A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization
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 boundar...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/10/4575 |
| _version_ | 1827692258594390016 |
|---|---|
| author | Eduardo Fernández Nelson Rangel-Valdez Laura Cruz-Reyes Claudia Gomez-Santillan |
| author_facet | Eduardo Fernández Nelson Rangel-Valdez Laura Cruz-Reyes Claudia Gomez-Santillan |
| author_sort | Eduardo Fernández |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:19:45Z |
| format | Article |
| id | doaj.art-21119ae625164e5fba71fec2dfcdba50 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:19:45Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-21119ae625164e5fba71fec2dfcdba502023-11-21T20:07:07ZengMDPI AGApplied Sciences2076-34172021-05-011110457510.3390/app11104575A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio OptimizationEduardo Fernández0Nelson Rangel-Valdez1Laura Cruz-Reyes2Claudia Gomez-Santillan3Dirección de Investigación y Posgrado, Universidad Autonoma de Coahuila, Saltillo 26200, MexicoDivisión de Estudios de Posgrado e Investigación, Cátedras CONACyT—Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, Los Mangos 89440, MexicoDivisión de Estudios de Posgrado e Investigación, Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, Los Mangos 89440, MexicoDivisión de Estudios de Posgrado e Investigación, Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, Los Mangos 89440, MexicoThis 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.https://www.mdpi.com/2076-3417/11/10/4575group multi-objective optimizationmulti-criteria classificationproject portfolio selectioninterval mathematicsevolutionary algorithms |
| spellingShingle | Eduardo Fernández Nelson Rangel-Valdez Laura Cruz-Reyes Claudia Gomez-Santillan A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization Applied Sciences group multi-objective optimization multi-criteria classification project portfolio selection interval mathematics evolutionary algorithms |
| title | A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization |
| title_full | A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization |
| title_fullStr | A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization |
| title_full_unstemmed | A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization |
| title_short | A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization |
| title_sort | new approach to group multi objective optimization under imperfect information and its application to project portfolio optimization |
| topic | group multi-objective optimization multi-criteria classification project portfolio selection interval mathematics evolutionary algorithms |
| url | https://www.mdpi.com/2076-3417/11/10/4575 |
| work_keys_str_mv | AT eduardofernandez anewapproachtogroupmultiobjectiveoptimizationunderimperfectinformationanditsapplicationtoprojectportfoliooptimization AT nelsonrangelvaldez anewapproachtogroupmultiobjectiveoptimizationunderimperfectinformationanditsapplicationtoprojectportfoliooptimization AT lauracruzreyes anewapproachtogroupmultiobjectiveoptimizationunderimperfectinformationanditsapplicationtoprojectportfoliooptimization AT claudiagomezsantillan anewapproachtogroupmultiobjectiveoptimizationunderimperfectinformationanditsapplicationtoprojectportfoliooptimization AT eduardofernandez newapproachtogroupmultiobjectiveoptimizationunderimperfectinformationanditsapplicationtoprojectportfoliooptimization AT nelsonrangelvaldez newapproachtogroupmultiobjectiveoptimizationunderimperfectinformationanditsapplicationtoprojectportfoliooptimization AT lauracruzreyes newapproachtogroupmultiobjectiveoptimizationunderimperfectinformationanditsapplicationtoprojectportfoliooptimization AT claudiagomezsantillan newapproachtogroupmultiobjectiveoptimizationunderimperfectinformationanditsapplicationtoprojectportfoliooptimization |