Abdominal surgery process modeling framework for simulation using spreadsheets

Boshkoska, Biljana Mileva; Damij, Talib; Jelenc, Franc; Damij, Nadja

doi:10.1016/j.cmpb.2015.05.001

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…To implement the model, universal modeling languages, programming languages, standard mathematical packages can be used. To solve the problem under consideration, tabular modeling was chosen [19,20], since the tools of the Excel mathematical package provide the ability to store data, step-by-step calculations, and also process data using the built-in VBA language. As a result, such a model is visual for the user: calculation formulas can be viewed and intermediate results analyzed.…”

Section: Fig 1 Histogram Of Demand Distributionmentioning

confidence: 99%

Development of spreadsheet simulation models of gas cylinders inventory management

2022

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The solution of the problem of managing the inventory of an enterprise whose activities are related to the purchase and sale of gas cylinders is considered. To solve the problem, it was necessary to investigate and choose the best inventory management strategy that provides the minimum value of the average inventory balance in the warehouse with the established upper limit of the average deficit. The problem of determining the best strategy is presented as a discrete programming problem, the required variables of which depend on the replenishment method. With a periodic replenishment strategy, the controlled variables are the volume of the delivery line and the delivery interval, with a threshold one, the minimum inventory level and the volume of the delivery line. Let’s also consider replenishment with a predicted inventory level, where the delivery level and the minimum inventory level are used as control variables. Three tabular simulation models with a given delivery time and random demand are proposed. Using the Chi-square test, it was found that the quantity demanded has a normal distribution law. By carrying out computational experiments, the optimal values of controlled variables were determined. The best objective function values were obtained using a model with a predicted inventory level and a threshold replenishment strategy. Experiments conducted on the basis of historical data have shown the advantage of the two model strategies compared to the strategy currently used in the enterprise. The use of a model with a predictable inventory level would reduce the average inventory balance by 46 %, and, consequently, save working capital. The results of the study can be useful for managers of enterprises whose activities are related to inventory management

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Section: Fig 1 Histogram Of Demand Distributionmentioning

confidence: 99%

Development of spreadsheet simulation models of gas cylinders inventory management

2022

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“…This may take a long time or require efforts to rework the model. So, in some tabular simulation models [16][17][18] changes in the input data of the model can lead to a change and addition of calculation formulas.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for Solving Inverse Problems of Simulation Modeling

Gribanova¹

2021

IJC

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This paper is devoted to solving inverse problems of simulation modeling, which are presented in the form of an optimization problem. The article discusses the use of direct search methods taking into account the specifics of the problem under consideration. Due to the fact that these methods require a lot of computational experiments, two algorithms based on approximation were proposed for solving the problem. The first algorithm consists in determining and evaluating the parameters of the function of dependence (which can be linear or non-linear) of the output variable on the input variables and solving the inverse problem by minimizing increments of the arguments. In the second algorithm a linear function of dependence is iteratively constructed using the data set generated by changing the input variables in given increments, and the inverse problem is solved by minimizing increments of the arguments. The classical inventory management model with a threshold strategy is considered as an example. The inverse problem was solved using direct search and approximation-based methods.

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