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A Support Vector Machine model for due date assignment in manufacturing operations

A Support Vector Machine model for due date assignment in manufacturing operations
A Support Vector Machine model for due date assignment in manufacturing operations

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.

Due date, flow time, job shop, kernel function, optimization, support vector machine
68-85
Dalalah, Doraid
ca514595-915f-4173-95a8-3ddb10b0ccce
Ojiako, Udechukwu
39f57398-8b7b-422c-9186-6a87c75e0f8f
Alkhaledi, Khaled
a91e4a35-b16c-4ac7-bf9a-ef7c5d0b2387
Marshall, Alasdair
93aa95a2-c707-4807-8eaa-1de3b994b616
Dalalah, Doraid
ca514595-915f-4173-95a8-3ddb10b0ccce
Ojiako, Udechukwu
39f57398-8b7b-422c-9186-6a87c75e0f8f
Alkhaledi, Khaled
a91e4a35-b16c-4ac7-bf9a-ef7c5d0b2387
Marshall, Alasdair
93aa95a2-c707-4807-8eaa-1de3b994b616

Dalalah, Doraid, Ojiako, Udechukwu, Alkhaledi, Khaled and Marshall, Alasdair (2023) A Support Vector Machine model for due date assignment in manufacturing operations. Journal of Industrial and Production Engineering, 40 (1), 68-85. (doi:10.1080/21681015.2022.2059791).

Record type: Article

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.

Text
flow time prediction-rev4 - 22-oct-2021 - FULL - Accepted Manuscript
Restricted to Repository staff only until 8 April 2023.
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More information

Accepted/In Press date: 20 March 2022
e-pub ahead of print date: 8 April 2022
Published date: 2 January 2023
Additional Information: Publisher Copyright: © 2022 Chinese Institute of Industrial Engineers.
Keywords: Due date, flow time, job shop, kernel function, optimization, support vector machine

Identifiers

Local EPrints ID: 456071
URI: http://eprints.soton.ac.uk/id/eprint/456071
PURE UUID: 1698c3b6-a289-4348-984c-570b59fa874b
ORCID for Alasdair Marshall: ORCID iD orcid.org/0000-0002-9789-8042

Catalogue record

Date deposited: 25 Apr 2022 16:51
Last modified: 02 Feb 2023 02:41

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Contributors

Author: Doraid Dalalah
Author: Udechukwu Ojiako
Author: Khaled Alkhaledi

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