Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization

2026-05-13 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

The Context-Aware Synthesis of Optimization Pipelines (CASOP) framework automates the construction and evaluation of optimization pipelines for order fulfillment in manual picker-to-goods warehouses. It integrates a modular repository of 22 algorithms for item assignment, order batching, picker routing, and scheduling with semantic data and algorithm cards, and a problem taxonomy. A pipeline synthesizer identifies applicable algorithms and composes valid optimization pipelines, which are then assessed by a pipeline evaluator. The framework was demonstrated on 7 benchmark instance sets across four problem classes (SPRP, SPRP-SS, OBRP, OBRSP), generating 1,063,044 valid pipelines. CASOP assists in designing, synthesizing, and selecting high-performing algorithmic solutions for warehouse operations.

Key takeaway

For Operations Professionals or Research Scientists tasked with optimizing order fulfillment in diverse warehouse environments, CASOP offers a critical tool. It enables you to move beyond manual algorithm selection by automatically synthesizing and evaluating valid, context-aware optimization pipelines. You should explore this framework to systematically design and benchmark strategies, particularly when facing heterogeneous warehouse characteristics, varying instance sizes, or multiple performance objectives like distance, makespan, or on-time rate. This ensures robust, high-performing solutions tailored to specific operational contexts.

Key insights

CASOP systematically synthesizes and evaluates context-aware optimization pipelines for warehouse order fulfillment using semantic descriptions.

Principles

• Decompose complex warehouse problems into manageable subproblems.
• Contextualize algorithm applicability using semantic data and algorithm cards.
• Automate pipeline generation and evaluation for systematic optimization.

Method

CASOP uses data cards to describe warehouse context and algorithm cards for requirements. A taxonomy structures problems into subproblems. A synthesizer maps applicable algorithms and composes valid pipelines, which a pipeline evaluator then assesses.

In practice

• Use CASOP's open-source software for warehouse optimization.
• Define warehouse context via data cards for automated algorithm selection.
• Evaluate diverse algorithmic pipelines to identify best-performing configurations.

Topics

• Warehouse Optimization
• Order Fulfillment
• Algorithm Selection
• Pipeline Synthesis
• Context-Aware Optimization
• Manual Warehouses

Code references

• kit-dsm/ware_ops_pipes
• kit-dsm/ware_ops_algos

Best for: AI Scientist, Research Scientist, Operations Professional, Automation Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.