Component testing of repairable systems in multistage missions

We consider the component testing problem of a device that is designed to perform a mission consisting of a sequence of stages. Once a stage is over, the device is overhauled to replace all failed components before the next stage starts to improve mission reliability. The components fail exponentially where the failure rate depends on the stage of the mission. The reliability structure of the device involves a series connection of redundant subsystems. The optimal component testing problem is formulated as a semi-infinite linear programme. We present an algorithmic procedure to compute optimal test times based on the column generation technique, and illustrate it with numerical results.     

An efficient memetic algorithm for the graph partitioning problem

We consider the component testing problem of a device that is designed to perform a mission consisting of a random sequence of phases with random durations. Testing is done at the component level to attain desired levels of mission reliability at minimum cost. The components fail exponentially where the failure rate depends on the phase of the mission. The reliability structure of the device involves a series connection of nonidentical components with different failure characteristics. The optimal component testing problem is formulated as a semi-infinite linear program. We present an algorithmic procedure to compute optimal test times based on the column generation technique, and illustrate it with numerical examples.     

Finding and identifying optimal inventory levels for systems with common components

In this article, we consider the problem of finding the optimal inventory level for components in an assembly system where multiple products share common components in the presence of random demand. Previously, solution procedures that identify the optimal inventory levels for components in a component commonality problem have been considered for two product or one common component systems. We will here extend this to a three products system considering any number of common components. The inventory problem considered is modeled as a two stage stochastic recourse problem where the first stage is to set the inventory levels to maximize expected profit while the second stage is to allocate components to products after observing demand. Our main contribution, and the main focus of this paper, is the outline of a procedure that finds the gradient for the stochastic problem, such that an optimal solution can be identified and a gradient based search method can be used to find the optimal solution.     

面向多阶段任务的武器系统备件优化配置建模

资源优化配置是作战单元维修保障的关键因素.当作战单元执行单阶段任务时,讨论了部件结构为串联系统和k/n系统的任务成功概率建模问题,在此基础上,建立了k/n结构动态变化的多阶段任务系统的任务成功概率模型.在满足系统任务成功概率约束条件下,给出了防空作战单元备件携行量优化模型,并运用边际分析法进行了求解,通过示例表明了模型的正确性.     

Supply planning models for a remanufacturer under just-in-time manufacturing environment with reverse logistics

We study a supply planning problem in a manufacturing system with two stages. The first stage is a remanufacturer that supplies two closely-related components to the second (manufacturing) stage, which uses each component as the basis for its respective product. The used products are recovered from the market by a third-party logistic provider through an established reverse logistics network. The remanufacturer may satisfy the manufacturer’s demand either by purchasing new components or by remanufacturing components recovered from the returned used products. The remanufacturer’s costs arise from product recovery, remanufacturing components, purchasing original components, holding inventories of recovered products and remanufactured components, production setups (at the first stage and at each component changeover), disposal of recovered products that are not remanufactured, and coordinating the supply modes. The objective is to develop optimal production plans for different production strategies. These strategies are differentiated by whether inventories of recovered products or remanufactured components are carried, and by whether the order in which retailers are served during the planning horizon may be resequenced. We devise production policies that minimize the total cost at the remanufacturer by specifying the quantity of components to be remanufactured, the quantity of new components to be purchased from suppliers, and the quantity of recovered used products that must be disposed. The effects of production capacity are also explored. A comprehensive computational study provides insights into this closed-loop supply chain for those strategies that are shown to be NP-hard.     

两类供应风险下装配商的最优订购策略

针对两类供应风险(不确定产能与随机产出率)下装配制造商的零部件订购决策这一难题,运用随机非线性规划方法,以装配商期望利润最大化为目标,建立零部件订购决策的多维优化模型,刻画了确定需求下的最优订购量,并对其进行了灵敏性分析。最后,通过数值算例验证了模型结论并进一步探讨不同类供应风险的影响,为装配商的零部件订购决策和风险管理提供有益的管理启示。     

面向任务保障的多组件系统效益优化策略

为了保障系统在执行任务期间高可靠、高效益的运行,从系统效益的角度出发,构建了系统可靠性模型,采用边际效应思想构建了效益重要度,提出了一种面向任务保障的多组件系统效益优化策略。当系统可靠度下降到设定阈值时,计算系统各组件的效益重要度,选择效益重要度最大的组件进行备件分配,如此进行迭代,直到完成任务保障,形成最优的备件分配序列。通过该策略实现了以运行效益最优为目标,以系统可靠度和任务保障时长为约束的备件分配序列。最后,通过数值仿真验证了该策略的可行性。     

Optimal replacement policies for a multicomponent reliability system

This paper studies a discrete time, infinite horizon, dynamic programming model for the replacement of components in a binary coherent system. Under quite general conditions, we show that it is optimal to follow a critical component policy (CCP), i.e., a policy specified by a critical component set and the rule: Replace a component if and only if it is failed and in the critical component set. We also discuss the problem of computing such policies.     

Multistate components assignment problem with optimal network reliability subject to assignment budget

The typical assignment problem for finding the optimal assignment of a set of components to a set of locations in a system has been widely studied in practical applications. However, this problem mainly focuses on maximizing the total profit or minimizing the total cost without considering component’s failure. In practice, each component should be multistate due to failure, partially failure, or maintenance. That is, each component has several capacities with a probability distribution and may fail. When a set of multistate components is assigned to a system, the system can be treated as a stochastic-flow network. The network reliability is the probability that d units of homogenous commodity can be transmitted through the network successfully. The multistate components assignment problem to maximize the network reliability is never discussed. Therefore, this paper focuses on solving this problem under an assignment budget constraint, in which each component has an assignment cost. The network reliability under a components assignment can be evaluated in terms of minimal paths and state-space decomposition. Subsequently an optimization method based on genetic algorithm is proposed. The experimental results show that the proposed algorithm can be executed in a reasonable time.     

Bayesian sequential testing for Lévy processes with diffusion and jump components

We study the Bayesian problem of sequential testing of two simple hypotheses about the Lévy-Khintchine triplet of a Lévy process, having diffusion component, represented by a Brownian motion with drift, and jump component of finite variation. The method of proof consists of reducing the original optimal stopping problem to a free-boundary problem. We show it is characterized by a second order integro-differential equation, that the unknown value function solves on the continuation region, and by the smooth fit principle, which holds at the unknown boundary points. Several examples are presented.     

Optimal maintenance of systems with Markovian mission and deterioration

We consider the maintenance of a mission-based system that is designed to perform missions consisting of a random sequence of phases or stages with random durations. A finite state Markov process describes the mission process. The age or deterioration process of the system is described by another finite state Markov process whose generator depends on the phases of the mission. We discuss optimal repair and optimal replacement problems, and characterize the optimal policies under some monotonicity assumptions. We also provide numerical illustrations to demonstrate the structure of the optimal policies.     

Optimal purchasing policy in a two-component assembly system with different purchasing contracts for each component

This paper considers an assembly system where a firm produces a single product which is assembled using two types of components (component 1 and component 2). The components are provided by individual suppliers (supplier 1 and supplier 2). We assume that the firm makes different procurement contracts with supplier 1 and supplier 2. To supplier 1, the firm specifies the maximum inventory level of component 1 and makes a commitment to purchase the component as long as its inventory level is below this target level. To supplier 2, the firm has the option of purchasing or rejecting component 2 at each instant supplier 2 provides it. Formulating our model as a Markov decision problem, we identify a component 2 purchasing policy which maximizes the firm’s profits subject to the costs of rejecting component 1, holding component 2, and purchasing component 2. We also investigate how the changes in the sales price and cost parameters affect the optimal purchasing policy. Finally, we present numerical study for the optimal performance evaluation.This material is based upon work supported by the Korea Science and Engineering Foundation (KOSEF) through the Northeast Asia e-Logistics Research Center at University of Incheon.     

基于承诺交货期的两阶通用件库存模型

在一个两阶段生产系统中,针对第二阶段应用单通用件的情况,引入承诺交货期因素,分别建立了第一阶段无通用件、单通用件、双通用件库存模型,考查了承诺交货期对通用件库存模型总成本的影响,分析了三类模型相应的最优库存水平。通过算例,说明了在一个第二阶段采用单通用件的两阶段生产系统中,当通用件与非通用件的单位采购成本相同时,并非第一阶段使用越多的通用件,总成本就越低。     

Cost effective scheduling of imperfect inspections in systems with hidden failures and rescue possibility

Motivated by real-world critical applications such as aircraft, medical devices, and military systems, this paper models non-repairable systems subject to a delay-time failure process involving hidden and fatal failures in two stages during their missions. A hidden failure cannot cause the system to stop functioning while a fatal failure causes the entire system loss. The system undergoes scheduled inspections for detecting the hidden failure. In the case of a positive inspection result, the system main mission is aborted and a rescue operation is started to mitigate the risk of the entire system loss. The inspections are imperfect and may produce false positive and negative failures. We propose probabilistic models for evaluating performance metrics of the system considered, including mission success probability, system survival probability, expected number of inspections during the mission, and total expected losses. Based on the evaluation models, we formulate and solve an optimization problem of finding the optimal inspection schedule on a fixed mission time horizon to minimize the total expected loss. Examples are provided to demonstrate the proposed methodology and effects of key system parameters on system performance and optimization solutions.     

The effect of testing equipment shift on optimal decisions in a repetitive testing process

Repetitive testing process is commonly used in the final testing stage of semiconductor manufacturing to ensure high outgoing product quality and to reduce testing errors. The decision on testing lot size and the number of testing repetitions ultimately determines the effectiveness of the testing process. Setting the retest rule is often difficult in practice due to uncertainties in the incoming product quality and testing equipment condition. In this paper, we study a repetitive testing process where the testing equipment may shift randomly to an inferior state. We develop a cost model that helps us to make optimal decisions on retesting rule. Through numerical analysis, we provide practical insights about the effects of testing equipment shift rate, testing errors, and different costs such as cost of testing and cost of rejecting conforming products on the optimal decision and the system performance. We find that significant penalty may result if the potential testing equipment shift is ignored.     

A hybrid algorithm for solving the economic lot and delivery scheduling problem in the common cycle case

The ELDSP problem is a combined lot sizing and sequencing problem. A supplier produces and delivers components of different types to a consumer in batches. The task is to determine the cycle time, i.e., the time between deliveries, which minimizes the total cost per time unit. This includes the determination of the production sequence of the component types within each cycle.We investigate the computational behavior of two published algorithms, a heuristic and an optimal algorithm. With large number of component types, the optimal algorithm has long running times. We devise a hybrid algorithm, which is both optimal and efficient.     

Minimizing the number of pickups on a multi-head placement machine

Multi-head gantry machines are becoming increasingly popular in surface mount technology (SMT), because they combine high printing speed with a moderate price. The optimization of their operation seems, however, to be very difficult. We formalize here a small subproblem of the scheduling problem of multi-headed SMT machines, namely the selection of nozzles which pick up and place components on printed circuit boards (PCB). The aim in this selection is to minimize the number of component pickups when manufacturing some PCB type. Given a sequence of component placement commands, a greedy nozzle usage policy picks, at each pickup, as many components next in the sequence as possible. If the nozzles are ‘universal’, that is, they can pick up any component, it is obvious that this policy is optimal. The situation gets more complicated once certain component types can be picked up only with certain nozzle types. We show that the greedy policy is optimal in this case, too. Finally, we do some experiments aimed at a better understanding of this subproblem.     

An optimal repair–replacement policy for a cold standby system with use priority

In this paper, the maintenance problem for a cold standby system consisting of two dissimilar components and one repairman is studied. Assume that both component 1 and component 2 after repair follow geometric process repair and component 1 is given priority in use when both components are workable. Under these assumptions, using geometric process repair model, we consider a replacement policy N under which the system is replaced when the number of failures of component 1 reaches N. Our purpose is to determine an optimal replacement policy N1 such that the average cost rate (i.e. the long-run average cost per unit time) of the system is minimized. The explicit expression for the average cost rate of the system is derived and the corresponding optimal replacement policy N1 can be determined analytically or numerically. Finally, a numerical example is given to illustrate some theoretical results and the model applicability.     

An optimum testing algorithm for some symmetric coherent systems

A symmetric coherent system (or k-out-of-n system) is a system composed of n components CO₁, CO₂ …, CO_n, each component existing in either a working or failing state. Such a system is in a working state if and only if k or more of its components are working, where 1 ? k ? n. It is assumed that the components can only be tested individually, and every test gives perfect information as to whether the tested component is working or failing. Let P_i, be the a priori probability that the component CO_i is working and C_i, be the cost of testing component CO_i. An optimal (minimum total expected cost) testing algorithm is an algorithm to determine the condition of a given symmetric coherent system by testing some of its components individually. In general, such an algorithm is a sequential process, that is, the next component to be tested is a function of the outcomes of the tests already applied. Every (optimal) testing algorithm corresponds to a (optimal) feasible testing policy which is basically a binary rooted tree with some component assigned to each node. In this paper an algorithm is presented for constructing an optimal feasible testing policy for symmetric coherent systems, where $\text{C}{}_{\mathrm{i}}\text{P}{}_{\mathrm{i}}\text{≠}\text{C}{}_{\mathrm{j}}\text{P}{}_{\mathrm{j}}$ and $\text{C}{}_{\mathrm{i}}\text{(1 ? P}{}_{\mathrm{i}}\text{)}\text{≠}\text{C}{}_{\mathrm{j}}\text{(1 ? P}{}_{\mathrm{j}}\text{)}$ whenever i ≠ j. This algorithm can be implemented as an optimal testing algorithm with polynomial complexity. Moreover, it is proven that any optimal testing algorithm corresponds to some feasible testing policy which can be generated by this algorithm.     

A branch and bound algorithm for minimizing the expected cost of testing coherent systems

We are interested in the problem of minimizing the expected cost of testing a coherent system. The concept of the Importance of Components is used to develop a branch and bound algorithm which determines the optimal testing policy for any coherent system.