scholarly journals Vehicle Assignment considering Battery Endurance for Electric Vehicle Carsharing Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Bo Zhang ◽  
Lei Wang ◽  
Li Li ◽  
Xu Lei ◽  
Yongfeng Ju
Keyword(s):  
Electric Vehicle ◽  
Discrete Event ◽  
Green Energy ◽  
Short Term ◽  
Agent Based ◽  
Event Simulation ◽  
Assignment Method ◽  
Vehicle Assignment ◽  
Access Mode ◽  
First Come First Serve

On-demand station-based one-way carsharing is widely adopted for battery electric vehicle sharing systems, which is regarded as a supplement of urban mobility and a promising approach to the utilization of green energy vehicles. The service model of these carsharing systems allows users to select vehicles based on their own judgment on vehicle battery endurance, while users tend to pick up vehicles with the longest endurance distances. This phenomenon makes instant-access systems lose efficiency on matching available vehicles with diverse user requests and limits carsharing systems for higher capacity. We proposed a vehicle assignment method to allocate vehicles to users that maximize the utility of battery, which requires the system to enable short-term reservation rather than instant access. The methodology is developed from an agent-based discrete event simulation framework with a first-come-first-serve logic module for instant access mode and a resource matching optimization module for short-term reservation mode. Results show that the short-term reservation mode can at most serve 20% more users and create 47% more revenue than instant access mode under the scenario of this research. This paper also points out the equilibrium between satisfying more users by efficiently allocating vehicles and distracting users by disabling instant access and suggests that the reservation time could be 15 minutes.

Reducing Handoffs and Improving Patient Flow in the ED

2021 ◽  
Author(s):  
Vishnunarayan Girishan Prabhu ◽  
Kevin Taaffe ◽  
Ronald Pirrallo ◽  
William Jackson ◽  
Michael Ramsay
Keyword(s):  
Performance Metrics ◽  
Patient Flow ◽  
Discrete Event ◽  
Common Source ◽  
Agent Based ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Healthcare Settings ◽  
Shift Schedules ◽  
Hybrid Agent

Abstract Over 145 million people visit US Emergency Departments annually. The diverse nature and overwhelming volume of patient visits make the ED one of the most complicated healthcare settings. In particular, handoffs, the transfer of patient care from one physician to another during shift transition are a common source of errors resulting from workflow interruptions and high cognitive workload. This research focuses on developing a hybrid agent-based discrete event simulation model to identify physician shifts that minimize handoffs without affecting other performance metrics. By providing overlapping shift schedules as well as implementing policies that restrict physicians from signing up a new patient during the last hour of the shift, we observed that handoffs and patient time in the emergency department could be reduced by as much as 42% and 17%, respectively.

Multi-Methodology Approach

2021 ◽  
pp. 98-128
Keyword(s):  
System Dynamics ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Multiple Methods ◽  
Soft Systems ◽  
Agent Based ◽  
Event Simulation ◽  
Pluralistic Approach ◽  
The Difference

The pluralistic approach in today's world needs combining multiple methods, whether hard or soft, into a multi-methodology intervention. The methodologies can be combined, sometimes from several different paradigms, including hard and soft, in the form of a multi-methodology so that the hard paradigms are positivistic and see the organizational environment as objective, while the nature of soft paradigms is interpretive. In this chapter, the combination of methodologies has been examined using soft systems methodologies (SSM) and simulation methodologies including discrete event simulation (DES), system dynamics (SD), and agent-based modeling (ABM). Also, using the ontological, epistemological, and methodological assumptions underlying the respective paradigms, the difference between SD, ABM, SSM; a synthesis of SSM and SD generally known as soft system dynamics methodology (SSDM); and a promising integration of SSM and ABM referred to as soft systems agent-based methodology (SSABM) have been proven.

A Combined Discrete Event, Agent - Based Approach to Modeling the Tensile Strength of One - Dimensional Fibrous Materials

2014 ◽  
Vol 9 (4) ◽  
pp. 155892501400900 ◽  
Author(s):  
Arkady Cherkassky ◽  
Eugene Bumagin
Keyword(s):  
Tensile Strength ◽  
Discrete Event ◽  
Fibrous Materials ◽  
New Approach ◽  
Agent Based ◽  
One Dimensional ◽  
Agent Based Modelling ◽  
Event Simulation ◽  
Slippage Effect ◽  
Basic Parameters

This paper presents a new approach to predict the tensile strength of one-dimensional fibrous materials. The approach combines discrete-event simulation of the fiber flow with agent-based modelling of the fiber slippage. The ability of the elaborated model of the fiber flow to track every fiber separately enables the calculation and analysis of all contacts and forces between the fibers, and the prediction of the material's tensile strength. The model is based on the phenomenon of the strength associated with the fiber slippage effect. Algorithms for modeling the cross-section and the segment tensile strength are developed. Implementation of this algorithm and the study of the behavior of the elaborated model by varying the basic parameters will be described in the Part 2 of the article.

scholarly journals Digital plant: methods of discrete-event modeling and optimization of production characteristics

2021 ◽  
Vol 15 (2) ◽  
pp. 7-20
Author(s):  
Valery Makarov ◽  
Albert Bakhtizin ◽  
Gayane Beklaryan ◽  
Andranik Akopov
Keyword(s):  
Simulation Model ◽  
Hybrid Methods ◽  
Discrete Event ◽  
Agent Based ◽  
Event Simulation ◽  
Event Modeling ◽  
Technical Control ◽  
Assembly Unit ◽  
Labor Resources ◽  
Production Characteristics

This article presents a new approach to the development of a ‘digital twin’ of a manufacturing enterprise, using a television manufacturing plant as the case study. The feature of the proposed approach is the use of hybrid methods of agent-based modeling and discrete-event simulation in order to implement a simulation model of a complex production process for assembling final products from supplied components. The most important requirement for such a system is the integration of all key chains of a digital plant: conveyor lines, warehouses with components and final products (TVs), sorting and conveyor system, assembly unit, technical control department, packing unit, etc. The proposed simulation model is implemented in the AnyLogic system, which supports the possibility of using agent-based and discrete-event modeling methods within one model. The system also supports using the built-in genetic algorithm to optimize the main parameters of the model: the most important production characteristics (for example, assembly time of a product, the number of employees involved in assembly, quality control and packaging processes). Optimization experiments were completed with the help of the developed model at various intensities of loading conveyor lines with components, various restrictions on labor resources, etc. Three scenarios of the production system behavior are investigated: the absence of the components deficit with the possibility of significantly increasing the labor resource involved, a components deficit while demand for final products is maintained, and the presence of hard restrictions on the number of employees who can be involved in the processes under conditions of components deficit.

Modelling and simulation of a fire department’s response to emergency incidents

2018 ◽  
Author(s):  
Adriano O. Solis ◽  
Jenaro Nosedal-Sánchez ◽  
Ali Asgary ◽  
Francesco Longo ◽  
Beatrice Zaccaro
Keyword(s):  
Simulation Model ◽  
Discrete Event ◽  
Fire Department ◽  
Simulation Models ◽  
Modelling And Simulation ◽  
Fire Departments ◽  
Simulation Experiments ◽  
Agent Based ◽  
Event Simulation ◽  
Principal Elements

"After statistical analysis of the database of a fire department covering eight years of consecutive incident records from January 2009 to December 2016, we developed a modelling and simulation (M&S) approach that could be replicated for fire departments across Canada. Our M&S framework involved two different simulation models running on separate platforms: (i) an Incident Generation Engine, which simulates the ‘arrival’ of emergency incidents, and (ii) a Response Simulation Model. The first model is a discrete event simulation model using CPNTools 4.0, generating inputs for the second model, which is an agent-based simulation model developed using AnyLogic. We discuss the principal elements of the two simulation models, and report on findings from our simulation experiments."

scholarly journals Modeling Electric Vehicle Charging Demand with the Effect of Increasing EVSEs: A Discrete Event Simulation-Based Model

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3734
Author(s):  
Neil Stephen Lopez ◽  
Adrian Allana ◽  
Jose Bienvenido Manuel Biona
Keyword(s):  
Electric Vehicle ◽  
Discrete Event Simulation ◽  
Internal Combustion Engines ◽  
Discrete Event ◽  
Significant Shift ◽  
Peak Demand ◽  
Event Simulation ◽  
Electric Vehicle Charging ◽  
Battery Capacity ◽  
Vehicle Charging

Electric vehicle (EV) use is growing at a steady rate globally. Many countries are planning to ban internal combustion engines by 2030. One of the key issues needed to be addressed before the full-scale deployment of EVs is ensuring energy security. Various studies have developed models to simulate and study hourly electricity demand from EV charging. In this study, we present an improved model based on discrete event simulation, which allows for modeling characteristics of individual EV users, including the availability of electric vehicle supply equipment (EVSE) outside homes and the charging threshold of each EV user. The model is illustrated by simulating 1000 random electric vehicles generated using data from an actual survey. The results agree with previous studies that daily charging demands do not significantly vary. However, the results show a significant shift in charging schedule during weekends. Moreover, the simulation demonstrated that the charging peak demand can be reduced by as much as 11% if EVSEs are made more available outside homes. Interestingly, a behavioral solution, such as requiring users to fully utilize their EV’s battery capacity, is more effective in reducing the peak demand (14–17%). Finally, the study concludes by discussing a few potential implications on electric vehicle charging policy.

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