| 1. |
- Goienetxea Uriarte, Ainhoa, 1983-, et al.
(author)
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Introducing simulation and optimization in the Lean continuous improvement standards in an automotive company
- 2019
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In: Proceedings of the Winter Simulation Conference, Gothenburg, December 9-12, 2018. - Piscataway, New Jersey : IEEE. - 9781538665725 - 9781538665701 - 9781538665718 - 9781538665732 ; , s. 3352-3363
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Conference paper (peer-reviewed)abstract
- The highly competitive automobile market requires automotive companies to become efficient by continuously improving their production systems. This paper presents a case study where simulationbased optimization (SBO) was employed as a step within a Value Stream Mapping event. The aim of the study was to promote the use of SBO to strengthen the continuous improvement work of the company. The paper presents all the key steps performed in the study, including the challenges faced and a reflection on how to introduce SBO as a powerful tool within the lean continuous improvement standards.
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| 2. |
- Lidberg, Simon, 1986-, et al.
(author)
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Using Aggregated Discrete Event Simulation Models and Multi-Objective Optimization to Improve Real-World Factories
- 2018
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In: Proceedings of the 2018 Winter Simulation Conference. - : IEEE. - 9781538665725 - 9781538665732 - 9781538665701 - 9781538665718 ; , s. 2015-2024
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Conference paper (peer-reviewed)abstract
- Improving production line performance and identifying bottlenecks using simulation-based optimization has been shown to be an effective approach. Nevertheless, for larger production systems which are consisted of multiple production lines, using simulation-based optimization can be too computationally expensive, due to the complexity of the models. Previous research has shown promising techniques for aggregating production line data into computationally efficient modules, which enables the simulation of higher-level systems, i.e., factories. This paper shows how a real-world factory flow can be optimized by applying the previously mentioned aggregation techniques in combination with multi-objective optimization using an experimental approach. The particular case studied in this paper reveals potential reductions of storage levels by over 30 %, lead time reductions by 67 %, and batch sizes reduced by more than 50 % while maintaining the delivery precision of the industrial system.
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| 3. |
- Renoux, Jennifer, 1989-, et al.
(author)
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Simulating Daily Activities in a Smart Home for Data Generation
- 2018
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In: 2018 Winter Simulation Conference (WSC). - New York, USA : Institute of Electrical and Electronics Engineers (IEEE). - 9781538665732 - 9781538665725 ; , s. 798-809
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Conference paper (peer-reviewed)abstract
- Smart Homes are currently one of the hottest topics in the area of Internet of Things or Augmented Living. In order to provide high-level intelligent solutions, algorithms for identifying which activities the inhabitants intend to perform are necessary. Sensor data plays here an essential role, for testing, for learning underlying rules, for classifying and connecting sensor patterns and to inhabitant activities, etc. However, only few and limited data sets are currently available. We present concepts and solutions for generating high-quality data using a flexible agent-based simulation tool. The basic idea is to integrate the simulation of a sensorized apartment with human behavior modelling based on constraint-based planning that produces a sequence of daily activities. The overall set-up is shown to generate data that exhibits the same relevant properties as data from a comparable real-world apartment.
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| 4. |
- Andersson Granberg, Tobias, et al.
(author)
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SIMULATION BASED PREDICTION OF THE NEAR-FUTURE EMERGENCY MEDICAL SERVICES SYSTEM STATE
- 2018
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In: 2018 WINTER SIMULATION CONFERENCE (WSC). - : IEEE. - 9781538665725 ; , s. 2542-2553
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Conference paper (peer-reviewed)abstract
- An ambulance dispatcher decides which ambulances to allocate to new calls, and how to relocate ambulances in order to maintain a good coverage. Doing this, it is valuable to have information about the future expected response times in different parts of the area of responsibility, as well as the expected number of available ambulances. We present a simulation model that can be used to predict this, and compare the results to a naive forecasting model. The results show that while it is difficult to accurately predict the future system state, the simulation based prediction manages this better than the naive model.
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| 5. |
- Feng, Kailun, et al.
(author)
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Machine learning based construction simulation and optimization
- 2018
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In: Proceedings of the 2018 Winter Simulation Conference. - : IEEE. - 9781538665725 ; , s. 2025-2036
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Conference paper (peer-reviewed)abstract
- Building construction comprises interaction and interdependence among processes. Discrete-event simulation (DES) is widely applied to model these processes interaction. To find optimal construction plans, optimization technique is usually integrated with DES. However, present simulation-optimization integrated method directly invokes simulation model within optimization algorithms, which is found significantly computationally expensive. This study proposes a machine learning based construction simulation and optimization integrated method. After trained by DES, the machine learning model accelerates simulation-optimization integration by nearly real-time providing fitness evaluation within optimization. This method was implemented into a real construction project for construction time-cost-environment optimization. Results show that proposed machine learning based method significantly reduce computing time compared with original simulation-optimization integration. Less than 1% of construction cost and time improvement were miss, while greenhouse gas emissions obtained same performance. The new method could be a more effective DES and optimization integration approach for practical engineering application.
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| 6. |
- L'ecuer, Pierre, et al.
(author)
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Modeling Bursts In The Arrival Process To An Emergency Call Center
- 2018
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In: Proceedings of the 2018 Winter Simulation Conference<em></em>. - : IEEE. - 9781538665725 ; , s. 525-- 536
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Conference paper (peer-reviewed)abstract
- In emergency call centers (for police, firemen, ambulances) a single event can sometimes trigger many incoming calls in a short period of time. Several people may call to report the same fire or the same accident, for example. Such a sudden burst of incoming traffic can have a significant impact on the responsiveness of the call center for other events in the same period of time. We examine data from the SOS Alarm center in Sweden. We also build a stochastic model for the bursts. We show how to estimate the model parameters for each burst by maximum likelihood, how to model the multivariate distribution of those parameters using copulas, and how to simulate the burst process from this model. In our model, certain events trigger an arrival process of calls with a random time-varying rate over a finite period of time of random length.
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| 7. |
- Roungas, Bill, et al.
(author)
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The future of contextual knowledge in gaming simulations : A research agenda
- 2018
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In: 2018 Winter Simulation Conference (WSC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538665725 ; , s. 2435-2446
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Conference paper (peer-reviewed)abstract
- Gaming simulations (games) are increasingly becoming the tool of choice for modeling and understanding the complexity of today's systems. This increased popularity has consequently revealed the weaknesses of games in several areas. These limitations range from inconsistencies on the game design to the unexploited explicit and tacit knowledge that games invoke. This paper focuses on games that do not aim at generalizing the produced knowledge but, instead, at understanding how a system works within a specific context. The first step of the analysis is identifying these limitations based on an extensive literature review. Based on this, different directions that could mitigate or even fully address these limitations are proposed. The paper concludes with a focused research agenda.
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