A support vector machine model for due date assignment in manufacturing operations

Doraid Dalalah, Udechukwu Ojiako*, Khaled A. Alkhaledi, Alasdair Marshall

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
26 Downloads (Pure)

Abstract

The relationship between product flow times and manufacturing system status is complex. This limits use of simple analytical functions for job shop manufacturing due date assigning, especially when dealing with orders involving multiple-resource manufacturing systems in receipt of random orders of different process plans. Our approach involves developing a Support Vector Machine classifier to articulate job shop manufacturing due date assigning in heterogeneous manufacturing environments. The emergent model allows not only for the complex relationships between flowtimes and manufacturing system status, but also for the prediction of random order flowtime of manufacturing systems with multiple resources. Our findings also suggest that service levels play a major role in negotiated due dates and eventual customer propensity to place manufacturing orders. In emphasizing negotiated due dates as against exogenous assigned due dates, the study focuses scholarly attention toward the need for participative, open and inclusive due date assignments.
Original languageEnglish
Pages (from-to)68-85
Number of pages18
JournalJournal of Industrial and Production Engineering
Volume40
Issue number1
Early online date8 Apr 2022
DOIs
Publication statusPublished - 2 Jan 2023

Keywords

  • due date
  • flow time
  • job shop
  • kernel function
  • optimization
  • support vector machine

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