An Intelligent Cooperative Approach Applied to Single Machine Total Weighted Tardiness Scheduling Problem

Abstract

In \u00a0this \u00a0research \u00a0work, \u00a0we \u00a0propose \u00a0an \u00a0intelligent \u00a0search \u00a0technique \u00a0called \u00a0genetic \u00a0simulated annealing algorithm (GASA) to obtain an approximate solution to the single machine total weighted tardiness job scheduling problem, which is a strong NP-hard. The developed approach is based on two metaheuristics: genetic algorithm (GA) and simulated annealing (SA) algorithm. In this context, when GA is exploited as a global search strategy to discover solution space, SA algorithm is used as a local search technique to enhance more \u00a0efficiently \u00a0the \u00a0visited \u00a0attractive \u00a0regions \u00a0to \u00a0improve \u00a0solution \u00a0quality. \u00a0Numerical \u00a0results \u00a0using \u00a0a \u00a0set \u00a0of benchmarks \u00a0have \u00a0shown \u00a0the \u00a0capability \u00a0of \u00a0the \u00a0proposed \u00a0method \u00a0to \u00a0produce \u00a0better \u00a0solutions \u00a0compared \u00a0to results given by some other recently literature works.