Data · dataset · 2024
Knowledge base for batch-processing machine scheduling research
Listed in Teesside University Research Data Repository
This knowledge base for batch-processing machine scheduling research provides a comprehensive literature data base comprising 425 research articles.
Description
These articles are classified according to two classification schemes: The first classification scheme is an adapted and extended Scheduling Problem Classification Scheme (SPCS) to comprehensively specify batch scheduling problems within the three fields “A - Machine characteristics”, “B - Job and processing characteristics”, and “C - Objective system”.
The second classification scheme is a completely new Scheduling Article Classification Scheme (SACS), consisting of five fields: “D - Theoretical insights”, “E - Model type”, “F - Solution method”, “G - Experimental evaluation”, and “H - Application case”. The core of the knowledge base is a binary matrix indicating which article has which characteristics (represented by attributes embedded in a hierarchical structure of categories and fields).
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To ensure transparency and reproducibility, not only batch-scheduling literature classification matrices are provided, but also a detailed description of the classification schemes (along with visualizations) and a detailed documentation of the applied methodology. The knowledge base complements the research article “Serial-batch scheduling: a systematic review and future research directions” by Gahm et al. (under review).
List of files: - Batch-scheduling literature classification matrices.xlsx This file includes the complete classification of 425 research articles on batch scheduling according to the SPCS and SACS. - Classification schemes for batch-processing machine scheduling research.pdf This file describes the Scheduling Problem Classification Scheme (SPCS) and the Scheduling Article Classification Scheme (SACS). - The SPCS at a glance.pdf - The SACS at a glance.pdf - Methodology for the development of the knowledge base.pdf This file documents the applied methodology.
Links
Where it is published
- DOI doi.org/10.17632/7cv58py5hk.4 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Data mining and knowledge discovery · Earth & Environmental Science
Provenance · 1 source records, 7 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/7cv58py5hk.4 | 16 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:460502 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Knowledge Discovery'] |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |