Simulation Tool for Transportation Problem

2018 ◽  
pp. 1-17
Author(s):  
Pratiksha Saxena ◽  
Abhinav Choudhary ◽  
Sanchit Kumar ◽  
Satyavan Singh
Keyword(s):  
Programming Languages ◽  
Transportation Problem ◽  
Analytical Approach ◽  
Model Simulation ◽  
Simulation Tool ◽  
Optimization Strategy ◽  
Performance Measurements ◽  
Product Allocation ◽  
Output Performance ◽  
Transportation Models

This chapter introduces simulation tool TRANSSIM (Transportation model Simulation) to simulate transportation models. TRANSSIM is a tool which simulates and compares the results of different transportation models. A combination of programming languages is used to design this tool and is based on analytical approach to guide optimization strategy. In TRANSSIM, inputs are provided in terms of resources available, requirement and cost associated. Output performance measurements are calculated in terms of product allocation and associated total cost.

Simulation Tool for Transportation Problem

2016 ◽  
pp. 111-130
Author(s):  
Pratiksha Saxena ◽  
Abhinav Choudhary ◽  
Sanchit Kumar ◽  
Satyavan Singh
Keyword(s):  
Programming Languages ◽  
Transportation Problem ◽  
Analytical Approach ◽  
Model Simulation ◽  
Simulation Tool ◽  
Optimization Strategy ◽  
Performance Measurements ◽  
Product Allocation ◽  
Output Performance ◽  
Transportation Models

This chapter introduces simulation tool TRANSSIM (Transportation model Simulation) to simulate transportation models. TRANSSIM is a tool which simulates and compares the results of different transportation models. A combination of programming languages is used to design this tool and is based on analytical approach to guide optimization strategy. In TRANSSIM, inputs are provided in terms of resources available, requirement and cost associated. Output performance measurements are calculated in terms of product allocation and associated total cost.

Simulation Tool for Assignment Models: SIMASI

2014 ◽  
Vol 13 (8) ◽  
pp. 4723-4728
Author(s):  
Pratiksha Saxena ◽  
Smt. Anjali
Keyword(s):  
Optimization Procedure ◽  
Simulation Tool ◽  
Optimization Strategy ◽  
Performance Measurements ◽  
Integrated Simulation ◽  
Assignment Model ◽  
Classical Models ◽  
Simulation Results ◽  
User Friendly ◽  
Assignment Models

In this paper, an integrated simulation optimization model for the assignment problems is developed. An effective algorithm is developed to evaluate and analyze the back-end stored simulation results. This paper proposes simulation tool SIMASI (Simulation of assignment models) to simulate assignment models. SIMASI is a tool which simulates and computes the results of different assignment models. This tool is programmed in DOT.NET and is based on analytical approach to guide optimization strategy. Objective of this paper is to provide a user friendly simulation tool which gives optimized assignment model results. Simulation is carried out by providing the required values of matrix for resource and destination requirements and result is stored in the database for further comparison and study. Result is obtained in terms of the performance measurements of classical models of assignment system. This simulation tool is interfaced with an optimization procedure based on classical models of assignment system. The simulation results are obtained and analyzed rigorously with the help of numerical examples. 

Simulation Tool for Inventory Models: SIMIN

2014 ◽  
Vol 3 (4) ◽  
pp. 109-124 ◽  
Author(s):  
Pratiksha Saxen ◽  
Tulsi Kushwaha
Keyword(s):  
Optimization Procedure ◽  
Inventory System ◽  
Total System ◽  
Simulation Tool ◽  
Optimization Strategy ◽  
Performance Measurements ◽  
Inventory Models ◽  
Input Parameters ◽  
Classical Models ◽  
Simulation Results

In this paper, an integrated simulation optimization model for the inventory system is developed. An effective algorithm is developed to evaluate and analyze the back-end stored simulation results. This paper proposes simulation tool SIMIN (Inventory Simulation) to simulate inventory models. SIMIN is a tool which simulates and compares the results of different inventory models. To overcome various practical restrictive assumptions, SIMIN provides values for a number of performance measurements. This tool is programmed in JAVA and is based on analytical approach to guide optimization strategy. Objective of this paper is to provide a user friendly simulation tool which gives optimized inventory model results. Simulation is carried out by providing the required values of input parameters and result is stored in the database for further comparison and study. Result is obtained in terms of the performance measurements of classical models of inventory system. Simulation results are stored in excel file and it also provides graphical results to compare the outcome. This simulation tool is interfaced with an optimization procedure based on classical models of inventory system. With the specified examples, the simulation results are obtained and analyzed rigorously. The result shows that input parameters, total system costs and capacity should be considered in the design of a practical system.

scholarly journals Simulation Tool for Queuing Models: QSIM

2013 ◽  
Vol 5 (2) ◽  
pp. 74-79 ◽  
Author(s):  
Dr. Pratiksha Saxena ◽  
Lokesh Sharma
Keyword(s):  
Performance Measurement ◽  
Analytical Approach ◽  
Arrival Rate ◽  
Simulation Software ◽  
Service Rate ◽  
Simulation Tool ◽  
Optimization Strategy ◽  
Queuing Models ◽  
Simulation Results ◽  
Excel File

This paper proposes simulation software QSIM (Queuing Simulation) to simulate queuing models. QSIM is a tool which simulates and compares the results of different queuing models. This tool is programmed in JAVA and is based on analytical approach to guide optimization strategy. In Qsim, Simulation is carried out by providing the inputs for arrival rate, service rate and number of servers and using these values the performance measurement of a particular model is stored in the database for further comparisons and study. Simulation results are stored in excel file and it also provides graphical results to compare the outcome.

scholarly journals Automated model optimisation using the Cylc workflow engine (Cyclops v1.0)

2017 ◽  
Author(s):  
Richard M. Gorman ◽  
Hilary J. Oliver
Keyword(s):  
Cost Function ◽  
Programming Languages ◽  
Model Simulation ◽  
Wave Model ◽  
Workflow Engine ◽  
Test Case ◽  
Spatial Average ◽  
Wave Hindcast ◽  
Geophysical Model ◽  
Error Metric

Abstract. Most geophysical models include a number of parameters that are not fully determined by theory, and can be ‘tuned’ to improve the model's agreement with available data. We might attempt to automate this tuning process in an objective way by employing an optimisation algorithm to find the set of parameters that minimises a cost function derived from comparing model outputs with measurements. A number of algorithms are available for solving optimisation problems, in various software programming languages, but interfacing such software to a complex geophysical model simulation, presents certain challenges. To tackle this problem, we have developed an optimisation suite ("Cyclops") based on the Cylc workflow engine (http://cylc.github.io/cylc/ and https://zenodo.org/badge/latestdoi/1836229) that implements a wide selection of optimisation algorithms from the NLopt python toolbox (Johnson, 2014). The Cyclops optimisation suite can be used to calibrate any modelling system that has itself been implemented as a (separate) Cylc model suite, provided it includes computation and output of the desired scalar cost function. A growing number of institutions are using Cylc to orchestrate complex distributed suites of interdependent cycling tasks within their operational forecast systems, and in such cases application of the optimisation suite is particularly straightforward. As a test case, we applied the Cyclops to calibrate a global implementation of the Wavewatch III™ (v4.18) third generation spectral wave model, forced by ERA-Interim input fields. This was calibrated over a one-year period (1997), before applying the calibrated model to a full (1979–2016) wave hindcast. The chosen error metric was the spatial average of the root-mean-square error of hindcast significant wave height compared with collocated altimeter records. We describe the results of a calibration in which up to 19 parameters were optimised.

scholarly journals Automated model optimisation using the Cylc workflow engine (Cyclops v1.0)

2018 ◽  
Vol 11 (6) ◽  
pp. 2153-2173 ◽  
Author(s):  
Richard M. Gorman ◽  
Hilary J. Oliver
Keyword(s):  
Cost Function ◽  
Programming Languages ◽  
Model Simulation ◽  
Wave Model ◽  
Workflow Engine ◽  
Test Case ◽  
Spatial Average ◽  
Wave Hindcast ◽  
Geophysical Model ◽  
Error Metric

Abstract. Most geophysical models include many parameters that are not fully determined by theory, and can be “tuned” to improve the model's agreement with available data. We might attempt to automate this tuning process in an objective way by employing an optimisation algorithm to find the set of parameters that minimises a cost function derived from comparing model outputs with measurements. A number of algorithms are available for solving optimisation problems, in various programming languages, but interfacing such software to a complex geophysical model simulation presents certain challenges. To tackle this problem, we have developed an optimisation suite (“Cyclops”) based on the Cylc workflow engine that implements a wide selection of optimisation algorithms from the NLopt Python toolbox (Johnson, 2014). The Cyclops optimisation suite can be used to calibrate any modelling system that has itself been implemented as a (separate) Cylc model suite, provided it includes computation and output of the desired scalar cost function. A growing number of institutions are using Cylc to orchestrate complex distributed suites of interdependent cycling tasks within their operational forecast systems, and in such cases application of the optimisation suite is particularly straightforward. As a test case, we applied the Cyclops to calibrate a global implementation of the WAVEWATCH III (v4.18) third-generation spectral wave model, forced by ERA-Interim input fields. This was calibrated over a 1-year period (1997), before applying the calibrated model to a full (1979–2016) wave hindcast. The chosen error metric was the spatial average of the root mean square error of hindcast significant wave height compared with collocated altimeter records. We describe the results of a calibration in which up to 19 parameters were optimised.

Model-Based Development

2011 ◽  
pp. 289-312 ◽  
Author(s):  
Juan D. Lara ◽  
Esther Guerra ◽  
Hans Vangheluwe
Keyword(s):  
Programming Languages ◽  
Graphical Models ◽  
Code Generation ◽  
Simulation Analysis ◽  
Model Simulation ◽  
General Purpose ◽  
Test Case ◽  
Model Driven ◽  
Model Based ◽  
Design Activities

Since the beginning of computer science more than 50 years ago, software engineers have sought techniques resulting in higher levels of quality and productivity. Some of these efforts have concentrated in increasing the level of abstraction in programming languages (from assembler to structured languages to object-oriented languages). In the last few years, we have witnessed an increasing focus on development based on high-level, graphical models. They are used not only as a means to documentthe analysis and design activities, but also as the actual “implementation” of the application, as well as for automatic analysis, code, and test case generation. The notations used to describe the models can be standard and general purpose (for example, UML) or tightly customized for the application domain. Code generation for the full application is only accomplished for specific, well-understood application domains. A key initiative in this direction is OMG’s Model-Driven Architecture (MDA), where models are progressively transformed until executable code is obtained. In this chapter, we give an overview of these technologies and propose ideas following this line (concerning metamodeling and the use of visual languages for the specification of model transformation, model simulation, analysis and code generation), and examine the impact of model-based techniques in the development process.

scholarly journals Optimal Cement Mixtures Containing Mineral Admixtures under Multiple and Conflicting Criteria

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Nitza M. García ◽  
Hildélix L. Soto-Toro ◽  
Mauricio Cabrera-Ríos ◽  
Oscar Marcelo Suárez
Keyword(s):  
Decision Making ◽  
Fly Ash ◽  
Construction Industry ◽  
Mineral Admixtures ◽  
Optimization Strategy ◽  
Objective Functions ◽  
Performance Measurements ◽  
Sustainable Concrete ◽  
Combined Use ◽  
Nanostructured Silica

In modern construction industry, fabrication of sustainable concrete has turned the decision-making process into a challenging endeavor. One alternative is using fly ash and nanostructured silica as cement replacements. In these modern mixtures, proper concrete bulk density, percentage of voids, and compressive strength normally cannot be optimized individually. Hereby, a decision-making strategy on the replacement of those components is presented while taking into account those three performance measurements. The relationships among those components upon concrete fabrication required a design of experiments of mixtures to characterize those mineral admixtures. This approach integrates different objective functions that are in conflict and obtains the best compromise mixtures for the performance measures being considered. This optimization strategy permitted to recommend the combined use of fly ash and nanosilica to improve the concrete properties at its early age.

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