考虑模具约束和工装生产的铸造车间作业计划联合方法

Joint Scheduling of Casting Workshops Considering Mold Constraints and Tooling Production

摘要

在家居企业铸造车间中，砂芯加工和浇铸加工是铸造工艺的关键环节。二者相互关联，砂芯加工需要在浇铸之前完成，而浇铸又依赖于前期砂芯的加工进度。在实际生产中，由于二者作业计划不协调，一方的延误会直接影响到另一方的进度，从而导致整个订单的拖期，同时生产过程还存在模具约束。基于以上问题，本文构建了以最小化订单总拖期时间为目标的联合调度优化模型，旨在通过改进灰狼优化算法解决铸造车间中的作业调度问题，并提出一种基于知识的进化搜索算子以增强全局搜索能力，提升求解的精度和效率。基于由企业实际订单及工艺结构生成的多个基准算例，结合正交试验法进行参数优化。实验结果表明，所提改进算法在求解效率和优化质量上优于传统的调度算法；在减少订单拖期时间方面，改进算法相较于对比算法具有显著的优势，且在实际生产中的应用效果良好，能够有效减少作业过程中的不确定性，提高生产调度的精度和灵活性。

Abstract

In the foundry workshop of a home furnishing enterprise, sand core manufacturing and casting are two key operations with strong interdependence and precedence constraints: sand core manufacturing must be completed before casting, while casting depends on the progress of upstream sand core production. In actual production, poor coordination between these two production schedules often causes delays in one stage to propagate to the other, resulting in overall order tardiness. In addition, mold constraints in the production process further increase scheduling complexity. To address these issues, this paper constructs a joint scheduling optimization model with the objective of minimizing total order tardiness. An improved grey wolf optimization algorithm is proposed to solve the job scheduling problem in casting workshops, and a knowledge-based evolutionary search operator is introduced to enhance the global search capability and improve solution accuracy and efficiency. Multiple benchmark instances generated from practical orders and process structures are used to optimize the parameters through orthogonal experiments. Experimental results show that the proposed algorithm is superior to traditional scheduling algorithms in both solution efficiency and optimization quality. The improved algorithm significantly outperforms the benchmark algorithm in reducing order tardiness and performs well in practical production, effectively reducing operational uncertainty and improving the accuracy and flexibility of production scheduling.

关键词

作业计划联合 / 模具约束 / 组装约束 / 知识 / 灰狼优化算法 (GWO)

Key words

joint scheduling / mold constraint / assembly constraint / knowledge / grey wolf optimization algorithm (GWO)

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基金资助

国家自然科学基金项目(61973089)