Statistics: principles and methods
Statistics: principles and methods
An algorithm for solving the job-shop problem
Management Science
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Routing and scheduling in a flexible job shop by tabu search
Annals of Operations Research - Special issue on Tabu search
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
A fast taboo search algorithm for the job shop problem
Management Science
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Weighted Modified Due Date Rule for Sequencing to Minimize Weighted Tardiness
Journal of Scheduling
Applying the clonal selection principle to find flexible job-shop schedules
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automated synthesis of analog electrical circuits by means ofgenetic programming
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Simulation optimization for industrial scheduling using hybrid genetic representation
Proceedings of the 40th Conference on Winter Simulation
Evolving human-competitive reusable 2D strip packing heuristics
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A particle swarm optimization algorithm for flexible jobshop scheduling problem
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Discrepancy search for the flexible job shop scheduling problem
Computers and Operations Research
Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Generating meta-heuristic optimization code using ADATE
Journal of Heuristics
A new dispatching rule based genetic algorithm for the multi-objective job shop problem
Journal of Heuristics
Scheduling flow shops with multiple processors: a flexible ANN-fuzzy simulation approach
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
A two-stage hybrid memetic algorithm for multiobjective job shop scheduling
Expert Systems with Applications: An International Journal
A simulation-based scheduling system for real-time optimization and decision making support
Robotics and Computer-Integrated Manufacturing
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Computers and Industrial Engineering
An indirect approach to the three-dimensional multi-pipe routing problem
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Supervised learning linear priority dispatch rules for job-shop scheduling
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Flexible job shop scheduling using a multiobjective memetic algorithm
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Rule-based scheduling in wafer fabrication with due date-based objectives
Computers and Operations Research
Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling
Applied Soft Computing
The automatic generation of mutation operators for genetic algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem
International Journal of Computer Applications in Technology
Computers and Industrial Engineering
Generating dispatching rules for semiconductor manufacturing to minimize weighted tardiness
Proceedings of the Winter Simulation Conference
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
An improved multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem
International Journal of Computer Applications in Technology
Journal of Intelligent Manufacturing
Reactive scheduling in a job shop where jobs arrive over time
Computers and Industrial Engineering
Computers and Industrial Engineering
Genetic programming for evolving due-date assignment models in job shop environments
Evolutionary Computation
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
How to design effective priority rules: Example of simple assembly line balancing
Computers and Industrial Engineering
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We solve the multi-objective flexible job-shop problems by using dispatching rules discovered through genetic programming. While Simple Priority Rules have been widely applied in practice, their efficacy remains poor due to lack of a global view. Composite dispatching rules have been shown to be more effective as they are constructed through human experience. In this paper, we evaluate and employ suitable parameter and operator spaces for evolving composite dispatching rules using genetic programming, with an aim towards greater scalability and flexibility. Experimental results show that composite dispatching rules generated by our genetic programming framework outperforms the single dispatching rules and composite dispatching rules selected from literature over five large validation sets with respect to minimum makespan, mean tardiness, and mean flow time objectives. Further results on sensitivity to changes (in coefficient values and terminals among the evolved rules) indicate that their designs are robust.