scholarly journals Complex Project Schedule Risk Simulation Based on Multi-agent

2018 ◽  
Author(s):  
Haolin Wen ◽  
Tao Hu ◽  
Gongda Yan
Keyword(s):  
Project Schedule ◽  
Simulation Based ◽  
Schedule Risk ◽  
Multi Agent ◽  
Risk Simulation ◽  
Complex Project

scholarly journals Integrating Schedule Risk Analysis with Multi-Skilled Resource Scheduling to Improve Resource-Constrained Project Scheduling Problems

2021 ◽  
Vol 11 (2) ◽  
pp. 650
Author(s):  
Muritala Adebayo Isah ◽  
Byung-Soo Kim
Keyword(s):  
Risk Analysis ◽  
Project Scheduling ◽  
Resource Scheduling ◽  
Scheduling Problems ◽  
Project Schedule ◽  
Risk And Uncertainty ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Schedule Risk ◽  
Schedule Risk Analysis

Construction projects are planned in a complex and dynamic environment characterized by high risks and uncertainties amidst resource constraints. Assessing construction schedule risk facilitates informed decision-making, especially in a resource-constrained situation, and allows proactive actions to be taken so that project objectives are not jeopardized. This study presents a stochastic multiskilled resource scheduling (SMSRS) model for resource-constrained project scheduling problems (RCSPSP) considering the impact of risk and uncertainty on activity durations. The SMSRS model was developed by integrating a schedule risk analysis (SRA) model (developed in MS Excel) with an existing multiskilled resource scheduling (MSRS) algorithm for the development of a feasible and realistic schedule. The computational experiment carried out on three case projects using the proposed SMSRS model revealed an average percentage deviation of 10.50%, indicating the inherent risk and uncertainty in activity durations of the project schedule. The core contribution of the proposed SMSRS model is that it: (1) presents project practitioners with a simple tool for assessing the risks and uncertainty associated with resource-constrained project schedules so that necessary response actions can be taken to ensure project success; (2) provides the small-scale construction businesses with an affordable tool for evaluating schedule risk and developing a feasible and realistic project schedule.

scholarly journals Electrification of Urban Waste Collection: Introducing a Simulation-Based Methodology for Technical Feasibility, Impact and Cost Analysis

2021 ◽  
Vol 12 (3) ◽  
pp. 122 ◽  
Author(s):  
Ricardo Ewert ◽  
Alexander Grahle ◽  
Kai Martins-Turner ◽  
Anne Magdalene Syré ◽  
Kai Nagel ◽  
...  
Keyword(s):  
Open Source ◽  
Waste Collection ◽  
Commercial Vehicles ◽  
Urban Waste ◽  
Technical Feasibility ◽  
Routing Problem ◽  
Agent Based ◽  
Simulation Based ◽  
Multi Agent ◽  
Urban Emissions

Electrification is a potential solution for transport decarbonization and already widely available for individual and public transport. However, the availability of electrified commercial vehicles like waste collection vehicles is still limited, despite their significant contribution to urban emissions. Moreover, there is a lack of clarity whether electric waste collection vehicles can persist in real world conditions and which system design is required. Therefore, we introduce a multi-agent-based simulation methodology to investigate the technical feasibility and evaluate environmental and economic sustainability of an electrified urban waste collection. We present a synthetic model for waste collection demand on a per-link basis, using open available data. The tour planning is solved by an open-source algorithm as a capacitated vehicle routing problem (CVRP). This generates plausible tours which handle the demand. The generated tours are simulated with an open-source transport simulation (MATSim) for both the diesel and the electric waste collection vehicles. To compare the life cycle costs, we analyze the data using total cost of ownership (TCO). Environmental impacts are evaluated based on a Well-to-Wheel approach. We present a comparison of the two propulsion types for the exemplary use case of Berlin. And we are able to generate a suitable planning to handle Berlin’s waste collection demand using battery electric vehicles only. The TCO calculation reveals that the electrification raises the total operator cost by 16–30%, depending on the scenario and the battery size with conservative assumptions. Furthermore, the greenhouse gas emissions (GHG) can be reduced by 60–99%, depending on the carbon footprint of electric power generation.

Foraging Multi-Agent System Simulation Based on Attachment Theory

2015 ◽  
pp. 359-364 ◽  
Author(s):  
Carlos Alberto Riveros Varela ◽  
Ferney Beltrán Velandia ◽  
Miguel Alberto Melgarejo Rey ◽  
Nadya González Romero ◽  
Nelson Obregón Neira
Keyword(s):  
Attachment Theory ◽  
System Simulation ◽  
Multi Agent System ◽  
Agent System ◽  
Simulation Based ◽  
Multi Agent

Detection of Primitive Collective Behaviours in a Crowd Panic Simulation Based on Multi-Agent Approach

2012 ◽  
Vol 3 (3) ◽  
pp. 50-65 ◽  
Author(s):  
Jérémy Patrix ◽  
Abdel-Illah Mouaddib ◽  
Sylvain Gatepaille
Keyword(s):  
Real Time ◽  
Situation Awareness ◽  
Hidden Markov ◽  
Extended Model ◽  
Multi Agent System ◽  
Complex Behaviour ◽  
Simulation Based ◽  
Fusion Methods ◽  
Multi Agent ◽  
Using Data

In case of emergency and evacuation, it is often impossible to interpret manually the complex behaviour of a crowd, essentially due to the lack of staff and time needed to understand a situation. In the literature, a monitored system using data fusion methods makes it possible to perform automatic situation awareness. Using Swarm Intelligence domain, the authors propose an approach based on multi-agent system to simulate and detect primitive collective behaviours emerging from a crowd panic. It enables anticipating collective behaviours in real-time as well as their anomalies according to specific scenarios. Detection is the possibility to learn, recognize and anticipate different behaviours by a probabilistic model. The collective behaviour detection of a crowd panic in real-time is based on a learning method on an extended model of Hidden Markov Model. This paper presents experiments of simulation and detection using an implementation of a virtual environment.

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