title: Genetic programming for evolving due-date assignment models in job shop environments.
abstract: Due-date assignment plays an important role in scheduling systems and strongly influences the delivery performance of job shops. Because of the stochastic and dynamic nature of job shops, the development of general due-date assignment models (DDAMs) is complicated. In this study, two genetic programming (GP) methods are proposed to evolve DDAMs for job shop environments. The experimental results show that the evolved DDAMs can make more accurate estimates than other existing dynamic DDAMs with promising reusability. In addition, the evolved operation-based DDAMs show better performance than the evolved DDAMs employing aggregate information of jobs and machines.
authors.affiliation: Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand su.nguyen@ecs.vuw.ac.nz.
authors.forename: Su
authors.initials: S
authors.lastname: Nguyen
authors.forename: Mengjie
authors.initials: M
authors.lastname: Zhang
authors.forename: Mark
authors.initials: M
authors.lastname: Johnston
authors.forename: Kay Chen
authors.initials: KC
authors.lastname: Tan
mesh_terms: D000465:Algorithms; D003205:Computing Methodologies; D008962:Models, Theoretical; D010561:Personnel Staffing and Scheduling; D011381:Programming Languages