考虑维修时间的总质保需求预测模型

激烈的市场竞争迫使制造商们逐渐向以顾客需求为中心的公司转变。在近 20 年内,作为影响顾客满意度的主要因素,产品的质保服务管理的相关研究开始成为学术界的焦点。良好的质保服务会给企业节省较多的运营成本,故对于刚投入市场的新产品而言,准确地预测质保需求对制造商合理分配资金等具有重要意义。以往对质保需求的预测模型都局限于分析长期意义上一个产品的总质保成本,忽略了产品的维修时间和动态销售过程对准确预测产品的总质保需求及成本的影响。为此,以销售期内的产品所产生的维修需求为主要的研究对象,深入探讨维修时间对预测质保需求的影响。模型中,利用非齐次泊松过程模拟产品的动态销售过程,并利用复合随机过程中的交错更新理论来刻画维修时间对总质保需求的影响。最后的参数分析,为企业更好地管理质保服务提供了重要的现实依据。     

基于故障率减少的不完全维修模型

针对可修系统在实际的运行过程中无法通过维修手段恢复如新的情况,通过考虑维修后故障率的直接减少建立了可修系统的不完全维修模型,以此描述可修系统维修改善的效果.在不完全维修模型的基础上,构建了系统的成本函数,得到了系统最优的预防性维修次数和预防性维修时间间隔.最后,通过实例验证了模型的可行性和有效性.     

基于ε-EGA多准则调整的舰船维修资源配置决策方法与应用

为了提高舰船维修资源配置的科学性、有效性，针对舰船维修目标提出维修战斗力定义，统筹考虑了舰船维修资源配置过程中的财力、人力、物力和技术资源对于资源配置的影响，构建了舰船维修资源配置模型。求解方法上，以军事效益(维修战斗力)最大和维修成本最小为优化目标，将舰船维修资源配置问题转化为混合整数非线性规划模型的多目标寻优问题。在标准遗传算法(SGA)基础上，融合ε约束准则和精英保留策略，构建一种新型ε-EGA的多准则调整算法，搜索获取满意的Pareto解集与前沿。结合企业H的年度舰船维修任务实际进行实证检验，提高优化配置模型结论的科学性，增强模型与实际情况的吻合度。结果表明，ε-EGA多准则调整算法具有良好的适用性和延展性，计算速度快，方案优化度高，而维修资源配置模型对于其他建造工程行业的资源调度、计划安排等工作开展，也具有较强的借鉴意义。     

非正态总体控制图和预防维修策略耦合优化的研究

主要探讨非正态有偏总体的过程监控和预防维修耦合优化问题。假定设备故障率随时间递增,设备发生异常前在正常状态的停留时间服从威布尔分布,一旦发生异常将导致过程均值漂移。采用赋权方差法构造X控制图,将过程监控和预防维修策略联系起来,结合生产不合格品损失、抽样成本及维修成本等,构建综合损失模型,提出动态抽样方案、控制图参数和预防维修间隔的确定方法。最后对模型进行了灵敏度分析。     

延迟时间生产系统预防维修策略和X-bar控制图联合经济设计

针对生产过程输出质量特性服从正态分布的单部件延迟时间生产系统,研究了预防维修策略和X-bar控制图联合经济设计问题.首先,在将延迟时间系统界定为受控、失控、故障3种结果状态基本内容前提下,根据维修时系统可能存在的实际状态和控制图监测结果关系,分析构建了生产系统维修方式和控制图监测的耦合机制;在此基础上,以期望单位时间最小成本为目标,以系统维修成本、产品质量抽检成本、不合格品的生产费用、维修停机生产损失为考虑成本内容,利用更新过程理论建立了生产系统预防维修策略和X-bar控制图联合决策数学模型;然后基于数值仿真示例,利用遗传算法对模型的求解进行了分析验证.实例分析结果表明,文章模型可行有效.最后,利用部分因子试验设计方法对模型参数进行了敏感性分析.     

具有小修和不完全预防性维修的多状态退化系统的可用度分析

研究了一种多状态退化系统,该系统由于在工作过程中逐渐退化,导致系统的效率降低.为了减少系统的失效率和退化率,系统会受到随机失效后的小修和退化到最后一个可接受工作状态时的预防性维修.在这种具有小修和不完全预防性维修的多状态退化系统模型中,假定系统能连续退化成一些离散状态,并且这些离散状态是从正常工作状态一直到完全失效状态.当系统逐渐退化到某个临界值(这里把它称作不可接受状态)时,就视系统完全失效,那么在最后一个可接受状态时系统就会得到预防性维修;当系统从任意的工作状态随机失效后就会得到小修.在这个模型中,基于它是一个连续时间的马尔可夫过程,来计算稳态可用度指标.     

考虑非周期检测的视情维修与备件订购联合策略优化

针对单部件系统/关键部件提出视情维修与备件订购联合策略,其中系统退化服从两阶段延迟时间过程且采用非周期检测策略,退化初期以检测间隔T₁检查系统状态,而在第一次识别缺陷状态时,缩短检测周期为T₂、订购备件且进行不完美维修;若系统在随后的退化中被识别处于缺陷状态,执行不完美维修直至超过阈值次数N_max并采取预防性更换,但若在检测周期内发生故障则进行更换。根据系统状态和备件状态分析各种可能更新事件及相应的联合决策,利用更新报酬理论构建最小化单位时间内期望成本的目标函数,优化T₁,T₂, N_max。与对比模型策略相比,算例结果表明所提出的联合策略能有效降低单位时间内的期望成本。     

带有线性退化和随机振荡的系统可靠性及维修决策

研究了随时间发生线性退化和随机振荡导致瞬时退化的系统可靠度及定期维修策略。随机振荡的发生次数服从非时齐泊松过程,每次振荡造成系统的退化量独立同分布。当累积退化量达到阀值时,系统发生故障。为了改善系统工作状态,降低故障风险,每隔T时对系统进行不完全预防维修,维修后故障率函数将发生变化,维修成本与系统的退化程度有关。在NT时,对系统进行完全预防维修,使系统修旧如新。构建了系统的可靠度函数。在单位时间平均利润最大的前提下,提出不完全预防维修间隔T和完全预防维修周期NT的确定方法。分析了模型参数对维修决策的影响。     

考虑员工疲劳度和休假的退化系统可靠性建模与检测策略研究

在退化系统检测维修策略的研究中,很少考虑人为因素对检测正确率的影响。事实上,考虑员工疲劳度水平和其工作状态可以更精确地评估和优化维修检测策略,帮助企业降低运维成本。本文考虑维修工的工作状态对检测结果的影响,通过建立疲劳度和检测正确率的函数关系,构建检测维修模型,以最小化维修成本为目标,优化员工工作时间安排、休假时间安排以及检测时间间隔;最后进行算例分析,针对各成本参数做敏感度分析,讨论参数变化时最优休假策略和维修策略的变化规律,从而寻找更广泛的优化适用性。结果表明检测出错成本大的系统,决策者应提高员工休假时间,降低检测频率;预防性维修成本高的系统,决策者应适当提高员工在岗时间并降低检测频率;停机机会成本更高的系统应提高员工在岗时间,同时提高检测频率。     

基于模糊集合论的实物期权定价方法

实物期权的定价在风险投资决策过程中具有重要意义.传统的实物期权定价方法忽略标的资产价值和投资成本的模糊性,从而可能导致错误的投资决策.本文主要研究了具有模糊标的的资产价值和投资成本情形时的实物期权定价模型.文中将这些模糊因素分别视为模糊数和模糊变量,然后运用模糊集合论,结合B-S期权定价理论,对实物期权进行定价,得到了基于模糊集合论的实物期权定价模型.     

Stochastic optimization for real time service capacity allocation under random service demand

The problem of repeated allocation of limited renewable service resources to distributed service centers is considered here. The objective is to assure a given Quality of Service expressed through percentage of demand which is satisfied during a specified time period. Resource requirements are not fully known at the time when a decision about the service resource distribution is taken.     

基于可信性理论的PPP项目特许期-价格联合调整策略研究

需求预测误差是影响PPP项目收益预测准确性的主要因素。为减少谈判争议,确保风险和收益的动态均衡,本文基于模糊数学可信性理论,构建了考虑需求不确定的特许期-价格联合调整模型。将项目运营期间的需求预测误差作为模糊变量,将运营期内特许期和价格的联合调整策略作为决策变量,通过模糊模拟求解出不同特许期和价格调整组合下的期望收益误差以及正收益预期下的可信性,进而得到特许期和价格的联合调整策略可行解集。并将该模型应用于某污水项目中,结果表明,该模型能够有效地解决需求不确定性风险对特许期测算影响的问题,弥补了目前PPP项目特许期和价格调整决策研究中未考虑需求预测误差的不足,对PPP项目特许期和价格的调整决策有着重要的参考意义。     

Bounds on stochastic convex allocation problems

We consider a stochastic convex program arising in a certain resource allocation problem. The uncertainty is in the demand for a resource which is to be allocated among several competing activities under convex inventory holding and shortage costs. The problem is cast as a two–period stochastic convex program and we derive tight upper and lower bounds to the problem using marginal distributions of the demands, which may be stochastically dependent. It turns out that these bounds are tighter than the usual bounds in the literature which are based on limited moment information of the underlying random variables. Numerical examples illustrate the bounds.     

应急物资预置问题的可信性优化模型

基于可信性理论,研究了多受灾点、多出救点、多物资的应急设备选址和物资预置问题.考虑到运输费用、出救点的供应量、受灾点的需求量和道路容量的不确定性,用模糊变量来刻画,建立了模糊环境下应急物资预置的可信性优化模型以最小化期望总费用.当模型中的模糊变量相互独立且服从三角分布时,推导了总费用目标及服务质量和弧容量约束的解析表达式,从而将原模型转化为等价的确定模型.鉴于等价模型是一个混合整数规划,可采用Lingo软件编程求解.最后,数值算例演示所提建模思想.实验结果说明了所建模型的有效性.     

An integrated fuzzy DEA–Fuzzy simulation approach for optimization of operator allocation with learning effects in multi products CMS

This paper presents an integrated fuzzy data envelopment analysis (FDEA) and fuzzy computer simulation approach for optimization of operator allocation in multi product cellular manufacturing systems (CMS) with learning effects. Operator allocation with learning effects is a challenging issue in flexible manufacturing systems in general and in CMS in particular. The main contribution of this work is taking into consideration various operators layouts and learning effects using fuzzy simulation and fuzzy DEA. FDEA is utilized to assess simulation alternatives in various levels of uncertainty. Previous studies consider only one type of product with crisp inputs, whereas this study considers multi-products and fuzzy set up times and processing times for CMS modeling. In addition, this study considers and integrates learning effects for optimum operators’ allocation. Moreover, more robust CMS assessment indicators are used in the proposed model. A case study illustrates the practicability, effectiveness and superiority of the proposed methodology.     

Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses

To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality. We develop a stochastic programming model that exploits historical product quality delivery data to produce slaughterhouse allocation plans with reduced levels of uncertainty in received livestock quality. The allocation plans generated by this model fulfil demand for multiple quality features at separate slaughterhouses under prescribed service levels while minimizing transportation costs. We test the model on real world problem instances generated from a data set provided by an industrial partner. Results show that historical farmer delivery data can be used to reduce uncertainty in quality of animals to be delivered to slaughterhouses.     

Structural properties of the optimal resource allocation policy for single-queue systems

This paper studies structural properties of the optimal resource allocation policy for single-queue systems. Jobs arrive at a service facility and are sent one by one to a pool of computing resources for parallel processing. The facility poses a constraint on the maximum expected sojourn time of a job. A central decision maker allocates the servers dynamically to the facility. We consider two models: a limited resource allocation model, where the allocation of resources can only be changed at the start of a new service, and a fully flexible allocation model, where the allocation of resources can also change during a service period. In these two models, the objective is to minimize the average utilization costs whilst satisfying the time constraint. To this end, we cast these optimization problems as Markov decision problems and derive structural properties of the relative value function. We show via dynamic programming that (1) the optimal allocation policy has a work-conservation property, and (2) the optimal number of servers follows a step function with as extreme policy the bang-bang control policy. Moreover, (3) we provide conditions under which the bang-bang control policy takes place. These properties give a full characterization of the optimal policy, which are illustrated by numerical experiments.     

Dynamic resource allocation in fuzzy coalitions: a game theoretic model

We introduce an efficient and dynamic resource allocation mechanism within the framework of a cooperative game with fuzzy coalitions (cooperative fuzzy game). A fuzzy coalition in a resource allocation problem can be so defined that membership grades of the players in it are proportional to the fractions of their total resources. We call any distribution of the resources possessed by the players, among a prescribed number of coalitions, a fuzzy coalition structure and every membership grade (equivalently fraction of the total resources), a resource investment. It is shown that this resource investment is influenced by the satisfaction of the players in regard to better performance under a cooperative setup. Our model is based on the real life situations, where possibly one or more players compromise on their resource investments in order to help forming coalitions.     

Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach

We study a vehicle routing problem with soft time windows and stochastic travel times. In this problem, we consider stochastic travel times to obtain routes which are both efficient and reliable. In our problem setting, soft time windows allow early and late servicing at customers by incurring some penalty costs. The objective is to minimize the sum of transportation costs and service costs. Transportation costs result from three elements which are the total distance traveled, the number of vehicles used and the total expected overtime of the drivers. Service costs are incurred for early and late arrivals; these correspond to time-window violations at the customers. We apply a column generation procedure to solve this problem. The master problem can be modeled as a classical set partitioning problem. The pricing subproblem, for each vehicle, corresponds to an elementary shortest path problem with resource constraints. To generate an integer solution, we embed our column generation procedure within a branch-and-price method. Computational results obtained by experimenting with well-known problem instances are reported.