5. Optimization of Order-Admission Policies

A mass-customization enterprise offers personalized manufacturing services. Once an order is submitted to the enterprise by a client, the EIS needs to make a real-time decision on whether to accept or refuse this order. Based on the enterprise current capacity, and the order’s properties and requirements, an order is refused if its acceptance is not profitable for the enterprise. The order is accepted with the most appropriate due date in order to maximize the profit that can result from this order. We have developed an intelligent order-admission framework that provides admission decisions in real-time for new orders using machine-learning and decision-integration techniques. The framework consists of three classifiers: Support Vector Machine (SVM), Decision Tree (DT), and Bayesian Probabilistic Model (BPM). The classifiers are trained by history orders and used to predict completion status for new orders. A decision integration technique is implemented to combine the results of the classifiers and predict due dates. Experimental results derived using real factory data from a leading print-service provider and Weka open source software show that the order completion-status prediction accuracy is significantly improved by the decision-integration strategy. The proposed multi-classifier model also outperforms a stand-alone regression model.