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The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
configurational entropyexhaustive enumeration of stateslattice gas modeloptimizationorder lot-sizing problem

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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.