TY - JOUR
T1 - Adaptive large neighborhood search for the time-dependent profitable pickup and delivery problem with time windows
AU - Sun, Peng
AU - Veelenturf, Luuk P.
AU - Hewitt, M.
AU - van Woensel, Tom
PY - 2020/6
Y1 - 2020/6
N2 - The rise of e-commerce has increased the demands placed on pickup and delivery operations, as well as customer expectations regarding the quality of services provided by those operations. One strategy a logistics provider can employ for meeting these increases in demands and expectations is to complement and coordinate its fleet operations with those of for-hire, third-party logistics providers. Herein, we study an optimization problem for coordinating these operations: the time-dependent profitable pickup and delivery problem with time windows. In this problem, the logistics provider has the opportunity to use its fleet of capacitated vehicles to transport shipment requests, for a profit, from pickup to delivery locations. Owing to demographic and market trends, we focus on an urban setting, wherein road congestion is a factor. As a result, the problem explicitly recognizes that travel times may be time-dependent. The logistics provider seeks to maximize its profits from serving transportation requests, which we compute as the difference between the profits associated with transported requests and transportation costs. To solve this problem, we propose an adaptive large neighborhood search algorithm. The results of our extensive computational study show that the proposed algorithm can find high-quality solutions quickly on instances with up to 75 transportation requests. Furthermore, we study its impact on profits when explicitly recognizing traffic congestion during planning operations.
AB - The rise of e-commerce has increased the demands placed on pickup and delivery operations, as well as customer expectations regarding the quality of services provided by those operations. One strategy a logistics provider can employ for meeting these increases in demands and expectations is to complement and coordinate its fleet operations with those of for-hire, third-party logistics providers. Herein, we study an optimization problem for coordinating these operations: the time-dependent profitable pickup and delivery problem with time windows. In this problem, the logistics provider has the opportunity to use its fleet of capacitated vehicles to transport shipment requests, for a profit, from pickup to delivery locations. Owing to demographic and market trends, we focus on an urban setting, wherein road congestion is a factor. As a result, the problem explicitly recognizes that travel times may be time-dependent. The logistics provider seeks to maximize its profits from serving transportation requests, which we compute as the difference between the profits associated with transported requests and transportation costs. To solve this problem, we propose an adaptive large neighborhood search algorithm. The results of our extensive computational study show that the proposed algorithm can find high-quality solutions quickly on instances with up to 75 transportation requests. Furthermore, we study its impact on profits when explicitly recognizing traffic congestion during planning operations.
KW - ALNS
KW - Pickup and delivery problem
KW - Profitable
KW - Time-dependent travel time
UR - http://www.scopus.com/inward/record.url?scp=85084660772&partnerID=8YFLogxK
U2 - 10.1016/j.tre.2020.101942
DO - 10.1016/j.tre.2020.101942
M3 - Article
AN - SCOPUS:85084660772
VL - 138
JO - Transportation Research. Part E: Logistics and Transportation Review
JF - Transportation Research. Part E: Logistics and Transportation Review
SN - 1366-5545
M1 - 101942
ER -