Exploring the development trajectory of single-machine production scheduling

Abstract The Single-Machine Scheduling Problem (SMSP) serves as the cornerstone of scheduling theory. Almost all developments in production planning and control were initially introduced and tested within single-machine production settings. Exploring the literature on SMSPs illuminates the entire development trajectory of scheduling theory. This study employs the Main Path Analysis (MPA) for a systematic review that is the first of its kind in the literature on SMSPs. By analyzing 2904 articles, the main path and key branches that highlight the most significant documents in the development trajectory as well as the specifics in different development stages of the field are identified. The literature is analyzed in four stages of development, following a chronological order. Within this framework, the focus of the studies, key technologies, and characteristics of each period are discussed. Cluster and keyword analysis are employed alongside MPA to identify and explore the predominant themes in the field, including SMSPs with deterioration and/or learning effects, solution methods for SMSPs with various constraints, solving SMSPs integrated with maintenance-related variables and constraints, advanced solution methods and constructive heuristics for minimizing delays, as well as agent-based methods for single-machine scheduling. The review outcomes consist of directions for future research based on the development trajectories of SMSPs.

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Publication Details

Journal
Annals of Operations Research
Published
2024-11-25
DOI
https://doi.org/10.1007/s10479-024-06395-6
Citations
3
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
1.05

Funders

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article

Exploring the development trajectory of single-machine production scheduling

Tzu‐Ting Huang, Pourya Pourhejazy, Kuo‐Ching Ying
3 citations
Annals of Operations Research
Scheduling and Optimization Algorithms
1.05
article

Exploring the development trajectory of single-machine production scheduling

Tzu‐Ting Huang, Pourya Pourhejazy, Kuo‐Ching Ying
article en
3 citations

Abstract

Abstract The Single-Machine Scheduling Problem (SMSP) serves as the cornerstone of scheduling theory. Almost all developments in production planning and control were initially introduced and tested within single-machine production settings. Exploring the literature on SMSPs illuminates the entire development trajectory of scheduling theory. This study employs the Main Path Analysis (MPA) for a systematic review that is the first of its kind in the literature on SMSPs. By analyzing 2904 articles, the main path and key branches that highlight the most significant documents in the development trajectory as well as the specifics in different development stages of the field are identified. The literature is analyzed in four stages of development, following a chronological order. Within this framework, the focus of the studies, key technologies, and characteristics of each period are discussed. Cluster and keyword analysis are employed alongside MPA to identify and explore the predominant themes in the field, including SMSPs with deterioration and/or learning effects, solution methods for SMSPs with various constraints, solving SMSPs integrated with maintenance-related variables and constraints, advanced solution methods and constructive heuristics for minimizing delays, as well as agent-based methods for single-machine scheduling. The review outcomes consist of directions for future research based on the development trajectories of SMSPs.

Annals of Operations Research
National Taipei University of Technology (TW), Taiwan Semiconductor Manufacturing Company (Taiwan) (TW), UiT The Arctic University of Norway (NO)
Universitetet i Tromsø
Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Scheduling and Optimization Algorithms
1.05
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Exploring the development trajectory of single-machine production scheduling — Tzu‐Ting Huang, Pourya Pourhejazy, et al. · Annals of Operations Research (2024) | TGRS Research Map | TGRS