%X 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.
%K 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
%R 10.1145/3545801.3545810
%D 2022
%J ACM International Conference Proceeding Series
%L scholars16732
%O 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
%I Association for Computing Machinery
%A S.T. Ngo
%A J.B. Jaafar
%A I.A. Aziz
%A G.T. Tong
%A G.H. Nguyen
%A A.N. Bui
%T Different Approaches of Evolutionary Algorithms to Multiple Objective RCPSP
%P 58-66