考虑天车约束的铝合金铸造用内导式液压缸生产调度

Production Scheduling of Inner-Guided Hydraulic Cylinders for Aluminum Alloy Casting Considering Crane Constraints

摘要

内导式液压缸是铝合金铸造机的关键部件，天车在该类液压缸生产过程中的作用不容忽视，为此建立了加工、搬运与装卸协同的柔性作业车间调度模型，并提出一种改进灰狼算法来求解包含完工时间、设备能耗以及天车工作量差异在内的多目标优化问题。该算法在解码阶段着重考虑了天车调度规则，利用佳点集理论和混合策略生成多样性与质量并重的初始种群；设计了自适应的狩猎权重系数和非线性变化的收敛因子，引入速度辅助项，提高算法的寻优能力，并通过外部存档保留优质解；提出 4 种邻域搜索结构以加强局部搜索。采用多种评价指标与其他算法对比，证明改进灰狼算法能有效求解该考虑天车运行约束的协同调度问题。

Abstract

Inner-guided hydraulic cylinders are key components of aluminum alloy casting machines, and cranes play an indispensable role in the production. To address this issue, this study establishes a flexible job shop scheduling model integrating processing, transportation, and loading/unloading operations. Furthermore, an improved grey wolf optimizer is proposed to solve the established multi-objective optimization problem for minimizing makespan, equipment energy consumption and crane workload variance. In the decoding stage, crane scheduling rules are explicitly considered. The initial population is generated using the theory of good point sets along with a hybrid strategy to balance diversity and quality. Additionally, an adaptive hunting weight coefficient, a nonlinear convergence factor, and a velocity-assisted term are introduced to enhance the optimization performance, while high-quality solutions are maintained in an external archive. Four neighborhood search structures are further developed to strengthen local search. Comparative experiments using multiple performance indicators show that the improved grey wolf optimizer demonstrates its superior in solving the cooperative scheduling problem considering crane operation constraints.

关键词

内导式液压缸 / 柔性作业车间 / 天车 / 多资源协同调度 / 灰狼优化算法 (GWO)

Key words

inner-guided hydraulic cylinder / flexible job shop / crane / multi-resource cooperative scheduling / grey wolf optimizer (GWO)

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基金资助

国家自然科学基金项目(51705384)

国家自然科学基金项目(52075401)