scholarly journals Modelling and performance analysis of smart waste collection system: a Petri nets and discrete event simulation approach

2019 ◽  
Vol 4 (1) ◽  
pp. 18
Author(s):  
Daniel Jian Sun ◽  
Taha Benarbia ◽  
Abdel Moumen Darcherif
2004 ◽  
Vol 02 (04) ◽  
pp. 619-637 ◽  
Author(s):  
SIMON HARDY ◽  
PIERRE N. ROBILLARD

Petri nets are a discrete event simulation approach developed for system representation, in particular for their concurrency and synchronization properties. Various extensions to the original theory of Petri nets have been used for modeling molecular biology systems and metabolic networks. These extensions are stochastic, colored, hybrid and functional. This paper carries out an initial review of the various modeling approaches based on Petri net found in the literature, and of the biological systems that have been successfully modeled with these approaches. Moreover, the modeling goals and possibilities of qualitative analysis and system simulation of each approach are discussed.


2018 ◽  
Vol 21 (4) ◽  
pp. 416-422 ◽  
Author(s):  
Feng Pan ◽  
Odette Reifsnider ◽  
Ying Zheng ◽  
Irina Proskorovsky ◽  
Tracy Li ◽  
...  

Author(s):  
Ian Flood ◽  
Kenneth Worley

AbstractThis paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.


2020 ◽  
Vol 170 ◽  
pp. 03001 ◽  
Author(s):  
A. Hamroun ◽  
K. Labadi ◽  
M. Lazri

Car sharing systems emerged as a new answer to mobility challenges in smart and sustainable cities. Despite their apparent success, design and exploitation of such systems raise crucial strategic and operational challenges. To help planners and decision makers, simulation, analysis and optimization models are unavoidable. Based on the formal modelling and analysis power of stochastic Petri nets, this paper proposes a discrete event simulation model for electric car sharing systems for performance and analysis purposes, taking into account their complex dynamic behaviour, organization and parameters including capacities of the stations, battery and energy availability, locations of charging stations and also their car maintenance activities, not negligible compared to the case of bike-sharing systems.


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