Planning tools

Planning tools

With the increasing number of available electric vehicles, ranging from passenger cars to vans and trucks >12t, their use in freight transport and company fleets will increase rapidly. The technical characteristics of these vehicles, especially the usable range and the possible payload in connection with the still small number of public recharging sites make a parallel use of conventionally and electric vehicles necessary. In the mixed use of conventional and electric vehicles, daily operation must be adapted. Existing routes are to be adapted in such a way that the requirements of future users can be met with an optimised number of conventionally powered and electric vehicles. To this end, tools have been developed that can help interested parties to assess the possible use of electric vehicles. In the following some tools are described and their possibilities are shown.

 

Fleet integration with DynaTOP - Dynamic Transport Optimisation

Objectives:

The purpose of the tool is to enable planning of mixed fleets and the reorganisation of delivery tours.

Benefits for the users:

The main benefit of the DynaTOP tool is the optimized planning of the fleet composition with conventional and electric vehicles. Furthermore, the tool also allows the daily tours to be adapted to the technical characteristics of the vehicles, such as load volume, range, etc.

Description of the Tool:

DynaTOP is a tool developed by project partner AIT to solve all kinds dynamic optimisation problems. The tool offers the opportunity to check whether an existing fleet and the daily business of an interested party are suitable for the use of electric or alternative propelled vehicles. The tool offers the possibility to get an overview how mixed fleets can be composed and the number of conventional driven and electric vehicles regarding to the user’s requirements. Additionally, the tool also provides suggestions how the daily tours could be changed to enable the usage of electric driven vehicles.

DynaTOP is a powerful server-based software framework that uses a generic central optimization module to compute high-quality solutions for all sorts of optimization problems. Its architecture allows it to be used concurrently by multiple users. However, before a class of optimization problems can be handled by DynaTOP, it must first be formally described programmatically and linked to the framework’s optimization algorithms via its API, as it currently offers no user-friendly GUI access to its core algorithms. Since this task requires expertise in both modelling and programming, extending the framework to work with new problems usually requires an experienced researcher to handle the implementation. Once this integration is completed, other programmers can use DynaTOP to solve instances of the new optimization problems with relative ease, since they must only provide the program with the appropriate instance data.

Users of EUFAL-platform can access the corresponding optimization facilities of DynaTOP via a tailor-made browser-based web user interface (see Link below). There, the user can easily input all the data required by the optimization algorithms, such as the addresses of their depot and their customers. They can furthermore customize the size and composition of their fleet, which can include a variety of different vehicle types like conventionally- and electrically powered transporters or regular and electric cargo bikes. This allows the users to both optimize their current delivery strategies, as well as easily explore the feasibility and potential of future alterations to their fleet (such as replacing some of their conventionally powered trucks by electrically powered ones). All this data can, of course, also be uploaded in a machine-readable format.

The data is then sent to the DynaTOP server, where our optimization framework quickly computes a set of delivery routes that visits all provided customers in an efficient manner. Our algorithms can incorporate a variety of side constraints on these tours, including maximum route length and duration, limited vehicle capacity and multiple delivery time windows for each customer. These routes, which are based on the delivery area’s actual street layout, are then visualized on a map, where they can also be downloaded by the customer in a machine-readable format.

Figure 1 - GUI of the DynaTOP optimization framework

Source: own illustration of AIT Austrian Institute of Technology

Figure 2 - Example of optimises delivery tours using 2 electric vehicles

Source: own illustration of AIT Austrian Institute of Technology

Data Requirements

In the free-to-use level of the platform the tool provides an example dataset that includes typical tours of a delivery company focusing on urban and rural areas. The dataset is implemented in the tool and could not be changed by the user. With the help of the user interface the user is able to change the number of the electric vehicles and the types of the cars of the investigated fleet.

Links

The provided link is only for tests regarding the integration in the web based EUFAL platform. This link belongs to another tool of AIT called “beelivery+”. This link will be changed as soon as the EUFAL-DynaTOP fleet integration tool will be available.

http://62.218.45.16:8080/eufal-ui/

For further information please contact:

Jürgen Zajicek

AIT Austrian Institute of Technology GmbH

Juergen.zajicek@ait.ac.at

+43 664 6207836

Mixed-fleet route optimization with temperature battery usage

Description of the Tool:

A route optimization tool to determine the deployment of an available fleet of electric and conventional vehicles for specific tasks characterized by various locations, time windows, demand quantities, service time durations, and compatibility with drivers. The tool minimizes the sum of the routing costs (energy and maintenance) and vehicle usage costs. This tool is primarily applicable for freight logistics in urban areas. The tool allows one recharging of the electric vehicles at most once during the day. To better account for the vehicles’ power consumption, the tool examines the energy impact of cabin climate control power in addition to mechanical power. Thus, this tool eliminates to a large extent, the mismatch in planned driving range and realized driving range, which has been a major concern to early adopters.

Objectives:

The purpose of this tool is to obtain routes that minimize the cost of energy used by electric vehicles (through recharging cost) and conventional vehicles (through fuel cost) in the available fleet.

Benefits for the users:

The tool optimizes the tours based on the different energy consumption of the auxiliary units in the electric vehicles over the course of the year.

Data Requirements:

The tool requires information about the engine and battery characteristics of the available fleet, and the current outside temperature. Moreover, the list of tasks to perform, the quantities to be transported, and the time windows and service times of each task are required.

Links

N/A

For further information please contact:

Assoc. Prof. Dario Pacino

DTU Management, Technical University of Denmark

darpa@dtu.dk

Mixed-fleet optimizer with a TCO perspective

A tool to determine the optimal fleet mix of electric and conventional vehicles that may operate on specific tasks characterized by various locations, time windows, demand quantities, service time durations, and compatibility with drivers. The tool analyzed the company driving requirements and minimizes the total cost of ownership (TCO) over a 10.6-year horizon with the vehicles operating on 227 days per year. The TCO is calculated here as the sum of operational cost (routing, maintenance, insurance, driver wages, etc.) and acquisition cost (with tax).

Objectives:

The purpose of the tool is to enable assessment of mixed electric fleet ownership from a TCO perspective. Results from the tool can be used to support the decision maker on how many and which types of electric vehicles can be used for a given company. The results also provide a sensitivity analysis of the costs based on changes in fuel and electricity prices and changes in the costs of acquisition.

Data Requirements:

The tool requires information about the engine and battery characteristics of the considered vehicle types and the seasonal outside temperature. Historical data from the company is required to analyse driving patterns and transport demands. Traffic data and road elevation can also be considered by the tool.

Benefits for the users:

The tool optimizes the fleet mix of electric and conventional vehicles from a perspective of the TCO over a long-time horizon. The user will be able to determine how many and which vehicles to acquire from this perspective.

Links

Examples for this planning tool are available here:

For further information please contact:

Assoc. Prof. Dario Pacino

DTU Management, Technical University of Denmark

darpa@dtu.dk

Vehicle Route Planning with mixed fleets and battery electric vehicles (jsprit)

Objectives:

The purpose of the tool is to optimize tours of vehicles of several different types. Given the location of one or several depots and a set of transport orders, the tool calculates the optimum number and types of vehicles that are needed as well as the assignment of transport orders to one of these vehicles and the sequence of stops in the single vehicle tours. Relevant decision variables are the fixed and variable costs of the single vehicle types. Tours are built in a way that ensures cost minimal operations subject to capacity and range constraints of the vehicles and time window constraints of the shipments. A fleet mix of electric and diesel driven vehicles that takes into account the capabilities and limitations of the various power units is obtained.

Data Requirements:

For each vehicle types the decision relevant figures, i.e. fixed and distance and/or time dependent costs are needed. Constraints for the vehicle types are capacity (mandatory) hours of operation (optional) and range (optional). For each shipment, origin (mandatory), destination (mandatory), size (mandatory), stop times for loading and unloading (mandatory) and time windows (optional) are needed.

Benefits for the users:

The tool optimizes tours and fleet compositions depending on the requirements of the shipments and the capabilities and restrictions of the available vehicles. The code is available open source and can be adapted to the requirements of the users and also be integrated in their own existing planning programs

Links

https://github.com/graphhopper/jsprit

For further information please contact:

Tilman Matteis

German Aerospace Center (DLR)

Institute of Transport Research

Tilman.Matteis@dlr.de

 

References

Knopp (2018): Mobility Offer Allocations for Corporate Mobility as a Service

https://arxiv.org/abs/1810.05659

Hiermann (2016): The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

https://www.sciencedirect.com/science/article/pii/S0377221716000837

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