Reducing the Carbon Footprint of Logistics Service Providers

Reducing the Carbon Footprint of Logistics Service Providers: A Case Study at DHL Parcel Benelux

van Hemert, Dick (TU Delft Mechanical, Maritime and Materials Engineering)

Atasoy, B. (mentor)
Schott, D.L. (graduation committee)

Delft University of Technology

Marine Technology | Transport Engineering and Logistics

2020-02-24

Parcel deliveries have seen a massive grow over the past several years, mainly in the B2C sector due to rise of online shopping. Due to this growing market more transport movements in the last mile are required. These extra driven kilometers add up to an increase in Greenhouse Gas (GHG) emissions. In a world where the ask for more sustainable operations is on a rise, this can be seen as a problem.
Logistics Service Providers (LSPs) are trying to come with solutions to tackle this problem, where most of them are of strategic kind, pushing the outcome to future instead of achieving results in the present. One of the solutions to realize emission reduction is the use of zero-emissions vehicles in the last mile delivery phase. However, current operations of these zero-emissions vehicles are not optimal. For that reason, a model is designed to optimize vehicle allocation to predetermined routes in order to keep the GHG emis- sions to a minimum.
This optimization model requires a method on how to determine the GHG emissions values per driven kilo- meter and energy source used by the vehicle. People tend to think solely about CO2 when talking about environmental harming gasses. However, the largest human influence of global warming is not caused by CO2 alone, but through a combination of gasses, summarized under the term greenhouse gasses. The sum of emissions caused by these gasses is standardised with the help of a Global Warming Potential (GWP), and is presented as CO2 equivalents. Calculating and reporting on these GHG emissions can be done according to a variety of methodologies and standards.In order to achieve sector wide comparable results, the EN 16258 standard, issued by the European Committee for Standardization, is used as a guideline to specify emission values.
The optimization model is setup to cope with a heterogeneous vehicle fleet. A heterogeneous vehicle fleet is considered a fleet where multiple vehicle types exist with individual characteristics in terms of range and parcel capacity. It also considers the possibility that a vehicle is able to drive multiple routes on a single given day, taking a decreasing range in to account.
The effect of the optimization model is determined with the help of adequate Key Performance Indicators (KPIs). These KPIs are total GHG emissions, total energy consumption, capacity utilization, electric vehicle utilization ratio, fuel costs and network performance. The network performance calculates the emitted grams of GHG emissions per parcel. In order to create a more insightful result the result of the KPI is rearranged to represent the number of parcels delivered per kg emitted GHG emissions.
The impact of the optimization model on these KPIs was simulated and compared in a LSP’s last mile delivery network. Results from the case study showed that allocating vehicles to routes on a day to day basis, instead of a fixed schedule, improves the environmental impact as the total GHG emissions decreased and the network performance improved. Depots with higher average route distance had a negative impact when accounting for a decrease in battery capacity of electric vehicles, concluding that the model should account for cold win- ter days. By allowing vehicles to charge between routes during a single given day, depending on the charging power and time only showed a positive effect at depots with these longer average route distances. Finally, as the total fuel costs also decreased, it was concluded that no argument can be given that the model might impose extra costs, and thus a reason for not implementing it.
Although the case study showed promising results for the scenario where vehicles are to roam freely between depots of a LSP at the end of each day, emissions caused by the relocation of these vehicles cancels out the achieved savings. For future research it is therefor recommended to add these relocation emissions so that a truly dynamic vehicle fleet for depots in vicinity of each other can be established.

logistics
GHG emissions reduction
Last Mile
Optimization model

http://resolver.tudelft.nl/uuid:fe0d0502-1799-491d-88fe-e26f209a1157

Student theses

master thesis

© 2020 Dick van Hemert