A Simulation Metamodel for Response‐Time Planning*

Watson, Edward; Chawda, Prabhulal P.; McCarthy, Brad; Drevna, Michael J.; Sadowski, R.

doi:10.1111/j.1540-5915.1998.tb01350.x

Cited by 27 publications

(20 citation statements)

References 18 publications

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“…New hires, training, skill, contracts, customer satisfaction, service types, product types, shifts, call arrival rates, job completion probability, interruptions, travel time distribution, skill-based process times, dispatching algorithms Watson et al 9 Staffing and response time planning…”

Section: Simulation Of Field Service Networkmentioning

confidence: 99%

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A simulation-based decision support system for industrial field service network planning

et al. 2013

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Technical field services for industrial machinery and equipment have become increasingly important for original equipment manufacturers. To deliver services to their customers, companies have to build up new core competencies and infrastructure, a challenge due to the high complexity and dynamics of this business. To assist companies in the strategic design of their network and the planning of resources for delivering industrial field services, we present a model-driven decision support system that uses discrete event simulation to support decision makers in various aspects of strategic design and tactical planning. The benefits of the decision support system include the creation of a generic framework that makes it possible to create simulation models of different field service networks for multiple purposes. Specifically, the system can be used to support various tactical planning and strategic design decisions while keeping investments low in terms of time consumption and money spending. In addition, the paper closes an identified gap involving a lack of decision support for the management of field service networks. An application of the decision support system in an exemplary case is used to illustrate potential applications and benefits.

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Section: Simulation Of Field Service Networkmentioning

confidence: 99%

“…2,8 Today, most methods used in field service network (FSN) planning in practice are 'outdated and overly simplistic'. 9,10 Similar to the supply chain area years ago, decision makers are missing ''a comprehensive methodology and engineering design method that allows them to rapidly prototype and evaluate several [. ] configurations [.…”

Section: Introductionmentioning

confidence: 99%

A simulation-based decision support system for industrial field service network planning

et al. 2013

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“…E, is a random error term composed of the error of any excluded or unidentified input factors and the error of fitting the polynomial. This latter component is assumed to distribute according to N (0, 0); Regression metamodels have been applied to manufacturing and operations management [17,25,261 and Sartori and Smith [21] extended their use to the field of engineering economy. They employed experimental design and regression metamodels in the sensitivity analysis of capital investment decisions and showed that this approach provided more insight than traditional sensitivity analysis.…”

Section: The Conceptmentioning

confidence: 99%

Economic Valuation of Capital Projects Using Neural Network Metamodels

Chaveesuk

Smith

2003

The Engineering Economist

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It is often useful to incorporate a metamodel into the sensitivity analysis of the economics of capital projects. While statistical models are straightforward and understandable, they are not always appropriate, especially where there are discontinuities or complex relationships in the economic data. This paper puts forth a neural network approach that uses design of experiments to generate metamodel training and validation data from the project(s) under consideration. Both backpropagation multi-layered perceptron networks and cascade correlation networks are explored and compared with the statistical approach. It is shown that the backpropagation networks work well for a variety of data sets and can be superior to regression in prediction accuracy when the economic data is especially complicated.Evaluation and selection of capital investments are generally based on an economic measure such as net present value (NPV), which is calculated from the estimates of input factors such as interest rate and amount and timing of cash flows. An investment decision made on the basis of point estimates of these input factors may not be robust. Sensitivity analysis thus becomes crucial as it allows the decision maker to determine the relative effect of changes in the input estimates on the indicated decision. Sensitivity analysis will also identify the factors that are most influential to the outcome of the investment decision.Precise estimates of important input factors are critical to a correct decision. Moreover, it will be beneficial to monitor these factors during implementation of Downloaded by [North West University] at 12:13 22 December 2014 the investment so that the decision maker can take corrective action in a timely manner.One factor at a time and scenario generation approaches are popular sensitivity analysis techniques. The one factor at a time approach is carried out by assuming a likely range or a single value for each input factor and recalculating tbe economic measure. The results are then presented in a graphical or tabular format (see for example [S]). Drawbacks to this approach are that it does not consider interactions among factors and it becomes more difficult to portray as the number of factors increases. Scenario generation explores the effect on the economic criterion when different factors take on the most likely, optimistic and pessimistic estimates in various combinations [3]. ,The results are usually tabulated. However, the size of the matrix display in the table grows quickly, thereby complicating the comprehension of useful information when multiple factors are analyzed.A metamodel is a simplified or approximated descriptive model of another descriptive model. It, itself, is not prescriptive, however it can assist with that function using optimization algorithms or interactive user queries. It is constructed from input-output data gathered from calculation, simulation or experimentation. Design of experiments is often used to collect data so that a meaningful metamodel is obtained in an efici...

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“…When the response time exceeds a predefined value, assistance is requested from service engineers from other territories. Sometimes, a ''floater'' field engineer is used to provide assistance across territories (Watson et al, 1998). Other examples include call centers where the number of customer service representatives is varied depending on the call arrival rates, and communication networks where the number of network connections is varied depending on the traffic congestion.…”

Section: Introductionmentioning

confidence: 99%