Optimal RSUs deployment with delay bound along highways in VANET

In order to broadcast an alert message from the accident site to the control center as soon as possible, the roadside units (RSUs) act as the critical component in vehicular ad hoc networks (VANETs). It is not possible to make a pervasive RSU deployment due to the huge cost and market requirement. Hence, how to deploy a minimum number of RSUs in a given region becomes a challenge problem. In this paper, we present an analysis for the total delay of broadcasting alert messages in VANETs along highways. Based on the analysis, the relationship between optimal number of RSUs with the highway distance is given. Moreover, the experiment results verifies the delay analysis and the optimization of RSUs deployment.     

考虑服务水平与动态转移规律的共享单车投放策略研究

为实现共享单车行业的精细化、科学化的运营管理，解决共享单车的过度投放问题。首先，对单车运营系统进行分析和描述，明确投放量优化涉及的核心研究问题。将研究范围界定在单车投放环节，利用马尔可夫链与状态转移矩阵来分析和描述单车的流转过程；从需求被满足概率的角度描述共享单车系统的服务水平并据此分析各投放点的单车需求量。在分析和描述的基础上，提出单车投放量优化的核心问题。其次，基于需求量的约束与单车流转规律，构建不同调度方案下的投放量优化模型，结合可行解空间变化对目标函数最优值的影响，分析最优单车投放量的性质。得出结论认为：（1）对时空的细分，有助于更准备地把握需求，最优单车投放量也会增加。（2）可以通过调度频率的增加，减少单车的投放量，但投放量有上、下限。最后，结合案例数据，对状态转移概率矩阵和服务水平约束下节点的需求量进行了计算，并据此对优化模型进行数值求解，展示了所构建模型的应用过程，说明了模型对于解决具体问题的可操作性和有效性。文章的模型及相应的求解过程，可为解决资源的优化配置问题提供参考和借鉴。     

An Efficient Algorithm for Delay Buffer Minimization

A data flow machine is said to be synchronized if for any vertex u in the underlying data flow graph, all inputs to vertex u arrive at the same time. An unsynchronized data flow machine with an acyclic underlying data flow graph can be transformed into a synchronized system by adding unit delay buffers to the system. This synchronization process can increase pipelining and throughout. Since the addition of delay buffers introduces hardware and area costs, it is desirable to insert the minimum number of delay buffers to synchronize a given data flow machine. Due to important applications in computer design, various delay buffer minimization problems have been studied by many researchers. Several optimal algorithms and heuristic algorithms have been proposed for slightly different models. In this paper, we introduce the concept of extensions of a directed acyclic graph to generalize and formalize several delay buffer minimization problems studied in the literature and present a polynomial time algorithm for computing the minimum delay buffer synchronization of a given data flow machine. Examples are provided to illustrate our algorithm and to show that our algorithm requires fewer delay buffers than previously published optimal algorithms for various models.     

考虑差异化服务时间的多车型电动汽车路径优化与充电策略研究

在政府政策大力支持以及社会环境意识不断增长的背景下,电动汽车在物流配送行业快速普及。电动汽车参与的物流配送服务需要物流专员、电动汽车和顾客三方协作完成。因此,在传统车辆配送路径优化的基础上,车辆的多样性、充电策略、人车的匹配以及服务时间差异化等因素都会影响物流运营成本。本文提出了考虑差异化服务成本的多车型电动汽车路径优化与充电策略问题并建立了该问题的整数规划数学模型。其次,提出了混合启发式算法MCWGATS,并通过多组算例验证了算法的有效性。最后,采用多组算例分析了多车型和差异化服务时间对运营成本的影响。实验结果表明,该模型有助于物流企业提高人员、物流车辆、服务时间等资源的利用效率,降低运营成本。     

Approximations to Optimal k‐Unit Cycles for Single‐Gripper and Dual‐Gripper Robotic Cells

We consider the problem of scheduling operations in bufferless robotic cells that produce identical parts using either single‐gripper or dual‐gripper robots. The objective is to find a cyclic sequence of robot moves that minimizes the long‐run average time to produce a part or, equivalently, maximizes the throughput. Obtaining an efficient algorithm for an optimum k‐unit cyclic solution (k ≥ 1) has been a longstanding open problem. For both single‐gripper and dual‐gripper cells, the approximation algorithms in this paper provide the best‐known performance guarantees (obtainable in polynomial time) for an optimal cyclic solution. We provide two algorithms that have a running time linear in the number of machines: for single‐gripper cells (respectively, dual‐gripper cells), the performance guarantee is 9/7 (respectively, 3/2). The domain considered is free‐pickup cells with constant intermachine travel time. Our structural analysis is an important step toward resolving the complexity status of finding an optimal cyclic solution in either a single‐gripper or a dual‐gripper cell. We also identify optimal cyclic solutions for a variety of special cases. Our analysis provides production managers valuable insights into the schedules that maximize productivity for both single‐gripper and dual‐gripper cells for any combination of processing requirements and physical parameters.     

动态需求下车辆路径问题的周期性优化模型及求解

针对客户点不断更新的动态需求车辆路径问题,依据滚动时域对配送中心工作时间进行划分,提出基于延迟服务的周期性客户点实时重置策略,策略中延迟服务机制能结合车辆启动延迟系数对照当前时域的时间进行检验,满足所有客户点的服务需求,保证车辆满足中心时间窗约束。设计多阶段求解的混合变邻域人工蜂群算法对各时间片内子问题进行连续迭代优化,算法中子路径动态转变的设计能较好平衡原有客户点和新客户点对路径更新和车辆实时信息匹配的要求。算例验证及对比分析表明本文策略和算法在求解动态问题时的有效性和可行性。     

Defense Resource Distribution Between Protection and Redundancy for Constant Resource Stockpiling Pace

The article considers the optimal resource distribution in a parallel system between increasing protection and providing redundancy in a situation when the attacker's and defender's resources are stockpiling and the resource increment rate is constant. It is assumed that the system must perform within an exogenously given time horizon and the attack time probability is uniformly distributed along this horizon. The defender optimizes the resource distribution in order to minimize the system destruction probability during the time horizon. First, we find the optimal pace of construction of the new redundant elements assuming that the construction must start in the initial stage of the stockpiling process. We show that starting construction of new elements in the beginning of the system's existence results in its high initial vulnerability. Introducing the time delay before starting the construction can reduce the initial system vulnerability and the entire system destruction probability. The problem of optimization of time delay and new element construction pace is considered with and without constraint on the initial system vulnerability. Examples illustrating the methodology of the optimal defense strategy analysis are presented.     

考虑充电策略与电池损耗的电动汽车路径优化问题研究

针对目前研究电动物流车辆路径问题的文章未考虑电池损耗对运营成本的影响,且多在充电速率为恒定值的情况下对充电策略进行优化,本文将电动物流车辆在配送货物途中的充电时间和电池损耗成本纳入目标函数并建立了线性规划数学模型,统筹安排车辆行驶路径和充电策略使得物流企业整体运营成本最低。其次,提出了求解该问题的多阶段启发式算法MCWIGALNS。随后,通过多组算例验证了模型和算法的准确性。实验结果表明,考虑充电时间与深度放电成本的模型可以在配送距离不变或略有增加的情况下,较大幅度减少充电时间与电池损耗成本,到达降低运营成本的目的。最后,将算法实验结果与本领域已发表的成果进行比较,证明了MCWIGALNS算法对车辆路径问题具有出色的求解能力,提升了该问题理论成果的实用性。可以为物流企业电动汽车路径策略提供良好借鉴与帮助。     

Resource constrained scheduling with general truncated job-dependent learning effect

Scheduling with general truncated job-dependent learning effect and resource-dependent processing times is studied on a single machine. It is assumed that the job processing time is a function of the amount of resource allocated to the job, the general job-dependent learning effect and the job-dependent control parameter. For each version of the problem that differs in terms of the objective functions and the processing time functions, the optimal resource allocation is provided. Polynomial time algorithms are also developed to find the optimal schedule of several versions of the problem.     

缺陷汽车召回所需安排的最优维修店数目

讨论了缺陷汽车产品制造商在汽车召回事件中如何安排最优维修店数目。假设汽车制造商在召回期限内维修几乎全部召回汽车,车主逗留时间和维修时间都服从指数分布,并且假设维修人员会根据库存水平(车位)调整维修速度。选取制造商安排组织的维修店数目为决策变量,利用有限源排队理论建立了成本控制模型。搜索算法确定了最优的维修店数目,使得期望总成本最小。     

Determining the optimal double-component assignment for a stochastic computer network

This study determines the optimal double-component assignment based on the system reliability criterion for a computer system, in which the computer system is represented as a network with a set of links and a set of vertices. The double-component assignment is to assign a set of transmission lines (resp. facilities) to the links (resp. vertices) of the network, in which each transmission line (resp. facility) has multiple states due to maintenance or failure. Thus, the computer system according to any double-component assignment is called a stochastic computer network. The system reliability is the probability that the specific units of data are successfully transmitted through the stochastic computer network. An optimization algorithm which integrates the genetic algorithm, minimal paths, and Recursive Sum of Disjoint Products is utilized to find the optimal double-component assignment with maximal system reliability. Several computer networks are utilized to demonstrate the efficiency of the proposed algorithm compared with other algorithms. By solving this problem, data can be more reliably transmitted and thus the organization operation is executed more smoothly.     

A multi-depot dial-a-ride problem with heterogeneous vehicles and compatibility constraints in healthcare

In this paper, a multi-depot dial-a-ride problem arising from a real-world healthcare application is addressed, concerning the non-emergency transportation of patients. The problem presents several constraints and features, such as heterogeneous vehicles, vehicle–patient compatibility constraints, quality of service requirements, patients׳ preferences, tariffs depending on the vehicles׳ waiting. Variable Neighborhood Search and Tabu Search algorithms are proposed able to tackle all the characteristics of the problem. A Mixed Integer Linear Programming formulation is also presented. Computational results on large real-world and random instances based on real data show the effectiveness of the proposed approaches.     

Determining an Optimal Penetration Among Weighted Regions in Two and Three Dimensions

We present efficient algorithms for solving the problem of computing an optimal penetration (a ray or a semi-ray) among weighted regions in 2-D and 3-D spaces. This problem finds applications in several areas, such as radiation therapy, geological exploration, and environmental engineering. Our algorithms are based on a combination of geometric techniques and optimization methods. Our geometric analysis shows that the d-D (d = 2, 3) optimal penetration problem can be reduced to solving O(n ^2(d–1)) instances of certain special types of non-linear optimization problems, where n is the total number of vertices of the regions. We also give implementation results of our 2-D algorithms.     

Genetic algorithms for optimization of a mine system under uncertainty

The production planning of a mine system associated with mining, processing and refining stages dictates to determine optimal system parameters such as optimal production rates, location of refining facility and the best reconstruction time of production rates. This paper proposes a combination of the chance constrained programming (CCP) and the genetic algorithms (GA) to find the optimal system parameters simultaneously. In generic form the problem is expressed as the maximization of net present value of future cash flows such that the capacity constraint and predefined specifications are satisfied. The blending requirements expressed in the CCP are transformed into deterministic equivalents. A new form of the problem is solved by the GA. The approach was demonstrated on extraction, processing and refining of four iron ore mines with varying reserves, ore qualities, geological and topographic conditions, four mineral processing units and one refining facility. The results showed that the proposed algorithm could be used to determine optimal production rates, the facility location and the best reconstruction time.     

Robust,multi-objective optimization for the military medical evacuation location-allocation problem

Determining where to locate mobile aeromedical staging facilities (MASFs) as well as identifying how many aeromedical helicopters to allocate to each MASF, commonly referred to as the medical evacuation (MEDEVAC) location-allocation problem, is vital to the success of a deployed MEDEVAC system. Within this research, we develop an integer mathematical programming formulation to determine the location and allocation of MEDEVAC assets over the phases of a military deployment to support operations ranging from peacekeeping through combat to post-combat resolution. Our model seeks to address the multi-objective problem of maximizing the expected demand coverage as a measure of solution effectiveness, minimizing the maximum number of located MASFs in any deployment phase as a measure of solution efficiency, and minimizing the total number of MASF relocations throughout the deployment as a measure of solution robustness. This research makes two contributions. First, it formulates a representative mathematical programming formulation and identifies an accompanying solution methodology (i.e., the ε-constraint Method) to assess and recommend improvements to deployed military MEDEVAC systems designed to provide large-scale emergency medical response for contingency operations that range in casualty-inducing intensity (i.e., demand) over the phases of a deployment. Second, the research illustrates the application of the model for a realistic, synthetically generated medical planning scenario in southern Azerbaijan. Comparisons are made between the model’s (multi-phase) optimal solution and the phase-specific optimal solutions that disregard concerns of transitions between phases. The results highlight the conflicting nature between the objectives and illustrate the trade-offs between objectives as restrictions applied to the second and third objectives are respectively tightened or relaxed.     

Approximate event detection over multi-modal sensing data

Composite event detection is one of the fundamental tasks for heterogeneous wireless sensor networks (WSNs). Multi-modal data generated by heterogeneous sensors bring new challenges for composite event monitoring in heterogeneous WSNs. By exploiting the correlations between different types of data, the approximate composite event detection problem is investigated in this paper. The optimal transmitting scheme problem is proposed to calculate the optimal transmitting scheme with minimum cost on the constraint that the confidence of the composite event must exceed the threshold. The optimal transmitting scheme problem is proved to belong to NP-complete. A dynamic programming based algorithm is presented for simple linear confidence combination operators, which runs in pseudo-polynomial time. The greedy based approximate algorithm is also designed for general confidence combination operators and the approximate ratio is proved to be 2 for “+” as the confidence combination operator. The simulation results show that our algorithms can reduce energy consumption significantly.     

Fast On-Line/Off-Line Algorithms for Optimal Reinforcement of a Network and its Connections with Principal Partition

The problem of computing the strength and performing optimal reinforcement for an edge-weighted graph G(V, E, w) is well-studied. In this paper, we present fast (sequential linear time and parallel logarithmic time) on-line algorithms for optimally reinforcing the graph when the reinforcement material is available continuously on-line. These are the first on-line algorithms for this problem. We invest O(|V|³|E|log|V|) time (equivalent to (|V|) invocations of the fastest known algorithms for optimal reinforcement) in preprocessing the graph before the start of our algorithms. It is shown that the output of our on-line algorithms is as good as that of the off-line algorithms. Thus our algorithms are better than the fastest off-line algorithms in situations when a sequence of more than (|V|) reinforcement problems need to be solved. The key idea is to make use of ideas underlying the theory of Principal Partition of a Graph. Our ideas are easily generalized to the general setting of polymatroid functions. We also present a new efficient algorithm for computation of the Principal Sequence of a graph.     

时变供需下基于商业信用的零售商最优订货策略

论文在时变供需环境下,即市场需求为斜坡式时间函数及补货率依赖于市场需求情境下,探讨了基于商业信用的供应链中零售商最优订货策略。首先,根据商业信用期和零售商补货周期的关系,分两种情形构建了零售商库存模型;其次,根据零售商的库存模型,证明了其最优解的存在,并给出相关的命题和算法来求解零售商的最优库存策略;最后,通过数值算例和灵敏度分析来论证文中的有关结论。研究发现:当供应商给予零售商的商业信用期较短时,此时市场需求仍处于增长期,随着市场需求稳定时间点的增大,零售商的最优订货周期逐渐减小,最优订货量和年费用也逐渐减小;当供应商给予零售商的商业信用期较长时,此时市场需求已趋于稳定期,随着市场需求稳定时间点的增大,零售商的最优订货周期逐渐增大,最优订货量和年费用也逐渐增大;当供应商生产与市场需求依赖性逐渐增大时,零售商的最优订货周期逐渐增大,最优订货量及年费用也逐渐增大。     

电子商务下基于改进两阶段算法的有时间窗车辆调度优化

为满足电子商务下的物流配送需求,将传统车辆调度模型进行修改,将目标函数改为基于费用最小,在约束条件中增加时间约束、货物容积约束、车辆最大工作时间、多种车型、载重量限制和最大行驶距离等,以提高模型的适用性和通用性。由于有时间窗的车辆调度问题是NP难问题,采用改进两阶段算法进行求解。即第一阶段用模糊分层聚类法将客户群分成若干区域,在每个区域又用扫描算法分解成若干符合约束条件的小规模子集;第二个阶段对各个分组内客户点,就是一个个单独TSPTW模型的线路优化问题,因此,采用改进混合遗传算法进行优化求解,最后的算例仿真表明了算法的有效性和可行性。     

基于遗传算法的虚拟企业协同资源优化问题研究

面向复杂零件的协同制造,以工艺流程为核心将协同制造任务进行分解,并有效利用"逻辑制造单元"和"逻辑加工路线"等概念描述复杂零件的协同制造任务,最终目标是形成基于复杂零件工艺流程的、可支撑异地协同生产的加工路线。对复杂零件协同制造的制造资源优化配置问题进行了数学分析和描述,建立了问题的目标函数与约束条件。本文以加工时间、运输费用和加工质量作为目标,约束条件包括顺序约束、释放期约束、时间约束、交货期约束、成本约束和质量约束,最终将资源优化配置问题归结为多目标优化问题,并利用遗传算法进行求解,得到了较为满意的结果。通过实例分析,将模型应用于某型号发动机叶片协同制造,说明采用本论文的模型可以有效解决复杂零件协同制造的资源优化配置问题。