Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization

2026-06-25

AI Summary

This work addresses the tightly coupled decisions in "person-to-goods" warehouse order fulfillment—such as item allocation, order batching, and picker routing—and the absence of a general mechanism to automatically compose and evaluate optimization algorithms tailored to specific operational contexts. To bridge this gap, the authors propose CASOP, a novel framework that enables, for the first time, context-aware automatic synthesis of end-to-end warehouse optimization pipelines. CASOP integrates a modular algorithm library, semantically annotated algorithm cards, a problem taxonomy, a pipeline synthesizer, and an automated evaluator to construct and validate customized solutions for given warehouse settings. Empirical evaluation across seven benchmark datasets yields 1,063,044 valid pipelines, and the accompanying open-source toolkit provides researchers and practitioners with robust support for high-performance pipeline design and selection.

Abstract

Order fulfillment in manual picker-to-goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing. While integrated models capture interactions between these decisions, practical warehouse systems often require decomposed approaches due to organizational boundaries, differing responsibilities, or limited data availability. Existing studies primarily evaluate algorithms for isolated subproblems or fixed subproblem combinations for specific warehouse settings, but lack a general mechanism to determine applicable algorithm configurations, compose them into valid solution pipelines, and assess their performance. With Context-Aware Synthesis of Optimization Pipelines (CASOP), we propose a framework for constructing and evaluating context-specific optimization pipelines and apply these to order fulfillment. The framework comprises: (1) a modular repository of algorithms for common order fulfillment problems; (2) semantic data and algorithm cards to describe warehouse context and algorithm requirements; (3) a taxonomy that structures order fulfillment problems into relevant subproblems; (4) a pipeline synthesizer that identifies applicable algorithms for a given warehouse context and composes all valid optimization pipelines; and (5) a pipeline evaluator that assesses all resulting pipelines. We demonstrate the framework on 7 benchmark instance sets covering four problem classes, resulting in 1,063,044 valid pipelines. The framework supports researchers and practitioners in designing, automatically synthesizing, and selecting valid, high-performing algorithmic pipelines for warehouse operations. The software is open-source and available at https://github.com/kit-dsm/ware_ops_pipes and https://github.com/kit-dsm/ware_ops_algos. Keywords: Warehouse optimization, Algorithm selection, Pipeline synthesis, Order fulfillment

Problem

• Warehouse optimization
• Algorithm selection
• Pipeline synthesis
• Order fulfillment

Innovation

• Pipeline synthesis
• Context-aware optimization
• Algorithm selection
• Warehouse optimization
• Order fulfillment