A branch-and-cut-and-price approach for the pickup and delivery problem with shuttle routes

•We introduce the Pickup and Delivery Problem with Shuttle routes (PDPS).•We propose one arc based model and two path based model for the PDPS.•We develop a branch-and-cut-and-price algorithm.•The method is evaluated on generated and real-world instances with up to 193 requests.•Instances with up to...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:European journal of operational research 2014-08, Vol.236 (3), p.849-862
Hauptverfasser: Masson, Renaud, Ropke, Stefan, Lehuédé, Fabien, Péton, Olivier
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•We introduce the Pickup and Delivery Problem with Shuttle routes (PDPS).•We propose one arc based model and two path based model for the PDPS.•We develop a branch-and-cut-and-price algorithm.•The method is evaluated on generated and real-world instances with up to 193 requests.•Instances with up to 87 requests are solved to optimality in less than one hour. The Pickup and Delivery Problem with Shuttle routes (PDPS) is a special case of the Pickup and Delivery Problem with Time Windows (PDPTW) where the trips between the pickup points and the delivery points can be decomposed into two legs. The first leg visits only pickup points and ends at some delivery point. The second leg is a direct trip – called a shuttle – between two delivery points. This optimization problem has practical applications in the transportation of people between a large set of pickup points and a restricted set of delivery points. This paper proposes three mathematical models for the PDPS and a branch-and-cut-and-price algorithm to solve it. The pricing sub-problem, an Elementary Shortest Path Problem with Resource Constraints (ESPPRC), is solved with a labeling algorithm enhanced with efficient dominance rules. Three families of valid inequalities are used to strengthen the quality of linear relaxations. The method is evaluated on generated and real-world instances containing up to 193 transportation requests. Instances with up to 87 customers are solved to optimality within a computation time of one hour.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2013.08.042