Analysis of dispatching rules of AGV systems with multiple vehicles

2001 ◽  
Vol 33 (10) ◽  
pp. 889-895 ◽  
Author(s):  
HIDEAKI YAMASHITA
Keyword(s):  
Dispatching Rules ◽  
Multiple Vehicles ◽  
Agv Systems

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Vehicle Miles Traveled in Multivehicle Households

2005 ◽  
Vol 1926 (1) ◽  
pp. 198-205
Author(s):  
Somchai Pathomsiri ◽  
Ali Haghani ◽  
Paul M. Schonfeld
Keyword(s):  
Least Squares Method ◽  
Substitution Effect ◽  
Mitigation Measures ◽  
Vehicle Miles Traveled ◽  
Emission Mitigation ◽  
Conservation Policies ◽  
Multiple Vehicles ◽  
Single Vehicle ◽  
Household Travel ◽  
Behavior Shift

Vehicle miles traveled (VMT) is an important factor in the development of transportation plans, emission mitigation measures, and energy conservation policies. Therefore, estimation of VMT is a crucial task supporting such plans and policies. This research addresses the estimation of VMT in households owning multiple vehicles. This sector is expected to use vehicles differently from single-vehicle households because usage of any vehicle may depend on usage of other vehicles. Previous studies concluded that there is a substitution effect between usages of two vehicles (i.e., greater usage of one vehicle lessens usage of the other). In view of more recent changes in sociodemographic structure, the problem was revisited with the 2001 National Household Travel Survey database. The proposed VMT model is a system of simultaneous equations. Each equation explains the VMT for one of the household's vehicles. The three-stage least-squares method was used to estimate the coefficients. A case study of two-vehicle households was investigated. The resulting model shows that VMT can be explained by variables such as the vehicle's newness, number of potential car users in a household, and household income. Surprisingly, the results show not a substitution effect but a spilling effect. The VMT of the first vehicle does not depend on how much the second vehicle is driven. However, increased use of the first vehicle tends to spill over and increase the use of the second one. Some explanation of this behavior shift is provided.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

Urban Impacts of Mobile Fuel Delivery Service

2020 ◽  
pp. 036119812097541
Author(s):  
Andrea Broaddus
Keyword(s):  
San Francisco ◽  
Traffic Congestion ◽  
Data Set ◽  
Gas Stations ◽  
Bay Area ◽  
Urban Impacts ◽  
Fuel Delivery ◽  
Declining Demand ◽  
Multiple Vehicles ◽  
Gas Station

Mobile fuel delivery (MFD) uses a fueling truck to fill up personal and commercial fleet vehicles while they are parked overnight. This study used a sample data set provided by a San Francisco Bay Area company to explore the potential impacts on vehicle miles traveled (VMT), carbon dioxide (CO2) emissions, and traffic congestion. An analysis of vehicle travel associated with gas station trips was conducted to establish a basis for comparison. Future scenarios comparing the potential impacts of scaled-up MFD services in 2030 were also developed. The study concluded that MFD services compared favorably to gas stations in relation to environmental and traffic benefits in the longer term, even though personal fueling trips tended to generate low VMT. Benefits stemmed from efficiencies achieved by fueling multiple vehicles per delivery trip, replacing car share vehicle fueling trips, and removing trips from the network during peak hours. This analysis estimated that total annual CO2 emissions associated with fuel delivery operations in the Bay Area were 76 metric tons, which is less than a typical gas station with 97 metric tons. Under assumptions of declining demand for gasoline and significantly fewer gas stations, and with highly efficient optimized operations, mobile delivery could gain up to 5% market share for gas and not add additional VMT over the business as usual scenario.

scholarly journals Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1090
Author(s):  
Charilaos Latinopoulos ◽  
Aruna Sivakumar ◽  
John W. Polak
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Power Grids ◽  
Local Distribution ◽  
Technical Problems ◽  
Vehicle To Grid ◽  
Distribution Transformers ◽  
Demand Models ◽  
Multiple Vehicles ◽  
Infrastructure Investments

The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments.

scholarly journals Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2626
Author(s):  
Carlos Hidalgo ◽  
Ray Lattarulo ◽  
Carlos Flores ◽  
Joshué Pérez Rastelli
Keyword(s):  
Trajectory Planning ◽  
Traffic Congestion ◽  
Efficient Solutions ◽  
Test Case ◽  
Cruise Control ◽  
Automated Driving ◽  
Modular Architecture ◽  
The Road ◽  
Cooperative Adaptive Cruise Control ◽  
Multiple Vehicles

Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among multiple vehicle platoons is needed to improve, effectively, the traffic flow. In this paper, a global solution to merge two platoons is presented. This approach combines: (i) a longitudinal controller based on a feed-back/feed-forward architecture focusing on providing CACC capacities and (ii) hybrid trajectory planning to merge platooning on straight paths. Experiments were performed using Tecnalia’s previous basis. These are the AUDRIC modular architecture for automated driving and the highly reliable simulation environment DYNACAR. A simulation test case was conducted using five vehicles, two of them executing the merging and three opening the gap to the upcoming vehicles. The results showed the good performance of both domains, longitudinal and lateral, merging multiple vehicles while ensuring safety and comfort and without propagating speed changes.

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