Double-horizon based heuristics for the dynamic pickup and delivery problem with time windows
Snezana Mitrovic-Minic,
Ramesh Krishnamurti and
Gilbert Laporte
Transportation Research Part B: Methodological, 2004, vol. 38, issue 8, 669-685
Abstract:
The dynamic Pickup and Delivery Problem with Time Windows (PDPTW) is faced by courier companies serving same-day pickup and delivery requests for the transport of letters and small parcels. This article focuses on the dynamic PDPTW for which future requests are not stochastically modelled or predicted. The standard solution methodology for the dynamic PDPTW is the use of a rolling time horizon as proposed by Psaraftis. When assigning a new request to a vehicle it may be preferable to consider the impact of a decision both on a short-term and on a long-term horizon. In particular, better managing slack time in the distant future may help reduce routing cost. This paper describes double-horizon based heuristics for the dynamic PDPTW. Computational results show the advantage of using a double-horizon in conjunction with insertion and improvement heuristics.
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (66)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0191-2615(03)00095-X
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:transb:v:38:y:2004:i:8:p:669-685
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
Access Statistics for this article
Transportation Research Part B: Methodological is currently edited by Fred Mannering
More articles in Transportation Research Part B: Methodological from Elsevier
Bibliographic data for series maintained by Catherine Liu ().