Minimizing the Total Completion Time and Total Earliness Time Functions for a Machine Scheduling Problem Using Local Search Methods

  • Faez Hassan Ali Deparment of Mathematics, College of Science, Mustansiryah University, Baghdad, Iraq
  • Aseel Aboud Jawad Deparment of Mathematics, College of Science, Mustansiryah University, Baghdad, Iraq
Keywords: Machine Scheduling Problem, Multiple Objective Functions, Simulated Annealing, Particle Swarm Optimization

Abstract

In this paper we investigate the use of two types of local search methods (LSM), the Simulated Annealing (SA) and Particle Swarm Optimization (PSO), to solve the problems ( ) and . The results of the two LSMs are compared with the Branch and Bound method and good heuristic methods. This work shows the good performance of SA and PSO compared with the exact and heuristic methods in terms of best solutions and CPU time.

Published
2020-05-17
How to Cite
AliF. H., & JawadA. A. (2020). Minimizing the Total Completion Time and Total Earliness Time Functions for a Machine Scheduling Problem Using Local Search Methods. Iraqi Journal of Science, 126-133. https://doi.org/10.24996/ijs.2020.SI.1.17