Open AccessLocal Search Algorithms for Multiobjective Scheduling Problem
Author(s) -
Adawiya Al-Nuaimi
Publication year - 2021
Publication title -
magallaẗ kulliyyaẗ al-rāfidayn al-ǧāmi'aẗ al-'ulūm/maǧallaẗ kulliyyaẗ al-rāfidayn al-ǧāmiʻaẗ li-l-ʻulūm
Language(s) - English
Resource type - Journals
eISSN - 2790-2293
pISSN - 1681-6870
DOI - 10.55562/jrucs.v36i2.255
Subject(s) - tardiness , simulated annealing , mathematical optimization , local search (optimization) , computer science , guided local search , single machine scheduling , hill climbing , scheduling (production processes) , tabu search , algorithm , job shop scheduling , schedule , search algorithm , genetic algorithm , mathematics , operating system
This paper presents local search algorithms for finding approximation solutions of the multiobjective scheduling problem within the single machine context, where the problem is the sum of the three objectives total completion time, maximum tardiness and maximum late work. Late work criterion estimates the quality of a schedule based on durations of late parts of jobs. Local search algorithms descent method (DM), simulated annealing (SA) and genetic algorithm (GA) are implemented. Based on results of computational experiments, conclusions are formulated on the efficiency of the local search algorithms.