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.

本面向批处理机器调度研究的知识库（knowledge base）收录了425篇学术文献，构建了一套完备的文献数据库。所有文献均依据两套分类体系完成归类：
第一套分类体系为经改造与扩展的调度问题分类方案（Scheduling Problem Classification Scheme, SPCS），用于全面规范“机器特性（A - Machine characteristics）”、“工件与加工特性（B - Job and processing characteristics）”及“目标体系（C - Objective system）”三大领域内的批调度问题。
第二套分类体系为全新研发的调度文献分类方案（Scheduling Article Classification Scheme, SACS），涵盖五大研究领域：“理论洞察（D - Theoretical insights）”、“模型类型（E - Model type）”、“求解方法（F - Solution method）”、“实验评估（G - Experimental evaluation）”及“应用案例（H - Application case）”。
该知识库的核心为二元分类矩阵，用于标注各文献所具备的特征，这些特征以嵌入于分类层级结构与领域中的属性形式呈现。
为保障研究的透明性与可复现性，本知识库不仅提供批调度文献分类矩阵，还附带分类体系的详细说明（含可视化内容）以及所采用研究方法的完整文档。
本知识库可作为Gahm等人所撰写、目前处于投稿待审阶段的学术论文《Serial-batch scheduling: a systematic review and future research directions》的补充资料。

文件清单：
- 批调度文献分类矩阵.xlsx：本文件包含425篇批调度研究文献依据SPCS与SACS完成的完整分类结果。
- 批处理机器调度研究分类体系.pdf：本文件详细阐述了调度问题分类方案（SPCS）与调度文献分类方案（SACS）。
- 调度问题分类方案速览.pdf（The SPCS at a glance.pdf）
- 调度文献分类方案速览.pdf（The SACS at a glance.pdf）
- 知识库构建方法论.pdf（Methodology for the development of the knowledge base.pdf）：本文件记录了本知识库构建所采用的研究方法。