Computer Simulation Success: On the Use of the End-User Computing Satisfaction Instrument: A Comment

This comment is part of a comprehensive study to develop a contingency model of simulation success. The current study focuses on the psychometric stability of the end-user computing satisfaction (EUCS) instrument by Doll and Torkzadeh (1988) when applied to users of computer simulation. Using a survey of 411 users, the researchers provide evidence that the EUCS instrument is a valid and reliable measure of computer simulation success. Given this evidence, managers and simulation software product developers can confidently apply the instrument in the investigation of competing tools, features, and technologies.     

A decision support methodology for strategic planning in maritime transportation

This paper presents a decision support methodology for strategic planning in tramp and industrial shipping. The proposed methodology combines simulation and optimization, where a Monte Carlo simulation framework is built around an optimization-based decision support system for short-term routing and scheduling. The simulation proceeds by considering a series of short-term routing and scheduling problems using a rolling horizon principle where information is revealed as time goes by. The approach is flexible in the sense that it can easily be configured to provide decision support for a wide range of strategic planning problems, such as fleet size and mix problems, analysis of long-term contracts and contract terms. The methodology is tested on a real case for a major Norwegian shipping company. The methodology provided valuable decision support on important strategic planning problems for the shipping company.     

基于多智能体的城市群政策协调建模与仿真

以城市群多政府互动与政策偏好演化为例,通过概念模型、数学模型和计算机模型的完整建模过程构建一个多智能体仿真模型,基于动力系统理论构建微观决策主体的偏好演化机制,基于复杂网络模型模拟宏观社会网络的拓扑演化规则,并对仿真模型和模拟结果进行效度和信度的检验。模拟实验探讨了全局交互周期比例、局部交互连接概率、行政/激励调控措施及其交叉作用对城市群政策协调演化的动态影响,并结合大样本模拟数据的统计分析,为促进城市群协调合作和区域一体化进程提供决策依据和政策参考,是多智能体建模与仿真(ABMS)在公共管理和政策领域的前沿拓展,提供了新的研究视角和方法论体系,也是政府组织模拟实验研究(行政学科计算化与实验化)的一次新尝试。     

Development of simulation-based production execution system in a shipyard: a case study for a panel block assembly shop

The management of a panel block shop in a shipyard is a complex process that entails the largest amount of work and in which many decisions are involved. Shipbuilders have considered the process as a bottleneck since every panel for every ship must be processed through the shop. The objective of this research is to carry out a materials flow analysis to maximise process productivity and to place simulation optimisation technology in the hands of decision makers, such as production planners and supervisors. In this article, a production execution planning system is proposed for panel block operations utilising discrete-event simulation and simulated annealing. The simulation model was validated using a real production scenario and the comparison showed a very favourable agreement between the actual panel shop and the simulation model. The proposed system supports production planners by general dispatching rules and optimisation to make better scheduling decisions on the shop floor. The system will provide a complete schedule that is at least as clear and accurate as any schedule currently obtained.     

The Use of Stochastic Simulation in Knowledge-Based Systems

Knowledge-based systems support the decision-making process with the help of domain specific knowledge bases. The knowledge bases almost always have uncertainty associated with them. A variety of approaches have been proposed in the artificial intelligence (AI) literature for the construction of and reasoning with uncertain knowledge bases. Building on this stream of research, we focus on how stochastic simulation can be used to construct and reason with knowledge bases that have uncertainties. An advantage of the simulation methodology is that it may not have to make many of the assumptions made by other approaches. It also allows the designer of the knowledge-based system to control the methodology based on accuracy and time requirements. The simulation approach to knowledge base construction is a modified version of the concept induction procedure used in AI. However, it incorporates, as does simulation modeling, statistical tests to identify the best rule that describes the relationship among the variables. We show that when simulation is used to reason with uncertain knowledge bases, under certain conditions, the number of simulation trials needed to achieve a given level of accuracy is independent of the characteristics, such as the size, of the knowledge base. Empirical results obtained from an experiment confirm our theoretical results and provide evidence that simulation methodology is practical for real life knowledge-based systems.     

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.     

Passenger Rail Security,Planning, and Resilience: Application of Network,Plume, and Economic Simulation Models as Decision Support Tools

We built three simulation models that can assist rail transit planners and operators to evaluate high and low probability rail‐centered hazard events that could lead to serious consequences for rail‐centered networks and their surrounding regions. Our key objective is to provide these models to users who, through planning with these models, can prevent events or more effectively react to them. The first of the three models is an industrial systems simulation tool that closely replicates rail passenger traffic flows between New York Penn Station and Trenton, New Jersey. Second, we built and used a line source plume model to trace chemical plumes released by a slow‐moving freight train that could impact rail passengers, as well as people in surrounding areas. Third, we crafted an economic simulation model that estimates the regional economic consequences of a variety of rail‐related hazard events through the year 2020. Each model can work independently of the others. However, used together they help provide a coherent story about what could happen and set the stage for planning that should make rail‐centered transport systems more resistant and resilient to hazard events. We highlight the limitations and opportunities presented by using these models individually or in sequence.     

A Framework for Analysis of Production Authorization Card‐Controlled Production Systems

A framework for the analysis of manufacturing systems operating under a production authorization card (PAC) system is outlined. The PAC system provides a single model, which encompasses a broad variety of control strategies, including Kanban and CONWIP. This paper describes a framework for the performance analysis and comparison of both specific and families of control strategies. The framework starts with system performance measures estimated by simulation. These simulations in turn provide training data for neural network metamodels. The metamodels allow for a variety of analysis and optimization approaches, including the construction of optimal policy curves, which can provide considerable insight into the systems under study.     

A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making*

Owing to its inherent modeling flexibility, simulation is often regarded as the proper means for supporting decision making on supply chain design. The ultimate success of supply chain simulation, however, is determined by a combination of the analyst's skills, the chain members' involvement, and the modeling capabilities of the simulation tool. This combination should provide the basis for a realistic simulation model, which is both transparent and complete. The need for transparency is especially strong for supply chains as they involve (semi)autonomous parties each having their own objectives. Mutual trust and model effectiveness are strongly influenced by the degree of completeness of each party's insight into the key decision variables. Ideally, visual interactive simulation models present an important communicative means for realizing the required overview and insight. Unfortunately, most models strongly focus on physical transactions, leaving key decision variables implicit for some or all of the parties involved. This especially applies to control structures, that is, the managers or systems responsible for control, their activities and their mutual attuning of these activities. Control elements are, for example, dispersed over the model, are not visualized, or form part of the time‐indexed scheduling of events. In this article, we propose an alternative approach that explicitly addresses the modeling of control structures. First, we will conduct a literature survey with the aim of listing simulation model qualities essential for supporting successful decision making on supply chain design. Next, we use this insight to define an object‐oriented modeling framework that facilitates supply chain simulation in a more realistic manner. This framework is meant to contribute to improved decision making in terms of recognizing and understanding opportunities for improved supply chain design. Finally, the use of the framework is illustrated by a case example concerning a supply chain for chilled salads.     

SIMULATION AND SEWAGE

Faced with increasing pressures on environmental issues, municipal and industrial planners must incorporate changing technology into planning processes. Conventional approaches to wastewater treatment plant design severely limit the ability of the designer to evaluate alternative design configurations. This paper suggests that computer simulation techniques can provide solutions to many of the problems which confront a designer. A simulation model of a hypothetical sewage treatment plant is described, and some of the design trade-offs that can be evaluated with the model are presented.     

An integrated modelling approach for raw material management in a steel mill

An integrated modelling approach combining optimisation models with simulation is proposed for coordinated raw material management at steel works throughout the whole supply chain management process. The integrated model is composed of three components: ship scheduling, yard operation simulation and material blending models. The ship scheduling model determines which brands, how much and when they should be arrived, and the problem is modelled as mixed integer linear programming. The simulation model is used to simulate the whole processes from ships’ arrivals to the retrieval of the materials through the berthing and unloading the raw materials. Finally, the raw material blending model is developed for determining the brand and quantity of raw materials to be used. The proposed integrated modelling approach for raw material management has been successfully implemented and applied at steelwork to provide shipping schedules and predict future inventory levels at stock yards. By coordinating all the activities throughout the entire raw material supply chain management process, this article proposes an integrated approach to the problem and suggests a guideline by the appropriate simplification. The quantitative nature of the optimisation model and simulation facilitates an assessment of the risk factors in the supply chain, leading to an evaluation of a wide variety of scenarios and the development of multiple contingency plans. Further research is expected to supplement the ship scheduling models with heuristics for the idiosyncratic constraints of maritime transportation.     

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.     

Quantitative Assessment of Exposure to the Mycotoxin Ochratoxin A in Food

This article presents the methodology and the simulation results concerning the quantitative assessment of exposure to the fungus toxin named Ochratoxin A (OA) in food, in humans in France. We show that is possible to provide reliable calculations of exposure to OA with the conjugate means of a nonparametric-type method of simulation, a parametric-type method of simulation, and the use of bootstrap confidence intervals. In the context of the Monte Carlo simulation, the nonparametric method takes into account the consumptions and the contaminations in the simulations only via the raw data whereas the parametric method depends on the random samplings from distribution functions fitted to consumption and contamination data. Our conclusions are based on eight types of food only. Nevertheless, they are meaningful due to the major importance of these foodstuffs in human nourishment in France. This methodology can be applied whatever the food contaminant (pesticides, other mycotoxins, Cadmium, etc.) when data are available.     

电子市场环境下供应链运作的鲁棒优化模型

本文研究了电子市场环境下的供应链运作问题,提出了不确定环境下的鲁棒优化模型.这一研究的实质是在外界需求最差条件下,如何得到电子市场中供应链最优供应量的策略.文中采用区间方法,设计供应链运作的鲁棒最优策略.进一步,在电子市场不确定环境下,进行了鲁棒策略仿真工作,结果表明鲁棒策略能为决策者提供最坏情况下供应商提供产品数量的鲁棒解决方案.     

动态车辆路径问题排队模型分析

分析了一类动态车辆路径问题,其中顾客需求以泊松流形式出现,现场服务时间服从一般分布.提出解决该问题的两种策略:顺序服务策略和中点改进策略,利用排队论、几何概率论等领域的知识分别求出了这两种策略的系统时间,并通过仿真数据实验验证了这两种策略的有效性.     

Functional and Behavioral Application

Simulation techniques have been recommended in the recent literature as vehicles for improving the analysis of the corporate capital budgeting decision. Such techniques are alleged to provide more helpful measures of both return and risk than do single-point discounted cash flow estimates of project worth. This contention is challenged here. The conclusion is reached that the information provided by simulation is, at best, no better than is generated by the traditional single-point present value approach and, in one very important respect, is markedly inferior.     

Nonparametric Instrumental Variable Estimation of Structural Quantile Effects

We study the asymptotic distribution of Tikhonov regularized estimation of quantile structural effects implied by a nonseparable model. The nonparametric instrumental variable estimator is based on a minimum distance principle. We show that the minimum distance problem without regularization is locally ill‐posed, and we consider penalization by the norms of the parameter and its derivatives. We derive pointwise asymptotic normality and develop a consistent estimator of the asymptotic variance. We study the small sample properties via simulation results and provide an empirical illustration of estimation of nonlinear pricing curves for telecommunications services in the United States.     

Reducing Surgical Ward Congestion Through Improved Surgical Scheduling and Uncapacitated Simulation

High surgical bed occupancy levels often result in heightened staff stress, frequent surgical cancellations, and long surgical wait times. This congestion is in part attributable to surgical scheduling practices, which often focus on the efficient use of operating rooms but ignore resulting downstream bed utilization. This paper describes a transparent and portable approach to improve scheduling practices, which combines a Monte Carlo simulation model and a mixed integer programming (MIP) model. For a specified surgical schedule, the simulation samples from historical case records and predicts bed requirements assuming no resource constraints. The MIP model complements the simulation model by scheduling both surgeon blocks and patient types to reduce peak bed occupancies. Scheduling guidelines were developed from the optimized schedules to provide surgical planners with a simple and implementable alternative to the MIP model. This approach has been tested and delivered to planners in a health authority in British Columbia, Canada. The models have been used to propose new surgical schedules and to evaluate the impact of proposed system changes on ward congestion.     

Management science models for evaluating regional government policies

This paper discusses the use of econometric models in evaluating alternative courses of action for public investment and governmental programs. Included are an extensive statewide and regional input-output analysis of the Texas economy, a simulation model to be used by government officials for fiscal policy-making and a model which simulates the demand for and use of water resources. These models provide a means whereby government planners and policy makers can plan and understand the consequences of investing limited resources in various public programs.     

INVESTMENT STRATEGY FOR A SMALL GROWTH COMPANY*

The purpose of this article is to illustrate the impact of the simultaneous investment-financing decisions required by a small growth company when accepting relatively large investment projects. By such illustration, the capital budgeting rationale for using “rules of thumb” will be clarified, and correspondingly, the implicit assumptions for approximating a company's “cost of capital” will be exposed. To provide quantitative estimates of the various impacts of different capital budgeting strategies, a relatively simple simulation model was constructed. Once the implicit assumptions are exposed, the importance of a strategy combining the rules of thumb and cost of capital criteria is evident. It is believed that this combined strategy provides a practical bridge between the “art” and science of finance.