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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.

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Catalogue records · 1

Topics

Provenance · 1 source records, 7 field assertions
SourceKeyLast seenRaw
Teesside University Research Data Repositoryoai:data.mendeley.com/7cv58py5hk.416 h agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[field].anzsrc:field:460502mapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Knowledge Discovery']
concepts[field].local:field:earth-environmentalmapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
descriptionsource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0/metadata/dc/description
licensesource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0/metadata/dc/rights
publication_datesource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
titlesource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0/metadata/dc/title