Dynamic routing of prioritized warranty repairs

Recent years have seen a strong trend toward outsourcing warranty repair services to outside vendors. In this article we consider the problem of dynamically routing warranty repairs to service vendors when warranties have priority levels. Each time an item under warranty fails, it is sent to one of the vendors for repair. Items covered by higher priority warranty receive higher priority in repair service. The manufacturer pays a fixed fee per repair and incurs a linear holding cost while an item is undergoing or waiting for repair. The objective is to minimize the manufacturer's long‐run average cost. Because of the complexity of the problem, it is very unlikely that there exist tractable ways to find the optimal routing strategies. Therefore, we propose five heuristic routing procedures that are applicable to real‐life problems. We evaluate the heuristics using simulation. The simulation results show that the index‐based “generalized join the shortest queue” policy, which applies a single policy improvement step to an initial state‐independent policy, performs the best among all five heuristics. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

军事通信中ad hoc网络路由协议性能分析

移动ad hoc组网灵活,抗毁性很强,将在未来的军事通信中发挥重要作用。因此,对于移动ad hoc网络研究中的首要的路由问题作了探讨,先简要介绍了两种典型的路由协议OLSR(Optimized Link State Routing)和AODV(Ad hoc On-Demand Distance Vector Routing),接着利用网络仿真工具OPNET分析了AODV的协议特点,最后比较了两种路由协议的性能。仿真结果表明AODV路由开销小,但时延大,节点移动过快时存在路由稳定性的问题;OLSR路由开销很大,但时延相对较小。因此在搭建军用adhoc网时,应根据实际使用情形做出选择。     

具有战时随机延误与损耗的多配送中心路径优化

建立了具有战时随机延误与损耗的多配送中心配送路径安排模型,给出了基于随机模拟的蚁群算法。算法通过给定残存率、用时与置信度阈值,把多目标问题作为单目标来处理。用随机模拟的方法来求路径的置信度,并以此为基础搜索转移策略的临域与判断未遍历点的插入位置。算法设计了符合问题特点的从虚拟点出发的转移策略与对两类路段不同的信息素更新策略,确保算法的实现。最后,通过算例说明了该方法的可行性与有效性。     

需求可拆分的应急物资调度问题的蚁群算法

应急物资调度问题是个典型的需求可拆分的车辆路径问题,区别于传统的车辆路径问题,将每个需求节点只能由一辆车访问的约束去除,允许需求节点由多辆车进行访问。针对应急物资调度问题的特点,建立相应的多目标车辆路径数学规划模型(SDVRP),并根据模型特点设计改进蚁群优化算法。最后,进行相应的算例分析,验证了该模型和算法的有效性。     

航空集群机载网络Failure-Oblivious路由策略

航空集群机载网络作为集群成员间信息交互的纽带,其路由策略性能优劣直接影响信息传输实时性与可靠性,从而制约网络化集群作战效能发挥.考虑到航空集群机载网络具有诸多不确定性,为应对路由失效以及尽可能避免路由更新,从路由选择算法的角度,在软件定义网络架构下提出Failure-Oblivious路由策略.与传统路由策略不同的是,...     

Flexibility and complexity in periodic distribution problems

In this paper, we explore trade‐offs between operational flexibility and operational complexity in periodic distribution problems. We consider the gains from operational flexibility in terms of vehicle routing costs and customer service benefits, as well as the costs of operational complexity in terms of modeling, solution methods, and implementation challenges for drivers and customers. The period vehicle routing problem (PVRP) is a variation of the classic vehicle routing problem in which delivery routes are constructed for a period of time; the PVRP with service choice (PVRP‐SC) extends the PVRP to allow service (visit) frequency to become a decision of the model. For the periodic distribution problems represented by PVRP and PVRP‐SC, we introduce operational flexibility levers and a set of quantitative measures to evaluate the trade‐offs between flexibility and complexity. We develop a Tabu Search heuristic to incorporate a range of operational flexibility options. We analyze the potential value and the increased operational complexity of the flexibility levers. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Inventory/routing: Reduction from an annual to a short-period problem

The inventory-routing problem is a distribution problem in which each customer maintains a local inventory of a product such as heating oil and consumes a certain amount of that product each day. Given a central supplier, the objective is to minimize the annual delivery costs while attempting to insure that no customer runs out of the commodity at any time. In this article we present a procedure for reducing the long-term version of this problem to a single-period problem, which can be attacked using standard routing algorithms. The reduction procedure involves the definition of single-period costs that reflect long-term costs, the definition of a safety stock level and a specification of the customer subset to be considered during a single period.     

Split delivery routing

This article examines a relaxed version of the generic vehicle routing problem. In this version, a delivery to a demand point can be split between any number of vehicles. In spite of this relaxation the problem remains computationally hard. Since only small instances of the vehicle routing problem are known to be solved using exact methods, the vehicle route construction for this problem version is approached using heuristic rules. The main contribution of this article to the existing body of literature on vehicle routing issues in (a) is presenting a new vehicle routing problem amenable to practical applications, and (b) demonstrating the potential for cost savings over similar “traditional” vehicle routing when implementing the model and solutions presented here. The solution scheme allowing for split deliveries is compared with a solution in which no split deliveries are allowed. The comparison is conducted on six sets of 30 problems each for problems of size 75, 115, and 150 demand points (all together 540 problems). For very small demands (up to 10% of vehicle's capacity) no significant difference in solutions is evident for both solution schemes. For the other five problem sets for which point demand exceeds 10% of vehicle's capacity, very significant cost savings are realized when allowing split deliveries. The savings are significant both in the total distance and the number of vehicles required. The vehicles' routes constructed by our procedure tend to cover cohesive geographical zones and retain some properties of optimal solutions.     

Outsourcing warranty repairs: Dynamic allocation

In this paper we consider the problem of minimizing the costs of outsourcing warranty repairs when failed items are dynamically routed to one of several service vendors. In our model, the manufacturer incurs a repair cost each time an item needs repair and also incurs a goodwill cost while an item is awaiting and undergoing repair. For a large manufacturer with annual warranty costs in the tens of millions of dollars, even a small relative cost reduction from the use of dynamic (rather than static) allocation may be practically significant. However, due to the size of the state space, the resulting dynamic programming problem is not exactly solvable in practice. Furthermore, standard routing heuristics, such as join‐the‐shortest‐queue, are simply not good enough to identify potential cost savings of any significance. We use two different approaches to develop effective, simply structured index policies for the dynamic allocation problem. The first uses dynamic programming policy improvement while the second deploys Whittle's proposal for restless bandits. The closed form indices concerned are new and the policies sufficiently close to optimal to provide cost savings over static allocation. All results of this paper are demonstrated using a simulation study. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

运用动态规划的智能卫星集群星间通信路由算法

通过建立智能卫星集群网络模型,把智能卫星集群星间通信路由问题转换为时延最短路径问题,进而提出一种求解此问题的智能卫星集群星间通信路由算法.该路由算法采用动态规划策略分阶段规划智能卫星集群两个成员之间的星间通信路由,在每个规划阶段,负责发送数据的智能卫星自主调用一种星间通信路由静态规划算法,以求出其在当前时刻的后继卫星来...     

Multiaction maintenance under markovian deterioration and incomplete state information

Much research been devoted to modeling the replacement problem under incomplete state information. Almost no work has been done on the maintenance problem under incomplete information with multiple maintenance actions that may not return the system to as good as new. We model this problem and derive structural results concerning the optimal maintenance policy. For the case where the effect of maintenance actions is state dependent, we give conditions under which the optimal policy is finitely computable. Where maintenance is state independent we show a specific structure, consisting of monotonic waiting times and constant maintenance actions, to be optimal.     

Branch‐and‐price‐and‐cut for the manpower routing problem with synchronization constraints

In this article, we propose a branch‐and‐price‐and‐cut (BPC) algorithm to exactly solve the manpower routing problem with synchronization constraints (MRPSC). Compared with the classical vehicle routing problems (VRPs), the defining characteristic of the MRPSC is that multiple workers are required to work together and start at the same time to carry out a job, that is, the routes of the scheduling subjects are dependent. The incorporation of the synchronization constraints increases the difficulty of the MRPSC significantly and makes the existing VRP exact algorithm inapplicable. Although there are many types of valid inequalities for the VRP or its variants, so far we can only adapt the infeasible path elimination inequality and the weak clique inequality to handle the synchronization constraints in our BPC algorithm. The experimental results at the root node of the branch‐and‐bound tree show that the employed inequalities can effectively improve the lower bound of the problem. Compared with ILOG CPLEX, our BPC algorithm managed to find optimal solutions for more test instances within 1 hour. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 138–171, 2016     

C4ISR分布式信息融合系统拓扑结构设计

提出一种构造分布式信息融合系统的拓扑结构的新方法.首先,简要介绍了该问题的研究现状;然后,在对目前使用的网络结构进行深入分析的基础上,提出了基于多层de Bruijn网的分布式信息融合系统网络拓扑结构及其构造方法;最后讨论了上述网络结构的路由问题.     

The effectiveness of the longest delivery time rule for the flow shop delivery time problem

In the flow shop delivery time problem, a set of jobs has to be processed on m machines. Every machine has to process each one of the jobs, and every job has the same routing through the machines. The objective is to determine a sequence of the jobs on the machines so as to minimize maximum delivery completion time over all the jobs, where the delivery completion time of a job is the sum of its completion time, and the delivery time associated with that job. In this paper, we prove the asymptotic optimality of the Longest Delivery Time algorithm for the static version of this problem, and the Longest Delivery Time among Available Jobs (LDTA) algorithm for the dynamic version of this problem. In addition, we present the result of computational testing of the effectiveness of these algorithms. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

Iterative methods for dynamic stochastic shortest path problems

We consider a routing policy that forms a dynamic shortest path in a network with independent, positive and discrete random arc costs. When visiting a node in the network, the costs for the arcs going out of this node are realized, and then the policy will determine which node to visit next with the objective of minimizing the expected cost from the current node to the destination node. This paper proposes an approach, which mimics the classical label-correcting approach, to compute the expected path cost. First, we develop a sequential implementation of this approach and establish some properties about the implementation. Next, we develop stochastic versions of some well-known label-correcting methods, including the first-in-first-out method, the two-queue method, the threshold algorithms, and the small-label-first principle. We perform numerical experiments to evaluate these methods and observe that fast methods for deterministic networks can become very slow for stochastic networks. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 769–789, 1998     

一种基于节点资源利用率的无标度网络路由策略

本文研究了节点负载处理能力异质条件下的无标度网络交通动力学过程,提出了一种基于节点资源利用率的全局动态路由策略。该策略利用网络中节点资源利用率构建了一种全局代价函数,选择使该代价函数最小的路径来传输负载。实验结果表明该路由策略在略微增加平均路径长度的情况下成倍地提高了网络负载传输能力,与有效路由策略的比较进一步验证了该策略的有效性。     

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.     

Continuous-time markov decision processes with nonzero terminal reward

In this article we consider a continuous-time Markov decision process with a denumerable state space and nonzero terminal rewards. We first establish the necessary and sufficient optimality condition without any restriction on the cost functions. The necessary condition is derived through the Pontryagin maximum principle and the sufficient condition, by the inherent structure of the problem. We introduce a dynamic programming approximation algorithm for the finite-horizon problem. As the time between discrete points decreases, the optimal policy of the discretized problem converges to that of the continuous-time problem in the sense of weak convergence. For the infinite-horizon problem, a successive approximation method is introduced as an alternative to a policy iteration method.     

Solving min-max shortest-path problems on a network

In this article we consider the problem of determining a path between two nodes in a network that minimizes the maximum of r path length values associated with it. This problem has a direct application in scheduling. It also has indirect applications in a class of routing problems and when considering multiobjective shortest-path problems. We present a label-correcting procedure for this problem. We also develop two pruning techniques, which, when incorporated in the label-correcting algorithm, recognize and discard many paths that are not part of the optimal path. Our computational results indicate that these techniques are able to speed up the label-correcting procedure by many orders of magnitude for hard problem instances, thereby enabling them to be solved in a reasonable time. © 1992 John Wiley & Sons, Inc.     

MANET多路径路由中最大可靠性路径选择算法

如何选择路径的数量和质量对多路径路由机制的性能有着重要的影响。已有的多路径算法没有深入研究如何选择多路径的问题。对目前存在的两个典型问题进行了分析,在此基础上研究了路径可靠性模型和虚拟完全非交叉多路径模型,然后提出一个最大可靠性多路径选择算法。算法利用路径权重作为路径可靠性的近似解决方案,以此克服路径可靠性度量问题(NP难题)研究的复杂性,根据路径可靠性模型和完全非交叉多路径模型来选择可靠的路径集,使用这组路径集并行分布流量。应用OPNET模拟平台实现了算法,结果表明,本算法能增加聚合带宽,优化网络带宽的应用,提高网络的吞吐率和多路径路由的性能。