Tag: fleet-telematics

Researchers Sahar Bsaybes, Alain Quilliot y Annegret K.Wagler from the University Clermont Auvergne, Clermont-Ferrand, France, published an article online (by Hal.archives-ouvertes.fr) 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.

By Horacio de la Fuente, industry expert

Researcher Adam Redmer, from the Poznan University of Technology, Poznan, Poland, published an article online (by LogForum.net) 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.