整车物流网络规划集成优化模型研究

优化整车物流系统配送网络可降低成本,为此,建立了综合考虑运输规模效应、库存控制策略、设施和服务质量等决策因素的整车物流网络规划集成优化模型。给出了一种流预测和遗传算法相结合的求解方法。在遗传算法中采用二进制码和自然码组合的编码方式,使得每个合法染色体都代表一种可行物流网络结构。为了解决适应度函数中的工厂与分销中心之间的运输成本计算困难的问题,提出了流预测算法,用于确定产品在工厂、集货中心和分销中心构成的凹费用流网络中的最优运输路径,进而获得适应度函数值。最后,通过仿真试验验证了优化模型的正确性和算法的有效性。     

考虑运输-生产-库存集成的精益供应链模型

研究了考虑运输、生产、库存集成的精益供应链模型.模型从成本的角度集成了供应链的生产、供应和分销环节,并且将采购与分销运输进行合并.将运输路径长度,运输频次作为决策变量,建立了一个多变量的非线性规划模型.首先采用改进蚁群算法求出最优运输路径和路径长度,然后再求出最优运输频次和运输批量.最后结合案例验证模型,结果表明模型简单、实用,对生产实践具有较好的指导意义.     

跨国供应链环境下的批量订货模型

针对当前供应链批量订货模型研究的不足,提出了跨国供应链环境下的非线性混合整数规划批量订货模型。与以往研究不同,除了传统的决策变量——订货次数外,模型的决策变量还包括转移价格和运输成本分配因子,目标函数为跨国供应链税后利润最大化。由于模型属于NP难题,求解不便,根据问题的具体特点将其转化成为若干个线性规划模型,然后利用MATLAB的优化工具箱进行了数据分析,计算结果表明,转让价格和运输费用分配的调整对跨国供应链最优订货批量及税后利润具有显著影响。     

基于改进蚁群算法的汽车混流装配调度模型求解

针对某汽车总装车间混流装配过程涉及大量人工以及人机协同操作而导致工位过载、整车装配质量无法得到保证的问题,建立了瓶颈选装工位负载平衡化、考虑换装与提前作业时间的加工滞后次数最小化的分层序列双目标优化模型,同时设计了一种改进蚁群算法。该算法在信息素全局更新以及概率转移规则过程中,使用一种特定启发式函数,并更改迭代过程中最优解的评价方法。仿真对比实验结果表明,该算法在优化目标函数过程中的收敛速度、收敛精度、最优解质量等方面均优于传统蚁群算法和对比遗传算法,验证了模型和算法的有效性。此外,该算法还可反向求解加工滞后次数为零时的计划生产节拍,具有一定的生产指导意义。     

一类多品种小批量混流制造系统作业组织模式与工件排序

为解决考虑搬运时间的混流制造系统的资源优化与调度问题,对作业组织模式与工件排序进行了深入研究。建立了以设备停机次数、工件搬运次数、搬运设备数量、搬运次数均衡程度作为第一、第二、第三和第四优化目标,以生产周期作为约束的多目标决策模型,利用该模型选择作业组织模式并确定最优排序方案。分别研究了用于求解该多目标决策模型的各分目标和约束:为不同类型批量工件建立加工时间模型;设计开始和结束搬运时间优化模型;给出平行移动模式的设备停机次数算法。开发了考虑搬运时间的混流制造系统软件程序,并设计了所需搬运设备情况的全局优化算法。通过分析和求解企业订单数据验证了所提方法的有效性。     

基于遗传模拟退火算法的大件公路运输路径选择优化

针对大件公路运输路径选择优化难题,以最小化运输成本为目标,在考虑带配送时间窗、客户服务时限、车辆超载惩罚、车辆载重限制、车辆容积限制的基础上,构建了大件公路运输路径选择优化模型,并提出改进遗传模拟退火算法对此类问题进行优化.该算法首先基于满足车辆容积和承载量的两层编码方式产生多个初始种群,然后各种群之间通过相互竞争实现优秀个体的迁移共享,最终搜索到最优解.最后,通过实例仿真验证了该算法解决此类特殊运输问题的有效性,并通过与其他算法进行比较,证明了该算法的先进性,为大件公路运输路径选择问题提供了新的解决思路.     

用于供水系统直接优化调度的蚁群改进算法

在城市供水系统中建立了多目标在线直接优化调度模型,并对影响优化调度的各方面因素进行了系统的分析和挑选。使用化多为一的乘除法,将该多目标决策问题转化为单目标问题求解,提出了使用乘法形式的罚函数将模型中的约束函数转化为目标函数。采用蚁群算法求解调度模型。为了更好地得到全局最优解,对算法进行了改进,加入了更多的决策点,实现蚁群算法的二进制编码方法,并采用单只最优蚂蚁更新路径上的外激素值、外激素值限定在一定范围内等改进方法。使用改进算法实现了某小区供水系统的直接优化调度,并与遗传算法优化调度的过程进行了对比,新算法在优化时间及得到最优解的次数上都优于遗传算法。     

基于交货期窗口约束的多级供应链批量调度问题研究

为解决三级供应网络中供应商和制造商的加工任务顺序优化和合理性问题,建立了交货期窗口约束下供应商与制造商协同决策的批量调度模型.以最小化供应链的物流和交付总成本为目标,综合考虑了供应商与制造商的库存成本、运输成本和未按期交付的惩罚成本.提出了采用两级遗传算法和两级编码的模型求解算法.通过一个仿真算例,验证了调度模型和求解策略的可行性.     

一种求解柔性多目标JIT作业车间调度优化问题的方法

针对JIT作业车间多种工艺路线的工件调度问题,考虑了生产过程受多因素的影响。在混合遗传算法与拉格朗日松弛法结合的基础上,提出一种混合改进算法,采用分层多目标分层协调策略,建立了柔性多目标优化的适应函数模型,利用遗传算法更新拉格朗日乘子得到问题的最优解,仿真实例验证了该模型与求解方法是现实可行的。     

考虑运输成本的单级多资源约束生产批量问题研究

构建了考虑运输成本的单级多资源约束生产批量计划模型,并提出了用于求解该问题的二进制粒子群算法,阐明了该算法的具体实现过程。通过对仿真实例进行计算和结果比较,表明了在生产批量计划决策时考虑运输成本可以大大降低运输费用,从而降低企业运行的总费用。     

供应链中基于不同运输主体的批量模型比较研究

在单位产品运输成本对批量敏感时,为了使订货商的最佳订货批量与供应商的最佳生产批量趋同,建立了供应链中不同主体在产品运输时,供、需双方在分散决策和集中决策情形下的最佳批量模型。研究表明,首先,在供应商负责产品运输时,运输能力柔性越强,其最佳生产批量越小,从而与订货商要求的短周期、小批量的订货更趋近;其次,订货商负责产品运输时,其最佳订货批量与供应商的最佳生产批量更趋一致,从而供、需方可以在双赢的前提下使整个供应链的利益更优。     

Estimation of Part Waiting Time and Fleet Sizing in AGV Systems

As manufacturing systems have grown in size and complexity, material flow control has become one of the key issues for system efficiency, and determination of the number of vehicles required is an important issue in the design of the AGV (automatic guided vehicle) systems for automated material flow control. In an AGV system, a part issues a delivery request for its transportation, and then an empty vehicle is assigned based on a pre-determined vehicle selection rule and provides delivery service. This research presents a fleet sizing procedure for an AGV system with multiple pickup and delivery stations. A queueing model is used to estimate part waiting times. The fleet sizing procedure estimates the minimum number of vehicles needed to ensure a predefined part waiting time limit. While most stochastic models assume first-come-first-served or random vehicle selection rules for the selection of an empty vehicle, this model considers such additional rules as the nearest vehicle selection rule, which is the most popular among all vehicle selection rules. The performance of the proposed model is examined through computational experiments. This work was supported by Korea Research Foundation Grant (KRF-2003-042-D20136).     

A new approach to solve time–cost trade-off problem with fuzzy decision variables

Time–cost trade-off problem is one of the main aspects of project scheduling. Due to variations in the real world, usually, risks in estimation of project parameters are considerably high. Therefore, use of uncertain models, which is capable of formulating vagueness in the real world, to solve time–cost trade-off problems, gives a scheduling with more stability against environmental variations. On the other hand, crisp decision making in uncertain environment causes loss of some parts of information. This paper presents a new optimal model for time–cost trade-off problem in a fuzzy environment. In order to solve this problem, a new solution method for possibility goal programming problems is developed. The significant feature of this model is the determination of optimal duration for each activity in the form of triangular fuzzy numbers. To validate the algorithm developed here, a case study will be presented.     

Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models

Cloud manufacturing is an emerging service-oriented manufacturing paradigm that integrates and manages distributed manufacturing resources through which complex manufacturing demands with a high degree of customization can be fulfilled. The process of service selection optimization and scheduling (SSOS) is an important issue for practical implementation of cloud manufacturing. In this paper, we propose new mixed-integer programming (MIP) models for solving the SSOS problem with basic composition structures (i.e., sequential, parallel, loop, and selective). Through incorporation of the proposed MIP models, the SSOS with a mixed composition structure can be tackled. As transportation is indispensable in cloud manufacturing environment, the models also optimize routing decisions within a given hybrid hub-and-spoke transportation network in which the central decision is to optimally determine whether a shipment between a pair of distributed manufacturing resources is routed directly or using hub facilities. Unlike the majority of previous research undertaken in cloud manufacturing, it is assumed that manufacturing resources are not continuously available for processing but the start time and end time of their occupancy interval are known in advance. The performance of the proposed models is evaluated through solving different scenarios in the SSOS. Moreover, in order to examine the robustness of the results, a series of sensitivity analysis are conducted on key parameters. The outcomes of this study demonstrate that the consideration of transportation and availability not only can change the results of the SSOS significantly, but also is necessary for obtaining more realistic solutions. The results also show that routing within a hybrid hub-and-spoke transportation network, compared with a pure hub-and-spoke network or a pure direct network, leads to more flexibility and has advantage of cost and time saving. The level of saving depends on the value of discount factor for decreasing transportation cost between hub facilities.     

开孔率对竖井型城市隧道阻滞工况自然通风影响的分析

为了分析开孔率对竖井型城市隧道在交通阻滞工况下对自然通风的影响,本文采用无量纲分析和模型试验的方法对其速度场、温度场和污染物浓度场进行了研究。通过对阻滞工况下隧道内空气所受的热压浮升力与重力的无量纲分析,得到了模型试验所需的热压通风相似准则数和温度分布相同所需散热量比例常数。利用所得相似条件搭建了开孔率分别为2.8%、3.7%、4.5%的3组试验模型进行试验。试验结果表明,随着开孔率的增大隧道内最不利通风段的通风效果得到改善,但竖井的建设费用大幅度提高。选择合适的竖井开孔率既能满足阻滞工况下隧道的通风要求,也能避免开孔率盲目增大导致的初投资浪费,对城市隧道建设具有重要意义。     

Financial analyses of on-line preemption-restart scheduling subject to cost penalties

This paper considers the on-line production order scheduling problem where each preemption causes a penalty, and the net profit is the total revenues of completed jobs minus the total penalties caused by preemptions. We discuss a preemptive version of the single-machine scheduling problem with the total flow time criterion. Two models with different kinds of preemption cost penalties are considered in this paper. In the first model, cost penalties are constant. In the second model, the preemption penalties are proportional to the lost part of the preempted job. Uncertain release times and processing times are modeled by an M/M/1 queue system. We calculate a lower bound probability for preemption of each job. Also, some financial analyses of the models are presented.     

铁路货车轮轴裂纹萌生规律及机理研究

为阐明铁路货车轮轴的断裂失效原因,对轮轴上的裂纹进行了全面的分析研究。通过对某厂的57根裂纹轴进行统计分析,得出铁路货车轮轴典型裂纹的萌生区域、位置、形貌和萌生周期等规律,并通过对典型裂纹的检验,掌握了裂纹的萌生机理,提出了应对的建议。研究结果有利于减少轮轴的裂纹,提高铁路货运安全性。     

物流产业发展与国民经济整体水平提升的相关性分析

通过对实际数据的分析，论述物流产业发展与国民经济整体水平提升的相关性。选取GDP、物流成本、物流增加值三个指标，从宏观物流成本占GDP的比重、物流产业增加值占GDP的比重两个方面来展开研究。用最小二乘法建立一般的回归模型，由此衍生出物流产业的发展趋势模型、货运量与GDP回归模型、物流成本与GDP回归模型，并以天津市统计数据为样本，利用模型得出结论，以此来印证前期论证和假设。     

服务型制造车间关键任务调度的Stackelberg博弈研究

针对服务型制造车间关键任务调度问题,提出了两层次嵌套的Stackelberg博弈调度模型。该博弈模型由Stackelberg子博弈与非合作静态子博弈构成。其中Stackelberg子博弈模型用于解决关键任务与非关键任务的之间的调度决策问题,非合作静态子博弈模型则用于实现非关键任务之间的调度决策。在该博弈调度模型中,将关键任务映射为领导者,将其余非关键任务映射为追随者,将与各任务包含的工序集所对应的可选加工设备映射为可行方案集,将各任务的综合成本指标映射为收益函数。为实现对模型的Stackelberg均衡点的有效求解,设计了基于爬山搜索的混合自适应遗传算法。算例仿真结果验证了所提出的模型与解算方法的正确性。