Showing papers on "Fleet management published in 2020"
TL;DR: A review of latest AGVs and AMRs research results in the past decade is presented and novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.
Abstract: In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.
107 citations
TL;DR: The findings of this study suggest that while the most important criteria are security, visibility and audit, the most feasible logistics operations proved to be transportation, materials handling, warehousing, order processing and fleet management in a possible blockchain implementation.
Abstract: The main purpose of this study is to investigate the feasibility of blockchain technology in logistics industry using a quantitative approach. To this end, a decision framework is proposed based on a multi-criteria decision structure that incorporates AHP into VIKOR under Intuitionistic Fuzzy Theory. This integration presents different solutions and rankings based on different decision-making strategies and also captures uncertainty in the evaluation process. While Intuitionistic Fuzzy AHP calculates the importance weights of the proposed criteria indicated as scalability, privacy, interoperability, audit, latency, visibility, trust, and security, Fuzzy VIKOR ranks the logistics operations demonstrated as materials handling, warehousing, order processing, transportation, packaging, fleet management, labeling, vehicle routing and product returns management. The proposed decision framework was applied in a large-scale logistics company located in Turkey. The findings of this study suggest that while the most important criteria are security, visibility and audit, the most feasible logistics operations proved to be transportation, materials handling, warehousing, order processing and fleet management in a possible blockchain implementation. The decision framework in this study may enable decision makers to evaluate the feasibility of blockchain in logistics operations, which is one of the main research gaps in the current blockchain research. Furthermore, this is the first study that integrates AHP and VIKOR methods under Intuitionistic Fuzzy Theory in the context of blockchain.
78 citations
TL;DR: An optimal vehicle scheduling approach for next generation PT systems, considering the instance of mixed electric / hybrid fleet, is developed and may be a key part of advanced decision support systems for policymakers and operators that are dealing with the on-going transition from conventional bus fleets towards greener transport solutions.
Abstract: Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport (PT), are increasingly becoming key objectives for policymakers worldwide. In this work we develop an optimal vehicle scheduling approach for next generation PT systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators. We propose a Mixed Integer Linear Program (MILP) to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, coupled with an ad-hoc decomposition scheme aimed at enhancing the scalability of the proposed MILP. Two case studies arising from the PT network of the city of Luxembourg are employed in order to validate the model; sensitivity analysis to fleet design parameters is performed, specifically in terms of fleet size and fleet composition. Conclusions point to the fact that careful modelling and handling of mixed-fleet conditions are necessary to achieve operational savings, and that marginal savings gradually reduce as more conventional buses are replaced by their electric counterparts. We believe the methodology proposed may be a key part of advanced decision support systems for policymakers and operators that are dealing with the on-going transition from conventional bus fleets towards greener transport solutions.
46 citations
TL;DR: The results have highlighted that the RL provides good performance improvement in case of green generation and an important aspect arose is the ability of RL to increase the saved energy even if it is not considered as a target of the controller.
37 citations
TL;DR: It is argued that it is intractable to exactly solve for the optimal policy using exact dynamic programming methods and therefore deep reinforcement learning is applied to develop a near-optimal control policy.
Abstract: The proliferation of ride sharing systems is a major drive in the advancement of autonomous and electric vehicle technologies. This paper considers the joint routing, battery charging, and pricing problem faced by a profit-maximizing transportation service provider that operates a fleet of autonomous electric vehicles. We first establish the static planning problem by considering time-invariant system parameters and determine the optimal static policy. While the static policy provides stability of customer queues waiting for rides even if consider the system dynamics, we see that it is inefficient to utilize a static policy as it can lead to long wait times for customers and low profits. To accommodate for the stochastic nature of trip demands, renewable energy availability, and electricity prices and to further optimally manage the autonomous fleet given the need to generate integer allocations, a real-time policy is required. The optimal real-time policy that executes actions based on full state information of the system is the solution of a complex dynamic program. However, we argue that it is intractable to exactly solve for the optimal policy using exact dynamic programming methods and therefore apply deep reinforcement learning to develop a near-optimal control policy. The two case studies we conducted in Manhattan and San Francisco demonstrate the efficacy of our real-time policy in terms of network stability and profits, while keeping the queue lengths up to 200 times less than the static policy.
31 citations
TL;DR: A novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes is presented.
Abstract: This article presents a novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes. The proposed stochastic optimization formulation considers geological uncertainty in addition to uncertainty related to equipment performances and truck cycle times. The method is applied at a real-world mining complex, stressing the benefits of optimizing the short-term production schedule and fleet management simultaneously. Compared to a conventional two-step approach, where the production schedule is optimized first before optimizing the allocation of the mining fleet, the costs generated by shovel movements are reduced by 56% and lost production due to shovel relocation is cut by 54%. Furthermore, the required number of trucks shows a more balanced profile, reducing total truck operational costs by 3.1% over an annual planning horizon, as well as the required haulage capacity in the most haulage-intense periods by 25%. A metaheuristic solution method is utilized to solve the large optimization problem in a reasonable timespan.
31 citations
TL;DR: In this paper, a micro-modeling TNC operation is essential for large-scale transportation network companies (TNCs) in metropolitan areas across the world, and it is shown that such a system can increase the share of total trips in cities across the globe.
Abstract: Transportation Network Companies (TNCs) have been steadily increasing the share of total trips in metropolitan areas across the world. Micro-modeling TNC operation is essential for large-scale tran...
30 citations
TL;DR: Simulation results have shown that the total energy consumption of an EV fleet is decreased significantly by improving the estimation accuracy, and it is demonstrated how the uncertainties in EV energy consumption estimation limit the overall performance of a FMS.
19 citations
DiDi1
TL;DR: A novel framework of driver repositioning system which meets various requirements in practical situations, including robust driver experience satisfaction and multi-driver collaboration is described, which has been fully deployed in the online system of DiDi Chuxing and serves millions of drivers on a daily basis.
Abstract: E-hailing platforms have become an important component of public transportation in recent years. The supply (online drivers) and demand (passenger requests) are intrinsically imbalanced because of the pattern of human behavior, especially in time and locations such as peak hours and train stations. Hence, how to balance supply and demand is one of the key problems to satisfy passengers and drivers and increase social welfare. As an intuitive and effective approach to address this problem, driver repositioning has been employed by some real-world e-hailing platforms. In this paper, we describe a novel framework of driver repositioning system, which meets various requirements in practical situations, including robust driver experience satisfaction and multi-driver collaboration. We introduce an effective and user-friendly driver interaction design called “driver repositioning task”. A novel modularized algorithm is developed to generate the repositioning tasks in real time. To our knowledge, this is the first industry-level application of driver repositioning. We evaluate the proposed method in real-world experiments, achieving a 2% improvement of driver income. Our framework has been fully deployed in the online system of DiDi Chuxing and serves millions of drivers on a daily basis.
15 citations
TL;DR: The total cost of ownership for the bus-to-route energy management strategy, has been improved a 7.65 % at fleet level against a charge-sustaining charge-depleting strategy and the neuro-fuzzy learns from the global optimal solutions.
14 citations
TL;DR: In this paper, the Matronit BRT system in Haifa, which is the first mass transit network of its kind in Israel, is analyzed and the interrelation between service reliability, fleet management and service utilization is analyzed.
Abstract: Cities worldwide are looking for expanding the capacity of their public transport system while considering budget limitations. Bus Rapid Transit (BRT) systems are increasingly considered as alternatives for designing a mass public transport in mid-size cities in developed countries. While operations have been recognized as an important success factor, previous studies have focused on infrastructure design and planning principles of BRT. We study the operations of BRT related to service reliability and service utilization and derive lessons for planning and operations. The study is centered on the performance of the Matronit BRT system in Haifa, which is the first mass transit network of its kind in Israel. The inter-relation between service reliability, fleet management and service utilization are analysed. The speed and reliability improvements attained by the infrastructure and technological priority measures need to be complemented with control instruments to yield further gains for both service users and service provider.
TL;DR: An operational mechanism design for fleet management coordination in humanitarian operations and its implications for peacekeeping and humanitarian operations are studied.
Abstract: An operational mechanism design for fleet management coordination in humanitarian operations
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TL;DR: This is the world’s first academic paper quantitatively investigating 5G and construction machines’ cooperation, and demonstrated the scenarios where 5G can have a significant effect on the construction machines industry.
Abstract: The fleet management of mobile working machines with the help of connectivity can increase safety and productivity. Although in our previous study, we proposed a solution to use IEEE 802.11p to achieve the fleet management of construction machines, the shortcoming of WIFI may limit the usage of this technology in some cases. Alternatively, the fifth-generation mobile networks (5G) have shown great potential to solve the problems. Thus, as the world's first academic paper investigating 5G and construction machines' cooperation, we demonstrated the scenarios where 5G can have a significant effect on the construction machines industry. Also, based on the simulation we made in $ns-3$, we compared the performance of 4G and 5G for the most relevant construction machines scenarios. Last but not least, we showed the feasibility of remote-control and self-working construction machines with the help of 5G.
TL;DR: The results show that the extended DF model is effective in solving the MFS problem and has the potential to be applied to solving real-life MFS problems of large-scale, multi-line and multi-terminal AMPT systems.
Abstract: Emerging technologies, such as connected and autonomous vehicles, electric vehicles, and information and communication, are surrounding us at an ever-increasing pace, which, together with the conce...
TL;DR: This paper presents efficient strategies for computing very short image representations suitable for classifying various types of traffic scenes in fleet management systems, and indicates that excellent classification results can be achieved with very shortimage representations, and that fine-tuning on the target dataset image data is not mandatory.
Abstract: Visual cues can be used alongside GPS positioning and digital maps to improve understanding of vehicle environment in fleet management systems. Such systems are limited both in terms of bandwidth and storage space, so minimizing the size of transmitted and stored visual data is a priority. In this paper, we present efficient strategies for computing very short image representations suitable for classifying various types of traffic scenes in fleet management systems. We anticipate that the set of interesting classes will change over time, so we consider image representations that can be trained without knowing the labels of the target dataset. We empirically evaluate and compare the presented methods on a contributed dataset of 11447 labeled traffic scenes. Our results indicate that excellent classification results can be achieved with very short image representations, and that fine-tuning on the target dataset image data is not mandatory. Image descriptors can be as short as 128 components while still offering good performance, even in presence of adverse weather or illumination conditions.
TL;DR: A QRewriter - Dueling Deep Q-Network (QRewriter-DDQN) algorithm, to dispatch multiple available vehicles in ahead to the locations with high demand to serve more orders.
Abstract: Inefficient supply-demand matching makes the fleet management a research hotpot in ride-sharing platforms. With the booming of mobile network services, it is promising to abate the supply-demand gap with effective vehicle dispatching. In this article, we propose a QRewriter - Dueling Deep Q-Network (QRewriter-DDQN) algorithm, to dispatch multiple available vehicles in ahead to the locations with high demand to serve more orders. The QRewriter-DDQN algorithm factorizes into a Dueling Deep Q-Network (DDQN) module and a QRewriter module, which are parameterized by neural networks and Q-table with Reinforcement Learning (RL) methods, respectively. Particularly, DDQN module utilizes the Kullback-Leibler (KL) distribution distance between supply (available vehicles) and demand (orders) as excitation to capture the complex dynamic variations of supply-demand. Afterwards, the QRewriter module learns to improve the DDQN dispatching policy with the streamlined and effective Q-table in RL. Importantly, the higher performance improvement space of the DDQN dispatching policy can be obtained by aggregating QRewriter state into low-dimension meta state. A simulator is designed to train and test the performance of QRewriter-DDQN, the experiment results show the significant improvement of QRewriter-DDQN in terms of order response rate.
TL;DR: A branch-and-cut algorithm is designed to solve the two-echelon multi-depot inventory-routing problem with Fleet Management, and a matheuristic, in which vehicle routes are handled by an adaptive large neighborhood mechanism, while input pickups, product deliveries, and fleet planning are performed by solving several subproblems to optimality.
TL;DR: Evidence of adequate operation is shown in sending and receiving messages from and to four prototypes developed for the vehicles, also complying with the established requirements of location, tracking, exchanged data, and security, which allows continuing the development of the proposed FMCS, with some adjustments.
Abstract: In medium-sized cities in developing countries, transit services without dedicated lanes have issues related to route compliance, schedules, speed control, and safety. An efficient way for dealing with this issue is the use of Information and Communication Technologies (ICT), to implement a Fleet Management and Control Systems (FMCS). Such implementation can be performed using Intelligent Transportation Systems (ITSs), which allow integration of services and adequate standardization. This article features: (a) a literature review, related to FMCS based on ITS and enabling technologies, (b) design of the ITS architecture of an FMCS, and (c) some advances in the development of the proposed FMCS in a Colombian city (Popayan). The results of the literature review allowed identifying the most important requirements of FMCS in order to design the ITS architecture and build a prototype featuring the suggested technologies. Finally, some experiments were performed to evaluate the operation of the developed prototype. The results showed evidence of adequate operation in sending and receiving messages from and to four prototypes developed for the vehicles, also complying with the established requirements of location, tracking, exchanged data, and security. This allows continuing the development of the proposed FMCS, with some adjustments.
19 Oct 2020
TL;DR: In this article, the authors proposed an analytical model for machines to estimate the ad-hoc network performance, i.e., the delay and the packet loss probability in real-time based on the simulation results they made in $ns-3$.
Abstract: The fleet management of mobile working machines with the help of connectivity can increase not only safety but also productivity. However, rare mobile working machines have taken advantage of V2X. Moreover, no one published the simulation results that are suitable for evaluating the performance of the ad-hoc network at a working site on the highway where is congested, with low mobility, and without building. In this paper, we suggested that IEEE 802.11p should be implemented for fleet management, at least for the first version. Furthermore, we proposed an analytical model for machines to estimate the ad-hoc network performance, i.e., the delay and the packet loss probability in real-time based on the simulation results we made in $ns-3$ . The model of this paper can be further used for determining when shall ad-hoc or cellular network be used in the corresponding scenarios.
TL;DR: This paper proposes the structure and composition of the FMCOS, the method of operating strip segmenting, and a new algorithm for strip state updating between successive field operations under an optimal strategy for waiting time conditioning between sequential operations.
Abstract: In large-scale arable farming, multiple sequential operations involving multiple machines must be carried out simultaneously due to restrictions of short time windows. However, the coordination and planning of multiple sequential operations is a nontrivial task for farmers, since each operation may have its own set of operational features, e.g., operating width and turning radius. Taking the two sequential operations—hoeing cultivation and seeding—as an example, the seeder has double the width of the hoeing cultivator, and the seeder must remain idle while waiting for the hoeing cultivator to finish two rows before it can commence its seeding operation. A flow-shop working mode can coordinate multiple machines in multiple operations within a field when different operations have different implement widths. To this end, an auto-steering-based collaborative operating system for fleet management (FMCOS) was developed to realize an in-field flow-shop working mode, which is often adopted by the scaled agricultural machinery cooperatives. This paper proposes the structure and composition of the FMCOS, the method of operating strip segmenting, and a new algorithm for strip state updating between successive field operations under an optimal strategy for waiting time conditioning between sequential operations. A simulation model was developed to verify the state-updating algorithm. Then, the prototype system of FMCOS was combined with auto-steering systems on tractors, and the collaborative operating system for the server was integrated. Three field experiments of one operation, two operations, and three operations were carried out to verify the functionality and performance of FMCOS. The results of the experiment showed that the FMCOS could coordinate in-field fleet operations while improving both the job quality and the efficiency of fleet management by adopting the flow-shop working mode.
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TL;DR: This paper presents a dynamic and demand aware fleet management framework for combined goods and passenger transportation that is deployable independently within each vehicle as this minimizes computational costs associated with the growth of distributed systems and democratizes decision-making to each individual.
Abstract: In this paper, we present a dynamic, demand aware, and pricing-based matching and route planning framework that allows efficient pooling of multiple passengers and goods in each vehicle. This approach includes the flexibility for transferring goods through multiple hops from source to destination as well as pooling of passengers. The key components of the proposed approach include (i) Pricing by the vehicles to passengers which is based on the insertion cost, which determines the matching based on passenger's acceptance/rejection, (ii) Matching of goods to vehicles, and the multi-hop routing of goods, (iii) Route planning of the vehicles to pick. up and drop passengers and goods, (i) Dispatching idle vehicles to areas of anticipated high passenger and goods demand using Deep Reinforcement Learning, and (v) Allowing for distributed inference at each vehicle while collectively optimizing fleet objectives. Our proposed framework can be deployed independently within each vehicle as this minimizes computational costs associated with the gorwth of distributed systems and democratizes decision-making to each individual. The proposed framework is validated in a simulated environment, where we leverage realistic delivery datasets such as the New York City Taxi public dataset and Google Maps traffic data from delivery offering businesses.Simulations on a variety of vehicle types, goods, and passenger utility functions show the effectiveness of our approach as compared to baselines that do not consider combined load transportation or dynamic multi-hop route planning. Our proposed method showed improvements over the next best baseline in various aspects including a 15% increase in fleet utilization and 20% increase in average vehicle profits.
TL;DR: An original model and a solution procedure for solving jointly three main strategic fleet management problems, taking into account interdependencies between them are developed allowing for the reduction of the fleet exploitation costs by adjusting fleet size, types of exploited vehicles and their exploitation periods.
Abstract: The purpose of this paper is to develop an original model and a solution procedure for solving jointly three main strategic fleet management problems (fleet composition, replacement and make-or-buy), taking into account interdependencies between them.,The three main strategic fleet management problems were analyzed in detail to identify interdependencies between them, mathematically modeled in terms of integer nonlinear programing (INLP) and solved using evolutionary based method of a solver compatible with a spreadsheet.,There are no optimization methods combining the analyzed problems, but it is possible to mathematically model them jointly and solve together using a solver compatible with a spreadsheet obtaining a solution/fleet management strategy answering the questions: Keep currently exploited vehicles in a fleet or remove them? If keep, how often to replace them? If remove then when? How many perspective/new vehicles, of what types, brand new or used ones and when should be put into a fleet? The relatively large scale instance of problem (50 vehicles) was solved based on a real-life data. The obtained results occurred to be better/cheaper by 10% than the two reference solutions – random and do-nothing ones.,The methodology of developing optimal fleet management strategy by solving jointly three main strategic fleet management problems is proposed allowing for the reduction of the fleet exploitation costs by adjusting fleet size, types of exploited vehicles and their exploitation periods.
TL;DR: Results show that, while critical for enabling UAM, the performance of the UAM ecosystem is robust to variations in ground infrastructure and fleet design decisions, while being sensitive to decisions for fleet and traffic management policies.
Abstract: A significant challenge in estimating operational feasibility of Urban Air Mobility (UAM) missions lies in understanding how choices in design impact the performance of a complex system-of-systems. This work examines the ability of the UAM ecosystem and the operations within it to meet a variety of demand profiles that may emerge in the coming years. We perform a set of simulation driven feasibility and scalability analyses based on UAM operational models with the goal of estimating capacity and throughput for a given set of parameters that represent an operational UAM ecosystem. UAM ecosystem design guidelines, vehicle constraints, and effective operational policies can be drawn from our analysis. Results show that, while critical for enabling UAM, the performance of the UAM ecosystem is robust to variations in ground infrastructure and fleet design decisions, while being sensitive to decisions for fleet and traffic management policies. We show that so long as the ecosystem design parameters for ground infrastructure and fleet design fall within a sensible range, the performance of the UAM ecosystem is affected by the policies used to manage the UAM traffic.
08 Oct 2020
TL;DR: The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost.
Abstract: Automobile maintenance has gained increasing attention in recent years. If the failure time of an automobile can be predicted, it can bring tangible benefits to automobile fleet management. The Multi-Grained Cascade Forest (gcForest) is a tree-based deep learning algorithm, which was originally developed for image classification, but is potentially a helpful tool in automobile maintenance. This study aims to introduce the gcForest into automobile maintenance based on automobile historical maintenance data and geographical information system (GIS) data. The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost.
01 Jan 2020
TL;DR: This research is focused on implementation of the Ant Colony Optimisation (ACO) technique to solve an advanced version of the Vehicle Routing Problem (VRP), called fleet management system (FMS).
Abstract: This research is focused on implementation of the Ant Colony Optimisation (ACO) technique to solve an advanced version of the Vehicle Routing Problem (VRP), called fleet management system (FMS). An optimum solution of VRP can bring lots of benefits for the fleet operators as well as contributing to the environment. Nowadays, particular considerations and modifications are needed to be applied in the existing FMS algorithms in response to the rapid growth of electric vehicles (EVs). For example, current FMS algorithms don’t consider the limited range of EVs, their charging time or battery degradation. In this study, a new ACO-based FMS algorithm is developed for a fleet of EVs. A simulation platform is built in order to evaluate performance of the proposed FMS algorithm under different simulation case-studies. The simulation results are validated against a well-established method in the literature called Nearest-Neighbour technique. At each case-study, the overall mileage of the fleet is considered as an index to measure the performance of the FMS algorithm.
TL;DR: An intelligent distributed Fleet Management System architecture for an open pit mine that allows mining vehicles control in a real time context, according to users’ requirements is proposed.
Abstract: Fleet management systems are currently used to coordinate mobility and delivery services in a wide range of areas. However, their traditional control architecture becomes a critical bottleneck in open and dynamic environments, where scalability and autonomy are key factors in their success. In this article, we propose an intelligent distributed Fleet Management System architecture for an open pit mine that allows mining vehicles control in a real time context, according to users’ requirements. Enriched by an intelligence layer made possible by the use of high-performance artificial intelligence algorithms and a reliable and efficient perception mechanism based on Internet of Things technologies and governed by an smart and integrated decision system that allows the fleet management system to improve its agility and its response to user requirements, our architecture presents numerous contributions to the domain. These contributions enable the fleet management system to meet the interoperability and autonomy requirements of the most widely used standards in the field, such as ISA 95.
TL;DR: The most useful parameters for fleet management are FC, CE, CT; Taguchi DOE and full factorial DOE have identified FC and RG as a most critical parameters forfleet health/performance monitoring.
Abstract: The purpose of this study is to investigate the best fleet for a new purchase based on multi-objective optimization on the basis of ratio (MOORA), reference point and multi-MOORA methods This study further identifies critical parameters for fleet performance monitoring and exploring optimum range of critical parameters using Monte Carlo simulation At the end of this study, fleet maintenance management and operations have been discussed in the perspectives of risk management,Fleet categories and fleet performance monitoring parameters have been identified using the literature survey and Delphi method Further, real-time data has been analyzed using MOORA, reference point and multi-MOORA methods Taguchi and full factorial design of experiment (DOE) are used to investigate critical parameters for fleet performance monitoring,Fleet performance monitoring is done based on fuel consumption (FC), CO2 emission (CE), coolant temperature (CT), fleet rating, revenue generation (RG), fleet utilization, total weight and ambient temperature MOORA, reference point and multi-MOORA methods suggested the common best alternative for a particular category of the fleet (compact, hatchback and sedan) FC and RG are the critical parameters for monitoring the fleet performance,The geographical aspects have not been considered for this study,A pilot run of 300 fleets shows saving of Rs 2,611,013/- (US$36,264065), which comprises total maintenance cost [Rs 1,749,033/- (US$24,292125)] and FC cost [Rs 861,980/- (US$11,97194)] annually,Reduction in CE (483%) creates a positive impact on human health The reduction in the breakdown maintenance of fleet improves the reliability of fleet services,This study investigates the most useful parameters for fleet management are FC, CE, CT Taguchi DOE and full factorial DOE have identified FC and RG as a most critical parameters for fleet health/performance monitoring
TL;DR: In this paper, the authors investigate the strategic fleet management practices in the air transport industry regarding airline fleet standardization (AFS) and conduct an econometric analysis of the key drivers of AFS in Brazil.
TL;DR: In this article, the authors project the introduction of battery-electric and fuel cell technologies into the medium-duty and heavy-duty vehicle markets and identify which markets will be most suitable for each of technologies and the factors (technical, economic, operational) which will be the most critical to their successful introduction.
Abstract: Author(s): Burke, Andrew; Sinha, Anish Kumar | Abstract: The objective of this study is to project the introduction of battery-electric and fuel cell technologies into the medium-duty and heavy-duty vehicle markets and to identify which markets will be most suitable for each of technologies and the factors (technical, economic, operational) which will be most critical to their successful introduction. The use of renewable energy sources to generate electricity and produce hydrogen are key considerations of the analysis. The present status of the battery-electric and hydrogen/fuel cell technologies are reviewed in detail and the futures of these technologies are projected. The design and performance of various types of buses and trucks are described based on detailed simulations of the various electrified vehicles. The total cost of ownership (TCO) of each bus/truck type were calculated using EXCEL spreadsheets and their market prospects projected for 2020-2040. It was concluded that before any of the electrified vehicles can be cost competitive with the corresponding diesel powered vehicle, the unit cost of batteries must be $80-100/kWh and the unit cost of the fuel cell system must be $80-100/kW. The long term economics of battery-electric buses and trucks looks more favorable than that for the fuel cell/hydrogen option if the range requirement (miles) for the vehicle can be met using batteries. This is primarily due to the significantly lower energy operating cost ($/mi) using electricity than hydrogen.View the NCST Project Webpage
TL;DR: The results showed that the proposed fleet control model has the ability of local asymptotic stability and global nonstrict string stability and the Lyapunov stability principle to analyze and prove the stability of the fleet in dense fog environment.
Abstract: Adverse weather conditions have a significant impairment on the safety, mobility, and efficiency of highway networks. Dense fog is considered the most dangerous within the adverse weather conditions. As to improve the traffic flow throughput and driving safety in dense fog weather condition on highway, this paper uses a mathematical modeling method to study and control the fleet mixed with human-driven vehicles (HDVs) and connected automatic vehicles (CAVs) in dense fog environment on highway based on distributed model predictive control algorithm (DMPC), along with considering the car-following behavior of HDVs driver based on cellular automatic (CA) model. It aims to provide a feasible solution for controlling the mixed flow of HDVs and CAVs more safely, accurately, and stably and then potentially to improve the mobility and efficiency of highway networks in adverse weather conditions, especially in dense fog environment. This paper explores the modeling framework of the fleet management for HDVs and CAVs, including the state space model of CAVs, the car-following model of HDVs, distributed model predictive control for the fleet, and the fleet stability analysis. The state space model is proposed to identify the status of the feet in the global state. The car-following model is proposed to simulate the driver behavior in the fleet in local. The DMPC-based model is proposed to optimize rolling of the fleet. Finally, this paper used the Lyapunov stability principle to analyze and prove the stability of the fleet in dense fog environment. Finally, numerical experiments were performed in MATLAB to verify the effectiveness of the proposed model. The results showed that the proposed fleet control model has the ability of local asymptotic stability and global nonstrict string stability.