自动组合算法生成仓库订单优化方案，适配不同场景。
Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization
- 构建模块化算法库与上下文描述体系，支持按需组合。
- 在7个基准数据集上生成超百万条有效优化流水线。
- 适合研究者和从业者快速设计高效仓库调度方案。
人工拣选式仓库中的订单履行涉及物品分配、订单分批和拣货路径等多个相互关联的决策。虽然集成模型能捕捉这些决策间的交互，但实际系统常因组织边界、职责分离或数据不足而采用分解式方法。现有研究多针对孤立子问题或固定组合进行评估，缺乏通用机制来确定适用算法、组合成有效方案并评估性能。本文提出上下文感知的优化流水线合成框架（CASOP），包含：（1）常见订单履行问题的算法模块库；（2）用于描述仓库上下文与算法需求的语义数据与算法卡片；（3）将订单履行问题结构化的分类体系；（4）根据给定上下文识别适用算法并生成所有有效优化流水线的合成器；（5）评估所有生成流水线性能的评估器。我们在涵盖四类问题的7个基准实例集上验证框架，共生成1,063,044条有效流水线。该框架支持研究人员与实践者设计、自动生成并选择适用于特定场景的高性能算法流水线。相关软件开源，地址为 https://github.com/kit-dsm/ware_ops_pipes 与 https://github.com/kit-dsm/ware_ops_algos。
原文摘要 · Abstract (English)
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
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