环形穿梭车系统的设计与调度

在自动化立体仓库等多种行业的生产中，环形穿梭车的应用非常广泛。采用最优化原理、动态规划模型等方法针对环形穿梭车系统的调度问题进行研究。对于不计穿梭车实际长度穿梭车调度问题，根据总完工时间最小的目标思想，以下一个窗口编号为决策变量，以系统中最迟完成搬运任务的总时间为目标函数，建立基于最优化原理的动态规划模型，当穿梭车数量分别为3、6、9时，最短完工时间分别为12699、6785.7、2540.7；对于考虑穿梭车长度的情况下，在动态规划模型中加入穿梭车长度等参数，当穿梭车数量分别为3、6、9时，求得最短完工时间分别为14195、7093.9、5199.7。综合考虑总完工时间、循环次数、拥堵时间、有效搬运距离比四个因素的几种运行效率的评价结果分别为：0.149、0.127、0.100、0.148、0.131、0.145。     

基于模糊决策与进化算法的生产设备布局优化

为解决复杂情况下制造系统的生产设备布局优化问题,提出了一种将模糊决策与进化算法相结合的设备布局优化方法。进一步完善了优化模型,优化目标包括总成本最小、设备相邻要求最大化和面积利用率最大化等优化目标;其中总成本最小目标考虑了物料搬运成本,设备重置导致的设备拆装、移动成本,生产停工造成的产能损失成本。该方法考虑了用户对于成本、利用率及相邻性要求等存在的满意度、优先度等模糊情况,基于模糊决策理论,对多目标优化模型进行了模糊化处理,设计了模糊适应度函数,用以根据用户的优先关系评价pareto解集。基于求解模型的特点,对多目标进化算法的染色体编码方式与交叉、变异等遗传操作方式进行改进,以提高求解该模型的实用性与效率。最后以实际案例的优化结果证明了该方法的有效性。     

考虑元素烧损和仓储成本的特种铝合金配料优化

配料计算是特种铝合金熔炼的重要准备工序,直接影响产品的最终性能.为提高产品质量和配料效率,降低原料和仓储物流成本,建立考虑元素烧损和旧料循环利用等因素的特种铝合金配料优化模型.针对该模型的目标多样性和非线性等特点,设计以投料量和投料时间为决策变量的实数编码规则,提出一种基于第3代非支配遗传算法并融入分布式估计策略的多目标优化算法用于求解该模型.通过基于真实生产数据的仿真实验进行模型和算法验证.实验结果表明,所提出模型能够有效地解决特种铝合金配料优化问题,与传统的多目标优化算法相比,所提出求解算法能够获得更优的结果.     

逆向物流多产品两阶段再生产网络鲁棒优化

考虑不同周期内逆向物流回收数量的不确定性和再生产设备的生产能力约束,将逆向物流回收数量、再生产成本和市场需求作为不确定参数,以成本最小为目标,建立了包含生产成本、设备运作成本、库存成本在内的多周期多产品两阶段逆向物流网络鲁棒优化模型。通过算例验证了模型的有效性。     

一种识别复杂流程参数的优化方法

对化工流程中的关键工艺参数进行辨识,对认识过程操作规律具有重要意义。本文以TDI生产过程为对象,发展了一种辨识流程参数的优化方法。所要辨识的工艺参数F_A和F_B属于循环物流中的参数,故以模拟初值与实际计算值平方差最小为优化目标,F_A和F_B为决策变量,结合2个等式约束方程建立优化模型。通过求解出满足目标最优化的解,使隐藏在流程中的特征参数即F_A和F_B被挖掘出来。同时对模拟结果进行了分析,找出了决策变量与环境因素之间的关系。此优化方法对解决类似隐性参数辨识问题以及分析复杂化工流程具有指导意义。     

面向炼钢—连铸调度过程的两阶段优化模型与算法

以某钢厂多台转炉及多台精炼炉对多台连铸机的复杂生产线为研究对象,针对其调度过程涉及多设备、多目标、多约束等调度要素,且离散和连续变量混杂,采用常规建模方法难以满足现场对调度的精度及排产速度的需求问题,提出一种新型的两阶段优化建模方法.首先,证明了炉次从炼钢到连铸总等待时间最小的调度目标与该炉次在转炉开始作业时间最大是等价的事实,并以离散型的设备变量为决策变量,以转炉开始作业时间最大为动态规划最优指标,建立设备指派多阶段动态规划基本方程和设备指派优化模型;然后,以炉次在设备开始作业时间的连续型变量为决策变量,并将准时开浇的非线性调度指标转化成与之等价的线性优化目标,以在同一台连铸机上浇铸的炉次之间断浇的时间间隔最小、钢包在设备之间的冗余等待时间最小、提前与滞后理想开浇时间的时间间隔最小为目标,建立线性规划冲突解消模型.工业实验表明所提出两阶段优化建模方法在求解速度与求解精度均满足现场要求.     

基于人工蜂群算法的轨道交通列车行车间隔优化

轨道交通运营组织作为轨道交通运营企业管理的核心,在降低企业运营成本、提升服务水平和旅客出行效率方面起着非常重要的作用。提出一种基于人工蜂群（ABC）优化算法的列车行车间隔优化策略,在考虑运营企业和旅客各自利益的基础上,以列车发车间隔为决策变量,建立旅客平均候车时间最小和列车等候时间最大的双目标非线性规划模型。采用ABC算法对模型进行优化求解,结合京津城际铁路某日不同时段客流基础数据进行仿真,实例验证了所提算法和模型的有效性。     

低层人工拣货仓库货位优化问题研究

在现代物流总成本中,仓储成本占很大比重,合理的储位分配是提高仓储拣选效率,降低仓储成本的核心所在。通过对低层人工拣货仓库的出库过程分析,同时考虑商品热销程度、商品之间的关联关系及货架位置等影响因素,设计了基于社区划分的货位优化算法。首先,根据商品关联性构建无向有权网络,并采用社区划分算法进行多次划分;然后,以社区为单位存放到货架,并通过调整阶段补齐货架;最后根据评估指标从多个方案中选出最优方案。评估指标根据缩短行走路径、缓解堵塞和减少拣选人员数量3个优化目标构建。实验结果表明,提出的货位优化算法无论是时间消耗还是货位摆放方案质量,与其他对比方案相比均具有显著优势。     

烟草辅料入库系统的仿真与优化

本文基于AutoMod仿真平台,应用进程交互式仿真算法,对卷烟生产自动化辅料入库系统进行可视化三维建模与仿真,研究了穿梭车的调度,利用AutoStat对穿梭车数量和速度参数进行统计分析和优化研究,为生产实际提供了可靠的设计依据.     

关联变量分组的分解多目标进化算法研究

含有大规模决策变量的优化问题是当前多目标进化算法领域中的研究热点和难点之一。在解决大规模变量问题时,目前的进化算法并没有寻找决策变量之间的关联信息,而都只是将所有变量视为一个整体来进行优化。但随着优化问题中决策变量的增多,“变量维度”成为瓶颈,从而影响算法的性能。针对上述问题,提出关联变量分组策略,通过识别决策变量间内在的关联信息把关联变量分配到同组中,将复杂高维变量的优化问题分解为简单低维的子问题来求解。该策略通过增加关联变量分配到同组中的概率来使算法尽可能地保留变量之间的关联性,减少分组后子问题间的依赖性,从而提高子问题最优解的质量并最终获得最佳的Pareto最优解集。将该算法在标准测试函数上进行变量扩展后再进行仿真对比实验,采用性能指标对算法的收敛性和多样性进行对比分析。实验结果表明,该算法在解决大规模变量的多目标优化问题中,随着决策变量维度的增加,比经典的多目标进化算法NSGA-II、MOEA/D以及RVEA具有更佳的收敛和更好的分布性能,所求得的Pareto解集质量更高。     

Free balancing for a shuttle-based storage and retrieval system

Shuttle-based storage and retrieval systems (SBS/RSes) – a type of automated storage and retrieval system (AS/RS) – have recently been developed to increase throughput capacity. An SBS/RS can increase throughput by using multiple shuttles as storage and retrieval machines (SRMs), while traditional AS/RSes are limited because they use a single stacker crane as an SRM to handle multiple jobs. However, when setting up an SBS/RS, a significant investment is required to integrate a shuttle for each tier in the storage construction. To lower costs, a reduction in the number of shuttles is an adequate solution if the SBS/RS can maintain the designated throughput. However, this could also lead to operational problems. Namely collision prevention and workload balances could be affected because each shuttle would be able to travel to any tier for pickups and drop-offs. Thus, shuttle position control is important when the system operates with a mismatch between the number of shuttles and tiers. Ideally, free balancing should be achieved. When free balanced, each shuttle's position is monitored to prevent collisions or blockages, as well as to make sure jobs are evenly assigned and the required throughput is maintained. This paper suggests system controls to prove the effectiveness of free balancing, and it runs a simulation analysis to verify the system with the suggested controls. As a result, free balancing shows better performances in terms of throughput and utilization compared to a basic system control. Specifically, in our case, free balancing can achieve targeted throughput with less number of shuttles.     

Simulation based performance analysis of an autonomous vehicle storage and retrieval system

In this paper, simulation based performance analysis of an autonomous vehicle storage and retrieval system (AVS/RS) is presented. AVS/RS represents relatively a new system for automated unit-load (UL) storage based on autonomous vehicle (AV) technology. This new system consists of AVs, lifts and storage racks. It is implemented at scores of facilities in Europe. The aim of this study is to find out near optimum values for number of AVs and lifts in the system that result in high performance under various pre-defined storage rack configuration scenarios. The storage rack of the system is defined in terms of number of aisles, bays and tiers. We consider seven rack configuration scenarios, ten and nine vehicles per zone and two arrival rates – 450 and 500 pallets/h. The performance measures considered are: average cycle time of storage and retrieval transactions, average utilizations of AVs and lifts. The simulation model of the system is developed using ARENA 12.0, a commercial software. We find that having a large number of lifts (zones) and a large footprint yield better performance.     

Determination of an optimal configuration of operating policies for direct-input-output manufacturing systems using the Taguchi method

Some modern manufacturing systems have workstations directly integrated with a centralized storage and handling system for work-in-process. We consider a Direct-Inlet-Output manufacturing system(DIOMS) which has a number of machine centers placed along a built-in automated storagejretrieval system(AS/RS). The storagdretrievai(S/R) machine handles parts placed on pallets for the machine centers located at either one or both sides of the AS/RS.

This paper investigates the operational aspect of DIOMS by the Taguchi method. Four operating policies including input sequencing control. dispatching rule for the S/R machine, machine center-based part type selection rule, and storage assignment policy are treated as design factors. For the performance characteristics, man flow time and throughput are adopted. The number of machine centers, the number of part types, demand rate, processing time and the rate of each part type. vertical and horizontal speed of the SIR machM and the size of a local buffer in the machine centers are considered as noise factors in generating various DIOMS environments. A robust design experiment with inner and outer orthogonal arrays are conducted by computer simulation, and an optimal configuration of operating policies is presented which consists of a combination of the level of each design factor. The validity of the optimal configuration is investigated by comparing its SN ratios with those obtained by an experiment with fiill factorial designs.     

自动化立体仓库货位分配与作业调度集成优化

针对动态提高单载具堆垛机式自动化立体仓库拣选效率的问题,文中提出了一种基于共享货位存储与动态订单拣选策略下的货位分配与作业调度集成优化方法。将动态移库优化扩展到仓库的整个拣选生命周期,建立以双指令循环下堆垛机拣选任务所需的总作业时间最短为评价目标的数学模型,提出了一种基于K-Medoids聚类的粒子群优化(Particle Swarm Optimization,PSO)算法,用K-Medoids算法通过产品与订单的相关性进行初始货位的聚类分析,筛除劣质解的货位范围,并在K-Medoids聚类算法生成的解类簇基础上获得精确解。实验结果表明,考虑动态移库可以使仓库拣选效率提高20%,且该算法与传统PSO算法相比求解时间下降66%左右。     

不确定环境下的岸桥集卡协调调度耦合模型建立与求解

集装箱码头系统是一个由多个子系统组成的复杂的生产系统,系统内资源的调度也是非线性的复杂问题,同时涉及多种多样的不确定性因素。从不确定性的角度出发,主要考虑码头装卸设备运行参数的概率分布,研究岸桥和集卡之间的协调调度问题。采用多学科变量耦合优化设计的方法,同时考虑了集装箱任务的时间窗约束,分别建立集卡分派子模型和集卡配置子模型。并将完工时刻和集卡数量作为公用设计变量连接两个子模型,建立了协调调度耦合模型。选取上海港某码头的数据编写算例,在Visual Studio 2012环境下调用Gurobi4.0求解该耦合模型,反复迭代计算后得出最优的集卡分派方案相对于最初的调度方案,总延误时间成本下降了90.69%,集卡数量下降了30.76%,验证了本模型的有效性和实用性。     

基于DPDK的高速存储I/O优化方法

网络I/O在Redis存储过程中是限制存储性能的关键因素,而默认参数或人工参数配置会制约存储性能。针对参数配置不当导致存储吞吐性能下降及时延较高的问题,提出一种存储I/O优化方法GTS。考虑各阶段参数对存储性能的影响,在DPDK的优化原理基础上通过分析处理特性,采用分层模型策略实现对存储性能预测,从而寻找出最优参数调优方案。实验结果表明,与默认参数相比,GTS方法能够有效提升存储吞吐量,且在写密集下较ATH算法具有更低的时延。     

Hierarchy of processing equipment configuration design problems for multiproduct chemical plants

A procedure for performing one stage of designing multiproduct chemical plant—definition of the processing equipment configuration is proposed. At this stage, a three-level hierarchical structure of problems is formed—at the upper level, the parameters of the operational mode of the chemical engineering system that ensures the desired output of each product are found; at the middle level, the size and number of equipment units and methods of product processing for all processing stages are chosen; at the lower level, technological and mechanical design of individual pieces of equipment (machines) are performed. The decomposition of the processing equipment design problem into three problems described above reduces the original mixed integer nonlinear programming problem to one nonlinear programming problem and a set of integer programming problems, where the number of integer programming problems is equal to the number of processing stages. The reduced problems can be solved using available optimization methods, which considerably reduces the solution time and improves the quality of design solutions. Basic information links between the problems of different hierarchical levels are determined, general problem statements and an algorithm for the simultaneous solution of the upper and middle level problems is proposed. By way of example, the problem of selection and design of a mechanical agitator for a vertical bulk-capacity storage is considered.     

Multi-objective reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm

A genetic algorithm approach is used to solve a multi-objective discrete reliability optimization problem in a k dissimilar-unit non-repairable cold-standby redundant system. Each unit is composed of a number of independent components with generalized Erlang distributions arranged in a series–parallel configuration. There are multiple component choices with different distribution parameters available for being replaced with each component of the system. The objective of the reliability optimization problem is to select the best components, from the set of available components, to be placed in the standby system in order to minimize the initial purchase cost of the system, maximize the system MTTF (mean time to failure), minimize the system VTTF (variance of time to failure) and also maximize the system reliability at the mission time. Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed GA approach.     

基于混合蚁群算法的冷链电动汽车车辆路径问题

用电动汽车进行冷链物流配送符合绿色物流的发展趋势。针对电动汽车冷链配送需消耗更多能源以维持低温环境,而电动汽车续驶里程短、充电时间长,致使运营成本高的现象,思考了电动汽车配送中的冷链车辆路径问题（REVRP）。考虑电动汽车能耗特点和社会充电站的充电需求,构建了以总配送成本最小为优化目标的线性规划模型,而目标函数由固定成本和可变成本构成,其中可变成本包含运输成本和制冷成本。模型考虑容量约束和电量约束,并设计混合蚁群（HACO）算法对其进行求解,其中重点设计了适合社会充电站的转移规则以及4种局部优化算子。在改进Solomon基准算例的基础上,形成了小规模和大规模两个算例集,并通过实验比较了蚁群（ACO）算法和局部优化算子的性能。实验结果表明,在小规模算例集中,传统ACO算法与CPLEX求解器均能找到精确解,而ACO算法在运算时间方面可节省99.6%;而在大规模算例集中,与ACO算法相比,结合4种局部优化算子的HACO算法的平均优化效率提升了4.45%。所提算法能够在有限时间内得出电动汽车REVRP的可行解。