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A logical strategy to contain the Covid-19 pandemic is to completely isolate everyone for 2 weeks (the incubation period of the virus). However, such a strategy can have prohibitive economic and social costs and, therefore, will be difficult to implement. At the same time, the current situation is leading to an expanding humanitarian, health and economic crisis. Based on principles of the Theory of Constraints, we propose in this article the “Shutting-down Transmission Of Pandemic” (STOP Covid-19) plan that would reliably contain the pandemic, mitigate its economic consequences, and boost societal confidence. This plan requires the implementation of four strategies over 90 days: (a) stop all international, domestic passenger air and intercity bus/train travel; (b) create administrative zones of about 1 million people; (c) stop all non-emergency cross-zonal travel except for transportation of goods, and (d) deploy an information-driven service value chain to control the spread of the pandemic within a zone.

A logical strategy to contain the COVID-19 pandemic is to completely isolate everyone for two weeks (the incubation period of the virus). However, such a strategy can have prohibitive economic and social costs and, therefore, will be difficult to implement. At the same time, the current situation is leading to an expanding humanitarian, health and economic crisis. Based on principles of the Theory of Constraints, we propose in this article the “Shutting-down Transmission Of Pandemic” (STOP COVID-19) plan that would reliably contain the pandemic, mitigate its economic consequences, and boost societal confidence. This plan requires the implementation of four strategies over 90 days – (a) Stop all international, domestic passenger air and intercity bus/train travel; (b) Create administrative zones of about 1 million people; (c) Stop all non-emergency cross-zonal travel except for transportation of goods, and (d) Deploy an information-driven service value chain to control the spread of the pandemic within a zone.

We consider a limited-liability firm that owns a finite single-product inventory subject to periodic-review replenishment and a corporate treasury that mediates the firm’s financial transactions related to inventory operations. The firm may elect to borrow money to purchase product via a revolving line of credit, secured by a compensating balance which determines the credit limit. The line of credit is subject to rollover risk, namely, each period the funding entity may, with some probability, refuse to roll over the line of credit. In response, the firm can search for an alternate funding entity, but in so doing it may incur search costs, primarily in the form of lost sales. The firm optimizes inventory replenishment order sizes and decides whether it should declare bankruptcy, as function of its inventory and available capital. The recent credit crunch has rendered illiquidity a critical concern for funding and operating decisions in enterprises. This paper addresses optimal inventory management in the face of liquidity shocks and supply disruptions. We show that rollover risk and supply disruption are important considerations for firms that rely on external funding. Rollover risk alone results in optimal inventory replenishment policies that differ materially from those specified by traditional supply chain models; differences manifest as state-dependent precautionary replenishment or cash hoarding. Inventory management models which fail to take rollover risk and supply disruption risk into account can prescribe suboptimal replenishment policies. Such policies would generate suboptimal profits for firms that rely on short-term financing to fund their working capital.

This paper models a cash conversion system in a subsidiary of a parent company where there is an active internal capital market, but otherwise the subsidiary has no access to additional external funds. The cash conversion system consists of a treasury, a single-product make-to-stock inventory, and a receivables pool. It implements a perpetual flow cycle, where funds convert to product and back to funds. The system is stationary, and revenues and costs flow directly to the parent company. The parent company aims to maximize equilibrium (long-run) financial metrics in terms of net profit rate and rate of return. To this end, we model this system as a discrete-state continuous-time Markov process and compute its equilibrium state distribution using analytic and numerical methods. These are then used to derive statistics of the equilibrium cash conversion cycle and define equilibrium financial rate metrics and cumulative counterparts that incorporate the time value of money. We further optimize the financial and operational designs of the system and, specifically, the internal capital allocation and inventory base stock level. Finally, noting the potential for friction in the parent–subsidiary relationship, we study numerically the impact of moral hazard and internal capital market inefficiency on optimal designs. The online appendix is available at https://doi.org/10.1287/msom.2017.0625 .

Understanding the link between project complexity and innovation is highly pertinent. Yet, the challenge of innovative complex projects has received limited research attention and little theory development. This article provides a retrospective analysis of the difficulties experienced by Boeing during the development project of its highly innovative Dreamliner aircraft. Eventually successful, this project suffered extensive delays and cost overruns. The article analyzes the project's complex nature of innovation, while using several frameworks to provide an integrative view of its challenges and suggesting possible alternative ways to address them. Insights for complex project teams and future research directions are offered.

This paper addresses Supply Chain Financial Management (SCFM), namely, the analysis of combined operational and financial aspects of supply chains, an important emerging topic in Supply Chain Management (SCM). It further aims to bridge the gap between practical SCM research and analytical tools for modeling and optimization purposes by illustrating how analytical and numerical methods can be used in combination with simple financial and accounting concepts to obtain financial/accounting metrics for supply chains. To this end we introduce Equilibrium Rate Analysis (ERA) as a modeling approach to stochastic systems and their performance metrics, including the domain of SCFM. ERA posits stochastic equilibrium as the mathematical setting of models, and utilizes rate metrics instead of cumulative (time-additive) ones. This approach brings about major conceptual and computational simplifications, which facilitate evaluation and optimization of SCFM metrics. The paper explains in broad outline how to formulate E...

Boeing Corporation launched its 787 Dreamliner development program in the early 2000s, anticipating quick benefits from a growing demand for next-generation, advanced, and highly efficient aircraft. Budgeted at US$20 billion and designed by a global network of more than 700 subcontractors around the world, the Dreamliner had all the characteristics of a megaproject. Boeing expected that a collection of strategic innovations would add substantial business benefits, but that dream led to years of painful delays, cost overruns, and service introduction problems. Boeing’s previous extensive experience in commercial aircraft building was insufficient to deal with new challenges of a highly innovative program, and the Dreamliner’s difficulties typify many modern megaprojects. With accelerated technological growth, increased complexity of systems, and intensified demand for shorter time cycles, the challenge of strategic innovation in megaprojects becomes a universal struggle. This chapter presents a retrospective analysis of Boeing’s experience and offers a collection of global lessons for future megaprojects and programs.

MARM (Multivariate Autoregressive Modular) processes constitute a versatile class of multidimensional stochastic sequences which can exactly fit arbitrary multi-dimensional empirical histograms and approximately fit the leading empirical autocorrelations and cross-correlations. A companion paper (Part I) presented the general theory of MARM processes. This paper (Part II) proposes practical MARM modeling and forecasting methodologies of considerable generality, suitable for implementation on a computer. The purpose of Part II is twofold: (1) to specialize the general class of MARM processes to a practical subclass, called Empirically-Based MARM (EB-MARM) processes, suitable for modeling of empirical vector-valued time series, and devise the corresponding fitting and forecasting algorithms; and (2) to illustrate the efficacy of the EB-MARM fitting and forecasting algorithms. Specifically, we shall consider MARM processes with iid step-function innovation densities and distortions based on an empirical multi-dimensional histogram, as well as empirical autocorrelation and cross-correlation functions. Finally, we illustrate the efficacy of these methodologies with an example of a three-dimension time series vector, using a software environment, called MultiArmLab, which supports MARM modeling and forecasting.

A cluster-based server consists of a front-end dispatcher and multiple back-end servers. The dispatcher receives incoming jobs, and then decides how to assign them to back-end servers, which in turn serve the jobs according to some discipline. Cluster-based servers have been widely deployed, as they combine good performance with low costs. Several assignment policies have been proposed for cluster-based servers, most of which aim to balance the load among back-end servers. There are two main strategies for load balancing: The first aims to balance the amount of workload at back-end servers, while the second aims to balance the number of jobs assigned to back-end servers. Examples of policies using these strategies are Dynamic and LC (Least Connected), respectively. In this paper we propose a policy, called LC*, which combines the two aforementioned strategies. The paper shows experimentally that when preemption is admitted (i.e., when jobs execute concurrently on back-end servers), LC* substantially outperforms bothDynamic and LC in terms of response-time metrics. This improved performance is achieved by using only information readily available to the dispatcher, rendering LC* a practical policy to implement. Finally, we study a refinement, called ALC* (Adaptive LC*), which further improves on the response-time performance of LC* by adapting its actions to incoming traffic rates.

This paper proposes a new scheduling policy for cluster-based servers called DAS (Deferred Assignment Scheduling). The main idea in DAS is to defer scheduling as much as possible in order to make better use of the accumulated information on job sizes. In broad outline, DAS operates as follows: (1) incoming jobs are held by the dispatcher in a buffer; (2) the dispatcher monitors the number of jobs being processed by each server; (3) when the number of jobs at a server queue drops below a prescribed threshold, the dispatcher sends to it the shortest job in its buffer. To gauge the efficacy of DAS, the paper presents simulation studies, using various data traces. The studies collected response times and slowdowns for two cluster configurations under multi-threaded and multi-process back-end server architectures. The experimental results show that in both architectures, DAS outperforms the Round-Robin policy in all traffic regimes, and the JSQ (Join Shortest Queue) policy in medium and heavy traffic regimes.

Manufacturing-related models have traditionally made independence assumptions on associated stochastic processes in order to achieve tractability of analytical models and simplify Monte Carlo models. This paper aims to alert users to potential deleterious implications stemming from unfounded independence assumptions in traditional stochastic models of manufacturing systems. Specifically, it demonstrates the dramatic impact that appreciable autocorrelations can have on manufacturing performance measures through a preliminary study of prediction errors incurred in ignoring dependence. To this end, the study compared performance measures of common manufacturing models with renewal components to their autocorrelated counterparts, drawn from the TES (Transform-Expand-Sample) class. TES models constitute a versatile class of stochastic processes, designed to capture empirical distributions and autocorrelations, simultaneously, and as such, are suitable for both Monte Carlo simulation and analytical modeling of autocorrelated time series. A brief overview of simple TES processes and their generation algorithms is also included.

TEStool is a visual interactive software environment for modeling autocorrelated time series, using a versatile class of stochastic processes called TES (Transform-Expand-Sample). The novel feature of the TES modeling approach is that it strives to fit a model to empirical records by simultaneously capturing both the empirical distribution and the leading empirical autocorrelations. Thus, TES models can have a high degree of fidelity, since both first-order and second-order aspects of the statistical signature of empirical time series are targeted for capture. TEStool has been used extensively to model empirical data from a variety of application domains, including compressed video traffic in high-speed communications networks, financial time series and machine reliability. This paper explains the TES approach to modeling empirical autocorrelated time series adopted in TEStool, as well as TEStool's graphical user interface (GUI). Special emphasis is placed on features that increase the efficiency and quality of modeling based on visualization and real-time interaction. In particular, TEStool casts a heuristic search activity into an intuitive process which is guided by visual feedback. Consequently, the software makes TES modeling accessible to modelers who have a basic understanding of time series, but lack expert knowledge of TES theory.

The letter considers modeling methodologies of variable bit-rate (VBR) video sources for performance evaluation of integrated networks. The authors consider an example in which compressed H.261 standard VBR video is transmitted over a local area network carrying both video and data. A group-of-block (GOB) level source model has been constructed using TES (transform-expand-sample)-a new general methodology for generating stationary autocorrelated time series, which can simultaneously capture a prescribed marginal distribution and autocorrelation function. The TES model is used to drive CSMA/CD network simulations for a performance evaluation study. Numerical comparisons with a conventional autoregressive (AR) modeling approach are given. >

The area of telecommunications network design and management is complex and solutions can become algorithmically intractable for moderately large networks. It is, therefore, a promising applications area for expert systems; however, a survey of the published literature reveals a paucity of integrated systems combining design and optimization of network-based problems. We present a distributed expert telecommunications provisioning system which uses a simulation-based optimization methodology for queueing networks. Our architecture admits parallel simulation of multiple configurations. A knowledge-based search drives our performance optimization of the network. The search process is a randomized combination of Steepest Descent and Branch and Bound algorithms, where the generating function of new states uses qualitative reasoning, and the gradient of the objective function is estimated using a heuristic Score Function method. We found a random search based on the relative order of the performance gradient components to be a powerful qualitative reasoning technique. The system (P3) is implemented as a loosely coupled expert system with components written in PROLOG, SIMSCRIPT, and C. We demonstrate the efficacy of our method through an example from the domain of Jackson queueing networks.

This chapter focuses on goodness assessment, and specifically on its two components: verification and validation. Verification assesses the correctness of the formal representation of the intended model (in the present context, a computer simulation program), by inspecting computer code and test runs, and performing consistency checks on their statistics. Validation assesses how realistic the modeling assumptions are, by comparing model performance metrics (predictions), obtained from model test runs, to their counterparts in the system under study. A model might exhibit satisfactory goodness even though its underlying assumptions are quite unrealistic. Conversely, an ostensibly realistic model may reveal its shortcomings by exhibiting poor predictive power. It is worth noting that a complete and sure verification and validation of models is rarely possible. Consequently, verification and validation activities must be alert to circumstantial evidence of coding errors and faulty logic.

Simulation modeling is a common paradigm for analyzing complex systems. This paradigm creates a simplified representation of a system under study. The paradigm then proceeds to experiment with the system, guided by a prescribed set of goals, such as improved system design, cost-benefit analysis, and sensitivity to design parameters. Experimentation consists of generating system histories and observing system behavior over time, as well as its statistics. Thus, the representation created describes system structure, while the histories generated describe system behavior. The chapter overviews the elements of simulation modeling and introduces basic concepts relevant to such modeling.

Manufacturing facilities in a host of industrial sectors are structured as a series of production stages. Jobs in a variety of forms are transferred from one stage to another to be processed in a prescribed order; these jobs eventually leave the system as finished or semifinished products. Such systems are referred to as production lines. The flexibility afforded by computer-controlled machinery enables production lines to handle a broad range of operations. Various operations permit the deployment of a sequence of intelligent workstations on the shop floor for processing or assembling various products. The chapter discusses simulation modeling of workstation sequences in the production line context. Design problems in production lines are primarily resource allocation problems. These problems include workload allocation and buffer capacity allocation for a given set of workstations with associated processing times. Generally, design problems are quite difficult to solve in manufacturing systems. This is partly because of the combinatorial nature of such problems.

A supply chain system (supply chain, for short) is a network that mediates the flow of entities involved in a product life cycle, from production to vending. Such a network consists of nodes and arcs. Nodes represent suppliers, manufacturers, distributors, and vendors (e.g., retail stores), as well as their inventory facilities for storing products and transportation facilities for shipping them among nodes. Arcs represent routes connecting the nodes along which goods are transported in a variety of modes (trucking, railways, airways, and so on). From a sufficiently high vantage point, supply chains have essentially a feed-forward network structure, with upstream and downstream components arranged in echelons (stages), such that raw materials, parts, products, and so on flow downstream, payments flow upstream, and information flows in both directions. However, there are supply chains where entity flows may not be strictly directional because products may be returned for repair or refund. This chapter focuses on inventory management and material flow among echelons.

The growing Internet/Web infrastructure has placed increasingly larger demands on servers as well as the networks connecting them. In particular, a crowded Internet can slow the delivery of responses (latency) to customer queries and transactions to a crawl, resulting in poor quality of service (QoS). The result is disappointed users and loss of business. Thus, performance evaluation of distributed client/server and web-based applications has become extremely important. A capacity planning is the activity of predicting system performance for a given set of resource requirements to achieve a desired level of performance. A capacity planning activity would launch a performance evaluation study via an analytical or simulation model to predict certain statistics of response times, and unearth any “performance bottlenecks” in the system. The chapter focuses on simulation modeling of computer-based information systems, especially those configured in a client/server architecture. A number of examples illustrate capacity planning for such systems.

Monte Carlo simulation is an invaluable tool for studying transportation systems and solving their attendant problems. Generally, strategic decisions concerning transportation systems often involve identifying system capacities and eliminating bottlenecks so as to increase system throughput. The associated operations are extremely expensive, and therefore need to be studied carefully before any action is taken. Fortunately, simulation studies can be helpful and even instrumental in identifying correct designs for large-scale transportation projects. Transportation-related simulation models employ entities to model the moving objects, such as cars, trucks, trains, planes, ships, tugboats, humans, and so on. Contention for transportation-oriented service by entities is modeled through resources, such as road pavement, traffic lights, tollbooths, airport taxiways and runways, and berths and piers, as well as material handling equipment. To make modeling more realistic, resources may experience stoppages for various reasons (failures, weather, strikes, etc.), which can further exacerbate delays in the system. This chapter provides an overview of Arena facilities for modeling transportation systems. Their use is illustrated in a number of transportation-related examples.

A modeling and simulation tool ought to provide adequate facilities for model testing and debugging to help the modeler ensure that the simulation model under construction is free of syntactic and logical errors. Such modeling activities may be viewed as part of model verificationnamely, checking that the simulation code correctly represents the conceptual model. Arena provides a rich set of facilities to support model testing and debugging throughout the life cycle of a simulation model. These facilities allow the user to access Arena objects, code, and data that underlie the model image on the screen, such as model parameters and SIMAN code (blocks). It further allows the modeler to inspect model runs, either in visual animation mode or in textual command mode. The chapter reviews the Arena testing and debugging facilities supported by the Run Interaction toolbar.

TES (Transform-Expand-Sample) is a versatile methodology for modeling stationary time series with general marginal distributions and a broad range of dependence structures. From the viewpoint of Monte Carlo simulation, TES constitutes a new and flexible input analysis approach whose principal merit is its potential ability to simultaneously capture first-order and second-order statistics of empirical time series. That is, TES is designed to fit an arbitrary empirical marginal distribution (histogram), and to simultaneously approximate the leading empirical autocorrelations. This paper is a tutorial introduction to the theory of TES processes and to the modeling methodology based on it. It employs a didactic approach which relies heavily on visual intuition as a means of conveying key ideas and an aid in building deep understanding of TES. This approach is in line with practical TES modeling which itself is based on visual interaction under software support. The interaction takes on the form of a heuristic search in a large parameter space, and it currently relies on visual feedback supplied by computer graphics. The tutorial is structured around an illustrative example both to clarify the modeling methodology and to exemplify its efficacy.

A cluster-based server consists of a front-end dispatcher and several back-end servers. The dispatcher receives incoming requests, and then assigns them to back-end servers for processing. Our goal is to devise an assignment policy that has good response time performance, and is practical to implement in that the amount of information used by the dispatcher is relatively small, so that the attendant computation and communication overheads are low. In contrast to extant assignment policies that apply the same assignment policy to all incoming jobs, our approach calls for the dispatcher to classify incoming jobs as long or short, and then use class-dependent assignment policies. Specifically, we propose a policy, called CDA (Class Dependent Assignment), where short jobs are assigned in Round-Robin manner as soon as they arrive, while long jobs are deferred and assigned only when a back-end server becomes idle. Furthermore, when processing a long job, a back-end server is not assigned any other jobs.Our approach is motivated by empirical evidence suggesting that the sizes of files traveling on the Internet follow power-law distributions, where long jobs constituting a small fraction of all incoming jobs actually account for a large fraction of the overall load. To gauge the performance of the proposed policy, we exercised it on empirical data traces measured at Internet sites serving the 1998 World Cup. Since the assignment of long jobs incurs computational overhead as well as extra communication overhead, we studied the performance of CDA as function of the fraction of jobs classified as long. Our study shows that classification of even a small fraction of jobs as long can have a profound impact on overall response time performance. More speciafically, our experimental results show that if less than 3% of the jobs are classified as long, then CDA outperforms traditional policies, such as Round-Robin, by two orders of magnitude. From an implementation viewpoint, these results support our contention that CDA-based assignment is a practical policy combining low overhead and greatly improved performance.

A variable-bit-rate (VBR) MPEG video compression encoder is introduced, and the performance of a statistically multiplexed asynchronous transfer mode (ATM) network supporting a number of such VBR video sources is evaluated. Bit-rate characteristics obtained from a detailed simulation are provided for a VBR MPEG encoder for CCIR601 video (operating in the 5-10 Mb/s regime) appropriate for medium-quality multimedia or broadcasting applications. The results presented include bit-rate traces and signal-to-noise-ratio data for typical test sequences, along with summary statistics such as the marginal distribution of frame rate. Data from a study of statistical multiplexing on an ATM network are also given. Simulation results for an ATM statistical multiplexer with N>>1 VBR MPEG sources are presented in terms of key performance measures such as cell loss rate and delay versus throughput. The results confirm that ATM channel efficiencies of approximately 80-90% can be obtained at reasonable cell loss rate and delay levels. >

The authors propose a novel methodology (called transform-expand-sample or TES) for modeling video sources. The main characteristic of this method is that it can model an arbitrary distribution for the bits generated in a frame and approximate its correlation structure. The authors present an example of the use of this method by using data from a video coding algorithm employing a layering technique. Using the source model, they develop a simulation model of a multiplexer of video sources. It is shown that this model can be used to address a number of design issues that arise in this class of problems. It is suggested that the methods presented could be useful in the design and understanding of broadband ISDN (integrated services digital network) systems used for transporting video images. >

The fluid-flow paradigm is an alternative approach to the traditional queueing paradigm, where workload movement in a network is described in terms of a fluid metaphor rather than the familiar customer metaphor. Stochastic fluid network models can considerably speed up the simulation of complex flow networks, such as high-speed packet-based telecommunications networks, where traditional queueing-based simulation routinely gives rise to enormous numbers of events. Furthermore, they are also amenable to fast, nonparametric, and unbiased estimation (unlike traditional queueing) of IPA (Infinitesimal Perturbation Analysis) gradients (derivatives of performance measures with respect to various parameters), which may be used to make decisions on network design, replenishment, management or control. This paper outlines the design of a fluid-oriented hybrid simulator admitting both packet flows and fluid flows. It focuses on the fluid processing design and on efficiency issues.

TES (Transform-Expand-Sample) is a versatile class of stochastic sequences defined via an autoregressive scheme with modulo-1 reduction and additional transformations. The scope of TES encompasses a wide variety of sample path behaviors, which in turn give rise to autocorrelation functions with diverse functional forms - monotone, oscillatory, alternating, and others. TES sequences are readily generated on a computer, and their autocorrelation functions can be numerically computed from accurate analytical formulas at a modest computational cost.This paper presents the empirical TES modeling methodology which uses TES process theory to model empirical records. The novel feature of the TES methodology is that it expressly aims to simultaneously capture the empirical marginal distribution (histogram) and autocorrelation function. We draw attention to the non-parametric nature of TES modeling in that it always guarantees an exact match to the empirical marginal distribution. However, fitting the corresponding autocorrelation function calls for a heuristic search for a TES model over a large parametric space. Consequently, practical TES modeling of empirical records must currently rely on software assistance. A visual interactive software environment, called TEStool, has been designed and implemented to support TES modeling. The paper describes the empirical TES modeling methodology as implemented in TEStool and provides numerically-computable formulas for TES autocorrelations. Two examples illustrate the efficacy of the TES modeling approach. These examples serve to highlight the ability of TES models to capture first-order and second-order properties of empirical sample paths and to mimic their qualitative appearance.

TES (Transform-Expand-Sample) is a versatile methodology for modeling general stationary time series, and particularly those that are autocorrelated. The salient feature of TES lies in its ability to simultaneously capture first order and second-order properties of empirical time series; given a sample data sequence, TES is designed to simultaneously capture any arbitrary marginal distribution and approximate the leading autocorrelations. Practical TES modeling is computationally intensive and can be effectively carried out only with software support. A computerized modeling environment, TEStool, has been designed to support the TES modeling methodology, through an interactive heuristic search approach facilitated by state of-the-art data visualization techniques. The purpose of this paper is to present four examples of the effective use of the TES methodology to model various types of time series that arise in a variety of disciplines, ranging from manufacturing to financial modeling, with particular emphasis on video compression. These examples serve to highlight the efficacy and versatility of the TES modeling methodology.

The authors consider modeling methodologies of variable-bit-rate video sources for performance evaluation of integrated networks. They consider an example in which compressed video is transmitted over a local area network carrying both video and data. A new modeling methodology called transform-expand-sample (TES) which is a method for generating autocorrelated variates, is used. TES is a nonparametric method which can accurately capture the histogram and approximate autocorrelation function of any data set. The TES model was used to drive a network simulation for performance measures. >

Considerable effort is being expended worldwide towards the design and implementation of modeling support environments (MSEs). Efforts range from simulation to analytical support environments, and combinations of the two. Originally text-only, MSEs are increasingly designed to incorporate extensive graphics to facilitate user interactions with the software.