ECSIM: DISCRETE EVENT SIMULATION USING EVENT CALCULUS

1995 ◽  
Vol 04 (01n02) ◽  
pp. 135-156
Author(s):  
LODE R. MISSIAEN
Keyword(s):  
Discrete Event Simulation ◽  
Intelligent Agents ◽  
Scheduling Algorithm ◽  
Inductive Learning ◽  
Discrete Event ◽  
Event Calculus ◽  
Event Simulation ◽  
Classical Simulation ◽  
History Of ◽  
Planning Decision

This paper presents the theory and implementation of a logic based discrete event simulation system ECSIM, Event Calculus SIMulation. ECSIM’s representation language is PROLOG extended with temporal predicates derived from the event calculus. The theory defines the truth value of a property given a history of events. ECSIM can represent actions that happen at a particular point in time, and activities that happen over a period of time; it can represent properties that change discretely and continuously over time. ECSIM’s scheduling algorithm uses activity scanning to generate event notices for all future activities. ECSIM’s major distinction with other simulation systems is its reference to the complete history of simulated time. A given event schedule can be analyzed by deriving the properties of the world at any time in the simulated history. ECSIM’s logic programming framework enables classical simulation to be extended with explanation generation, inductive learning, planning, decision support, simulation of intelligent agents, and symbolic simulation.

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

2019 ◽  
Vol 31 (3) ◽  
pp. 67-82
Author(s):  
Yu Huang ◽  
Wanxing Sheng ◽  
Peipei Jin ◽  
Baicuan Nie ◽  
Meikang Qiu ◽  
...  
Keyword(s):  
Discrete Event Simulation ◽  
Packet Loss ◽  
Large Scale ◽  
Network Simulation ◽  
Scheduling Algorithm ◽  
Discrete Event ◽  
Event Simulation ◽  
Resource Limited ◽  
Event Scheduling ◽  
Low Performance

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.

scholarly journals PIH61 CALIBRATION OF A DISCRETE EVENT SIMULATION MODEL OF NATURAL HISTORY OF HPV-RELATED DISEASES

2010 ◽  
Vol 13 (7) ◽  
pp. A386
Author(s):  
P Quon ◽  
DJ Vanness ◽  
A Kansal ◽  
P Hillemanns ◽  
V Remy ◽  
...  
Keyword(s):  
Natural History ◽  
Simulation Model ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
History Of

scholarly journals A Comprehensive TCO Evaluation Method for Electric Bus Systems Based on Discrete-Event Simulation Including Bus Scheduling and Charging Infrastructure Optimisation

2020 ◽  
Author(s):  
Dominic Jefferies ◽  
Dietmar Göhlich
Keyword(s):  
Discrete Event Simulation ◽  
Evaluation Method ◽  
Scheduling Algorithm ◽  
Discrete Event ◽  
Local Conditions ◽  
Charging Infrastructure ◽  
Event Simulation ◽  
Electric Bus ◽  
Bus Network ◽  
Bus Networks

Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies, however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to re-schedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO, however, under some circumstances, the technologies are on par. This emphasises the need for detailed analysis of the local bus network in order to make an informed procurement decision.

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