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Related Experiment Video

Updated: Jul 19, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Generalized data model for real-world capacitated lot-sizing problems with linked lot sizes and backorders.

Michael Simonis1, Stefan Nickel1

  • 1Department of Economics and Management, Karlsruhe Institute of Technology, Kaiserstraße 89, Building 05.20-4A, Karlsruhe 76133, Germany.

Data in Brief
|August 9, 2023
PubMed
Summary

This study introduces the first data model for the Multi-Level Capacitated Lot-Sizing Problem with Linked Lot Sizes and Backorders (MLCLSP-L-B) in tablet manufacturing. The dataset supports research by providing standardized, real-world manufacturing process data.

Keywords:
Lot-SizingMLCLSP-L-BProduction planningTablets manufacturing processes

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Industrial Engineering

Background:

  • Capacitated lot-sizing problems are crucial in manufacturing for optimizing production and inventory.
  • Existing models often lack the granularity to handle multi-level production and linked lot-size constraints with backorders.
  • Tablet manufacturing presents unique challenges due to complex multi-level processes and demand patterns.

Purpose of the Study:

  • To establish the first comprehensive data model for the Multi-Level Capacitated Lot-Sizing Problem with Linked Lot Sizes and Backorders (MLCLSP-L-B).
  • To provide a standardized dataset compatible with both single- and multi-level lot-sizing problems.
  • To facilitate research and development in optimizing tablet manufacturing processes.

Main Methods:

  • Development of a novel data model comprising nine interconnected tables.
  • Detailed description of each table's fields and data types for clarity and usability.
  • Inclusion of anonymized, real-world data from four single-level packaging and five multi-level tablet manufacturing processes.

Main Results:

  • A fully specified data model for MLCLSP-L-B is presented.
  • The dataset is compatible with single- and multi-level lot-sizing problem instances.
  • Anonymized real-world data from diverse manufacturing scenarios is shared.

Conclusions:

  • The introduced data model and dataset provide a valuable resource for the lot-sizing research community.
  • This work enables more accurate modeling and optimization of complex manufacturing systems, particularly in the pharmaceutical industry.
  • The standardized template promotes reproducibility and further investigation into lot-sizing strategies.