摘要
在分时电价背景下,制造成本和人力成本往往难以取得平衡:晚上电价较低但人员加班费较高,白天人员时薪较低而电价却较高。若将两个问题联合建模,则规模较大,不易求解。在实际应用中,较多采用先进行任务调度,再对人员排班的分阶段建模求解方法,但该求解思路难以保证得到较低成本的解。针对这一问题,提出一种代理模型的方法,以GA算法生成两个子问题的多组较优可行解作为训练样本,利用BP神经网络、深度学习及宽度学习系统分别拟合组合问题的代理模型,并采用BFGS法寻优。随着工件与工序数目的增加,本文所提供的自适应采样算法能有效解决维数灾问题。算例结果表明,新方法能得到明显优于利用遗传算法分阶段求解得到的结果,能为企业节省高达11.91%的电费与人力总成本。
Task and personnel scheduling problems are critical to production management.In the condition of time-of-use electricity tariffs,it is difficult to strike a balance between manufacturing and labor costs:electricity prices are lower at night but the salary for working-at-night is higher,while hourly wages are lower during the day but electricity prices are higher.If the two problems are jointly modeled,the resulting large-scale model is not easy to solve.In applications,it is usually addressed in the way that the task scheduling is firstly solved,and then the personnel scheduling is determined afterwards;however,it is difficult to guarantee a low-cost solution.To resolve this issue,this paper proposes a surrogate model method,which uses:1)multiple sets of relatively optimal feasible solutions of the two sub-problems generated by genetic algorithm,as training samples;and 2)BP neural network,deep learning and broad learning systems to respectively fit the surrogate model for the combination problem.Then,BFGS algorithm is used to search for optima.The proposed adaptive sampling algorithm can effectively simplify the problem in terms of dimensions as the number of jobs and processes increases.Results show that the new method can obtain significantly better results than those obtained by genetic algorithm.In addition,it can save up to 11.9%of the total cost of electricity and manpower for enterprises.
作者
赖信君
黄金晓
刘艺涵
张恪
毛宁
陈庆新
LAI Xinjun;HUANG Jinxiao;LIU Yihan;ZHANG Ke;MAO Ning;CHEN Qingxin(School of Electromechanical Engineering,Guangdong University of Technology,Guangzhou 510006,China;GAC AION New Energy Automobile Co.Ltd,Guangzhou 511434,China)
出处
《工业工程》
2024年第1期65-77,共13页
Industrial Engineering Journal
基金
国家自然科学基金资助项目(61973089)
广东省自然科学基金资助项目(2114050003127)。
关键词
代理模型
基于仿真的优化
宽度学习系统
变尺度法
自适应采样
surrogate model
simulation-based optimization
broad learning system
variable metric method
adaptive sampling
