Knowledge base for batch-processing machine scheduling research

This knowledge base for batch-processing machine scheduling research provides a comprehensive literature data base comprising 425 research articles. 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).
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.