A ENHANCED EVOLUTIONARY PROGRAMMING FOR JOB SHOP SCHEDULING PROBLEMS
Pan Quan-ke, Zhu Jian-ying · Shandong Nongye Daxue xuebao · 2005
An enhanced evolutionary programming is proposed for the job shop scheduling problems. The simulated annealing algorithm replaces the classical mutation operator to intensify the neighborhood search and to avoid premature convergence. The neighborhood search template that employs a critical path is adopted to decrease the search area and improve the efficiency of the exploration. Numerical simulation demonstrates that within the framework of the newly designed evolutionary programming, the NP-hard classic job-shop scheduling problem can be efficiently solved with higher quality.