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Scaling Industrial Logistics: Tackling Multi-Batching Problems via Sequential Solving

Authors Emmanuelle Dietz , Guillaume Povéda , Karl Henning , Clara Buire

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Planning and scheduling
  • Theory of computation → Constraint and logic programming
  • Applied computing → Industry and manufacturing
  • Applied computing → Transportation
Keywords
  • Mixed Integer Linear Programming
  • Constraint Programming
  • Discrete Optimization
  • Declarative Problem Solving
  • Industrial Logistics
  • Scalability
  • Constraint Answer Set Programming

Metrics

Abstract

Logistic optimization frequently involves complex routing decisions bound by tight numerical constraints such as vehicle capacities. This paper addresses a real-world industrial multi-batching problem where products must be routed between distributed sites. The objective is to determine optimal routes, travel frequencies, and packing configurations at minimum cost. The problem corresponds to a minimum cost flow problem coupled a bin packing problem. We investigate direct formalizations, decompositions, and scalable sequential approaches across three base technologies: Mixed-Integer Linear Programming, Constraint Programming, and Constraint Answer Set Programming. Our contributions are threefold: we propose a direct formalization of the problem, additional distinct approaches that scale for an industrial use case, and finally an empirical evaluation. By comparing these approaches we highlight the most effective configurations. Results suggests that a three-step approach provides the best results: combining MILP for flow routing, a greedy bin packing and CP for refinement.

Cite As Get BibTex

Emmanuelle Dietz, Guillaume Povéda, Karl Henning, and Clara Buire. Scaling Industrial Logistics: Tackling Multi-Batching Problems via Sequential Solving. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 20:1-20:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.CP.2026.20

Author Details

Emmanuelle Dietz
  • Airbus Central R&T, Hamburg, Germany
Guillaume Povéda
  • Airbus Central R&T, Toulouse, France
Karl Henning
  • Airbus Central R&T, Hamburg, Germany
Clara Buire
  • Airbus Central R&T, Toulouse, France

Supplementary Materials

References

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