TY - GEN
T1 - Adaptation of a Collaborative Truck and Robotic Vehicle for Sustainable Supply Chain Operations
AU - Foumani, Mehdi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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.
KW - distribution centers
KW - energy solutions
KW - inbound and outbound logistics
KW - mixed-integer programming
KW - supply chains
KW - sustainability
UR - http://www.scopus.com/inward/record.url?scp=85210805696&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70684-4_24
DO - 10.1007/978-3-031-70684-4_24
M3 - Conference Proceeding
AN - SCOPUS:85210805696
SN - 9783031706837
T3 - Lecture Notes in Networks and Systems
SP - 289
EP - 301
BT - Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
A2 - Abdul Majeed, Anwar P.P.
A2 - Yap, Eng Hwa
A2 - Liu, Pengcheng
A2 - Huang, Xiaowei
A2 - Nguyen, Anh
A2 - Chen, Wei
A2 - Kim, Ue-Hwan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
Y2 - 6 December 2023 through 8 December 2023
ER -
