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Updated: Sep 10, 2025
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Order Lot Sizing: Insights from Lattice Gas-Type Model
Margarita Miguelina Mieras1, Tania Daiana Tobares1, Fabricio Orlando Sanchez-Varretti1
1San Rafael Regional Faculty, Institute of Applied Physics (INFAP), CONICET, National Technological University (UTN), Gral. Urquiza 314, San Rafael, Mendoza 5600, Argentina.
Entropy (Basel, Switzerland)
|August 28, 2025
Summary
This study applies statistical physics lattice-gas models to supply chain order lot-sizing. The novel framework uses physics principles to find optimal ordering strategies and measure decision robustness.
Area of Science:
- Interdisciplinary research bridging statistical physics and supply chain management.
- Application of lattice-gas models from statistical mechanics to operations research.
Background:
- Classical order lot-sizing often uses deterministic or heuristic methods.
- These traditional approaches may not capture the probabilistic and dynamic nature of supply chain decisions.
Purpose of the Study:
- To introduce a novel framework applying statistical physics to the order lot-sizing problem.
- To develop a new method for identifying optimal ordering strategies in supply chains.
Main Methods:
- Mapping inventory decisions to lattice-gas models in statistical physics.
- Utilizing thermodynamic potentials and free energy minimization for optimization.
- Employing analytical tools from statistical mechanics.
Main Results:
- Developed a framework representing order placements as particles on a lattice.
- Identified optimal ordering strategies through free energy functional minimization.
- Introduced configurational entropy as a measure of decision variability and robustness.
Conclusions:
- The lattice-gas model effectively captures key features of the order lot-sizing problem.
- The framework offers a robust theoretical foundation for supply chain optimization.
- Suggests potential extensions to multi-item systems and time-varying demand.
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