Result: The vehicle routing problem with flexible time windows and traveling times

Title:
The vehicle routing problem with flexible time windows and traveling times
Source:
Discrete algorithms and optimization, in honor of professor Toshihide Ibaraki at his retirement from Kyoto UniversityDiscrete applied mathematics. 154(16):2271-2290
Publisher Information:
Amsterdam; Lausanne; New York, NY: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 25 ref
Original Material:
INIST-CNRS
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
Department of Informatics, School of Science and Technology, Kwansei Gakuin University, Japan
Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo, Japan
ISSN:
0166-218X
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Operational research. Management
Accession Number:
edscal.18240645
Database:
PASCAL Archive

Further Information

We generalize the standard vehicle routing problem by allowing soft time window and soft traveling time constraints, where both constraints are treated as cost functions. With the proposed generalization, the problem becomes very general. In our algorithm, we use local search to determine the routes of vehicles. After fixing the route of each vehicle, we must determine the optimal start times of services at visited customers. We show that this subproblem is NP-hard when cost functions are general, but can be efficiently solved with dynamic programming when traveling time cost functions are convex even if time window cost functions are non-convex. We deal with the latter situation in the developed iterated local search algorithm. Finally we report computational results on benchmark instances, and confirm the benefits of the proposed generalization.