Supply chain transformation is an emerging service area in the market which aims at helping clients improve their operational efficiency and reduce costs. As a generic technique for the analysis of complex and dynamic...
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ISBN:
(纸本)9781424405008
Supply chain transformation is an emerging service area in the market which aims at helping clients improve their operational efficiency and reduce costs. As a generic technique for the analysis of complex and dynamic systems, simulation could play an important role in this field. This paper presents a case study showing how simulation could be the key enablement for a supply chain transformation project. A methodology and tool developed by IBM China Research Lab has been applied in this joint project with a world-class supplier of home improvement tools. By applying simulation, the client is provided an insightful view about their business processes and inventory allocation strategy, which serves as the basis for the transformation implementation. Financial results show that simulation has addressed the key issues and provided the client accountable evaluation results for decision-making.
The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from...
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ISBN:
(纸本)9781538634288
The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose.
Modern enterprises are large complex systems operating in highly dynamic environments thus requiring quick response to a variety of change drivers. Moreover, they are systems of systems wherein understanding is availa...
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ISBN:
(纸本)9781728132839
Modern enterprises are large complex systems operating in highly dynamic environments thus requiring quick response to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We present an approach that combines ideas from modeling & simulation, reinforcement learning and control theory to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. The paper describes illustration of approach in two real world use cases.
Maintenance processes of repairable systems have been extensively studied in the past. The resulting simple solutions have proven to be remarkably effective. It requires complex and time-consuming simulations to impro...
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ISBN:
(纸本)9781509044863
Maintenance processes of repairable systems have been extensively studied in the past. The resulting simple solutions have proven to be remarkably effective. It requires complex and time-consuming simulations to improve on those simple solutions, and reliable input data is even harder to get. However, new technologies, epitomized by Big Data and the Internet of Things, change the data-availability part of the equation. As a result, there are new exciting possibilities for modeling more subtle effects, and developing processes for easily (and therefore frequently) updated inputs. Modeling decisions can be repeatedly tested on the data, and the models can be quickly adjusted to better reflect reality and even to compensate for missing pieces of the data. In this context, the transparency and simplicity of models becomes a larger virtue. Several examples of the insights based on real-world large-scale applications of predictive analytics using simulation are discussed.
Outpatient surgery scheduling involves the coordination of several activities in an uncertain environment. Due to the very customized nature of surgical procedures there is significant uncertainty in the duration of a...
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ISBN:
(纸本)9781424405008
Outpatient surgery scheduling involves the coordination of several activities in an uncertain environment. Due to the very customized nature of surgical procedures there is significant uncertainty in the duration of activities related to the intake process, surgical procedure, and recovery process. Furthermore, there are multiple criteria which must be traded off when considering how to schedule surgical procedures including patient waiting, operating room (OR) team waiting, OR idling, and overtime for the surgical suite. Uncertainty combined with the need to tradeoff many criteria makes scheduling a complex task for OR managers. In this article we present a simulation model for a multiple OR surgical suite, describe some of the scheduling challenges, and illustrate how the model can be used as a decisions aid to improve strategic and operational decision making relating to the delivery of surgical services. All results presented are based on real data collected at Mayo Clinic in Rochester, MN.
This paper aims to further motivate the use of simulation of complex systems in optimizing healthcare operations under uncertainty. One argument to use optimization only such as mathematical programming instead of sim...
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ISBN:
(纸本)9781509044863
This paper aims to further motivate the use of simulation of complex systems in optimizing healthcare operations under uncertainty. One argument to use optimization only such as mathematical programming instead of simulation optimization in makingdecisions is the ability of the former to account for constraints and to consider a large number of alternatives. However, current state-of-the art of simulation optimization has opened the possibilities of using both simulation and optimization in the case of multiple performance measures. We consider the case of hospital bed allocation and give an example on how a stochastically constrained optimization via simulation can be applied. Nested Partitions are used for the search algorithm and combined with OCBA-CO, an efficient simulation budget allocation, as simulation is time-consuming.
By explicitly modeling the decision making of heterogeneous individuals, agent-based models can compute the resulting emergent phenomena on the micro-level. This lets planners evaluate new planning approaches for prob...
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ISBN:
(纸本)9781538634288
By explicitly modeling the decision making of heterogeneous individuals, agent-based models can compute the resulting emergent phenomena on the micro-level. This lets planners evaluate new planning approaches for problems that depend on individual decisions. Examples include airline revenue management or traffic control. However, when decision support relies on agent-based modeling, its applicability to real-world problems depends on the model's validity. This paper introduces a novel methodological concept to decompose agent-based models for calibration and validation. This concept enables modelers to isolate agents from the evolution of the model's state variables, allowing greater choice of calibration and validation approaches. The approach first parameterizes and validates individual agents, and subsequently re-calibrates the agent-collective within the entire model.
Despite simulation offers tremendous promise for designing and analyzing complex production systems, manufacturing industry has been less successful in using it as a decision support tool, especially in the early conc...
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ISBN:
(纸本)9781457721090
Despite simulation offers tremendous promise for designing and analyzing complex production systems, manufacturing industry has been less successful in using it as a decision support tool, especially in the early conceptual phase of factory flow design. If simulation is used today for system design, it is more often used in later phases when important design decisions have already been made and costs are locked. With an aim to advocate the use of simulation in early phases of factory design and analysis, this paper introduces FACTS Analyzer, a toolset developed based on the concept of integrating model abstraction, automatic model generation and simulation-based optimization under an innovative Internet-based platform. Specifically, it addresses a novel model aggregation and generation method, which when combined together with other system components, like optimization engines, can synthetically enable simulation to become much easier to use and speed up the time-consuming model building, experimentation and optimization processes, in order to support optimal decision making.
The development of large and complex simulated models often requires teams to collaborate. One approach is to break a large model into independently developed partial models that, when combined, capture the overall be...
ISBN:
(纸本)9781509044863
The development of large and complex simulated models often requires teams to collaborate. One approach is to break a large model into independently developed partial models that, when combined, capture the overall behavior. However, maintaining consistent world state across independently developed simulations is a challenge. In this paper, we introduce the Collaborative Aspect-Oriented Distributed Interactive simulation (CADIS) architecture and development platform. CADIS embodies a new paradigm for integrating independently developed time-discrete partial models and simulations, focusing on transparently maintaining synchronized shared state. Data is pulled and instantiated in the beginning of each time step, and pushed at the end of each time step. An urban simulation is used to demonstrate CADIS capabilities and performance. We show how simple optimizations can bring the performance of the framework to acceptable levels, making CADIS a viable modeling and simulation methodology supporting separation of concerns.
Emergency department (ED) expansion and redesign is a complex design task which must take into account many operational processes (current and proposed) as well a projected changes in the system, e.g., patient volume....
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