Knowledge base for batch-processing machine scheduling research
Description
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 the batch-scheduling literature classification matrix is provided, but also a detailed description of the classification schemes and a detailed documentation of the applied methodology: - Batch-scheduling literature classification matrix 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 This file describes the Scheduling Problem Classification Scheme (SPCS) and the Scheduling Article Classification Scheme (SACS). - Methodology for the development of the knowledge base This file documents the applied methodology. The knowledge base complements the research article “Serial-batch scheduling: a systematic review and future research directions” by Wahl et al. (2023).