Using Swarm Intelligence to Solve Bicriteria and Biobjective Machine Scheduling Problems

Safanah Y.  Faisal; Manal G.  Ahmed; Manal H.  Ibrahim; Faez H.  Ali

doi:10.24996/ijs.2025.66.11.26

Authors

Safanah Y. Faisal Mathematics Department, College of Science, Mustansiriyah University, Bagdad, Iraq
Manal G. Ahmed Mathematics Department, College of Science, Mustansiriyah University, Bagdad, Iraq
Manal H. Ibrahim Mathematics Department, College of Science, Mustansiriyah University, Bagdad, Iraq
Faez H. Ali Mathematics Department, College of Science, Mustansiriyah University, Bagdad, Iraq

DOI:

https://doi.org/10.24996/ijs.2025.66.11.26

Keywords:

Particle Swarm Optimization, Branch and Bound technique, Range of Lateness Jobs Times, Maximum Early Jobs Time, the Bicriteria Machine Scheduling Problems

Abstract

In this paper, solving the Bicriteria Machine Scheduling Problems (BCMSP) and Bi-Objective Machine Scheduling Problems (BOMSP) are proposed using Swarm Intelligence (AI) represented by Particle Swarm Optimization (PSO). The discussed BCMSP is a single machine with maximum early job time and range of late jobs (), and the BOMSP is . Comparison results of a simulation for exact (complete enumeration and Branch and Bound), heuristic method, and simulated annealing with proposed PSO has been made. The results prove the good efficiency of PSO in solving the two problems. All the results obtained by constructing simulation programs using MATLAB language.