TY - GEN
T1 - Adaptation of a Collaborative Truck and Robotic Vehicle for Sustainable Supply Chain Operations
AU - Foumani, Mehdi
PY - 2024
Y1 - 2024
N2 - Supply chains and their distribution centres today are under high pressure from carbon markets to manage energy resilience. With the focus on inbound and outbound logistics, this paper proposes a modelling framework to consider operational and environmental challenges of scheduling smart trucks with supporting robotic vehicles. Specifically, we consider a warehouse or distribution center with smart trucks to optimize the driving speed of each truck on each route segment, supported by energy-efficient robotic vehicles. Considering the distance-limited radius, the truck dispatches robotic vehicles at a location to deliver parcels to customers within that vicinity, and then collect them at the same location. A mixed-integer linear programming (MILP) is initially developed adapting the goal as the minimization of the weighted sum of delivery completion time and energy consumption. Using the available solution methods, we explore their suitability for randomly generated instances of the network typology. The results reveal insights for logistics systems to use robotic vehicles as a solution within a supply chain network context.
AB - Supply chains and their distribution centres today are under high pressure from carbon markets to manage energy resilience. With the focus on inbound and outbound logistics, this paper proposes a modelling framework to consider operational and environmental challenges of scheduling smart trucks with supporting robotic vehicles. Specifically, we consider a warehouse or distribution center with smart trucks to optimize the driving speed of each truck on each route segment, supported by energy-efficient robotic vehicles. Considering the distance-limited radius, the truck dispatches robotic vehicles at a location to deliver parcels to customers within that vicinity, and then collect them at the same location. A mixed-integer linear programming (MILP) is initially developed adapting the goal as the minimization of the weighted sum of delivery completion time and energy consumption. Using the available solution methods, we explore their suitability for randomly generated instances of the network typology. The results reveal insights for logistics systems to use robotic vehicles as a solution within a supply chain network context.
M3 - Conference Proceeding
T3 - Lecture Notes in Networks and Systems
BT - Lecture Notes in Networks and Systems
PB - Springer
ER -
