| 1. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Adaptive Runtime Response Time Control in PLC-based Real-Time Systems using Reinforcement Learning
- 2018
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Ingår i: ACM/IEEE 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2018, , co-located with International Conference on Software Engineering, ICSE 2018; Gothenburg; Sweden; 28 May 2018 through 29 May 2018; Code 138312. - New York, NY, USA : ACM. ; , s. 217-223
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Konferensbidrag (refereegranskat)abstract
- Timing requirements such as constraints on response time are key characteristics of real-time systems and violations of these requirements might cause a total failure, particularly in hard real-time systems. Runtime monitoring of the system properties is of great importance to detect and mitigate such failures. Thus, a runtime control to preserve the system properties could improve the robustness of the system with respect to timing violations. Common control approaches may require a precise analytical model of the system which is difficult to be provided at design time. Reinforcement learning is a promising technique to provide adaptive model-free control when the environment is stochastic, and the control problem could be formulated as a Markov Decision Process. In this paper, we propose an adaptive runtime control using reinforcement learning for real-time programs based on Programmable Logic Controllers (PLCs), to meet the response time requirements. We demonstrate through multiple experiments that our approach could control the response time efficiently to satisfy the timing requirements.
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| 2. |
- Helali Moghadam, Mahshid, et al.
(författare)
-
An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning
- 2022
-
Ingår i: Software quality journal. - : Springer. - 0963-9314 .- 1573-1367. ; , s. 127-159
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Tidskriftsartikel (refereegranskat)abstract
- Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case-based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learned policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments in a simulated performance testing setup, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process and performs adaptively without access to source code and performance models. © 2021, The Author(s).
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| 3. |
- Helali Moghadam, Mahshid
(författare)
-
Intelligence-Driven Software Performance Assurance
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Software performance assurance is of great importance for the success of software products, which are nowadays involved in many parts of our life. Performance evaluation approaches such as performance modeling, testing, as well as runtime performance control methods, all can contribute to the realization of software performance assurance. Many of the common approaches to tackle challenges in this area involve relying on performance models or using system models and source code. Although modeling provides a deep insight into the system behavior, developing a detailed model is challenging. Furthermore, software artifacts such as models and source code might not be readily available at all times in the development lifecycle. This thesis focuses on leveraging the potential of machine learning (ML) and evolutionary search-based techniques to provide viable solutions for addressing the challenges in different aspects of software performance assurance efficiently and effectively.In this thesis, we first investigate the capabilities of model-free reinforcement learning to address the objectives in robustness testing problems. We develop two self-adaptive reinforcement learning-driven test agents called SaFReL and RELOAD. They generate effective platform-based test scenarios and test workloads, respectively. The output scenarios and workloads help testers and software engineers meet their objectives efficiently without relying on models or source code. SaFReL and RELOAD learn the optimal policies (ways) to meet the test objectives and can reuse the learned policies adaptively in other testing settings. Policy reuse can lead to higher test efficiency and cost savings, for example, when testing similar test objectives or software systems with comparable performance sensitivity.Next, we leverage the potential of evolutionary computation algorithms, i.e., genetic algorithms, evolution strategies, and particle swarm optimization, to generate failure-revealing test scenarios for robustness testing of AI systems. In this part, we choose autonomous driving systems as a prevailing example of contemporary AI systems. We study the efficacy of the proposed evolutionary search-based test generation techniques and evaluate primarily to what extent they can trigger failures. Moreover, we investigate the diversity of those failures and compare them to existing baseline solutions. Finally, we again use the potential of model-free reinforcement learning to develop adaptive ML-driven runtime performance control approaches. We present a response time preservation method for a sample type of industrial applications and a resource allocation technique for dynamic workloads in a data grid application. The proposed ML-driven techniques learn how to adjust the tunable parameters and resource configuration at runtime to keep the performance continually compliant with the requirements and to further optimize the runtime performance. We evaluate the efficacy of the approaches and show how effectively they can improve the performance and keep the performance requirements satisfied under varying conditions such as dynamic workloads and the occurrence of runtime events that lead to substantial response time deviations.
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| 4. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Intelligent Load Testing: Self-adaptive Reinforcement Learning-driven Load Runner
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Load testing with the aim of generating an effective workload to identify performance issues is a time-consuming and complex challenge, particularly for evolving software systems. Current automated approaches mainly rely on analyzing system models and source code, or modeling of the real system usage. However, that information might not be available all the time or obtaining it might require considerable effort. On the other hand, if the optimal policy for generating the proper test workload resulting in meeting the objectives of the testing can be learned by the testing system, testing would be possible without access to system models or source code. We propose a self-adaptive reinforcement learning-driven load testing agent that learns the optimal policy for test workload generation. The agent can reuse the learned policy in subsequent testing activities such as meeting different types of testing targets. It generates an efficient test workload resulting in meeting the objective of the testing adaptively without access to system models or source code. Our experimental evaluation shows that the proposed self-adaptive intelligent load testing can reach the testing objective with lower cost in terms of the workload size, i.e. the number of generated users, compared to a typical load testing process, and results in productivity benefits in terms of higher efficiency.
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| 5. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Learning-based Response Time Analysis in Real-Time Embedded Systems : A Simulation-based Approach
- 2018
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Ingår i: 1st International Workshop on Software Qualities and their Dependencies, located at the International Conference of Software Engineering (ICSE) 2018 SQUADE'18. - New York, NY, USA : ACM. - 9781450357371 ; , s. 21-24
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Konferensbidrag (refereegranskat)abstract
- Response time analysis is an essential task to verify the behavior of real-time systems. Several response time analysis methods have been proposed to address this challenge, particularly for real-time systems with different levels of complexity. Static analysis is a popular approach in this context, but its practical applicability is limited due to the high complexity of the industrial real-time systems, as well as many unpredictable runtime events in these systems. In this work-in-progress paper, we propose a simulationbased response time analysis approach using reinforcement learning to find the execution scenarios leading to the worst-case response time. The approach learns how to provide a practical estimation of the worst-case response time through simulating the program without performing static analysis. Our initial study suggests that the proposed approach could be applicable in the simulation environments of the industrial real-time control systems to provide a practical estimation of the execution scenarios leading to the worst-case response time.
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| 6. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Learning-Based Self-Adaptive Assurance of Timing Properties in a Real-Time Embedded System
- 2018
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Ingår i: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'18. - 9781538663523 ; , s. 77-80
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Konferensbidrag (refereegranskat)abstract
- Providing an adaptive runtime assurance technique to meet the performance requirements of a real-time system without the need for a precise model could be a challenge. Adaptive performance assurance based on monitoring the status of timing properties can bring more robustness to the underlying platform. At the same time, the results or the achieved policy of this adaptive procedure could be used as feedback to update the initial model, and consequently for producing proper test cases. Reinforcement-learning has been considered as a promising adaptive technique for assuring the satisfaction of the performance properties of software-intensive systems in recent years. In this work-in-progress paper, we propose an adaptive runtime timing assurance procedure based on reinforcement learning to satisfy the performance requirements in terms of response time. The timing control problem is formulated as a Markov Decision Process and the details of applying the proposed learning-based timing assurance technique are described.
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| 7. |
- Helali Moghadam, Mahshid
(författare)
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Machine Learning-Assisted Performance Assurance
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With the growing involvement of software systems in our life, assurance of performance, as an important quality characteristic, rises to prominence for the success of software products. Performance testing, preservation, and improvement all contribute to the realization of performance assurance. Common approaches to tackle challenges in testing, preservation, and improvement of performance mainly involve techniques relying on performance models or using system models or source code. Although modeling provides a deep insight into the system behavior, drawing a well-detailed model is challenging. On the other hand, those artifacts such as models and source code might not be available all the time. These issues are the motivations for using model-free machine learning techniques such as model-free reinforcement learning to address the related challenges in performance assurance.Reinforcement learning implies that if the optimal policy (way) for achieving the intended objective in a performance assurance process could instead be learnt by the acting system (e.g., the tester system), then the intended objective could be accomplished without advanced performance models. Furthermore, the learnt policy could later be reused in similar situations, which leads to efficiency improvement by saving computation time while reducing the dependency on the models and source code.In this thesis, our research goal is to develop adaptive and efficient performance assurance techniques meeting the intended objectives without access to models and source code. We propose three model-free learning-based approaches to tackle the challenges; efficient generation of performance test cases, runtime performance (response time) preservation, and performance improvement in terms of makespan (completion time) reduction. We demonstrate the efficiency and adaptivity of our approaches based on experimental evaluations conducted on the research prototype tools, i.e. simulation environments that we developed or tailored for our problems, in different application areas.
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| 8. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing
- 2022
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (μ+ λ) and (μ,λ) evolution strategies(ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints.
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| 9. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Machine learning testing in an ADAS case study using simulation-integrated bio-inspired search-based testing
- 2024
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Ingår i: Journal of Software. - : John Wiley and Sons Ltd. - 2047-7473 .- 2047-7481. ; :5
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (Formula presented.) and (Formula presented.) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints.
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| 10. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Machine Learning to Guide Performance Testing : An Autonomous Test Framework
- 2019
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Ingår i: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 2019.
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Konferensbidrag (refereegranskat)abstract
- Satisfying performance requirements is of great importance for performance-critical software systems. Performance analysis to provide an estimation of performance indices and ascertain whether the requirements are met is essential for achieving this target. Model-based analysis as a common approach might provide useful information but inferring a precise performance model is challenging, especially for complex systems. Performance testing is considered as a dynamic approach for doing performance analysis. In this work-in-progress paper, we propose a self-adaptive learning-based test framework which learns how to apply stress testing as one aspect of performance testing on various software systems to find the performance breaking point. It learns the optimal policy of generating stress test cases for different types of software systems, then replays the learned policy to generate the test cases with less required effort. Our study indicates that the proposed learning-based framework could be applied to different types of software systems and guides towards autonomous performance testing.
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| 11. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Performance Testing Using a Smart Reinforcement Learning-Driven Test Agent
- 2021
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Ingår i: 2021 IEEE Congress on Evolutionary Computation (CEC). - 9781728183930 ; , s. 2385-2394
-
Konferensbidrag (refereegranskat)abstract
- Performance testing with the aim of generating an efficient and effective workload to identify performance issues is challenging. Many of the automated approaches mainly rely on analyzing system models, source code, or extracting the usage pattern of the system during the execution. However, such information and artifacts are not always available. Moreover, all the transactions within a generated workload do not impact the performance of the system the same way, a finely tuned workload could accomplish the test objective in an efficient way. Model-free reinforcement learning is widely used for finding the optimal behavior to accomplish an objective in many decision-making problems without relying on a model of the system. This paper proposes that if the optimal policy (way) for generating test workload to meet a test objective can be learned by a test agent, then efficient test automation would be possible without relying on system models or source code. We present a self-adaptive reinforcement learning-driven load testing agent, RELOAD, that learns the optimal policy for test workload generation and generates an effective workload efficiently to meet the test objective. Once the agent learns the optimal policy, it can reuse the learned policy in subsequent testing activities. Our experiments show that the proposed intelligent load test agent can accomplish the test objective with lower test cost compared to common load testing procedures, and results in higher test efficiency.
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| 12. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Poster : Performance Testing Driven by Reinforcement Learning
- 2020
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Ingår i: 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728157771 ; , s. 402-405
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Konferensbidrag (refereegranskat)abstract
- Performance testing remains a challenge, particularly for complex systems. Different application-, platform- and workload-based factors can influence the performance of software under test. Common approaches for generating platform- and workload-based test conditions are often based on system model or source code analysis, real usage modeling and use-case based design techniques. Nonetheless, creating a detailed performance model is often difficult, and also those artifacts might not be always available during the testing. On the other hand, test automation solutions such as automated test case generation can enable effort and cost reduction with the potential to improve the intended test criteria coverage. Furthermore, if the optimal way (policy) to generate test cases can be learnt by testing system, then the learnt policy can be reused in further testing situations such as testing variants, evolved versions of software, and different testing scenarios. This capability can lead to additional cost and computation time saving in the testing process. In this research, we present an autonomous performance testing framework which uses a model-free reinforcement learning augmented by fuzzy logic and self-adaptive strategies. It is able to learn the optimal policy to generate platform- and workload-based test conditions which result in meeting the intended testing objective without access to system model and source code. The use of fuzzy logic and self-adaptive strategy helps to tackle the issue of uncertainty and improve the accuracy and adaptivity of the proposed learning. Our evaluation experiments show that the proposed autonomous performance testing framework is able to generate the test conditions efficiently and in a way adaptive to varying testing situations.
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| 13. |
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| 14. |
- Zinser, Markus, et al.
(författare)
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Comparison of microscopic and macroscopic approaches to simulating the effects of infrastructure disruptions on railway networks
- 2018
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Ingår i: Proceedings of 7th Transport Research Arena TRA 2018, April 16-19, 2018, Vienna, Austr. - : Zenodo.
-
Konferensbidrag (refereegranskat)abstract
- The current state-of-the-art in timetable analysis in the presence of disruptions is to use railway microsimulation, which typically yields detailed results on infrastructure or timetable performance. However, micro-simulation is time-consuming and requires a detailed infrastructure model. This paper outlines a macroscopic approach which aims at reducing execution time by restricting the level of detail to high-level relations between significant events. In particular, the effect of disruptions is modelled by sampling delay times from probability distributions obtained from historical data. In this paper, we test whether this approach, given common disruption scenarios, still allows accurate results on delays to be obtained. Two disruption scenarios were simulated in RailSys and with the new method, using limited parameter tuning. In the results, visually similar delay distributions were observed. Although there is some room for improvements in accuracy, the new approach appears promising, and we found no evidence against its suitability in the presence of disruptions.
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| 15. |
- Andersson, Tim, 1989-
(författare)
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Automated Tactile Sensing for Quality Control of Locks Using Machine Learning
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis delves into the use of Artificial Intelligence (AI) for quality control in manufacturing systems, with a particular focus on anomaly detection through the analysis of torque measurements in rotating mechanical systems. The research specifically examines the effectiveness of torque measurements in quality control of locks, challenging the traditional method that relies on human tactile sense for detecting mechanical anomalies. This conventional approach, while widely used, has been found to yield inconsistent results and poses physical strain on operators. A key aspect of this study involves conducting experiments on locks using torque measurements to identify mechanical anomalies. This method represents a shift from the subjective and physically demanding practice of manually testing each lock. The research aims to demonstrate that an automated, AI-driven approach can offer more consistent and reliable results, thereby improving overall product quality. The development of a machine learning model for this purpose starts with the collection of training data, a process that can be costly and disruptive to normal workflow. Therefore, this thesis also investigates strategies for predicting and minimizing the sample size used for training. Additionally, it addresses the critical need of trustworthiness in AI systems used for final quality control. The research explores how to utilize machine learning models that are not only effective in detecting anomalies but also offers a level of interpretability, avoiding the pitfalls of black box AI models. Overall, this thesis contributes to advancing automated quality control by exploring the state-of-the-art machine learning algorithms for mechanical fault detection, focusing on sample size prediction and minimization and also model interpretability. To the best of the author’s knowledge, it is the first study that evaluates an AI-driven solution for quality control of mechanical locks, marking an innovation in the field.
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| 16. |
- Andersson, Tim, et al.
(författare)
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Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks
- 2022
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Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031164064 ; , s. 27-34, s. 27-34
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Konferensbidrag (refereegranskat)abstract
- Historically, cylinder locks’ quality has been tested manually by human operators after full assembly. The frequency and the characteristics of the testing procedure for these locks wear the operators’ wrists and lead to varying results of the quality control. The consistency in the quality control is an important factor for the expected lifetime of the locks which is why the industry seeks an automated solution. This study evaluates how consistently the operators can classify a collection of locks, using their tactile sense, compared to a more objective approach, using torque measurements and Machine Learning (ML). These locks were deliberately chosen because they are prone to get inconsistent classifications, which means that there is no ground truth of how to classify them. The ML algorithms were therefore evaluated with two different labeling approaches, one based on the results from the operators, using their tactile sense to classify into ‘working’ or ‘faulty’ locks, and a second approach by letting an unsupervised learner create two clusters of the data which were then labeled by an expert using visual inspection of the torque diagrams. The results show that an ML-solution, trained with the second approach, can classify mechanical anomalies, based on torque data, more consistently compared to operators, using their tactile sense. These findings are a crucial milestone for the further development of a fully automated test procedure that has the potential to increase the reliability of the quality control and remove an injury-prone task from the operators.
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| 17. |
- Andersson, Tim, et al.
(författare)
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Interpretable ML model for quality control of locks using counterfactual explanations
- 2024
-
Konferensbidrag (refereegranskat)abstract
- This paper presents an interpretable machinelearning model for anomaly detection in door locks using torque data. The model aims to replace the human tactile sense in the quality control process, reducing repetitive tasks and improving reliability. The model achieved an accuracy of 96%, however, to gain social acceptance and operators' trust, interpretability of the model is crucial. The purpose of this study was to evaluate anapproach that can improve interpretability of anomalousclassifications obtained from an anomaly detection model. Weevaluate four instance-based counterfactual explanators, three of which, employ optimization techniques and one uses, a less complex, weighted nearest neighbor approach, which serve as ourbaseline. The former approaches, leverage a latent representation of the data, using a weighted principal component analysis, improving plausibility of the counter factual explanations andreduces computational cost. The explanations are presentedtogether with the 5-50-95th percentile range of the training data, acting as a frame of reference to improve interpretability. All approaches successfully presented valid and plausible counterfactual explanations. However, instance-based approachesemploying optimization techniques yielded explanations withgreater similarity to the observations and was therefore concluded to be preferable despite the higher execution times (4-16s) compared to the baseline approach (0.1s). The findings of this study hold significant value for the lock industry and can potentially be extended to other industrial settings using timeseries data, serving as a valuable point of departure for further research.
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| 18. |
- Andersson, Tim, et al.
(författare)
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Sample size prediction for anomaly detection in locks
- 2023
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Ingår i: Procedia CIRP. - : Elsevier B.V.. ; , s. 870-874
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Konferensbidrag (refereegranskat)abstract
- Artificial intelligence in manufacturing systems is currently most used for quality control and predictive maintenance. In the lock industry, quality control of final assembled cylinder lock is still done by hand, wearing out the operators' wrists and introducing subjectivity which negatively affects reliability. Studies have shown that quality control can be automated using machine-learning to analyse torque measurements from the locks. The resulting performance of the approach depends on the dimensionality and size of the training dataset but unfortunately, the process of gathering data can be expensive so the amount collected data should therefore be minimized with respect to an acceptable performance measure. The dimensionality can be reduced with a method called Principal Component Analysis and the training dataset size can be estimated by repeated testing of the algorithms with smaller datasets of different sizes, which then can be used to extrapolate the expected performance for larger datasets. The purpose of this study is to evaluate the state-of-the-art methods to predict and minimize the needed sample size for commonly used machine-learning algorithms to reach an acceptable anomaly detection accuracy using torque measurements from locks. The results show that the learning curve with the best fit to the training data does not always give the best predictions. Instead, performance depends on the amount of data used to create the curve and the particular machine-learning algorithm used. Overall, the exponential and power-law functions gave the most reliable predictions and the use of principal component analysis greatly reduced the learning effort for the machine-learning algorithms. With torque measurements from 50-150 locks, we predicted a detection accuracy of over 95% while the current method of using the human tactile sense gives only 16% accuracy.
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| 19. |
- Aronsson, Martin, et al.
(författare)
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MILP formulations of cumulative constraints for railway scheduling - A comparative study
- 2009. - 13
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Ingår i: The Proceedings of the 9th Workshop on Algorithmic Methods and Models for Optimization of Railways (ATMOS). - : Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany.
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Konferensbidrag (refereegranskat)abstract
- This paper introduces two Mixed Integer Linear Programming (MILP) models for railway traffic planning using a cumulative scheduling constraint and associated pre-processing filters. We compare standard solver performance for these models on three sets of problems from the railway domain and for two of them, where tasks have unitary resource consumption, we also compare them with two more conventional models. In the experiments, the solver performance of one of the cumulative models is clearly the best and is also shown to scale very well for a large scale practical railway scheduling problem.
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| 20. |
- Aronsson, Martin, et al.
(författare)
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Mixed integer-linear formulations of cumulative scheduling constraints - A comparative study
- 2007. - 1
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper introduces two MILP models for the cumulative scheduling constraint and associated pre-processing filters. We compare standard solver performance for these models on three sets of problems and for two of them, where tasks have unitary resource consumption, we also compare them with two models based on a geometric placement constraint. In the experiments, the solver performance of one of the cumulative models, is clearly the best and is also shown to scale very well for a large scale industrial transportation scheduling problem.
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| 21. |
- Backåker, Lars, 1984-
(författare)
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The Influence of Customer Agreements and Planning Principles on Rail Freight Performance
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Rail freight transportation is recognized worldwide as a suitable transportation mode when it comes to long haul transportation of heavy commodities. The industry is also known to be capital intensive, highly dependent on infrastructural developments and requires thorough planning of operations. Despite intensive planning of operations, great challenges remain in how to make best use of existing resources. Especially uncertainties related to up-coming daily freight volumes stand as central causes behind such planning challenges. This thesis focuses on rail freight carload transportation and concerns how customer commitments influence operational performance as well as potentials for improvements of operational planning principles. Problem statements are addressed using three separate studies and all experiments involve quantitative approaches. The first study investigates effects of a potential Volume Variation Allowance (VVA) policy through simulation. The policy dictates how much freight volumes are allowed to vary by day of week. Results indicate that effects of the policy are relatively small, but an overall decrease in transportation times is observed. The study also identifies improvement potentials with respect to the current operational planning principles used within Swedish railways. The second study proposes a new optimization-based approach for trip plan generation. The approach, including a number of extensions, is evaluated against the current industry practice. Results confirm the potentials for reduced transportation times, shunting activities as well as service frequencies. All experiments satisfy existing customer commitments. The third study explores effects of a Fixed Carload Capacity (FCC) concept which partially allows capacity reservation on services. The study adopts an extension of the previously developed optimization approach. Results confirm the hypothetical trade-off between customer groups and the dependency on capacity reservation levels, but indicate that the concept has relatively small effects with respect to regular carload customers. On the other hand, benefits in terms of guarantee of service and reliability in transportation times can be observed for the customer under the agreement.
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| 22. |
- Bajceta, Aleksandar, et al.
(författare)
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Using NLP Tools to Detect Ambiguities in System Requirements - A Comparison Study
- 2022
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS.
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Konferensbidrag (refereegranskat)abstract
- Requirements engineering is a time-consuming process, and it can benefit significantly from automated tool support. Ambiguity detection in natural language requirements is a challenging problem in the requirements engineering community. Several Natural Language Processing tools and techniques have been developed to improve and solve the problem of ambiguity detection in natural language requirements. However, there is a lack of empirical evaluation of these tools. We aim to contribute the understanding of the empirical performance of such solutions by evaluating four tools using the dataset of 180 system requirements from the electric train propulsion system provided to us by our industrial partner Alstom. The tools that were selected for this study are Automated Requirements Measurement (ARM), Quality Analyzer for Requirement Specifications (QuARS), REquirements Template Analyzer (RETA), and Requirements Complexity Measurement (RCM). Our analysis showed that selected tools could achieve high recall. Two of them had the recall of 0.85 and 0.98. But they struggled to achieve high precision. The RCM, an in-house developed tool by our industrial partner Alstom, achieved the highest precision in our study of 0.68.
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| 23. |
- Bashir, Sarmad, et al.
(författare)
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Requirement or Not, That is the Question : A Case from the Railway Industry
- 2023
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Ingår i: <em>Lecture Notes in Computer Science. </em>Volume 13975. Pages 105 - 121 2023. - : Springer Science and Business Media Deutschland GmbH. - 9783031297854 ; , s. 105-121
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Konferensbidrag (refereegranskat)abstract
- Requirements in tender documents are often mixed with other supporting information. Identifying requirements in large tender documents could aid the bidding process and help estimate the risk associated with the project. Manual identification of requirements in large documents is a resource-intensive activity that is prone to human error and limits scalability. This study compares various state-of-the-art approaches for requirements identification in an industrial context. For generalizability, we also present an evaluation on a real-world public dataset. We formulate the requirement identification problem as a binary text classification problem. Various state-of-the-art classifiers based on traditional machine learning, deep learning, and few-shot learning are evaluated for requirements identification based on accuracy, precision, recall, and F1 score. Results from the evaluation show that the transformer-based BERT classifier performs the best, with an average F1 score of 0.82 and 0.87 on industrial and public datasets, respectively. Our results also confirm that few-shot classifiers can achieve comparable results with an average F1 score of 0.76 on significantly lower samples, i.e., only 20% of the data. There is little empirical evidence on the use of large language models and few-shots classifiers for requirements identification. This paper fills this gap by presenting an industrial empirical evaluation of the state-of-the-art approaches for requirements identification in large tender documents. We also provide a running tool and a replication package for further experimentation to support future research in this area. © 2023, The Author(s)
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| 24. |
- Bohlin, Markus
(författare)
-
A Local Search System for Solving Constraint Problems of Declarative Graph-Based Global Constraints
- 2005. - 1
-
Ingår i: Applications of Declarative Programming and Knowledge Management: 15th International Conference on Applications of Declarative Programming and Knowledge Management, INAP 2004, and 18th Workshop on Logic Programming: Revised Selected Papers. - Berlin, Heidelberg : Springer. - 3540255605 ; , s. 166-184
-
Konferensbidrag (refereegranskat)abstract
- In this paper we present a local search constraint solver in which constraints are expressed using cost functions on graph structures of filter constraints of equal type. A similar theoretical approach has previously been used to model a large number of complex global constraints, which motivates the use of such a model in practice. In a local search context, we view global constraints as complex cost functions, encapsulating the structure of the constraints using a graph of variables, values and filter constraints. This representation gives us a declarative model, which can also be used to efficiently compute a cost as well as conflict levels of the variables in the constraints. We have implemented these ideas in a compositional C++ framework called Composer, which can be used to solve systems of graph-based constraints. We demonstrate the usability of this approach on several well-known global constraints, and show by experimental results on two problems that an approach using a graph basis for global constraint modeling is not only possible in practice, but also competitive with existing constraint-based local search systems.
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| 25. |
- Bohlin, Markus, 1976-
(författare)
-
A Study of Combinatorial Optimization Problems in Industrial Computer Systems
- 2009
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A combinatorial optimization problem is an optimization problem where the number of possible solutions are finite and grow combinatorially with the problem size. Combinatorial problems exist everywhere in industrial systems. This thesis focuses on solving three such problems which arise within two different areas where industrial computer systems are often used. Within embedded systems and real-time systems, we investigate the problems of allocating stack memory for an system where a shared stacks may be used, and of estimating the highest response time of a task in a system of industrial complexity. We propose a number of different algorithms to compute safe upper bounds on run-time stack usage whenever the system supports stack sharing. The algorithms have in common that they can exploit commonly-available information regarding timing behaviour of the tasks in the system. Given upper bounds on the individual stack usage of the tasks, it is possible to estimate the worst-case stack behaviour by analysing the possible and impossible preemption patterns. Using relations on offset and precedences, we form a preemption graph, which is further analysed to find safe upper-bounds on the maximal preemptions chain in the system. For the special case where all tasks exist in a single static schedule and share a single stack, we propose a polynomial algorithm to solve the problem. For generalizations of this problem, we propose an exact branch-and-bound algorithm for smaller problems and a polynomial heuristic algorithm for cases where the branch-and-bound algorithm fails to find a solution in reasonable time. All algorithms are evaluated in comprehensive experimental studies. The polynomial algorithm is implemented and shipped in the developer tool set for a commercial real-time operating system, Rubus OS. The second problem we study in the thesis is how to estimate the highest response time of a specified task in a complex industrial real-time system. The response-time analysis is done using a best-effort approach, where a detailed model of the system is simulated on input constructed using a local search procedure. In an evaluation on three different systems we can see that the new algorithm were able to produce higher response times much faster than what has previously been possible. Since the analysis is based on simulation and measurement, the results are not safe in the sense that they are always higher or equal to the true response time of the system. The value of the method lies instead in that it makes it possible to analyse complex industrial systems which cannot be analysed accurately using existing safe approaches. The third problem is in the area of maintenance planning, and focus on how to dynamically plan maintenance for industrial systems. Within this area we have focused on industrial gas turbines and rail vehicles. We have developed algorithms and a planning tool which can be used to plan maintenance for gas turbines and other stationary machinery. In such problems, it is often the case that performing several maintenance actions at the same time is beneficial, since many of these jobs can be done in parallel, which reduces the total downtime of the unit. The core of the problem is therefore how to (or how not to) group maintenance activities so that a composite cost due to spare parts, labor and loss of production due to downtime is minimized. We allow each machine to have individual schedules for each component in the system. For rail vehicles, we have evaluated the effect of replanning maintenance in the case where the component maintenance deadline is set to reflect a maximum risk of breakdown in a Gaussian failure distribution. In such a model, we show by simulation that replanning of maintenance can reduce the number of maintenance stops when the variance and expected value of the distribution are increased. For the gas turbine maintenance planning problem, we have evaluated the planning software on a real-world scenario from the oil and gas industry and compared it to the solutions obtained from a commercial integer programming solver. It is estimated that the availability increase from using our planning software is between 0.5 to 1.0 %, which is substantial considering that availability is currently already at 97-98 %.
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| 26. |
- Bohlin, Markus, et al.
(författare)
-
A Tool for Gas Turbine Maintenance Scheduling
- 2009. - 20
-
Ingår i: Proceedings of the Twenty-First Conference on Innovative Applications of Artificial Intelligence (IAAI'09). - : IEEE Computer Society. - 9781577354239
-
Konferensbidrag (refereegranskat)abstract
- We describe the implementation and deployment of a software decision support tool for the maintenance planning of gas turbines. The tool is used to plan the maintenance for turbines manufactured and maintained by Siemens Industrial Turbomachinery AB (SIT AB) with the goal to reduce the direct maintenance costs and the often very costly production losses during maintenance downtime. The optimization problem is formally defined, and we argue that feasibility in it is NP-complete. We outline a heuristic algorithm that can quickly solve the problem for practical purposes, and validate the approach on a real-world scenario based on an oil production facility. We also compare the performance of our algorithm with results from using mixed integer linear programming, and discuss the deployment of the application. The experimental results indicate that downtime reductions up to 65% can be achieved, compared to traditional preventive maintenance. In addition, using our tool is expected to improve availability with up to 1% and reduce the number of planned maintenance days with 12%. Compared to a mixed integer programming approach, our algorithm not optimal, but is orders of magnitude faster and produces results which are useful in practice. Our test results and SIT AB’s estimates based on operational use both indicate that significant savings can be achieved by using our software tool, compared to maintenance plans with fixed intervals.
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| 27. |
- Bohlin, Markus, et al.
(författare)
-
Ansatser för flexibel planering och schemaläggning av tågtidtabeller
- 2006. - 1
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Rapporten beskriver möjliga ansatser för att lösa det abstraherade tidtabellproblemet som bl.a. diskuteras i rapporten "Leveranstågplan: Specifikation och åtagande" (DDTP Arbetsdokument SICS-DDTP-002). Till grund för de olika ansatserna ligger en modell med avgångstider och hålltider (dvs. väntetider och i viss mån traverseringstider) som tillåts variera inom vissa tidsintervall. Huvudidén är att arbeta med förenklade kapacitetsvillkor på bana och bangård, för att på ett effektivt sätt kunna beräkna tidtabeller på en nivå som tillåter anpassning av tidtabellen till det gällande transportbehovet och den rådande trafiksituationen.
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| 28. |
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| 29. |
- Bohlin, Markus, et al.
(författare)
-
Bounding Shared-Stack Usage in Systems with Offsets and Precedences
- 2008
-
Ingår i: ECRTS 2008: PROCEEDINGS OF THE 20TH EUROMICRO CONFERENCE ON REAL-TIME SYSTEMS. - 9780769532981 ; , s. 276-285
-
Konferensbidrag (refereegranskat)abstract
- The paper presents two novel methods to bound the stack memory used in preemptive, shared stack, real-time systems. The first method is based on branch-and-bound search for possible preemption patterns, and the second one approximates the first in polynomial time. The work extends previous methods by considering a more general task-model, in which all tasks can share the same stack. In addition, the new methods account for precedence and offset relations. Thus, the methods give tight bounds for a large set of realistic systems. The methods have been implemented and a comprehensive evaluation, comparing our new methods against each other and against existing methods, is presented. The evaluation shows that our exact method can significantly reduce the amount of stack memory needed.
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| 30. |
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| 31. |
- Bohlin, Markus
(författare)
-
Constraint satisfaction by local search
- 2002. - 1
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The constraint satisfaction problem and its derivate, the propositional satisfiability problem (SAT), are fundamental problems in computing theory and mathematical logic. SAT was the first proved NP-complete problem, and although complete algorithms have been dominating the constraint satisfaction field, incomplete approaches based on local search has been successful the last ten years. In this report we give a general framework for constraint satisfaction using local search as well as an different techniques to improve this basic local search framework. We also give an overview of algorithms for problems of constraint satisfaction and optimization using heuristics, and discuss hybrid methods that combine complete methods for constraint satisfaction with local search techniques.
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| 32. |
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| 33. |
- Bohlin, Markus, et al.
(författare)
-
Designing Global Scheduling Constraints for Local Search: A Generic Approach
- 2002. - 1
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this work we present a novel method to automate the computation of global constraints cost for local search. The method is based on the representation of a global constraints as graph properties on a binary constraint network. This formulation simplifies the implementation of global constraints in local search, and provides a cost that can be readily compared to one obtained for subproblems using binary constraints exclusively. The cost obtained can be efficiently updated during the search using incremental methods. The representation of a global constraint as outlined above can also be used for generation of suitable neighborhoods for the constraint. This is done using simple repair functions applied on the elementary constraints in the global constraint graph. We show the usability of our approach by presenting formulations of global constraints in non-overlapping and cumulative scheduling.
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| 34. |
- Bohlin, Markus, et al.
(författare)
-
Evaluation of planning policies for marshalling track allocation using simulation
- 2012. - 10
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Planning the operational procedures in a railway marshalling yard is a complex problem. When a train arrives at a marshalling yard, it is uncoupled on an arrival yard and then its cars are rolled to a classification yard. All cars should eventually be rolled to the classification track that has been assigned to the train they’re supposed to depart with. However, there is normally not enough capacity to compound all trains at once. In Sweden, cars arriving before a track has been assigned to their train can be stored on separate tracks called mixing tracks. All cars on mixing tracks will be pulled back to the arrival yard, and then rolled to the classification yard again to allow for reclassification. Today all procedures are planned by experienced dispatchers, but there are no documented strategies or guidelines for efficient manual planning. The aim of this paper is to examine operational planning strategies that could help dispatchers find a feasible marshalling schedule that minimizes unnecessary mixing. In order to achieve this goal, two different online planning strategies have been tested using deterministic and stochastic simulation. The Hallsberg marshalling yard was used as a case study, and was simulated for the time period between December 2010 and May 2011. The first tested strategy simply assigns tracks to trains on a first come-first served basis, while the second strategy uses time limits to determine when tracks should be assigned to departing trains. The online planning algorithms have been compared with an offline optimized track allocation. The results from both the deterministic and the stochastic simulation show that the optimized allocation is better than all online strategies and that the second strategy with a time limit of 32 hours is the best online method.
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| 35. |
- Bohlin, Markus, et al.
(författare)
-
Hump Yard Track Allocation with Temporary Car Storage
- 2011. - 12
-
Ingår i: 4th International Seminar on Railway Operations Modelling and Analysis.
-
Konferensbidrag (refereegranskat)abstract
- In rail freight operation, freight cars need to be separated and reformed into new trains at hump yards. The classification procedure is complex and hump yards constitute bottlenecks in the rail freight network, often causing outbound trains to be delayed. One of the problems is that planning for the allocation of tracks at hump yards is difficult, given that the planner has limited resources (tracks, shunting engines, etc.) and needs to foresee the future capacity requirements when planning for the current inbound trains. In this paper, we consider the problem of allocating classification tracks in a rail freight hump yard for arriving and departing trains with predetermined arrival and departure times. The core problem can be formulated as a special list coloring problem. We focus on an extension where individual cars can temporarily be stored on a special subset of the tracks. An extension where individual cars can temporarily be stored on a special subset of the tracks is also considered. We model the problem using mixed integer programming, and also propose several heuristics that can quickly give feasible track allocations. As a case study, we consider a real-world problem instance from the Hallsberg Rangerbangård hump yard in Sweden. Planning over horizons over two to four days, we obtain feasible solutions from both the exact and heuristic approaches that allow all outgoing trains to leave on time.
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| 36. |
- Bohlin, Markus, et al.
(författare)
-
Hump Yard Track Allocation with Temporary Car Storage
- 2010. - 11
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In rail freight operation, freight cars need to be separated and reformed into new trains at hump yards. The classification procedure is complex and time consuming, and hump yards often constitute bottlenecks in the rail freight network. One of the problems is that planning for the allocation of tracks at hump yards is difficult, given that the planner has limited resources (tracks, shunting engines, etc.) and needs to foresee the future capacity requirements when planning for the current inbound trains. In this paper, we consider the problem of allocating classification tracks in a rail freight hump yard for arriving and departing trains. Arrival and departure times are predetermined and may originate in timetables or estimated arrival and departure times (in case of disturbances in the rail system). The core problem can be formulated as a special list coloring problem. We focus on an extension where individual cars can temporarily be stored on a special subset of the tracks. We model the problem using mixed integer programming, and also propose several heuristics that can quickly give feasible track allocations. As a case study, we consider a real-world problem instance from the Hallsberg Rangerbangård hump yard in Sweden. Planning over horizons over two to four days, we obtain feasible solutions from both the exact and heuristic approaches that allow all outgoing trains to leave on time.
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| 37. |
- Bohlin, Markus, et al.
(författare)
-
Maintenance optimization with duration-dependent costs
- 2015
-
Ingår i: Annals of Operations Research. - : Springer Science and Business Media LLC. - 0254-5330 .- 1572-9338. ; 224:1, s. 1-23
-
Tidskriftsartikel (refereegranskat)abstract
- High levels of availability and reliability are essential in many industries where production is subject to high costs due to downtime. Examples include the mechanical drive in natural gas pipelines and power generation on oil platforms, where gas turbines are commonly used as a power source. To mitigate the effects of service outages and increase overall reliability, it is also possible to use one or more redundant units serving as cold standby backup units. In this paper, we consider preventive maintenance optimization for parallel k-out-of-n multi-unit systems, where production at a reduced level is possible when some of the units are still operational. In such systems, there are both positive and negative effects of grouping activities together. The positive effects come from parallel execution of maintenance activities and shared setup costs, while the negative effects come from the limited number of units which can be maintained at the same time. To show the possible economic effects, we evaluate the approach on models of two production environments under a no-fault assumption. We conclude that savings were substantial in our experiments on preventive maintenance, compared to a traditional preventive maintenance plan. For single-unit systems, costs were on average 39 % lower when using optimization. For multi-unit systems, average savings were 19 %. We also used the optimization models to evaluate the effects of re-planning at breakdown and effects due to modeling of inclusion relations. Breakdown re-planning saved between 0 and 11 % of the maintenance costs, depending on which component failed, while inclusion relation modeling resulted in an 7 % average cost reduction.
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| 38. |
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| 39. |
- Bohlin, Markus, et al.
(författare)
-
Optimal Freight Train Classification using Column Generation
- 2012. - 9
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Ingår i: 12th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, September 13, 2012, Ljubljana, Slovenia. - Dagstuhl, Germany : Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. - 9783939897453 ; , s. 10-22
-
Konferensbidrag (refereegranskat)abstract
- We consider planning of freight train classification at hump yards using integer programming. The problem involves the formation of departing freight trains from arriving trains subject to scheduling and capacity constraints. To increase yard capacity, we allow the temporary storage of early freight cars on specific mixed-usage tracks. The problem has previously been modeled using a direct integer programming model, but this approach did not yield lower bounds of sufficient quality to prove optimality. In this paper, we formulate a new extended integer programming model and design a column generation approach based on branch-and-price to solve problem instances of industrial size. We evaluate the method on historical data from the Hallsberg hump yard in Sweden, and compare the results with previous approaches. The new method managed to find optimal solutions in all of the 192 problem instances tried. Furthermore, no instance took more than 13 minutes to solve to optimality using fairly standard computer hardware.
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| 40. |
- Bohlin, Markus, et al.
(författare)
-
Optimerad rangering: slutsatser och resultat från projektet RANPLAN
- 2013. - 7
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Sammanfattning Rapporten innehåller kortfattade slutsatser och resultat från en studie genomförd i projektet RANPLAN, som har utförts av SICS Swedish ICT AB på uppdrag av Trafikverket under åren 2010-2013. Fokus är på Hallsbergs rangerbangård, men resultaten är tillämpbara även på andra rangerbangårdar med vall. Datorkörningar visar att blanddragen kan öka kapaciteten på rangerbangårdar väsentligt, mätt i antalet samtidiga tåg som kan hanteras, till en kostnad av en ökad mängd vagnsrörelser. I en jämförande datorstudie av simulering och optimering framgick också att de optimala planerna var betydligt effektivare, mätt i antalet vagnsrörelser, än de simulerade planerna. Resultaten pekar tydligt på att datorstödd optimering av planeringsprocessen för rangerbangårdar både är praktiskt möjligt och kan ge stora effektivitetsvinster.
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| 41. |
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| 42. |
- Bohlin, Markus, et al.
(författare)
-
Optimization Methods for Multistage Freight Train Formation
- 2015. - 6
-
Ingår i: Transportation Science. - : Institute for Operations Research and the Management Sciences. - 0041-1655 .- 1526-5447. ; 50:3, s. 823-840
-
Tidskriftsartikel (refereegranskat)abstract
- This paper considers mathematical optimization for the multistage train formation problem, which at the core is the allocation of classification yard formation tracks to outbound freight trains, subject to realistic constraints on train scheduling, arrival and departure timeliness, and track capacity. The problem formulation allows the temporary storage of freight cars on a dedicated mixed-usage track. This real-world practice increases the capacity of the yard, measured in the number of simultaneous trains that can be successfully handled. Two optimization models are proposed and evaluated for the multistage train formation problem. The first one is a column-based integer programming model, which is solved using branch and price. The second model is a simplified reformulation of the first model as an arc-indexed integer linear program, which has the same linear programming relaxation as the first model. Both models are adapted for rolling horizon planning and evaluated on a five-month historical data set from the largest freight yard in Scandinavia. From this data set, 784 instances of different types and lengths, spanning from two to five days, were created. In contrast to earlier approaches, all instances could be solved to optimality using the two models. In the experiments, the arc-indexed model proved optimality on average twice as fast as the column-based model for the independent instances, and three times faster for the rolling horizon instances. For the arc-indexed model, the average solution time for a reasonably sized planning horizon of three days was 16 seconds. Regardless of size, no instance took longer than eight minutes to be solved. The results indicate that optimization approaches are suitable alternatives for scheduling and track allocation at classification yards.
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| 43. |
- Bohlin, Markus, et al.
(författare)
-
Optimization of condition-based maintenance for industrial gas turbines: Requirements and results
- 2009
-
Ingår i: Proceedings of the ASME Turbo Expo Volume 5. - 9780791848869 ; , s. 455-464
-
Konferensbidrag (refereegranskat)abstract
- In oil and gas applications, the careful planning and execution of preventive maintenance is important due to the high costs associated with shutdown of critical equipment. Optimization and lifetime management for equipment such as gas turbines is therefore crucial in order to achieve high availability and reliability. In this paper, a novel condition-based gas turbine maintenance strategy is described and evaluated. Using custom-madegas turbine maintenance planning software, maintenance is repeatedly reoptimized to fit into the time intervals where production losses are least costly and result in the lowest possible impact. The strategy focuses on accurate online lifetime estimates for gas turbine components, where algorithms predicting future maintenance requirements are used to produce maintenance deadlines. This ensures that the gas turbines are maintained in accordance with the conditions on site. To show the feasibility and economic effects of a customer-adapted maintenance planning process, the maintenance plan for a gas turbine used in a real-world scenario is optimized using a combinatorial optimization algorithm and input from gas turbine operation data, maintenance schedules and operator requirements. The approach was validated through the inspection of a reference gas turbine after a predetermined time interval. It is shown that savings may be substantial compared to a traditional preventivemaintenance plan. In the evaluation, typical cost reductions range from 25 to 65 %. The calculated availability increase in practice is estimated to range from 0.5 to 1 %. In addition, down-time reductions of approximately 12 % are expected, due solely to improved planning. This indicates significant improvements.
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| 44. |
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| 45. |
- Bohlin, Markus, 1976-, et al.
(författare)
-
Optimization of Railway Freight Shunting
- 2018
-
Ingår i: Handbook of Optimization in the Railway Industry. - Cham : Springer-Verlag New York. ; , s. 181-212
-
Bokkapitel (refereegranskat)abstract
- Railway freight shunting is the process of forming departing trains from arriving freight trains. The process is continuously performed at rail yards. The shunting procedure is complex and rail yards constitute bottlenecks in the rail freight network, often causing delays to individual shipments. One of the problems is that planning for the allocation of tracks at rail yards is difficult, given that the planner has limited resources (tracks, shunting engines, etc.) and needs to foresee the consequences of committed actions for the current inbound trains. The required schedules highly depend on the particular infrastructure of the rail yard, on the configuration of inbound and outbound trains, and on the business objectives. Thus, new optimization tools as active decision support for the dispatchers are closely tailored to the actual processes. Due to its practical relevance, a broad range of variants has been discussed and solved by the scientific community in recent years. For selected relevant variants, we describe their fruitful relation to scientific research topics such as graph coloring, sequence partitioning, and scheduling, we discuss their computational complexity and approximability, and we outline efficient optimization procedures. In particular, we consider a set of models and algorithms which are applicable in practice, and discuss their application to the shunting yards in Ludwigshafen, Germany and in Hallsberg, Sweden. We also discuss similarities and differences between the different approaches and outline the need for future research.
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| 46. |
- Bohlin, Markus, 1976-, et al.
(författare)
-
Optimization of railway freight shunting
- 2018
-
Ingår i: Handbook of Optimization in the Railway Industry. - Cham : Springer. - 9783319721521 ; , s. 181-212
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 47. |
- Bohlin, Markus, et al.
(författare)
-
Optimized shunting with mixed-usage tracks
- 2013. - 16
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- We consider the planning of railway freight classification at hump yards, where the problem involves the formation of departing freight train blocks from arriving trains subject to scheduling and capacity constraints. The hump yard layout considered consists of arrival tracks of sufficient length at an arrival yard, a hump, classification tracks of non-uniform and possibly non-sufficient length at a classification yard, and departure tracks of sufficient length. To increase yard capacity, freight cars arriving early can be stored temporarily on specific mixed-usage tracks. The entire hump yard planning process is covered in this paper, and heuristics for arrival and departure track assignment, as well as hump scheduling, have been included to provide the neccessary input data. However, the central problem considered is the classification track allocation problem. This problem has previously been modeled using direct mixed integer programming models, but this approach did not yield lower bounds of sufficient quality to prove optimality. Later attempts focused on a column generation approach based on branch-and-price that could solve problem instances of industrial size. Building upon the column generation approach we introduce a direct arc-based integer programming model, where the arcs are precedence relations between blocks on the same classification track. Further, the most promising models are adapted for rolling-horizon planning. We evaluate the methods on historical data from the Hallsberg shunting yard in Sweden. The results show that the new arc-based model performs as well as the column generation approach. It returns an optimal schedule within the execution time limit for all instances but from one, and executes as fast as the column generation approach. Further, the short execution times of the column generation approach and the arc-indexed model make them suitable for rolling-horizon planning, while the direct mixed integer program proved to be too slow for this. Extended analysis of the results shows that mixing was only required if the maximum number of concurrent trains on the classification yard exceeds 29 (there are 32 available tracks), and that after this point the number of extra car roll-ins increases heavily.
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| 48. |
- Bohlin, Markus, et al.
(författare)
-
Redesign of the Oz Compiler
- 2002. - 1
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This master of science thesis describes a new design and its implementation for an Oz compiler. The project is based on the existing Oz compiler. The new compiler is designed more modular, with separate software components that can be replaced and modified locally. A prototype has been implemented, but further development is necessary. We give an overview of the language Oz, its features and the underlying calculus. The features of Oz regarding object orientation, functional programming, logic and constraint programming are also discussed. The liveness analysis and register allocation problems in general and regarding Oz specific compilers are analyzed, together with current and future optimizations suitable for the Mozart platform. The design of the new compiler and information about the old one is presented, and future work regarding the compiler, optimizations, and analysis phases is discussed. Appendices describing the interfaces between the phases of the compiler is included, together with documentation regarding the internal code formats used.
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| 49. |
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| 50. |
- Bohlin, Markus, et al.
(författare)
-
Safe Shared Stack Bounds in Systems with Offsets and Precedences
- 2008
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The paper presents two novel methods to bound the stack memory used in preemptive, shared stack, real-time systems. The first method is based on branch-and-bound search for possible preemption patterns, and the second one approximates the first in polynomial time. The work extends previous methods by considering a more general taskmodel, in which all tasks can share the same stack. In addition, the new methods account for precedence and offset relations. Thus, the methods give tight bounds for a large set of realistic systems. The methods have been implemented and a comprehensive evaluation, comparing our new methods against each other and against existing methods, is presented. The evaluation shows that our exact method can significantly reduce the amount of stack memory needed. In our simulations, a decrease in the order of 40% was typical, with a runtime in the order of seconds. Our polynomial approximation consequently yields about 20% higher bound than the exact method.
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