A5029995660- Simulation Techniques and Applications
- Scheduling and Optimization Algorithms
- Advanced Multi-Objective Optimization Algorithms
- Business Process Modeling and Analysis
- Manufacturing Process and Optimization
- Assembly Line Balancing Optimization
- Advanced Manufacturing and Logistics Optimization
- Metaheuristic Optimization Algorithms Research
- Process Optimization and Integration
- Advanced Control Systems Optimization
- Advanced Database Systems and Queries
- Operations Management Techniques
- Fault Detection and Control Systems
- Statistical and Computational Modeling
- BIM and Construction Integration
- Advanced Data Processing Techniques
- Big Data and Business Intelligence
- Scientific Computing and Data Management
University of Skövde
2018-2024
Volvo (Sweden)
2012-2024
Volvo Cars (Sweden)
2012-2018
Abstract Reacting quickly to changing market demands and new variants by improving adapting industrial systems is an important business advantage. Changes are costly; especially when those already in place. Resources invested should be targeted so that the results of improvements maximized. One method allowing this combination discrete event simulation, aggregated models, multi-objective optimization, data-mining shown article. A real-world optimization case study problem conducted resulting...
Discrete Event Simulation is a comprehensive tool for the analysis and design of manufacturing systems. Over years, considerable efforts to improve simulation processes have been made. One step in these standardisation output data through development an appropriate system which presents results standardised way. This paper survey based on projects undertaken automotive industry. In addition, it implementation automated data-handling aims simplify project's documentation task engineers make...
This paper describes a case study of real-world simulation-based optimization an engine manufacturing line. The aims to maximize utilization machines and at the same time minimize tied-up capital by manipulating 56 unique decision variables. A recently proposed metaheuristic algorithm that has achieved successful results in various problem domains called Cuckoo Search is used perform optimization. To handle multiple objectives, extension original based on concept Pareto optimality study....
This paper describes a case study of real-world simulation-based optimization an engine manufacturing line. The aims to maximize utilization machines and at the same time minimize tied-up capital by manipulating 56 unique decision variables. A recently proposed metaheuristic algorithm that has achieved successful results in various problem domains called Cuckoo Search is used perform optimization. To handle multiple objectives, extension original based on concept Pareto optimality study....
The application of discrete event simulation methodology in the analysis higher level manufacturing systems has been limited due to model complexity and lack aggregation techniques for lines. Recent research introduced new methods preparing approaches or networks. In this paper one aggregated line modeling is successfully applied on a real world system, solving real-world problem. results demonstrate that technique adequate be plant wide models. Furthermore, particular case, there potential...
Improving production line performance and identifying bottlenecks using simulation-based optimization has been shown to be an effective approach. Nevertheless, for larger systems which are consisted of multiple lines, can too computationally expensive, due the complexity models. Previous research promising techniques aggregating data into efficient modules, enables simulation higher-level systems, i.e., factories. This paper shows how a real-world factory flow optimized by applying...
Discrete Event Simulation (DES) project data management is a complex and important engineering activity which impacts on an organization's efficiency. This efficiency could be decreased by the lack of provenance information or unreliability existing regarding previous simulation projects, all complicates reusability data. study presents analysis projects their data, according to different types scenarios usually found at manufacturing plant. A survey based automotive plant was conducted, in...
Simulation-based optimisation enables companies to take decisions based on data, and allows prescriptive analysis of current future production scenarios, creating a competitive edge.However, effectively visualising extracting knowledge from the vast amounts data generated by many-objective algorithms can be challenging.We present an open-source, web-based application in R language extract simulation-based optimisation.For tool useful for real-world industrial decision-making support, several...
Improving production line performance and identifying bottlenecks using simulation-based optimization has been shown to be an effective approach. Nevertheless, for larger systems which are consisted of multiple lines, can too computationally expensive, due the complexity models. Previous research promising techniques aggregating data into efficient modules, enables simulation higher-level systems, i.e., factories. This paper shows how a real-world factory flow optimized by applying...
Removing bottlenecks that restrain the overall performance of a factory can give companies competitive edge. Although in principle, it is possible to connect multiple detailed discrete-event simulation models form complete model, could be too computationally expensive, especially if connected are used for simulation-based optimizations. Observing computational speed running model significantly reduced by aggregating line-level into an aggregated level, this paper investigates, with some loss...
Discrete Event Simulation (DES) project data management is a complex and important engineering activity which impacts on an organization's efficiency. This efficiency could be decreased by the lack of provenance information or unreliability existing regarding previous simulation projects, all complicates reusability data. study presents analysis projects their data, according to different types scenarios usually found at manufacturing plant. A survey based automotive plant was conducted, in...
In an increasingly competitive market due to customer demands for customization and increasing rate of new product variant introductions, companies need explore tools support them better predict optimally re-configure their production networks. terms the factory flow level, discrete-event simulation simulation-based optimization represent type available engineers or managers. For a complex consisting multiple lines, creating detailed models these lines connecting can be used optimization,...
Simulation-based optimisation enables companies to take decisions based on data, and allows prescriptive analysis of current future production scenarios, creating a competitive edge. However, effectively visualising extracting knowledge from the vast amounts data generated by many-objective algorithms can be challenging. We present an open-source, web-based application in R language extract simulation-based optimisation. For tool useful for real-world industrial decision-making support,...