Petri net is a mathematical model for representing parallel, asynchronous, and distributed systems. Petri nets can model parallel and synchronous activities in manufacturing systems at various levels of abstraction. I...
详细信息
Petri net is a mathematical model for representing parallel, asynchronous, and distributed systems. Petri nets can model parallel and synchronous activities in manufacturing systems at various levels of abstraction. In this study, we propose data-driven modeling and scheduling for cellular manufacturing systems using process mining with Petri nets. In the proposed method, the event log data is extracted from a virtual plant and then the Petri net model considering the movement of products and operators is developed by using the process mining technique with the Petri net model. We also derived an approximate solution for the derived Petri net model from the event log using a local search method using a Petri net simulator. The analysis and modification of the model are conducted in the proposed method. Near-optimal schedules are derived using Petri net simulations. The validity of the proposed model is evaluated.
Numerous processes require dedicated scheduling of their to-be-executed activities. Various algorithms have been developed to computationally solve many different scheduling problems, allocating the available resource...
详细信息
ISBN:
(纸本)9783031345593;9783031345609
Numerous processes require dedicated scheduling of their to-be-executed activities. Various algorithms have been developed to computationally solve many different scheduling problems, allocating the available resources to predefined time slots of activity execution to (theoretically) maximize resource utilization efficiency. Yet, in industry, creating schedules for future process executions often remains a (primarily) manual, spreadsheet-based endeavor. Typically, manually created schedules are sub-optimal and potentially infeasible. At the same time, the event data stored in the information systems supporting the process can act as valuable input to further improve the general alignment of the schedule to the actual process execution. Therefore, in this paper, we propose a novel method that enables schedule feasibility checking based on historically recorded event data corresponding to the actual execution of the scheduled process. Our method serves as an input to detect significant issues in the project scheduling problems, which can be used to further improve the overall quality of the schedules computed. Our initial results confirm the general applicability of the proposed framework.
This research is concerned with finding the optimal solution of a job shop scheduling problem (JSSP) in as short time as possible. A JSSP can be formulated as a 0-1 mixed integer programming (0-1 MIP), and it is expec...
详细信息
This research is concerned with finding the optimal solution of a job shop scheduling problem (JSSP) in as short time as possible. A JSSP can be formulated as a 0-1 mixed integer programming (0-1 MIP), and it is expected that the optimal schedule can be obtained in a shorter time by predicting a good solution based on data of scheduling performed previously and using the solution as the initial solution for the optimization algorithm. In this concept, it is an important point to predict a solution which satisfy constraints of the 0-1 MIP. This paper provides an improved method based on this concept where the prediction is carried out so that the constraints are always satisfied. Numerical experiments showed that solution time of the proposed method is shorter than that of the previous method, which does not assure the constraints are satisfied, and learning time reduces about half. In addition, it turned out that there is a reason of the shorter solution time other than that the predicted solution always satisfies the constraints. One possible reason is that the number of circular sequences tends to be smaller than the previous method. Further analysis and additional evaluations from this point of view will be performed in a future work.
Security constrained unit commitment is an essential part of the day-ahead energy markets. The presence of discrete and continuous variables makes it a complex, mixed-integer, and time-hungry optimization problem. Gri...
详细信息
Security constrained unit commitment is an essential part of the day-ahead energy markets. The presence of discrete and continuous variables makes it a complex, mixed-integer, and time-hungry optimization problem. Grid operators solve unit commitment problems multiple times daily with only minor changes in the operating conditions. Solving a large-scale unit commitment problem requires considerable computational effort and a reasonable time. However, the solution time can be improved by exploiting the fact that the operating conditions do not change significantly in the day-ahead market clearing. Therefore, in this paper, a novel multi-layer data-driven approach is proposed, which significantly improves the solution time (90% time-reduction on average for the three studied systems). The proposed approach not only provides a near-optimal solution (<1% optimality gap) but also ensures that it is feasible for the stable operation of the system (0% infeasible predicted solutions). The efficacy of the developed algorithm is demonstrated through numerical simulations on three test systems, namely a 4-bus system and the IEEE 39-bus and 118-bus systems, and promising results are obtained.
Despite the success in developments of wind energy technology, there remain challenges, for example, in offshore wind energy installation. Due to changeable and unstable offshore weather conditions, it is hard to effe...
详细信息
ISBN:
(纸本)9781713814290
Despite the success in developments of wind energy technology, there remain challenges, for example, in offshore wind energy installation. Due to changeable and unstable offshore weather conditions, it is hard to effectively schedule the installation logistics. In this work, we propose a simulation-based scheduling strategy to help the operators and the project managers, to make the main decisions during the installation to increase efficiency, e.g. how many offshore wind turbines should be loaded onto the installation vessel. The offshore logistic concept is modeled using timed Petri nets (TPN) approach. The timed transitions in the TPN model are assigned with operation times estimated by means of discrete-time Markov chain (DTMC) approach, which uses historical weather data. Besides, operability is introduced in this work as an indicator to evaluate schedules of a certain time period.
暂无评论