Optimal Decision Tree for Cycle Time Prediction and Allowance Determination

Abstract

The due-date quotation is a key performance indicator for managing customer orders which would influence customer acceptance and/or the future potential lateness penalty. The production cycle time and allowance time are added and used as the due date of order. The objective is to maximize the hit rate which is the percentage of the orders fulfilled within the time limit of quoted due date. Under the framework of supervised machine learning, we explore the new developments in feature selection and the optimal decision tree to predict cycle time by using mixed-integer optimization. Cycle time allowance could be added to the predicted cycle time or incorporated in an optimization problem as a managerial decision variable. Case studies are used to demonstrate the effectiveness of this approach, and their performances are comparable to the other popular ensemble tree approaches, such as random forests and gradient boosting. INDEX TERMS Allowance determination, cycle time prediction, gradient boosting, hit rate, local search in a decision tree, mixed-integer optimization, optimal decision tree, random forests.

Simulation Based Resource Optimization Using a Decision Tree Clearing Function

“…Lee and Jun use a tree model in a logistic regression tree [24] and propose a split model based on separability. Hsu [25] uses decision trees to predict cycle time in manufacturing and share that decision trees perform better than other machine learning algorithms.…”

Subscribing prediction of term deposit based on decision tree, random forest and support vector machine

“…By generating many decision trees, random forest can get better result. However, if one repeats too few, the variable importance rankings will be different [7]. Identically, It will overfit when the trees too many.…”