eprintid: 16732
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/67/32
datestamp: 2023-12-19 03:23:15
lastmod: 2023-12-19 03:23:15
status_changed: 2023-12-19 03:06:47
type: conference_item
metadata_visibility: show
creators_name: Ngo, S.T.
creators_name: Jaafar, J.B.
creators_name: Aziz, I.A.
creators_name: Tong, G.T.
creators_name: Nguyen, G.H.
creators_name: Bui, A.N.
title: Different Approaches of Evolutionary Algorithms to Multiple Objective RCPSP
ispublished: pub
keywords: Ant colony optimization; Artificial intelligence; Combinatorial optimization; Decision making; Multiobjective optimization; Scheduling; Software design, Compromise programming; Multi-objective optimization problem; Multiple-objectives; NGSA-2; Planning process; Project planning; Project quality; RCPSP; Scheduling; Tasks assignments, Genetic algorithms
note: cited By 1; Conference of 7th International Conference on Big Data and Computing, ICBDC 2022 ; Conference Date: 27 May 2022 Through 29 May 2022; Conference Code:182616
abstract: Task assignments to the project members play a vital role in the project planning process. The project's quality, development time, and cost are criteria to indicate a success or failure project. These are affected by every single arrangement generated by the optimizer. Unfortunately, the task assignment problem is a multi-objective optimization problem (MOP) and complex scheduling problem in software development projects (MOP-PSP) and other domains. There are several approaches to MOP introduced in the literature. Our study aims to evaluate different evolutionary algorithms (EAs), including the compromise programming-based and Pareto frontier-based for the MOP-PSP. To make this work, we calibrate the parameters for the genetic algorithm (GA) developed in the previous research and design a new version of the ant colony algorithm (ACO) to solve the compromised problem. They are then compared with the NGSA-2, a Multi objective EA. Experiments show that compromise programming is an effective method for the decision-making process in practice. © 2022 ACM.
date: 2022
publisher: Association for Computing Machinery
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138368169&doi=10.1145%2f3545801.3545810&partnerID=40&md5=7278c8bbc529bf72d9d60d5f6d2b9c09
id_number: 10.1145/3545801.3545810
full_text_status: none
publication: ACM International Conference Proceeding Series
pagerange: 58-66
refereed: TRUE
isbn: 9781450396097
citation:   Ngo, S.T. and Jaafar, J.B. and Aziz, I.A. and Tong, G.T. and Nguyen, G.H. and Bui, A.N.  (2022) Different Approaches of Evolutionary Algorithms to Multiple Objective RCPSP.  In: UNSPECIFIED.