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Fleet management for autonomous vehicles using flows in Time-Expanded Networks

Researchers Sahar Bsaybes, Alain Quilliot y Annegret K.Wagler from the University Clermont Auvergne, Clermont-Ferrand, France, published an article online (by regarding the fleet management for autonomous vehicles.  The article provides an a-up to date review on this issue. Here there are some of the key issues.

VIPAFLEET is a framework to manage a fleet of Individual public autonomous vehicles (VIPA). Researchers consider a fleet of cars distributed at specified stations in an industrial area to supply internal transportation, where the cars can be used in different modes of circulation (tram mode, elevator mode, taxi mode).

The research is about the pickup and delivery problem related to the taxi mode by means of flows in time-expanded networks. This enables researchers to compute optimal offline solutions, to propose strategies for the online situation, and to evaluate their performance in comparison with the optimal offline solution.

A VIPA can operate in three different circulation modes:

– Tram mode VIPAs continuously run on predefined lines or cycles in a predefined direction and stop at a station if requested to let users enter or leave.

– Elevator mode VIPAs run on predefined lines and react to requests by moving to a station to let users enter or leave, thereby changing their driving direction if needed.

– Taxi mode VIPAs run on a connected network to serve transport requests (from any start to any destination station in the network within given time windows).

In this paper, the researchers treat the PDP related to the taxi mode as the most advanced circulation mode for VIPAs in the dynamic fleet management system. The transport requests are released over time and need to be served within a specified time window. The case in consideration is that, at each time, at most one customer can be transported by a VIPA (where one customer can be a group of people not exceeding the capacity of the VIPA), and a VIPA cannot serve other requests until the current one is delivered.

Note that, due to the time windows and the above additional restrictions, it is not always possible to serve all transport requests. Hence, the studied PDP includes firstly to accept/reject requests and secondly to generate tours for the VIPAs to serve the accepted requests. Thus, this article treat here both the quality-of-service aspect of the problem (with the goal of accepting as many requests as possible) and the economic aspect (with the goal of serving the accepted requests at minimum costs, expressed in terms of minimizing the total tour length of the constructed tours).

It´s necessary to distinguish between the online and the offline version of the problem: the online version occurs in practice (since the transport requests become known over time), whereas the offline version is important in theory to rate the quality of solutions for the online problem, by comparison with the optimal offline solution (computed knowing the entire request sequence already in advance).

To solve the Online TMP, three approaches are considered:

– A simple Earliest Pickup Heuristic that incrementally constructs tours by always choosing from the subsequence of currently waiting requests (i.e., already released but not yet served requests).

– The two well-known meta-strategies Replan and Ignore that determine which requests can be accepted and compute optimal (partial) tours to serve them, where Replan performs these tours until new requests are released, but – Ignore completely performs these tours before it checks for newly released Requests.

The conclusion is that IGNORE is not suitable for the Online TMP, since the way in which to construct tours may result in many rejected requests and the decision to accept/reject a request may be taken late, which does not comply with the quality-of-service aspect of the fleet management. Therefore, this paper focus on the other two approaches and perform computational results only for EPH and REPLAN, with the expectation that EPH is faster, but REPLAN achieves a higher acceptance rate.

In practice, EPH is faster, but REPLAN provides solutions of reasonable quality within a short time for each recomputation step and achieves a better acceptance rate.

The researchers highlight that the proposed REPLAN strategy is already a promising algorithm to handle the Online TMP for the taxi mode in the studied VIPAFLEET management system, with a reasonable ratio of computation times in each replanning step and an acceptance rate of about 55% compared to the optimal offline solution on realistic test instances.

Computational experiments revealed that the acceptance rate can be increased in some cases (on average about 13 % compared to the here studied REPLAN strategy), but that the possible increase strongly depends on the ratio of the request loads and the VIPA capacity.

On the other hand, the computation times in each replanning step are much higher, and the constructed tours are sometimes preemptive which causes inconveniences for the users (as they may have to change VIPAs and are not always transported along a shortest path from their origin to their destination).

Thus, the researchers conclude that the REPLAN strategy for the preemptive case has no clear advantage compared to the here proposed REPLAN strategy for the non-preemptive case, so that the operator of a VIPAFLEET system has to decide whether or not it is worth to have preemptive tours and longer computation times in each replanning step, taking the ratio of the average request loads and the VIPA capacity and, thus, the expected increase of the acceptance rate into account.

As future work, these researchers plan to improve the runtime of the here proposed REPLAN strategy by reducing the time-expanded network built in each replanning step without loss of optimality.

Fleet management: the replacement problem

By Horacio de la Fuente, industry expert

Researcher Adam Redmer, from the Poznan University of Technology, Poznan, Poland, published an article online (by regarding the fleet replacement problem. The article provides an up to date review on this issue. Here there are some of the key issues.

Fleets constitute the most important production means in transportation. Their appropriate management is crucial for all companies having transportation duties. The paper discusses ways of building replacement strategies for companies’ fleets of vehicles. It means deciding for how long to exploit particular vehicles in a fleet (the fleet replacement problem – FR). The essence of this problem lies in the minimization of vehicle / fleet exploitation costs by balancing ownership and utilization costs and taking into account budget limitations.

The solution shows that there exist optimal exploitation periods of particular vehicles in a fleet. However, combination of them gives a replacement plan for an entire fleet violating budget constraints. But it is possible to adjust individual age to replacement of particular vehicles to fulfill budget constraints without losing economical optimality of a developed replacement plan for an entire fleet.

The decision for how long to exploit particular vehicles in a fleet or when to dispose / replace them and by what type of a brand new or used vehicles, including selection of vehicles investment / acquisition option (e.g. to buy on cash, credit, lease or rent), is called a fleet replacement (FR) problem.

The FR problem can be considered on a level of single vehicles or on a level of entire fleets. It makes a significant difference in the way budget limitations are taken into account. When single vehicles are considered budget limitations can be skipped or they just simply can’t be taken into account since budgets are defined on a fleet level, not on a level of single vehicles. In contradiction, whereas entire fleets are considered budget limitations are crucial when developing replacement plans.

On a single vehicle level of considerations the essence of the FR problem is to exploit vehicles not too short and not too long. Too short exploitation periods result in high vehicle ownership costs, whereas too long exploitation periods result in high vehicle utilization costs. High ownership costs a caused by a steep decrease in a vehicles’ residual value (RV) in early years of their exploitation life. High utilization costs a caused by technical condition deterioration and increased downtimes associated with it.

Methods for solving the FR problem can be divided into the preventive and the failure based ones. But in the case of the preventive replacement methods it is necessary to define time to replacement, which is obvious in the case of the failure based methods since it is just a moment of a failure. There are two ways of defining that time (an exploitation period) when using the preventive based methods. They are: age-based replacement and group replacement. Considering vehicles, including trucks, the preventive age-based replacement methods can be applied. Instead of an age a cumulative utilization, e.g. mileage can be used as well.

Regardless if an age of a vehicle (an exploitation period) or a cumulative utilization (mileage) is used the general aim when planning replacement is to minimize overall exploitation costs, usually discounted ones. The general drawback of the existing solution methods for the FR problem is that they assume a constant utilization of equipment including vehicles, during its operational lifetime

Two different solutions of the problem have been found. The solutions named “optimal” and “smooth” one. The optimal solution means a replacement plan constructed based on the optimal age to replacement calculated separately for each one vehicle in the fleet. In this solution the budget constraint has been relaxed (skipped). The smooth solution means a replacement plan constructed based on the age to replacement calculated for particular vehicles in the fleet simultaneously (at the same time) trying to keep investment expenditures in a certain range (the budget).

Both solutions are very similar, equally good, when taking into account the average unit fleet exploitation costs they result in (less than 1% difference). Moreover, the average age to replacement is similar in both solutions as well. It is around 7 years of exploitation to the moment of replacement (based on the optimal solution 6.5 years and on the smooth one 7.1 years – precisely). The significant difference is when taking into account budget limitations and net fleet investments the both solutions result in within particular fiscal years.

The optimal solution from the budget limitations point of view is a significantly worse solution for the company operating analyzed fleet than the smooth one. The optimal solution results in the net fleet investments varying much from year to year. Moreover, this solution requires high expenditures in the first one fiscal year. On the contrary, the researcher highlight the smooth solution does not cause capital investment surges in time, requires small expenditures in the first three fiscal years and the expenditures in the further years are relatively flat.

Io-T based predictive maintenance for fleet management

By Horacio de La Fuente, Editor

During the 2nd International Conference on Emerging Data and Industry 4.0 (EDI40) April 29 – May 2, 2019, Leuven, Belgium, researchers from the University of Ottawa and Harbin University of Science and Technology, China, Patrick Killeen, Bo Ding, Iluju Kiringa and Tet Yeap presented an article regarding fleet management, then published in the scientific journal Procedia Computer Science, 151.

In the article researchers provide an IoT architecture designed to support Fleet Management. Here are some of the key issues.

1. Introduction

Internet of Things (IoT) is a new paradigm that is growing quickly. By 2020 many believe billions of devices will be connected to the internet. Some of the applications of IoT are smart farming, smart transport, smart health, smart cities, smart homes, and smart grids. Predictive maintenance, an example of smart transportation, attempts to predict the health of equipment using machine learning.

2. Background

IoT connects multiple devices, and the devices can sense and interact with the environment around them. IoT can be split into five layers: sensing, network, storage, learning, and application. The sensing layer gathers data from the environment and interacts with it using sensors and actuators. The network layer connects lower level nodes to the cloud/fog. A company named Libelium is working on providing global wireless sensors network coverage. The storage layer stores sensor data, aggregations, and other types of data. The learning layer performs data analytics on stored sensor data for knowledge discovery; for example, anomaly detection, or deviation detection, which attempts to detect when an instance deviates from its norm, can be performed in the learning layer. The application layer provides the interface to the IoT system by providing lower layer information access and control.

Predictive maintenance lowers costs by preventing failures, unscheduled maintenance, and downtime, and by ensuring the replacement of failing parts is done only when needed.

3. Predictive maintenance system architecture

The authors stress that the present work proposes an IoT architecture designed to support fleet management. It is divided into three layers (the perception layer, the middleware layer, and the application layer). The perception layer abstracts the fog and embedded systems. It performs sensing, lightweight storage, networking, and machine learning. It provides the interface to low-level nodes. The middleware layer abstracts the fog and the cloud and generally performs more heavy-duty storage, networking, and machine learning compared to the perception layer. It provides the interface to perception-layer nodes. The application layer is similar to the IoT application layer mentioned in section 2.

4. Semi-supervised sensor feature selection — ICOSMO

Researchers highlight that this work attempts to improve the sensor feature selection performed in COSMO, by using a semi- supervised machine learning approach, which is currently under development. A few definitions are necessary.

  1. a) Sensor Class: a J1939 sensor definition, defined as a PGN-SPN pair
  2. b) Sensor Instance: a physical J1939 sensor, which may or may not be installed on a vehicle
  3. c) COSMO Sensor: a sensor class that has been chosen as a selected feature in the unsupervised deviation detection model of the COSMO approach.

The algorithm proposed by the authors is named Improved Consensus self-organized models (ICOSMO) and makes the following assumptions: a) VSRDB, with repair records that describe details about faults that were repaired, is accessible; b) the repair records in the VSRDB are associated with true faults (not preventive repairs but instead reactive repairs); c) a document retrieval algorithm exists, which takes a mechanic’s repair record as a query, searches its indexed J1939 specification document, and estimates sensor classes involved in the failing/faulty components that the repair fixed (a black box document retrieval algorithm (BBDRA) is used in this work to simulate this assumption); and d) all buses in a fleet are of the same model, and each bus shares similar daily travel routes.

ICOSMO is designed in a data-driven fashion for conducting predictive maintenance, since mostly current and historical sensor data are accessible.

To verify the performance of ICOSMO, simulations and modeling will be conducted by generating data using the STO J1939 data dumps acquired from the MVP. The goal would be to design a fully-autonomous predictive maintenance system using fleet-wide and on-board data analytics.

5. Predictive maintenance system prototype

A minimally viable prototype (MVP) of the architecture mentioned in section has been implemented and is running in a live environment. The MVP does not have ICOSMO implemented yet, since ICOSMO is still currently under development.

The MVP is targeted for creating an IoT predictive maintenance fleet management system for the public transport buses of the Société de Transport de l’Outaouais (STO), Gatineau, Canada. Each bus will have a gateway installed, which reads sensor data and performs lightweight analytics. The goal is to discover novelties and to provide this information to the fleet managers to help them make better maintenance decisions. With each bus equipped with a gateway, fleet-wide data analysis will be possible. This enables the possibility of discovering some novelties that would not be obtainable when only monitoring individual vehicles.

Since deployment, the MVP allowed the authors to acquire J1939 data dumps daily. Approximately 1 GB of uncompressed J1939 data (200 MB when compressed) was acquired daily. Noteworthy sensor readings found in the data dump are listed below:

  • wheel speed information: axle angle and relative speed
  • vehicle distance information: trip distance, total distance, and remaining distance before running out of fuel
  • driver pedal positions
  • engine information: speed, torque, and temperature
  • oil information: temperature, pressure, and level
  • coolant information: temperature, pressure, and level
  • transmission fluid information: oil temperature and pressure

6. Conclusion

The researchers express that this work provides a novel IoT architecture for predictive maintenance. A semi-supervised machine learning approach is proposed for improving the sensor feature selection of the COSMO approach, which the authors name ICOSMO. A prototype of the architecture has been installed at a garage of the STO, Gatineau, Canada. Currently only a single bus is equipped with a gateway, but the plan is to expand the MVP and equip many more buses with gateways. This MVP is the foundation of a predictive maintenance machine learning and data analytic research. By using the J1939 data acquired from the buses, the researchers will train machine learning algorithms, and once complete these algorithms will be deployed to the MVP.

Future work will include completing the implementation of ICOSMO and running experiments.

Relieve The Fleet Europe Summit 2018 With Omoove

Omoove at the Fleet Europe Summit 2018

For the first time, the Fleet Europe Village contained a Smart Mobility Area that hosted Smart Mobility Talks on MaaS, safety and autonomous vehicles and shared mobility. It was also where start-ups could present their companies and pitch for the Smart Mobility Start-Up of the Year Award and where the candidates for the International Fleet Industry Awards defended their cases. We are present with our EVM solution and rank in the top 9 among flagship companies of the International Fleet Industry Award and we are proud to recognized as one of the leading companies in the Fleet Management sector.

Read the full article

Smart Mobility Talks at the Fleet Europe Summit 2018

The third session of the Smart Mobility Talks addressed shared mobility as a disruptor, delving into the way to implement this mobility service in corporate fleets. Omoove showed the do’s and don’ts of shared mobility, from their experience with this new mobility service. Omoove, for instance, brings shared mobility closer to the company, by providing tailor-made solutions for larger fleets or plug-and-play solutions for those who are not that keen on working out the analytics.Omoove offers end-to-end Shared Mobility, Fleet Management and Insurance Telematics technologies and solutions for Car and Ride Sharing Operators, corporate fleet and peer-to-peer Shared Mobility Communities.

Read the full article here

The Mobility Paradox

Omoove’s VP Sales and Marketing, Edwin Colella, presented his insights on how the future of mobility includes redefining the travel experience. The vehicle acts as a gate to a world of mobility-based services in which the user experience must be simple, functional and above all self-service. He explained how shared mobility, enhanced vehicle management and insurance telematics interface between end user and operator. Especially the concept of “mobility paradox”, the fact that mobility has become complex, exactly because there are so many solutions, led to conclude that tools can put the user, or the fleet manager, back at the centre.

Read the full article here

Firts ever Fleet Europe EV test drive: quite the buzz

Italian telematics company Omoove joined up with Spanish two and three-wheeler maker Silence to create the first connected sharable e-scooter in the world. A regular driver’s licence suffices to enjoy the quiet and emission-free riding fun- without having to own it.Omoove and Silence together for the first electric Connected Scooter in the world. The S02 model by Silence coming out of the factory, is equipped with the Connected Technologies for the Scooter Sharing. It means that the device is not installed in aftermarket but it’s already provided at the end of the production cycle in the factory premises. The technological solution is offered as a Software-as-a-Service solution built on a web-based platform that comprises a suite of essential functionalities required for efficient management of a Scooter Sharing service: user registration, booking, use and release of scooter, accounting of costs, fleet manager support, back office support including reporting and business intelligence tools. The sharing service, included in the standard version, is available thanks to the Omoove technology. The device allows real-time access to the main information on the vehicle, on the battery and on the engine. 

Read the full article here

Omoove Goes To Future Of Fleets

Future of Fleets invites global fleet managers and operators to discover the latest innovations in fleet technology and how connectedcars, electric vehicles and smartmobility will help reduce costs, better efficiency and shape the future of mobility. This October 19 in Paris, our own Edwin M. Colella will be on a round table “connected fleet” to talk about the latest fleet innovations and use these new connected, shared, and electric vehicle technologies to reduce costs. 

Hope to see you there!

We’re At The Global Fleet Conference 2018

For over 15 years, Octo and Omoove have been developing state of the art services in the mobility sector and leading global mobility initiatives, such as Sharemine. This 28-30 May, we will be the headline sponsor of the sixth edition of the Global Fleet Conference 2018 where experts in the field examine the biggest trends in Fleet Management and discuss what direction the future of mobility is heading.

Sign up here for the chance to speak directly to an Octo/Omoove industry expert!

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