A hybrid intelligent model for order allocation planning in make-to-order manufacturing

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.

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► The mathematical model for the order allocation planning problem is established.
► The problem considers a complicated environment with various realistic features.
► A hybrid intelligent optimization model is developed to tackle the problem.
► The proposed model integrates memetic algorithm and Monte Carlo simulation method.
► The proposed model performs better than NSGA-II-based optimization process.