Abstract
This study develops an integrated mathematical formulation for hybrid manufacturing, incorporating product platforms, multi-period lot-sizing, and fuzzy demand to address demand uncertainty and product variation challenges. Applying the fuzzy set theory, demand is modeled as fuzzy demand, providing a more effective approach to handling uncertainty than deterministic methods. The model includes a substitution strategy to accommodate dynamic changes in variant requirements, enhancing production flexibility. Additionally, based on the developed fuzzy optimization model, the fuzzy model is employed to train a regression model that predicts costs as a function of anticipated confidence levels. The proposed model is validated through a case study, demonstrating its effectiveness in minimizing total production costs and efficiently managing multiple product variants across different planning periods. The findings offer adaptive production planning strategies for manufacturers facing fluctuating demand and high product variety.
| Original language | English |
|---|---|
| Pages (from-to) | 243-252 |
| Number of pages | 10 |
| Journal | Manufacturing Letters |
| Volume | 44 |
| DOIs | |
| State | Published - Aug 2025 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2025 The Author(s)
Keywords
- Confidence level
- Fuzzy demand
- Fuzzy set theory
- Hybrid manufacturing
- Multi period
- Product platform
- Variant substitution
ASJC Scopus subject areas
- Mechanics of Materials
- Industrial and Manufacturing Engineering