制造系统通用作业计划与蚁群算法优化

分析了基本作业方式的“通用-特例”关系,提出了全面研究上述基本作业方式的通用作业计划的概念,建立了通用作业计划非连接图模型及基于蚁群算法的优化方法。算例研究假定一制造系统包含开放作业、异顺序作业、混杂流水作业三种作业方式,将其按照通用作业计划方式采用蚁群算法进行优化,所得的近优加工流程时间显著优于采用穷举法对三种作业方式分别优化所得的最优解。     

基于多Agent的多任务协作时间调度算法研究

为了合理安排多任务合作中的时间,设计了基于多Agent通信的多任务协作时间调度算法,实现了额外代价最小化和窗口时间内完成任务最大化。其中,算法的求解过程利用了0-1背包问题的最优值和最优解;对0-1背包问题的求解利用了改进的编码和进化的遗传算法,提高了运算的准确性。多任务协作时间调度算法的应用,有效地最小化了系统的额外代价,实现了多Agent系统的优化。     

具有随机加工时间和机器故障的流水车间调度

不同的流水车间往往具有不同的生产方式,为提高调度方案对不同生产方式下随机因素的处理能力,重点考虑了2种生产方式下3种不同情况的随机调度。针对这3种情况,以最小化最大完工时间为目标,研究了具有随机加工时间和随机机器故障的置换流水车间调度问题,提出了处理不同生产方式下随机因素的3种计算方法,通过预测机器的期望故障时刻来计算每个任务的完工时间。采用启发式规则和遗传算法相结合的方法,确定出最佳调度方案,并进行了实验分析和比较。     

Refinery scheduling with varying crude: A deep belief network classification and multimodel approach

In model‐based refinery scheduling, the varying composition of the crude being refined is a major challenge, especially for those reaction processes. A classification based, multimodel approach is proposed to handle the frequently varying crude. The idea is to build a scheduling model for each type of feed crude, and the type can be determined using an online classifier. The recently emerged deep belief network is introduced to develop the classifier, which provides more accurate classification than the traditional neural network. The proposed method is demonstrated through modeling a fluidized catalytic cracking unit (the mostly affected by varying crude), and then the scheduling of a refinery that was carefully simulated to mimic the actual operation of a refinery in northern China. The results reveal that the multimodel approach is effective in handling varying crude. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2525–2532, 2014     

Integration of scheduling and control for batch processes using multi‐parametric model predictive control

Integration of scheduling and control results in Mixed Integer Nonlinear Programming (MINLP) which is computationally expensive. The online implementation of integrated scheduling and control requires repetitively solving the resulting MINLP at each time interval. (Zhuge and Ierapetritou, Ind Eng Chem Res. 2012;51:8550–8565) To address the online computation burden, we incorporare multi‐parametric Model Predictive Control (mp‐MPC) in the integration of scheduling and control. The proposed methodology involves the development of an integrated model using continuous‐time event‐point formulation for the scheduling level and the derived constraints from explicit MPC for the control level. Results of case studies of batch processes prove that the proposed approach guarantees efficient computation and thus facilitates the online implementation. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3169–3183, 2014     

浅谈GPS技术在露天矿开采中的应用

介绍了GPS技术在露天矿开采中地表测量、开采设备的作业优化控制、卡车运输安全监控及边坡位移自动监测四个方面的应用特点、原理、优势,阐述了GPS技术在露天矿开采中应用存在的问题和解决办法,以推广GPS技术的应用。     

基于仿真和正交试验的优化调度研究

针对生产系统多点协调调度问题,提出了一种基于仿真和正交试验的优化调度方法。在动态生产环境中,为了支持快速仿真调度分析,以模型元素和控制逻辑分离的原则为指导,采用面向对象方法,实现了基于模块化控制逻辑的快速仿真建模。以仿真模型作为系统目标求解函数,各种决策参数作为系统输入变量,通过正交试验优化仿真实验,确定最佳调度决策参数组合,将其应用于生产系统的调度控制。以某研究所的数控车间为例,验证了该调度方法的可行性和有效性。     

求解作业车间调度问题的一种改进遗传算法

为克服传统遗传算法解决车间作业调度问题的局限性,综合遗传算法和局部搜索的优点,提出一种改进的遗传算法。为基于工序的编码提出了一种新的POX交叉算子。同时,为克服传统遗传算法在求解车间作业调度问题时的早熟收敛,设计了一种子代交替模式的交叉方式,并运用局部搜索改善交叉和变异后得到的调度解,将提出的改进遗传算法应用于MuthandThompson基准问题的实验运行,显示了该算法的有效性。     

采用多个体交叉的遗传算法求解作业车间问题

为改善目前求解Job-Shop问题中的遗传算法的性能，加快搜索最优调度解的速度，首先分析了目前Job-Shop问题自身的求解难点和遗传算法的特点，并借鉴生物学的依据，提出了多个体交叉的遗传算法。该算法在遗传过程中采用多个体遗传算子，充分利用个体的优良性质，对不可行调度解根据多个体修补原则进行修正，可保证遗传后代的合法性和多样性，能够加快最优调度解的搜索时间。仿真结果充分证明了该算法的有效性。     

Determining optimal combination of genetic operators for flow shop scheduling

Genetic algorithms (GAs) have gained wide research and applications in production scheduling fields, but the efficiency and effectiveness of a GA significantly depend on its parameters and operators. In contrast to the rich research on determination of optimal and adaptive parameters, little research has been done on determining optimal combination of genetic operators. Different from the traditional way by trial and error, this paper presents a novel and systematical approach based on ordinal optimisation (OO) and optimal computing budget allocation (OCBA) technique to determine optimal combination of genetic operators for flow shop scheduling problems. Simulation results show that the proposed methodology is able to determine optimal combination of genetic operators and simultaneously to provide a good solution with reasonable performance evaluation for scheduling problem.