situation and the process of policy intervention in the sci-tech finance environment. These findings offer theoretical guidelines for policymakers to improve their innovation process and remove possible obstacles by motivating financing institutions.

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Section: Coordination Mechanismmentioning

confidence: 99%

How Do Tech Companies Finance in the Context of Sci-Tech Finance?

Li,

Yusoff

2024

ChnStd

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“…Fernandez and Olmedo in [33] search for good consensus solutions by maximizing, (respectively, minimizing), the number of group members who are satisfied (resp. dissatisfied) with the current solution in the decision variable space.…”

Section: An Overview Of Gdm-mop Literaturementioning

confidence: 99%

“…With Condition (B), Fernandez and Olmedo in [33] and Fernandez et al in [48] rejected "majority dictatorship", which neglects the importance of the intensity of disagreement and degrades fairness and equity concerning minorities.…”

Section: Toward a Maximum Consensusmentioning

confidence: 99%

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

et al. 2021

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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.

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“…Through an iterative process, the subsystems attempt to reach a consensus in the form of a feasible solution that is not necessarily optimal [3]. In group decision making, the search for best agreement is not limited to AiO optimal solution but seeks a solution with a high measure of collective satisfaction [10].…”

Section: Introductionmentioning

confidence: 99%

“…In some studies, the collection assumes the form of a network. In group decision making, Fernandez and Olmedo [10] represent a complex decision problem as a network of MOPs with each decision maker assigned to a node of the network. Konnov [21] uses a network to model spatially distributed elements of complex systems encountered in transportation, communications, and economics.…”

Section: Introductionmentioning

confidence: 99%

Introducing multiobjective complex systems

Dietz

Klamroth

Konstantin

et al. 2020

European Journal of Operational Research

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This article focuses on the optimization of a complex system which is composed of several subsystems. On the one hand, these subsystems are subject to multiple objectives, local constraints as well as local variables, and they are associated with an own, subsystemdependent decision maker. On the other hand, these subsystems are interconnected to each other by global variables or linking constraints. Due to these interdependencies, it is in general not possible to simply optimize each subsystem individually to improve the performance of the overall system. This article introduces a formal graph-based representation of such complex systems and generalizes the classical notions of feasibility and optimality to match this complex situation. Moreover, several algorithmic approaches are suggested and analyzed.

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An outranking-based general approach to solving group multi-objective optimization problems

Cited by 10 publications

References 25 publications

How Do Tech Companies Finance in the Context of Sci-Tech Finance?

How Do Tech Companies Finance in the Context of Sci-Tech Finance?

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

Introducing multiobjective complex systems

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