An interactive city planning model

Increased man-machine communications capability can provide improvements in problem solving. This improvement can be achieved by designing flexibility of input and output through the use of interactive graphics. This paper outlines an interactive simulation system for use in city planning. In this system, simulation models of a city's demography, employment, transportation, utilities, and communications systems are interfaced with each other and with a graphics screen through the use of a module called the interface control program. This program functions as a system monitor and recognizes what, where, and when information should be transferred through the system. The interactive city planning model allows applications-oriented problem solvers to effectively use the problem solving capability of the computer to respond to events as they develop during a simulation run.     

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.     

基于灰色系统建模技术的人体疾病早期预测预警研究

传统健康体检主要通过对单次体检指标进行横向比较和静态分析,忽略个体差异,纯粹从指标高低来判断体检者健康状况与身体状态,难以及时发现体检者可能存在的疾病隐患。由于个体体检指标具有样本量小、信息不确定、数据类型异构、影响因素构成复杂等特征,传统以大样本为基础的数学模型均难以适应此类小数据系统的建模要求。为此,通过建立适用于人体主要指标趋势预测的灰色系统模型（简称HIGM（1,1）模型）,实现对人体主要健康指标的动态建模与趋势分析,从而可以掌握体检者未来一段时间身体指标的变化趋势及可能存在的疾病隐患。研究成果对提升体检效果、强化体检意义,促进灰色理论与现实问题的有效对接等方面,均具有积极意义。     

Decentralized WIP-oriented manufacturing control (DEWIP)

DEWIP is a manufacturing control system for job shop environments aiming at achieving short and reliable lead times by establishing WIP control loops between the manufacturing work centres. The paper describes the mode of function, the setting of parameters and simulation results of the new manufacturing control system. The setting of parameters is done with the aid of the funnel model and the theory of logistic operating curves, both developed at the Institute of Production Systems at the University of Hanover. The simulation is conducted using industrial data and makes it possible to assess DEWIP with regard to lead times, WIP level, performance and schedule reliability. DEWIP is compared both with an uncontrolled process and with the manufacturing control systems Load oriented order release (LOOR), Conwip and Polca. The results suggest that DEWIP and the models employed for the setting of parameters are suitable for job shop production and therefore offer a valuable alternative to prevailing centralized manufacturing control systems.     

Managing the Risk of Uncertain Threshold Responses: Comparison of Robust, Optimum, and Precautionary Approaches

Many commentators have suggested the need for new decision analysis approaches to better manage systems with deeply uncertain, poorly characterized risks. Most notably, policy challenges such as abrupt climate change involve potential nonlinear or threshold responses where both the triggering level and subsequent system response are poorly understood. This study uses a simple computer simulation model to compare several alternative frameworks for decision making under uncertainty -- optimal expected utility, the precautionary principle, and three different approaches to robust decision making -- for addressing the challenge of adding pollution to a lake without triggering unwanted and potentially irreversible eutrophication. The three robust decision approaches -- trading some optimal performance for less sensitivity to assumptions, satisficing over a wide range of futures, and keeping options open -- are found to identify similar strategies as the most robust choice. This study also suggests that these robust decision approaches offer a quantitative, decision analytic framework that captures the spirit of the precautionary principle while addressing some of its shortcomings. Finally, this study finds that robust strategies may be preferable to optimum strategies when the uncertainty is sufficiently deep and the set of alternative policy options is sufficiently rich.     

资源松弛视角下企业技术创新策略选择的实物期权模型

组织资源对企业的创新行为具有重要作用.本文通过建立数学模型,采用实物期权分析方法,分析了松弛资源与技术创新策略选择之间的关系.分析结果表明:发掘、利用的松弛资源达到某一临界点时,能对选择基于研发的自主创新策略产生足够的激励;在基于技术引进的技术创新策略越来越难以实现的情况下,正确辨识、发掘、利用松弛资源对于提高组织的自主创新能力和绩效,显得格外重要.     

SIMULATION FOR FIRE DEPARTMENT DEPLOYMENT POLICY ANALYSIS

This paper discusses the use of simulation in the determination and analysis of alternative deployment strategies for urban fire supression systems. The simulation model utilizes the New York City-Rand Institute simulator of fire department operations tailored for use with the Denver Fire department. A distinction is made between static deployment (where mathematical programming methods are used) and dynamic analysis (which employs the simulation model). The simulation model is described, as are its extensive data requirements. The results discuss measures of validation of the simulation and outline the recommendations resulting from this research.     

How Probabilistic Risk Assessment Can Mislead Terrorism Risk Analysts

Traditional probabilistic risk assessment (PRA), of the type originally developed for engineered systems, is still proposed for terrorism risk analysis. We show that such PRA applications are unjustified in general. The capacity of terrorists to seek and use information and to actively research different attack options before deciding what to do raises unique features of terrorism risk assessment that are not adequately addressed by conventional PRA for natural and engineered systems—in part because decisions based on such PRA estimates do not adequately hedge against the different probabilities that attackers may eventually act upon. These probabilities may differ from the defender's (even if the defender's experts are thoroughly trained, well calibrated, unbiased probability assessors) because they may be conditioned on different information. We illustrate the fundamental differences between PRA and terrorism risk analysis, and suggest use of robust decision analysis for risk management when attackers may know more about some attack options than we do.     

基于投影降维技术的期权组合非线性VaR模型

高维期权组合VaR值的计算时间和计算工作量随着市场风险因子维数的增加而迅速增加.为此,引入投影降维技术,用少数几个风险因子来解释高维期权组合总的风险,并结合快速卷积方法,建立了基于投影降维技术的市场风险因子呈厚尾分布情形下的期权组合非线性VaR模型,达到减少计算时间和计算工作量的目的,同时期权组合价值变化的信息又没有太大的损失.数值结果表明,投影降维技术能够达到与快速卷积方法、Monte-Carlo方法差不多的估算精度,而计算效率明显优于快速卷积方法、Monte-Carlo方法,计算时间和计算工作量明显减少.     

A simulation system for corporate planning

This paper describes the structure and use of a general purpose simulation package developed for the interactive construction of dynamic computer-based simulation models. The package consists of a system of computer programs written in the BASIC language for a Digital PDP 11/70 time-sharing computer.The framework upon which the package is based is essentially an amalgamation of the System Dynamics and Input-Output approaches to the modelling of complex organizations. Networks of levels and flows provide the basis for dynamic, modular representation, while matrix algebra features provide the basis for aggregational flexibility and the analysis of multi- product multi-process industrial systems.The package is designed to provide planning managers with the ability to construct flexible models, localized or ‘corporate’, financial or non-financial, without the need for any programming in the conventional sense. A flow diagram of the system to be modelled guides the model builder in responding to computer terminal prompts which determine the model structure as a set of programmed relationships. This in turn gives rise to a further series of prompts at the terminal for the entry of all data pertinent to the model. Model editing and computation then proceeds, with user interaction, if desired, for amendments, monitoring of computations, and report generation.Two specific applications of the package are discussed in the latter part of the paper, and sample output from runs of both of the resultant models is provided.     

ALLOCATE: A COMPUTER MODEL FOR SALES TERRITORY PLANNING

Recent years have witnessed a renewal of interest in the application of management science techniques to personal selling related problems. Some early applications are described in [2] [7]. Cloonan has employed simulation in examination of the salesman routing problem [3] [4]. Lodish, in CALLPLAN, has devised an interactive call planning system designed to assist sales management and/or salesmen in allocating sales call time more efficiently [6]. Armstrong has devised a system he labels SCHEDULE which estimates the value of calls on accounts [1]. Hess and Samuels have designed a computer based sales districting model which is an analogue of a legislative apportionment model [5]. The objective of this paper is to explore the nature of a call planning system entitled ALLOCATE. ALLOCATE was designed to be employed by upper sales management either as an input device for sales management decisions such as sales-territory-size, or as a vehicle for determining the effects of alternative call allocation strategies on territorial revenue over multiple time periods.     

Multiechelon (R,S) Inventory Model*

Most studies in multiechelon inventory systems have concentrated on understanding the specific aspects of a system's behavior. The problem of optimal policy computation has largely been ignored. In this paper, we investigate a two-echelon inventory system experiencing stochastic demand and a pull system of inventory allocation. Both echelons use an order-up-to-level type control policy. A mathematical model is developed to determine the optimal order level at all echelons and validated through simulation. Two simple algorithms to locate the optimum solution are presented. The use of graphical tools in optimal policy calculation is also discussed.     

Capacity Planning and Allocation for Web‐Based Applications

Motivated by the technology division of a financial services firm, we study the problem of capacity planning and allocation for Web‐based applications. The steady growth in Web traffic has affected the quality of service (QoS) as measured by response time (RT), for numerous e‐businesses. In addition, the lack of understanding of system interactions and availability of proper planning tools has impeded effective capacity management. Managers typically make decisions to add server capacity on an ad hoc basis when systems reach critical response levels. Very often this turns out to be too late and results in extremely long response times and the system crashes. We present an analytical model to understand system interactions with the goal of making better server capacity decisions based on the results. The model studies the relationships and important interactions between the various components of a Web‐based application using a continuous time Markov chain embedded in a queuing network as the basic framework. We use several structured aggregation schemes to appropriately represent a complex system, and demonstrate how the model can be used to quickly predict system performance, which facilitates effective capacity allocation decision making. Using simulation as a benchmark, we show that our model produces results within 5% accuracy at a fraction of the time of simulation, even at high traffic intensities. This knowledge helps managers quickly analyze the performance of the system and better plan server capacity to maintain desirable levels of QoS. We also demonstrate how to utilize a combination of dedicated and shared resources to achieve QoS using fewer servers.     

基于互联网金融模式的结构性理财产品风险度量研究进展

针对由期权和固定收益类产品组合而成的结构性理财产品，本文基于互联网金融模式对结构性理财产品进行风险度量理论、方法及应用研究进行了分析和述评，并对基于互联网金融环境下用不同分布类型来刻画风险因子相依关系的线性和非线性资产组合的风险集成度量模型进行了归类总结。最后，提出了基于互联网金融模式的结构性理财产品风险度量研究展望。     

Decision analysis and risk models for land development affecting infrastructure systems

Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential land development. Land development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of land development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of land development and land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of land development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential land development.     

Replenishment Analysis in Distribution Requirements Planning*

Most research on lot sizing has been for the case of a manufacturing system. In this paper, analogous issues are studied for a distribution network. Specifically, we consider the choice of shipment quantities within distribution requirements planning (DRP). A simulation model of DRP in a multi-echelon, rolling-schedule environment is used to examine, in conditions of both certain and uncertain demand, the performance of five lot-sizing rules. We conducted a full-factorial experiment in which four additional parameters were varied: distribution network structure (two options), demand distribution (three options), forecast error distribution (three options), and ordering cost (three values), as suggested by the consulting study which motivated our research. We found that for DRP, contrary to the “shop floor” wisdom on MRP, the choice of lot-sizing method can be important. Generally the Silver-Meal and Bookbinder-Tan heuristics were significantly better than the other methods.     

Cyber Risk Management for Critical Infrastructure: A Risk Analysis Model and Three Case Studies

Managing cyber security in an organization involves allocating the protection budget across a spectrum of possible options. This requires assessing the benefits and the costs of these options. The risk analyses presented here are statistical when relevant data are available, and system‐based for high‐consequence events that have not happened yet. This article presents, first, a general probabilistic risk analysis framework for cyber security in an organization to be specified. It then describes three examples of forward‐looking analyses motivated by recent cyber attacks. The first one is the statistical analysis of an actual database, extended at the upper end of the loss distribution by a Bayesian analysis of possible, high‐consequence attack scenarios that may happen in the future. The second is a systems analysis of cyber risks for a smart, connected electric grid, showing that there is an optimal level of connectivity. The third is an analysis of sequential decisions to upgrade the software of an existing cyber security system or to adopt a new one to stay ahead of adversaries trying to find their way in. The results are distributions of losses to cyber attacks, with and without some considered countermeasures in support of risk management decisions based both on past data and anticipated incidents.     

Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically‐oriented models to advanced physical‐flow‐based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large‐scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.     

Order batching in a pick-and-pass warehousing system with group genetic algorithm

An order batching policy determines how orders are combined to form batches. Previous studies on order batching policy focused primarily on classic manual warehouses, and its effect on pick-and-pass systems has rarely been discussed. Pick-and-pass systems, a commonly used warehousing installation for small to medium-sized items, play a key role in managing a supply chain efficiently because the fast delivery of small and frequent inventory orders has become a crucial trading practice because of the rise of e-commerce and e-business. This paper proposes an order batching approach based on a group genetic algorithm to balance the workload of each picking zone and minimize the number of batches in a pick-and-pass system in an effort to improve system performance. A simulation model based on FlexSim is used to implement the proposed heuristic algorithm, and compare the throughput for different order batching policies. The results reveal that the proposed heuristic policy outperforms existing order batching policies in a pick-and-pass system.