Implementation of "Algorithms for on-line order batching in an order picking warehouse" using Python

This seminar paper presents the implementation of Sebastian Henn's algorithm for solving On-Line Order Batching Problems (OOBP) in order picking warehouses, using Python. The primary objective is to enhance warehouse efficiency by reducing the total travel time of pickers through effective real-time order batching. The OOBP, characterized as NP-Hard due to its combinatorial nature, necessitates heuristic methods for practical solutions. Henn’s Iterated Local Search (ILS) algorithm, known for iteratively refining solutions by exploring neighboring solutions and applying perturbations to escape local optima, forms the core of this implementation.

The project involves designing a Python-based solution that can be deployed in real-world warehouse environments, handling dynamic order arrivals and providing efficient batching solutions. The implementation includes a robust core logic and a command-line interface (CLI) for user interaction, ensuring ease of use and adaptability for future integration into larger systems. Extensive testing validates the algorithm’s accuracy and efficiency, confirming its practical applicability in dynamic settings.
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Future enhancements are recommended to improve input validation, database integration, API development, and performance optimization, aiming to make the program more robust and scalable. This paper demonstrates the successful application of Henn's algorithm in a dynamic warehouse environment, providing a reliable solution for on-line order batching problems.