TY - GEN
T1 - Enhancing Inter-Terminal Transport via Early Information
AU - Brunetti, Matteo
AU - Lalla-Ruiz, Eduardo
AU - Mes, Martijn
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/8/31
Y1 - 2024/8/31
N2 - We focus on decoupling decisions of inter-terminal transport (ITT) vehicles at a logistics node, such as a port or business park. We assume the node faces a stochastic flow of trucks delivering and retrieving containers from logistics companies (LCs), i.e., warehouses and terminals. During peak hours, trucks may stop at a parking area, where the ITT fleet, composed of electric and automated vehicles (EAVs), takes over the transport of containers between the parking and the LCs. The decoupling decision (DD) determines whether trucks should proceed to their LC or park. The decision model is based on few parameters, such as the estimated workload of the ITT fleet over a time window. We integrate the decision model into a discrete event simulation of the Port of Moerdijk, the Netherlands, allowing experimentation with various arrival patterns, earliness of information, and DD parameters. The simulation involves a realistic traffic simulation, more than 130 LCs, and up to 100 EAVs. Through parameter calibration, the decision model capitalizes on early information to improve service levels and reduce kilometers driven by conventional trucks.
AB - We focus on decoupling decisions of inter-terminal transport (ITT) vehicles at a logistics node, such as a port or business park. We assume the node faces a stochastic flow of trucks delivering and retrieving containers from logistics companies (LCs), i.e., warehouses and terminals. During peak hours, trucks may stop at a parking area, where the ITT fleet, composed of electric and automated vehicles (EAVs), takes over the transport of containers between the parking and the LCs. The decoupling decision (DD) determines whether trucks should proceed to their LC or park. The decision model is based on few parameters, such as the estimated workload of the ITT fleet over a time window. We integrate the decision model into a discrete event simulation of the Port of Moerdijk, the Netherlands, allowing experimentation with various arrival patterns, earliness of information, and DD parameters. The simulation involves a realistic traffic simulation, more than 130 LCs, and up to 100 EAVs. Through parameter calibration, the decision model capitalizes on early information to improve service levels and reduce kilometers driven by conventional trucks.
KW - 2025 OA procedure
KW - Decoupling
KW - Electric vehicles
KW - Inter-terminal transport
KW - Logistics nodes
KW - Connected automated transport
UR - http://www.scopus.com/inward/record.url?scp=85212481572&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-61589-4_18
DO - 10.1007/978-3-031-61589-4_18
M3 - Conference contribution
AN - SCOPUS:85212481572
SN - 978-3-031-61588-7
T3 - Lecture Notes in Operations Research
SP - 215
EP - 227
BT - Business Analytics and Decision Making in Practice
PB - Springer Nature Brazil
T2 - International Conference on Business Analytics in Practice, ICBAP 2024
Y2 - 8 January 2024 through 11 January 2024
ER -
