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.
Highlights
The due-date quotation is a key performance index in competitive manufacturing and service environments [21]
For due date management (DDM) policy, one important performance index is the hit rate which is the percentage of the orders that are fulfilled within the time limit of quoted due date
Dunn [14] formulated the search of the optimal decision tree as a mixed-integer optimization (MIO) problem and circumvented the suboptimal solution of the traditional greedy approaches
Summary
The due-date quotation is a key performance index in competitive manufacturing and service environments [21]. A hybrid model of adaptive fuzzy c-means algorithm and parallel back propagation network was proposed to predict the cycle time and achieved better performance than backpropagation network and multivariate linear regression in terms of the mean absolute deviation and the cycle time variance Besides these methods, the other popular approaches include fuzzy logic [11], hybrid approach [10], LSTM [36], production simulation [19], and queueing theory [13]. The rest of the paper is organized as follows: Section II describes the methodology, Section III presents the illustrative examples and results, and Section IV includes the conclusion and further directions
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