Discrete event model-based simulation for train movement on a single-line railway

The aim of this paper is to present a discrete event model-based approach to simulate train movement with the con- sidered energy-saving factor. We conduct extensive case studies to show the dynamic characteristics of the traffic flow and demonstrate the effectiveness of the proposed approach. The simulation results indicate that the proposed discrete event model-based simulation approach is suitable for characterizing the movements of a group of trains on a single railway line with less iterations and CPU time. Additionally, some other qualitative and quantitative characteristics are investigated. In particular, because of the cumulative influence from the previous trains, the following trains should be accelerated or braked frequently to control the headway distance, leading to more energy consumption.

Delay Restoration of Single-Track Railway Rescheduling

On the basis of analysis of the principle of delay restoration in a disturbed schedule, a heuristic algorithm for rescheduling trains is developed by restoring the total delay of the disturbed schedule. A discrete event topologic model is derived from the original undisturbed train diagram and a back propagation analysis method is used to label the maximum buffer time of each point in the model. In order to analyze the principle of delay restoration, the concept of critical delay is developed from the labeled maximum buffer time. The critical delay is the critical point of successful delay restoration. All the disturbed trains are classified into the strong-delayed trains and the weak-delayed trains by the criterion of the critical delay. Only the latter, in which actual delay is less than its critical delay, can be adjusted to a normal running state during time horizon considered. The heuristic algorithm is used to restore all the disturbed trains according to their critical details. The cores of the algorithm are the iterative repair technique and two repair methods for the two kinds of trains. The algorithm searches iteratively the space of possible conflicts caused by disturbed trains using an earfiest-delay-first heuristics and always attempts to repair the earliest constraint violation. The algorithm adjusts the weak-delayed trains directly back to the normal running state using the buffer time of the original train diagram. For the strong-delayed trains,the algorithm uses an utility function with some weighted attributes to determine the dynamic priority of the trains, and resolves the conflict according to the calculated dynamic priority. In the end, the experimental results show that the algorithm produces ＂good enough＂ schedules effectively and efficiently in disturbed situations.

TABS: Transforming automatically BPMN models into blockchain smart contracts

Research on blockchains addresses multiple issues,with one being the automated creation of smart contracts.Developing smart contract methods is more difficult than mainstream software development as the underlying blockchain infrastructure poses additional complexity.We report on a new approach to developing smart contracts with the objective of automating the process to increase developer efficiency and reduce the risk of errors introduced by software developers.To support industry adoption,we use Business Process Model and Notation(BPMN)modeling to describe an application while targeting applications in the trade vertical.We describe a system that transforms a BPMN model into a multi-modal model that combines Discrete Event(DE)modeling for concurrency with Hierarchical State Machines(HSMs)to represent application functionality.Then,further transformations are used to transform the DE-HSM model into methods in smart contracts.The system lets the modeler decide which of the independent patterns should be transformed into methods of a separate smart contract that is deployed on a sidechain for the purpose of(i)reducing processing costs and/or(ii)providing privacy so that other participants in the smart contract do not have visibility into the processing of the pattern.We also briefly describe a proof-of-concept tool we built to demonstrate the feasibility of our approach.