计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (16): 302-310.DOI: 10.3778/j.issn.1002-8331.2305-0418

• 工程与应用 • 上一篇    下一篇

改进SHO求解自动化立体仓库能耗优化调度问题

刘凯,吉卫喜   

  1. 江南大学 机械工程学院,江苏 无锡 214122
  • 出版日期:2024-08-15 发布日期:2024-08-15

Improved SHO for Energy-Optimized Task Scheduling of Automated Warehouse

LIU Kai, JI Weixi   

  1. School of Mechanical Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2024-08-15 Published:2024-08-15

摘要: 针对带有完工时间约束的自动化立体仓库任务调度问题,提出一种货位再分配策略,对货物进行合理的货位分配,并产生任务先后约束,建立以堆垛机总能耗最低为优化目标的数学调度模型,并引入相应的惩罚函数,采用一种改进的海马优化算法(improved sea-horse optimizer,I-SHO)作为全局优化算法并进行求解。在原始海马优化算法(sea-horse optimizer,SHO)的基础上,融合混沌映射与对立学习策略,提高了初始解的质量。引入自适应t分布变异策略,避免陷入局部最优,并且设置修正机制,使解满足任务先后约束。引入混合种群寻优策略,进一步优化算法的搜索能力。最后通过实验进行验证,将海马优化算法、遗传算法(genetic algorithm,GA)和粒子群算法(particle swarm optimization,PSO)作为对比算法,验证了I-SHO在求解自动化立体仓库能耗优化调度问题上的有效性。

关键词: 任务调度优化, 能耗优化, 修正机制, 改进海马优化算法

Abstract: Aiming at the task scheduling problem in automated warehouse with completion time constraint, a bin reassignment strategy is proposed to allocate goods to reasonable bins and generate task sequence constraints. A mathematical scheduling model is established with the objective of minimizing the total energy consumption of the stacker crane, and a corresponding penalty function is introduced. An improved sea-horse optimizer (I-SHO) algorithm is employed as the global optimization algorithm to solve this problem. Based on the sea-horse optimizer (SHO), the chaotic Tent mapping and opposition-based learning strategy are integrated to improve the quality of initial solutions while ensuring the ergodic uniformity. An adaptive t-distribution mutation strategy is introduced to increase population quality and prevent convergence to local optima in the algorithm. Besides, a correction mechanism is set up to ensure that solutions meet task sequence constraints. A hybrid population optimization strategy is introduced to further improve population quality and search accuracy. Finally, through the verification of examples, the sea-horse optimizer, genetic algorithm (GA), and particle swarm optimization (PSO) are used as comparison algorithms, and the feasibility and effectiveness of the I-SHO algorithm in solving the energy optimization scheduling problem of automated warehouse are verified.

Key words: task scheduling optimization, energy consumption optimization, correction mechanism, improved sea-horse optimizer algorithm