Discrete Event Scheduler Research Articles

Evaluation of an autonomous, short sea shipping feeder-loop service through advanced simulations

Traditionally, container-freight being shipped from central Europe to the coast of Norway has been transported either by road, or by larger containerships to central ports. For the past 3 years the AEGIS consortium has worked to develop a new, disruptive short sea shipping feeder-loop service based on mother and daughter ships [1]. The hypothesis is that introducing smaller, autonomous, battery-powered vessels into the fjords of Norway would open new business areas, provide access to remote regions, and allow shipping companies to take on cargo that could not previously be transported by water. Such a transport system has the potential of reducing cost, GHG emissions and external costs, while increasing frequency of service and the waterborne cargo volume in Europe. One of the main challenges of the mother-daughter logistic system is how transshipment affects defined key performance indicators (KPIs), especially in terms of cost. For this purpose, the SIMPACT tool [2] was developed in the H2020 projects AEGIS and AUTOSHIP. The tool allows for rapid iterations of maritime logistic systems through discrete event scheduling, and estimation of energy, fuel, emission, and cost.This paper will present results from a case-study on two different daughter ship concepts. The concepts are evaluated through cost and environmental KPIs presented in [1], in addition to external costs based on the European handbook on the external costs of transport [3].Results from the case-studies indicate that transport systems including green daughter-vessels have the potential of being cost competitive and would lower externalities compared to the baseline truck transportation system.

A Deep Reinforcement Learning Approach for Optimal Scheduling of Heavy-haul Railway

This paper presents an approach based on deep reinforcement learning to solve the combinatorial optimization problem of heavy-haul railway scheduling. The problem is modeled as a Markov decision process. Considering that the problem is characterized by complex process and strong constraints, a heavy-haul railway simulation environment based on discrete event scheduling is established, in which the optimization objective calculation and complex constraints can be achieved naturally. A reinforcement learning algorithm based on two-stage action sampling is proposed to alleviate the curse of dimensionality, in which invalid actions are masked out through staged action masking. The Effectiveness of the approach is verified by the experimental results.

A Node-Oriented Discrete Event Scheduling Algorithm Based on Finite Resource Model

Discrete event simulation is the most important and essential part in network simulation. The node-oriented model of discrete event scheduling is a model that allocates computing resources as nodes and makes the discrete event simulation as a simulation task on nodes. In this article the reason of low performance in large-scale network simulation is analyzed, and an ideal node-oriented model of discrete event scheduling is presented and a resource-limited node-oriented model of discrete event scheduling by adding some restrictions on network resources is proposed. Then, the authors complete contrast experiments of the resource-limited node-oriented model of discrete event scheduling and NS2. Finally, packet loss in resource-limited node-oriented model of discrete event scheduling is examined. Also, NS2 is discussed in this article and the authors have proposed an improved method for the packet loss algorithm in a resource-limited node-oriented model of discrete event scheduling.

Platform for Multiagent Application Development Incorporating Accurate Communications Modeling

Multiagent systems are widely recognized as a method of choice for realization of distributed time-critical applications for the smart grid. However, no general solutions have been proposed for the difficult task of system development and validation, ready for deployment, which would fully account for the underlying communication network performance. We propose a novel platform designed for this purpose, which integrates a standard multiagent development framework [Java Agent Development (JADE)] and an industry standard communications network simulator (OPNET modeler). It was realized through generic extensions of the JADE framework to provide discrete event scheduling capabilities, while the OPNET modeler was extended to provide a generic method of associating the network nodes with agents running in JADE. The adopted method adheres to the high-level architecture standard. Importantly, applications developed using this platform may be deployed on the target system without manual modifications. A distributed protection application is presented and the performance is analyzed with respect to candidate agent behaviors and communication scenarios, demonstrating that the feasibility of the application critically depends on the choices made during its design and implementation.

Computing and Communication Quality of Service for Distributedtime-Scaled Simulation in Heterarchical Manufacturing Control

The authors present experimental results and empirical models that characterize the computing and communication workload for distributed time-scaled simulation used for distributed arrival time control (DATC) algorithms. In DATC application, real-time discrete event scheduling is achieved in a highly distributed manner by part entities (work pieces) that continuously control their arrival times at the queues for various resources based on feedback obtained from fast time-scaled distributed simulation. This study focuses on a range of systems of up to 180 parts and 16 machines. It focuses on real-time scheduling in heterarchical manufacturing systems using time-scaled distributed simulation. The quality (nearness to optimality) of schedules of such a system is limited by the speed and accuracy of the distributed simulations. In this paper designed experiments are used to characterize the Quality of Service (QoS). Based on experiments with time-scaled simulation for DATC algorithms, a regression model is built and is used to predict the latency and jitter on the network. The experimental results and regression model will enable systematic assessment of the QoS requirements and design of computing and communication architectures for the next generation of distributed manufacturing control systems.

Computing and Communication Quality of Service for Distributed Time-Scaled Simulation in Heterarchical Manufacturing Control

The authors present experimental results and empirical models that characterize the computing and communication workload for distributed time-scaled simulation used for distributed arrival time control (DATC) algorithms. In DATC application, real-time discrete event scheduling is achieved in a highly distributed manner by part entities (work pieces) that continuously control their arrival times at the queues for various resources based on feedback obtained from fast time-scaled distributed simulation. This study focuses on a range of systems of up to 180 parts and 16 machines. It focuses on real-time scheduling in heterarchical manufacturing systems using time-scaled distributed simulation. The quality (nearness to optimality) of schedules of such a system is limited by the speed and accuracy of the distributed simulations. In this paper designed experiments are used to characterize the Quality of Service (QoS). Based on experiments with time-scaled simulation for DATC algorithms, a regression model is built and is used to predict the latency and jitter on the network. The experimental results and regression model will enable systematic assessment of the QoS requirements and design of computing and communication architectures for the next generation of distributed manufacturing control systems.