Context-Aware Synthesis of Optimization Pipelines f...

Context-Aware Synthesis of Optimization Pipelines f... | AI Research

Key Takeaways

Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization Manual warehouses rely on a series of interconnected decisions—such as how to as...
Order fulfillment in manual picker-to-goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing.
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.
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.

Paper AbstractExpand

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 this https URL and this https URL . Keywords: Warehouse optimization, Algorithm selection, Pipeline synthesis, Order fulfillment

Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization
Manual warehouses rely on a series of interconnected decisions—such as how to assign items to storage, how to group orders, and how to route pickers—to fulfill customer requests efficiently. While integrated models can solve these problems simultaneously, real-world warehouses often require decomposed approaches due to organizational boundaries or limited data. Current research typically evaluates algorithms in isolation or uses fixed, manually selected combinations, leaving a gap in how to systematically determine which algorithms work best for a specific warehouse’s unique context. This paper introduces the Context-Aware Synthesis of Optimization Pipelines (CASOP) framework, which automates the design, construction, and evaluation of these optimization pipelines.

How the Framework Works

CASOP functions as a structured system for building tailored optimization strategies. It relies on five core components: a modular repository of algorithms, a taxonomy that categorizes warehouse subproblems, and a "pipeline synthesizer" that uses logic to identify which algorithms are compatible with a specific warehouse's requirements. To ensure the system understands the environment, it uses "semantic data and algorithm cards." These cards act as machine-readable descriptions that define the warehouse's layout, resources, and order characteristics, ensuring that only valid, applicable algorithms are selected for a given scenario.

Automated Pipeline Synthesis

Instead of relying on expert intuition to guess which combination of algorithms might perform well, CASOP automates the process. By using combinatory logic synthesis, the framework generates every possible valid pipeline that fits the constraints of a specific warehouse. Once these pipelines are synthesized, a built-in evaluator runs them to assess their performance. This allows researchers and practitioners to move beyond manual testing and instead explore a vast space of algorithmic configurations to find the most effective solution for their specific operational needs.

Performance and Validation

The authors demonstrated the framework’s capabilities by applying it to seven benchmark instance sets covering four distinct problem classes, including order batching, picker routing, and scheduling. Through this process, the framework successfully generated 1,063,044 valid optimization pipelines. This result highlights the framework's ability to handle complex, large-scale decision-making tasks that would be impossible to manage manually. By providing a standardized, open-source approach, the authors aim to improve the reproducibility and transferability of warehouse optimization research.

Practical Implications

The CASOP framework addresses the need for more flexible, context-aware decision-making in logistics. By shifting the focus from creating monolithic, "one-size-fits-all" models to synthesizing modular, context-specific pipelines, the framework helps bridge the gap between theoretical research and practical warehouse operations. The software is available as an open-source tool, providing a foundation for others to design, synthesize, and select high-performing algorithmic pipelines for their own warehouse environments.