ID: 173
/ D1S1T2: 1
Abstract LDIC Submission (for presentation only)
LDIC 2024 Topics: Sustainable and green logistics, Multi-modal transportation networks, Urban logisticsKeywords: City logistics, urban logistics, last mile, consolidation, sustainability
New approaches to city logistics
Quiter, Daniel; Engelhardt, Maximilian; Malzahn, Birte; Seeck, Stephan
Hochschule für Technik und Wirtschaft Berlin, Germany
City logistics has changed fundamentally since the 1990s, driven by digitization, changing customer demands, and the urgency of environmental protection. Earlier approaches such as freight distribution centers could not cope with the challenges, leading to new concepts.
This presentation analyzes the changes in city logistics and examines why earlier approaches such as freight distribution centers failed. It highlights current conditions, including the impact of urbanization and changing consumer behavior. Furthermore, it introduces a transfer roadmap - a strategic instrument to help companies implement multi-level logistics networks including sustainable transportation. The roadmap aims to minimize risks and ensure the success of city logistics solutions.
With a focus on practical examples and best practices, this paper will highlight the relevance of city logistics in today's environment and provide approaches to how companies can effectively respond to the changing climate. The presentation at the conference will offer the opportunity to share key insights and experiences in the field of city logistics and to work together on sustainable solutions to urban freight transport problems.
ID: 170
/ D1S1T2: 2
Abstract LDIC Submission (for presentation only)
LDIC 2024 Topics: Modeling and optimization methods, Uncertainty, risk, resilience, and performance, Sustainable and green logistics, Dynamics and complexity, Urban logisticsKeywords: dynamic, ridepooling, lookahead
Insertions with lookahead for dynamic ridepooling services
Schulz, Arne; Pfeiffer, Christian
Universität Hamburg, Germany
Due to the required reduction of emissions, modern mobility concepts are rapidly evolving. Ridepooling is one of these concepts. Beside the reduction of emissions due to electric vehicles, ridepooling services promise to reduce traffic due to pooling and to increase mobility access especially in suburban areas. In practice, ridepooling services receive customer orders dynamically and thus have to integrate them in the vehicles’ tours. In this talk, we discuss an efficient procedure to insert new customer requests into given tours while incorporating possible future customers with the objective to serve as many customer requests as possible over the time horizon.
ID: 149
/ D1S1T2: 3
Full Paper LDIC Submission
LDIC 2024 Topics: Modeling and optimization methods, Dynamics and complexity, Urban logisticsKeywords: Urban Air Logistics, Continuous approximation, Drone ports, UAM, Drones
Continuous Approximation Approach to Determine the Optimal Service Area for a Drone Port in Urban Air Logistics.
Jasmine, Arunika; Adikariwattage, Varuna Viraj; Rifan, Rafhan
University of Moratuwa, Sri Lanka
The aviation sector employs innovative technical involvements, applications, and operational practices. As a result, unmanned aerial vehicles that are remotely piloted from a ground station usher in the next phase of both passenger and freight transportation. This study is focused on freight transportation using drones. Although many studies in the past have focused on various drone delivery configurations, this study finds a critical research gap when evaluating the drone port location problem for a set of centralized ports where service is shared among multiple demand generators. Addressing the research gap, this study adapts the approach of continuous approximation (CA) in model development to find the optimum area allocated to a centralized drone port in an urban area. Findings indicate that the drone service range is a limiting factor for the optimal service area of the drone port. Furthermore, it was revealed that the optimal service area and the minimum total delivery operation cost have a low sensitivity to factors such as the shape of the service area, demand density and travel cost per unit distance.
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