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Genetic algorithms with multi-parent recombination
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Order Assignment and Scheduling in a Supply Chain
Operations Research
Genetic optimization of order scheduling with multiple uncertainties
Expert Systems with Applications: An International Journal
Intelligent production control decision support system for flexible assembly lines
Expert Systems with Applications: An International Journal
A multi-modal immune algorithm for the job-shop scheduling problem
Information Sciences: an International Journal
A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
Applied Soft Computing
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Improving productivity and firm performance with enterprise resource planning
Enterprise Information Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pig procurement plan considering pig growth and size distribution
Computers and Industrial Engineering
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This paper investigated a multi-objective order allocation planning problem in make-to-order manufacturing with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization (MOMO) process, a Monte Carlo simulation technique and a heuristic pruning technique, is developed to tackle this problem. The MOMO process, combining a NSGA-II optimization process with a tabu search, is proposed to provide Pareto optimal solutions. Extensive experiments based on industrial data are conducted to validate the proposed model. Results show that (1) the proposed model can effectively solve the investigated problem by providing effective production decision-making solutions; (2) the MOMO process has better capability of seeking global optimum than an NSGA-II-based optimization process and an industrial method.