不确定环境下服务资源配置优化

在服务资源配置过程中,按维修状态将服务对象分成三类:完全维修、部分维修及最小维修.由于维修服务的特性,服务成本与服务时间均是一个模糊数,并且维修对象在服务时所处的维修状态也具有一定的不确定性.针对这类情况下的服务资源配置问题,提出了同时考虑模糊服务成本和模糊服务时间及不确定维修状态的最小化服务成本为优化指标的服务资源配置模型.在模型的求解过程中采用多粒子群算法,通过仿真计算表明了该方法的可行性和有效性.     

风电场可靠性模型及其应用

考虑了风速分布的季节性差异和风速的随机变化的影响、风电机组的实际功率特性、风电场尾流效应以及风电机组故障状况,建立了风电场可靠性模型.根据某海上风电场实测数据进行数值仿真实验,结果验证了模型的正确性和有效性.模型为进一步研究大规模风电并网以及风电场功率预测等问题打下了基础.     

带有一个冷贮备部件的两部件串联系统机会维修策略

针对带有一个冷贮备部件的两部件串联系统,本文首先提出一种预防维修与机会维修相结合的维修策略,运用更新报酬定理求得长期运行情况下的单位时间期望维修成本函数的表达式,然后研究最优的机会维修阀值,运用微分学理论求解最优解,最后用实例验证理论的正确性,从实际例子说明本文提出的维修策略可明显节约维修成本,为相应的带有冷贮备的多部件串联系统的维修策略分析提供参考,对企业设备的维修有实际指导意义。     

具有学习效应的半导体晶圆制造绿色车间调度问题研究

针对最小化最大完工时间、总碳排放以及总拖期时间的具有学习效应的半导体晶圆制造绿色车间调度问题,构建了双影响因素的新型学习效应模型,提出了改进的多元宇宙优化算法,并对其收敛性进行证明。通过对初始种群进行反向学习、宇宙个体进行莱维飞行扰动和对外部档案中的个体进行邻域搜索变异更新,产生新的父代个体,扩大了种群的多样性,避免算法陷入局部最优。通过对小规模和大规模测试算例的仿真实验,以及利用改进算法求解具有异质性机器的学习型半导体晶圆制造绿色车间调度问题,验证了本文所提出的算法对于求解具有学习效应的半导体晶圆制造绿色车间调度问题的有效性和可行性。     

面向健康管理的维修资源调度系统关键模型研究

针对健康管理理论在维修资源调度决策研究中的不足,分析了面向健康管理进行维修资源调度优化的可行性,研究了维修资源调度系统与健康管理系统的连接关系.然后结合装备维修资源供应保障的实际,建立了系统中调度优化模块所用到的带软时间窗的多需求地-多供应地维修资源调度优化模型,并应用遗传算法对模型的求解进行了分析.最后,对方法进行了演算,验证了模型在维修资源调度系统中应用的正确性和可行性.     

云制造平台加工能力分享调度优化研究

云制造平台加工能力分享的调度优化是云制造模式的核心运营问题。为提高云制造平台对参与能力分享企业资源的利用率,解决实际云制造需求环境下企业级制造资源调度问题,达到云制造平台动态实时的要求,以最小化工期和成本为目标函数,构建了基于平台并面向分享的云加工能力调度模型。为快速求解多个云任务订单的最优调度序列,引入任务简易编码和随机权重,设计了改进的二阶粒子群算法,并提出可分享加工能力时间窗的更新策略。最后,通过算例验证云制造平台加工能力调度机制的可行性和有效性。     

基于可靠性理论的退化设备维修优化

设备可靠性、设备完好率等指标是衡量设备运行状态好坏的关键指标.以设备完好率、设备可靠性以及设备预防维修费用为决策目标,通过线性规划的方法对退化设备的预防维修策略进行了优化研究.在定量化模型中引入了设备检修的概念,并在此基础上构建了以可靠性为约束的预防维修费用模型和设备完好率模型,考虑到企业在生产活动的维修决策是靠多因素条件的约束,而构建了一个以维修费用、设备完好率、设备可靠性为决策目标的多目标规划模型,通过线性规划的方法求解了该策略下的最优维修策略.通过数值验证证明了模型的可行性.     

模糊环境下的差异作业平行机批调度优化问题研究

针对现实生产制造系统中存在的时间参数模糊化问题,采用梯形模糊数表征时间参数,给出了一种具有模糊加工时间与模糊批次间隔的,以最小化制造跨度为目标的差异作业平行机批调度问题模型。在问题求解方面,给出了一种基于粒子群优化和差异进化的混合优化算法,避免求解过程陷入局部最优,并通过改进的Batch First Fit算法获得优化的分批。仿真实验验证了该算法具有可行性和有效性。     

基于可用度的机群维修作业调度

针对面向多波次飞行任务的机群维修作业调度问题,以装备保障人员、保障时间为主要约束,以非机组保障方式为主要保障方式,以机群最大可用度为目标,用网络流结构模式表述机群处于飞行、停机和修理的流程,建立了机群维修作业的混合整数规划模型,采用分支界定法求解,通过实例分析验证,所建模型和方法可以有效的实现机群维修作业的调度,以满意的人员配置方案满足任务的可用度要求.     

改进回溯搜索算法求解多目标柔性作业车间调度问题

随着绿色制造的到来，在调度问题中考虑能源消耗相关的目标变得至关重要，这已经成为了当下热点研究领域。因此，本文建立以最小化最大完工时间、机器总负荷和总能量消耗为目标的柔性作业车间调度数学模型。就回溯搜索算法的缺点提出改进，该算法通过结合改变个体搜索幅度因子对变异操作进行动态控制，防止种群迭代过程中陷入局部最优，然后通过结合个体引导与随机数扰乱提出一种新的交叉算子，提高后期寻优能力，防止了算法过早收敛。最后，运用基准算例对该算法的求解性进行了验证，并与文献中其他算法从求解精度、求解多样性、求解最优值等方面进行对比，结果表明该改进算法具有优越的求解性能。最后为该问题后续研究提供了三个可行方向：考虑更多约束条件、增加局部搜索算子和考虑实例分析。     

A framework for evaluating remote diagnostics investment decisions for semiconductor equipment suppliers

With advances in information technology, service activities for expensive equipment used in semiconductor manufacturing can be performed from a remote location. This capability is called remote diagnostics (RD). Currently, there are intense development efforts in the semiconductor industry for implementing RD in wafer fabrication facilities to reduce maintenance and capital costs and improve productivity. In this paper, we develop a queueing-location model to analyze the capacity and location problem of after sales service providers, considering the effects of RD technology. Our model optimizes the location, capacity and the type of service centers while taking congestion effects into consideration. We solve this model using a simulation optimization approach in which we use a genetic algorithm to search the solution space. We demonstrate how our methodology can be used in strategic investment planning regarding the adoption of RD technology and service center siting through a realistic case study.     

An evolutionary approach to rehabilitation patient scheduling: A case study

Focusing on real settings, this study aimed to develop an evolutionary approach based on genetic algorithm for solving the problem of rehabilitation patient scheduling to increase service quality by reducing patient waiting time and improve operation efficiency by increasing the therapy equipment utilization. Indeed, due to partial precedence constraints of rehabilitation therapies, the problem can be structured as a hybrid shop scheduling problem that has received little attention to date. In addition, a mixed integer programming model was also constructed as a benchmark to validate the solution quality with small problems. Based on empirical data from a Medical Center in Taiwan, several experiments were conducted to estimate the validity of the proposed algorithm. The results showed that the proposed algorithm can reduce patient waiting time and enhance resource utilization and thus demonstrated the practicality of the proposed algorithm. Indeed, a decision support system embedded with the developed algorithm has been implemented in this medical center.     

考虑权力结构的产品服务供应链服务渠道选择及协调研究

在制造业服务化转型过程中,传统的产品供应链转变为了产品服务供应链,为顾客提供产品和服务,服务可以由制造商或零售商提供,权力结构会对服务渠道的选择产生影响。本文通过建立博弈模型,研究了制造商为核心、零售商为核心以及制造商与零售商权力均等三种权力结构下最优服务渠道的选择问题,并应用两部定价契约对最优服务渠道的结果进行协调。研究发现,当制造商为核心时,最优的服务渠道为零售商提供服务,当零售商为核心时,最优的服务渠道为制造商提供服务,当制造商与零售商权力均等时,不存在最优的服务渠道。两部定价契约可以实现最优服务渠道的协调,最后通过数值仿真对结论进行了验证。     

Supply chain network capacity competition with outsourcing: a variational equilibrium framework

This paper develops a supply chain network game theory framework with multiple manufacturers/producers, with multiple manufacturing plants, who own distribution centers and distribute their products, which are distinguished by brands, to demand markets, while maximizing profits and competing noncooperatively. The manufacturers also may avail themselves of external distribution centers for storing their products and freight service provision. The manufacturers have capacities associated with their supply chain network links and the external distribution centers also have capacitated storage and distribution capacities for their links, which are shared among the manufacturers and competed for. We utilize a special case of the Generalized Nash Equilibrium problem, known as a variational equilibrium, in order to formulate and solve the problem. A case study on apple farmers in Massachusetts is provided with various scenarios, including a supply chain disruption, to illustrate the modeling and methodological framework as well as the potential benefits of outsourcing in this sector.     

考虑加工速率相互干扰的平行机调度最优策略研究

随着智能互联网的应用深入、个性化消费时代的来临,制造服务企业开始注重利用网络平台为客户提供个性化的定制服务,在此过程中派生出了产品设计师可与多名客户在线同步交互的一种新型服务模式。本文根据设计师服务效率受并行服务客户数量影响的特征,将问题刻画为机器处理速度相互影响的一类平行机调度模型,以最小化总完工时间为优化目标,研究设计最优调度方案。首先,对于只有两名设计师且各自同时处理最多两个任务的情形,提出了改进的SPT调度规则,运用归纳法证明了该规则可以生成最优加工方案。其次,对改进的SPT规则进行任务分配方式的适当松驰以便更加易于操作,并证明松驰后的新分配方案保持了解的最优性。最后,将相关结论推广至多名设计师的一般情形。上述研究为个性化在线定制服务模式下的有效调度策略制定提供了良好的理论支撑。     

随机服务时间下异质患者门诊预约调度优化

考虑随机服务时间与行为特征互不相同的异质患者,建立随机混合整数规划模型对门诊预约调度问题展开研究。首先在给定服务顺序的假设下求解了两个患者的预约调度问题;在此基础上,设计启发式算法对多个患者预约方案和服务顺序同时进行优化。数值结果表明:当患者服务时间为独立同分布的随机变量时,患者预约时间间隔呈现先增加后减少的圆顶形状;当患者服务时间服从不同分布时,通过与样本平均近似方法对比,验证了启发式算法的计算效率和有效性。     

Control methods for dynamic time-based manufacturing under customized product lead times

For manufacturers, the integration of high performance manufacturing with customer-oriented practices plays an important role in improving the performance of their business system. The benefits from such integration can only be maximized when the two parts are designed to work cooperatively. Though previous research has contributed much to manufacturing control algorithms and customer service practices, there has been little consideration of the two parts as a whole; consequently, the methods proposed may not be well supported by the other practices adopted in the system. This study develops production control methods that support a customer-oriented lead time policy, and aims to increase the performance of both manufacturing and customer service. The control methods are proposed for hybrid flow shops handling orders arriving dynamically. Computer simulations are conducted on a large number of problem instances, and the results show that the designed distributed feedback and decision-making functions enable the proposed methods to significantly outperform existing methods in achieving just-in-time (JIT) job completion under customized product lead times. Even taking into account the possible tradeoff between JIT job completion and flow time length, the proposed methods still deliver competitive performance.     

Managing a portfolio of long term service agreements

Long-term service agreements (LTSAs) for the maintenance of capital-intensive equipments such as gas turbines and aircraft engines are gaining wide acceptance. A typical LTSA contract spans over a period of around 10 years making a manufacturer fully responsible for maintaining the customer equipment. In this paper, we address the management of a portfolio of such contracts from the manufacturer’s perspective. The goal is to meet all the service requirements imposed by the contracts while minimizing total cost incurred. We develop a deterministic integer programming model to generate the optimal maintenance schedules that minimize the total portfolio cost. We then propose two heuristic algorithms for the problem.     

Optimisation of fault-tolerant fabric-cutting schedules using genetic algorithms and fuzzy set theory

In apparel industry, manufacturers developed standard allowed minutes (SAMs) databases on various manufacturing operations in order to facilitate better scheduling, while effective production schedules ensure smoothness of downstream operations. As apparel manufacturing environment is fuzzy and dynamic, rigid production schedules based on SAMs become futile in the presence of any uncertainty. In this paper, a fuzzification scheme is proposed to fuzzify the static standard time so as to incorporate some uncertainties, in terms of both job-specific and human related factors, into the fabric-cutting scheduling problem. A genetic optimisation procedure is also proposed to search for fault-tolerant schedules using genetic algorithms, such that makespan and scheduling uncertainties are minimised. Two sets of real production data were collected to validate the proposed method. Experimental results indicate that the genetically optimised fault-tolerant schedules not only improve the operation performance but also minimise the scheduling risks.     

Operation and preventive maintenance scheduling for containerships: Mathematical model and solution algorithm

This paper considers the problem of determining operation and maintenance schedules for a containership equipped with various subsystems during its sailing according to a pre-determined navigation schedule. The operation schedule, which specifies working time of each subsystem, determines the due-date of each maintenance activity and the maintenance schedule specifies the actual start time of each maintenance activity. The main constraints are subsystem requirements, workforce availability, working time limitation, and inter-maintenance time. To represent the problem mathematically, a mixed integer programming model is developed. Then, due to the complexity of the problem, we suggest a heuristic algorithm that minimizes the sum of earliness and tardiness between the due-date and the actual start time for each maintenance activity. Computational experiments were done on various test instances and the results are reported. In particular, a case study was done on a real instance and a significant amount of improvement is reported over the experience based conventional method.