Optimal RSUs placement with delay bounded message dissemination in vehicular networks

In order to transfer emergency messages from the accident site to the control center as soon as possible, the Roadside Units (RSUs) act as the most important roles in vehicular ad hoc networks (VANETs). However, we always hope to deploy the minimum number of RSUs because of the low market penetration of VANET-enabled vehicles and the deployment cost of RSUs. This paper presents a transmission delay analysis in urban areas with IID vehicles, and then reveal the relationship between the transmission time and message dissemination distance. According to the analysis, the optimal RSUs deployment problem is formulated. We propose three heuristic greedy strategies as code methods of genetic algorithms to solve the optimal RSUs placement problem with delay bound. Simulation results show that the solutions are efficient and the time complexity is lower than those existing algorithms.     

Location optimization of strategic alert sites for homeland defense

This research uses a location analysis approach for selecting aircraft alert sites for the defense of important national areas of interest identified by the US Department of Defense. Solutions are generated in a two step approach where the minimum number of sites is first identified using the location set covering problem and then the result is improved by finding the minimum aggregate network distance or p-median solution from the alternate optimal solutions to the LSCP. This approach also identifies the p-center solution to the problem ensuring equitable response to all areas of interest. Sensitivity analysis is performed to determine the impact of altering aircraft launch and flying times on the number of required alert sites and the amount of coverage provided by selecting fewer locations. Results indicate a significant increase in the number of alert locations needed in comparison to original military estimates. The research points out significant implications about future military base closure decisions and the trade-offs between cost and required response times of aircraft in a defense emergency.     

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。     

订单不确定条件下的供应链协同决策研究

在产品客户化程度较高的装配生产中,制造商在获得客户的订单需求信息,但订单尚未签订的情况下,往往就要求供应商开始生产订单所需的零部件,以便客户订单签订后可以立刻开始产品的装配生产,从而实现尽快向客户交付订单的目的。但是,在客户订单不确定条件下,供应商提前生产零部件存在着一定的风险。一旦客户订单最终未能签订,由于零部件客户化程度一般也比较高,在相当长一段时间内很难被其它订单消化,从而形成呆滞库存。本文基于这样的运作环境,通过数学建模分析,研究了制造商何时向供应商下达零部件订单最优,以及供应商的最优生产决策问题,并给出了具体的决策方法。最后通过算例验证了模型的结论,并分析了生产延滞成本分担系数对供应商和制造商双方期望利润的影响。     

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.     

Reducing Risk Through Real Options in Systems Design: The Case of Architecting a Maritime Domain Protection System

Complex engineering systems are usually designed to last for many years. Such systems will face many uncertainties in the future. Hence the design and deployment of these systems should not be based on a single scenario, but should incorporate flexibility. Flexibility can be incorporated in system architectures in the form of options that can be exercised in the future when new information is available. Incorporating flexibility comes, however, at a cost. To evaluate if this cost is worth the investment a real options analysis can be carried out. This approach is demonstrated through analysis of a case study of a previously developed static system-of-systems for maritime domain protection in the Straits of Malacca. This article presents a framework for dynamic strategic planning of engineering systems using real options analysis and demonstrates that flexibility adds considerable value over a static design. In addition to this it is shown that Monte Carlo analysis and genetic algorithms can be successfully combined to find solutions in a case with a very large number of possible futures and system designs.     

Should Cities Invest in Sheltering‐in‐Place Measures Against Chlorine Truck Attacks by Terrorists?

After an intentional release of chlorine in an office district, public responses such as sheltering‐in‐place could save many lives if rapid enough. However, previous work does not estimate how fast and effective such responses would be for several possible investments in attack detection, public alert, and building ventilation, nor whether such measures would be cost effective. We estimate public response times with investment options in place, and resulting changes in fatalities as well as system costs, including false alarm costs, and cost effectiveness in terms of cost per net death avoided. The measures do have life‐saving potential, especially if all response times are at or near the lower limits of the ranges assumed in this article. However, due to uncertainties, it is not clear that responses would be rapid enough to save many people. In some cases total fatalities would increase, since sheltering after chlorine vapor has already entered buildings can increase occupants’ chlorine exposure. None of the options considered have median cost per statistical life saved meeting a cost‐effectiveness threshold of $6.5 million across all of the chlorine exposure dose‐response and ingress‐delay models considered here, even if there were one attack per year in the area covered by the system. Given these and other issues discussed in this article, at this point investments to improve sheltering‐in‐place capability appear not to be robust strategies for reducing fatalities from chlorine attack in an office district.     

An Evaluation of the NERJIT Priority Rule in a Kanban‐Controlled Flowshop

Significant progress in production and information technologies and innovations in management of operations during the last couple of decades have made the production of small lots and deployment of Just‐In‐Time (JIT) concepts in flowshops possible. As a result, some researchers and practitioners have been seeking to improve the performance of non‐repetitive systems using JIT concepts. In this process, the JIT concepts that were originally designed for mass production have been modified to adapt JIT to non‐repetitive systems. This article uses a priority rule that is based on real‐time demand and production information for sequencing jobs in a kanban‐controlled flowshop. The analysis of the effect of this priority rule; the number of kanbans; the length of the withdrawal cycle; First‐Come, First‐Served (FCFS); and Shortest Processing Time (SPT) on four performance measures—customer wait time, total inventory, input stock‐point inventory, and output stock‐point inventory, shows that the use of this priority rule results in a significant reduction of customer wait time and a slight decrease in inventory.     

An analysis of vendor innovation capability in the contract electronics manufacturing industry

Limited academic research has been given to analysing the innovation capabilities of vendors in outsourcing contracts. This paper seeks to address this gap in the literature by enhancing our understanding of how the innovation capability of vendors is deployed to win, run and renew outsourcing contracts with their customers. Employing the resource-based view as a theoretical basis and undertaking in-depth case study analysis of three vendors in the electronic manufacturing services industry, the research shows that to achieve the outsourcing objectives of winning, running and renewing the contract, vendors can use different configurations of the competitive priorities of cost, quality, delivery and flexibility. The research aggregates the capabilities that influence the innovative capability of a vendor into the innovation-related capabilities (IRCs) of design, new product introduction and manufacturing. Three strategies are identified for vendors on how to deploy these IRCs, and a number of propositions are developed to indicate the suitability of the three deployment strategies for different operational contexts.     

Food Risks on the Web: Analysis of the 2017 Fipronil Alert in the Italian Online Information Sources

In the summer of 2017, several European Union Member States were involved in a food alert caused by the presence of fipronil pesticide residues in chicken eggs. The food alert became a major news and received wide coverage both in the mass media and on the Internet. This article describes a study that analyzed how the Italian online information sources represented the fipronil alert, using web monitoring techniques and both manual and automatic content analysis methods. The results indicate that the alert was amplified because general news media could represent the alert within the frame of a political scandal, and because different social actors exploited the case. However, online information sources correctly communicated that the risks for consumers were low, reporting mainly what was officially communicated by the Italian health authorities. The study provides empirical evidence on how the online information sources represent food risks and food alerts and offers useful indications for health authorities in charge of the public communication of food risks.     

自动生产线的同步维修模型及实例研究

自动生产线故障停机时间长、停机费用高,其原因在于:设备种类多且相关联,存在各设备协同维修的难题。首先,寻求生产线故障时间和检查时间之间的平衡点,建立检查周期与总停机时间之间关系的目标函数。其次,借鉴同步维修的"对故障隐患集中式批处理,减少分散维修的停机时间"思想,以及时间延迟维修理论,建立生产线的同步维修模型,该模型利用生产线每天停机次数、每次停机时间及检查时发现的缺陷数,计算出合理的生产线检查周期,并以此为依据制定维修计划,实现"提高维修批量,减少生产线停机时间"的目标。最后,实例研究。收集某生产线近十年的故障记录数据,在分析生产流程、设备布局、各设备停机影响的基础上,提出对策指导生产线维修管理。     

A Game Theory Approach for Assessment of Risk and Deployment of Police Patrols in Response to Criminal Activity in San Francisco

An efficient police patrol schedule must ensure the allocation of an appropriate number of officers sufficient to respond to the danger of criminal incidents, particularly in an urban environment, even when the available number of personnel is limited. This study proposes a framework that incorporates two game theory models designed for the allocation of police officers to patrol shifts. In the first step, the interactions of three factors between the criminal and the operation captain are modeled as a zero-sum, noncooperative game, after which a mixed strategy Nash equilibrium method is used to derive the risk value for each district to be patrolled. In the second step, the risk values are used to compute the Shapley value for all 10 districts, for three different threat levels. A fair allocation of police personnel based on the Shapley value is made with a minimum set of personnel deployment costs. The efficacy of the proposed method is verified using openly available data from the San Francisco City Police detailing incidents from the year 2016. The experimental results show that police planners can use this framework to quantitatively evaluate the criminal threat in each district when deciding upon the deployment of patrol officers for three shifts per day.     

The maximum dispersion problem

In the maximum dispersion problem, a given set of objects has to be partitioned into a number of groups. Each object has a non-negative weight and each group has a target weight, which may be different for each group. In addition to meeting the target weight of each group, all objects assigned to the same group should be as dispersed as possible with respect to some distance measure between pairs of objects. Potential applications for this problem come from such diverse fields as the problem of creating study groups or the design of waste collection systems. We develop and compare two different (mixed-) integer linear programming formulations for the problem. We also study a specific relaxation that enables us to derive tight bounds that improve the effectiveness of the formulations. Thereby, we obtain an upper bound by finding in an auxiliary graph subsets of given size with minimal diameter. A lower bound is derived based on the relation of the optimal solution of the relaxation to the chromatic number of a series of auxiliary graphs. Finally, we propose an exact solution scheme for the maximum dispersion problem and present extensive computational experiments to assess its efficiency.     

Use of robust estimators of missing data in evaluating shiftwork performance

Abstract

Major problems apparent in studies evaluating the performance of employees across a large number of shifts often involve data missing due to a lack of employee participation in all possible shifts, and large variance around central tendency estimators owing to extreme responses on performance measures. These problems and possible solutions are considered here with reference to a data set collected from a number of shiftworkers in the steel industry. Data were collected from 28 employees over a morning shift and night-shift roster. The shift consisted of two cycles of MORNING-MORNING-NIGHT-NIGHT. The employees were male computer operators working a 12-h shift. The work required them to be constantly alert, and to read, monitor and respond to messages from multiple media channels. Included in the test battery were five performance indicators of cognitive speed and power. This battery was delivered using two IBM computers, which controlled the sequences and administration of the cognitive tasks. Administration of the battery was conducted in the work room and undertaken as close as possible to starting work, and as close as possible to completing work. The findings indicate that regression modelling was the most efficient way of estimating missing data. The use of M-estimators reduced the influence of extreme values on parameter estimation, and increased effect size over that observed using raw data.     

Human Error in Autonomous Underwater Vehicle Deployment: A System Dynamics Approach

The use of autonomous underwater vehicles (AUVs) for various applications have grown with maturing technology and improved accessibility. The deployment of AUVs for under-ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extremities in the Antarctic. A thorough analysis of risks is therefore crucial for formulating effective risk control policies and achieving a lower risk of loss. Existing risk analysis approaches focused predominantly on the technical aspects, as well as identifying static cause and effect relationships in the chain of events leading to AUV loss. Comparatively, the complex interrelationships between risk variables and other aspects of risk such as human errors have received much lesser attention. In this article, a systems-based risk analysis framework facilitated by system dynamics methodology is proposed to overcome existing shortfalls. To demonstrate usefulness of the framework, it is applied on an actual AUV program to examine the occurrence of human error during Antarctic deployment. Simulation of the resultant risk model showed an overall decline in human error incident rate with the increase in experience of the AUV team. Scenario analysis based on the example provided policy recommendations in areas of training, practice runs, recruitment policy, and setting of risk tolerance level. The proposed risk analysis framework is pragmatically useful for risk analysis of future AUV programs to ensure the sustainability of operations, facilitating both better control and monitoring of risk.     

基于时间延迟理论的预防维修模型及案例研究

本文旨在解决设备维修决策过程中预防维修检查数据缺乏情况下如何确定出合理的维修间隔期问题。首先,通过预防维修技术经济分析,提出了有关维修间隔期和总的停机时间之间关系的预防维修模型。其次,根据时间延迟维修理论,利用故障记录数据和预防维修检查数据的估计值,建立了统计模型并用来计算维修间隔期内故障次数的期望值。计算机仿真检验证明统计模型正确后,采用最大拟然法估计有关参数,这些参数包括缺陷发生率、时间延迟分布、检查出缺陷的概率等。最后是案例分析,应用估计参数和预防维修模型,计算出最佳的维修间隔期。     

The Impact of Revenue‐Maximizing Priority Pricing on Customer Delay Costs

Speed is an increasingly important determinant of which suppliers will be given customers' business and is defined as the time between when an order is placed by the customer and when the product is delivered, or as the amount of time customers must wait before they receive their desired service. In either case, the speed a customer experiences can be enhanced by giving priority to that particular customer. Such a prioritization scheme will necessarily reduce the speed experienced by lower‐priority customers, but this can lead to a better outcome when different customers place different values on speed. We model a single resource (e.g., a manufacturer) that processes jobs from customers who have heterogeneous waiting costs. We analyze the price that maximizes priority revenue for the resource owner (i.e., supplier, manufacturer) under different assumptions regarding customer behavior. We discover that a revenue‐maximizing supplier facing self‐interested customers (i.e., those that independently minimize their own expected costs) charges a price that also minimizes the expected total delay costs across all customers and that this outcome does not result when customers coordinate to submit priority orders at a level that seeks to minimize their aggregate costs of priority fees and delays. Thus, the customers are better off collectively (as is the supplier) when the supplier and customers act independently in their own best interests. Finally, as the number of priority classes increases, both the priority revenues and the overall customer delay costs improve, but at a decreasing rate.     

Approximation Algorithms for Certain Network Improvement Problems

We study budget constrained network upgrading problems. Such problems aim at finding optimal strategies for improving a network under some cost measure subject to certain budget constraints. Given an edge weighted graph G = (V, E), in the edge based upgrading model, it is assumed that each edge e of the given network also has an associated function ce (t) that specifies the cost of upgrading the edge by an amount t. A reduction strategy specifies for each edge e the amount by which the length (e) is to be reduced. In the node based upgrading model, a node v can be upgraded at an expense of c(v). Such an upgrade reduces the delay of each edge incident on v. For a given budget B, the goal is to find an improvement strategy such that the total cost of reduction is at most the given budget B and the cost of a subgraph (e.g. minimum spanning tree) under the modified edge lengths is the best over all possible strategies which obey the budget constraint.After providing a brief overview of the models and definitions of the various problems considered, we present several new results on the complexity and approximability of network improvement problems.