research-article

Data-Driven Optimization of Order Admission Policies in a Digital Print Factory

Authors:

Krishnendu Chakrabarty,

Gary DispotoAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems (TODAES), Volume 20, Issue 2

Article No.: 21, Pages 1 - 25

https://doi.org/10.1145/2699836

Published: 02 March 2015 Publication History

Abstract

On-demand digital print service is an example of a real-time embedded enterprise system. It offers mass customization and exemplifies personalized manufacturing services. Once a print order is submitted to the print factory by a client, the print service provider (PSP) needs to make a real-time decision on whether to accept or refuse this order. Based on the print factory's current capacity and the order's properties and requirements, an order is refused if its acceptance is not profitable for the PSP. 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 automated learning-based order admission framework that can be embedded into an enterprise environment to provide real-time admission decisions for new orders. 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 multiclassifier model also outperforms a standalone regression model.

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Cited By

Masod MZakaria S(2024)Application of Artificial Intelligence in Printing Industry: Systematic Review2024 IEEE 12th Conference on Systems, Process & Control (ICSPC)10.1109/ICSPC63060.2024.10861924(107-112)Online publication date: 7-Dec-2024
https://doi.org/10.1109/ICSPC63060.2024.10861924
Retto Uhlmann ILedoux Takeda Berger Sde Souza Silva CFrazzon E(2023)Digital and smart production planning and controlDesigning Smart Manufacturing Systems10.1016/B978-0-32-399208-4.00022-2(311-343)Online publication date: 2023
https://doi.org/10.1016/B978-0-32-399208-4.00022-2
Santoso IPurnomo MSulianto AChoirun A(2021)Machine learning application for sustainable agri-food supply chain performance: a reviewIOP Conference Series: Earth and Environmental Science10.1088/1755-1315/924/1/012059924:1(012059)Online publication date: 1-Nov-2021
https://doi.org/10.1088/1755-1315/924/1/012059
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Index Terms

Data-Driven Optimization of Order Admission Policies in a Digital Print Factory
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 20, Issue 2

February 2015

404 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/2742143

Editor:
Naehyuck Chang
Korea Advanced Institute of Science and Technology, Korea

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Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 02 March 2015

Accepted: 01 November 2014

Revised: 01 November 2014

Received: 01 September 2014

Published in TODAES Volume 20, Issue 2

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Cited By

Masod MZakaria S(2024)Application of Artificial Intelligence in Printing Industry: Systematic Review2024 IEEE 12th Conference on Systems, Process & Control (ICSPC)10.1109/ICSPC63060.2024.10861924(107-112)Online publication date: 7-Dec-2024
https://doi.org/10.1109/ICSPC63060.2024.10861924
Retto Uhlmann ILedoux Takeda Berger Sde Souza Silva CFrazzon E(2023)Digital and smart production planning and controlDesigning Smart Manufacturing Systems10.1016/B978-0-32-399208-4.00022-2(311-343)Online publication date: 2023
https://doi.org/10.1016/B978-0-32-399208-4.00022-2
Santoso IPurnomo MSulianto AChoirun A(2021)Machine learning application for sustainable agri-food supply chain performance: a reviewIOP Conference Series: Earth and Environmental Science10.1088/1755-1315/924/1/012059924:1(012059)Online publication date: 1-Nov-2021
https://doi.org/10.1088/1755-1315/924/1/012059
Bertolini MMezzogori DNeroni MZammori F(2021)Machine Learning for industrial applicationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114820175:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114820
Hafsaoui ADandoush AUrvoy-Keller GSiekkinen MCollange D(2018)A fine-grained response time analysis technique in heterogeneous environmentsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2017.11.006130:C(16-33)Online publication date: 15-Jan-2018
https://dl.acm.org/doi/10.1016/j.comnet.2017.11.006

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