Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Session Overview
D1S2T1: City and Last Mile Logistics
Wednesday, 23/Feb/2022:
1:30pm - 2:30pm

Session Chair: Ingrid Rügge

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ID: 121 / D1S2T1: 1
Full Paper LDIC Submission
LDIC 2022 Topics: Artificial Intelligence, Big Data and IT Platforms
Keywords: Artificial Intelligence, Urban Logistics, Last Mile Delivery, Sustainability, Explorative Expert Interviews

Artificial Intelligence in Urban Last Mile Logistics - Status Quo, Potentials and Key Challenges

Engelhardt, Maximilian; Seeck, Stephan; Geier, Ben

HTW Berlin, Germany

Artificial Intelligence (AI) has the potential to solve the sustainability and service issues of Urban Last Mile Logistics (ULML). High delivery costs, noisy and polluting traffic, bad working conditions and failed delivery attempts could be addressed by measures like AI-based demand forecasting, intelligent tour and route optimization or digital delivery assistance. However, there is little empirical evidence on the extent to which AI can do this. Thus, the purpose of this report is to elaborate the relevance of AI for solving ULML problems by identifying use cases, potentials and challenges of implementing AI in ULML planning and execution. Therefore, we conducted 15 explorative expert interviews with ULML companies and analyzed them using qualitative content analysis to obtain an initial orientation in this new empirical research field. The findings indicate, among others, that the ULML industry is in the very early stages of AI implementation and that there is relevant potential for efficiency and service improvement. However, one of the key challenges is the perceived high level of uncertainty about achieving economic benefits while having high investment and AI operating costs. The practical contribution of this paper is to provide guidance for ULML companies starting AI activities. The scientific contribution is to show the practical need for AI implementation and to derive concrete research needs for the development of suitable AI methods and algorithms.

ID: 155 / D1S2T1: 2
Abstract LDIC Submission (for presentation only)
LDIC 2022 Topics: Modeling and Optimization Methods
Keywords: City Logistics, Routing, Mixed-Integer Programming

Evaluation of combined distribution systems for city logistics

Himstedt, Barbara; Meisel, Frank

Christian-Albrechts-Universität zu Kiel, Germany

In order to cope with the ever-increasing volume of shipments over the last mile and to meet the increased demands of customer-friendly yet sustainable delivery, many urban logistics providers are already using cargo bikes, and delivery by autonomous robots or drones is also under research and testing. Each of these has its own characteristics and, thus, individual advantages and disadvantages, which is why a combination of different distribution systems could prove advantageous. To investigate which of these options should be seen as complementary or rather substitutive, we have developed a MILP-Model in which they can be linked together in a modular way. A first variant of our model provides for a 2-tier distribution, where vans are used at the first echelon both to deliver to a depot within the city and to drop off mobile hubs. At the second echelon, different combinations of cargo bikes, robots and drones serve customers and, if mobile hubs have been put off, can pick up further parcels there. The second variant of our model is similar, but vehicles being used at the first echelon can also serve customers at the second echelon. Preliminary results from realistic data simulations indicate that supporting bike deliveries by drones, robots or combinations of these can lead to cost savings in the two echelon model, even taking into account different cost structures and differing road infrastructure. If vans can be used to deliver to customers, the smaller delivery vehicles are hardly profitable.

ID: 150 / D1S2T1: 3
Full Paper LDIC Submission
LDIC 2022 Topics: Sustainable and Green Logistics, Humanitarian Logistics, Disaster Management and COVID-19 Logistics
Keywords: Sustainability, Last-Mile Delivery, Online Shopping

The Impact of the COVID-19 Pandemic on E-commerce Consumers’ Pro-Environmental Behavior

Koleva, Simona; Chankov, Stanislav

Jacobs University Bremen, Germany

The advent of COVID-19 led to an explosion of online shopping. As a result of the health crisis, companies reduced their priorities on environmental issues. However, consumers’ concern for sustainability is on the rise. Thus, the purpose of this paper is to examine the impact of the COVD-19 pandemic on e-commerce consumers’ pro-environmental behavior. Accordingly, we conduct an online survey exploring consumers’ online shopping frequency and engagement in environmentally-friendly practices before, during and after the COVD-19 pan-demic. Applying the Wilcoxon test to compare these three stages, we are able to investigate the shift in e-commerce consumer pro-environmental behavior trig-gered by COVID-19. The results indicate that the shopping frequency has in-creased substantially since the start of the pandemic, but will drop down after the end of the pandemic. Moreover, the COVID-19 pandemic was detrimental to consumers’ pro-environmental behavior: during the pandemic consumers showed a tendency towards less environmentally friendly behavior but they have strong intentions to adopt more eco-friendly practices after the pandemic ends.