Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Selecting jobs for a heavily loaded shop with lateness penalties
Computers and Operations Research
Job selection in a heavily loaded shop
Computers and Operations Research
Job shop scheduling for missed due-date performance
Computers and Industrial Engineering
Multiobjective evolutionary algorithm test suites
Proceedings of the 1999 ACM symposium on Applied computing
A tabu search algorithm for parallel machine total tardiness problem
Computers and Operations Research
Order acceptance with weighted tardiness
Computers and Operations Research
Computers and Industrial Engineering
Order acceptance using genetic algorithms
Computers and Operations Research
Block approach-tabu search algorithm for single machine total weighted tardiness problem
Computers and Industrial Engineering
Genetic programming heuristics for multiple machine scheduling
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A tabu search algorithm for order acceptance and scheduling
Computers and Operations Research
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
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Order acceptance and scheduling (OAS) is an important issue in make-to-order production systems that decides the set of orders to accept and the sequence in which these accepted orders are processed to increase total revenue and improve customer satisfaction. This paper aims to explore the Pareto fronts of trade-off solutions for a multi-objective OAS problem. Due to its complexity, solving this problem is challenging. A two-stage learning/optimising (2SLO) system is proposed in this paper to solve the problem. The novelty of this system is the use of genetic programming to evolve a set of scheduling rules that can be reused to initialise populations of an evolutionary multi-objective optimisation (EMO) method. The computational results show that 2SLO is more effective than the pure EMO method. Regarding maximising the total revenue, 2SLO is also competitive as compared to other optimisation methods in the literature.