A scenario-based planning for the pickup and delivery problem with time windows, scheduled lines and stochastic demands

Veaceslav Ghilas; Emrah Demir; Tom Van Woensel

doi:10.1016/j.trb.2016.04.015

A scenario-based planning for the pickup and delivery problem with time windows, scheduled lines and stochastic demands

Veaceslav Ghilas, Emrah Demir, Tom Van Woensel

Management Science & Engineering

Eindhoven University of Technology

Research output: Contribution to journal › Article › peer-review

77 Scopus citations

Abstract

The Pickup and Delivery Problem with Time Windows, Scheduled Lines and Stochastic Demands (PDPTW-SLSD) concerns scheduling a set of vehicles to serve a set of requests, whose expected demands are known in distribution when planning, but are only revealed with certainty upon the vehicles' arrival. In addition, a part of the transportation plan can be carried out on limited-capacity scheduled public transportation line services. This paper proposes a scenario-based sample average approximation approach for the PDPTW-SLSD. An adaptive large neighborhood search heuristic embedded into sample average approximation method is used to generate good-quality solutions. Computational results on instances with up to 40 requests (i.e., 80 locations) reveal that the integrated transportation networks can lead to operational cost savings of up to 16% compared with classical pickup and delivery systems.

Original language	British English
Pages (from-to)	34-51
Number of pages	18
Journal	Transportation Research Part B: Methodological
Volume	91
DOIs	https://doi.org/10.1016/j.trb.2016.04.015
State	Published - 1 Sep 2016

Keywords

Freight transportation
Heuristic algorithm
Pickup and delivery problem
Scheduled lines
Stochastic demands

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10.1016/j.trb.2016.04.015

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title = "A scenario-based planning for the pickup and delivery problem with time windows, scheduled lines and stochastic demands",

abstract = "The Pickup and Delivery Problem with Time Windows, Scheduled Lines and Stochastic Demands (PDPTW-SLSD) concerns scheduling a set of vehicles to serve a set of requests, whose expected demands are known in distribution when planning, but are only revealed with certainty upon the vehicles' arrival. In addition, a part of the transportation plan can be carried out on limited-capacity scheduled public transportation line services. This paper proposes a scenario-based sample average approximation approach for the PDPTW-SLSD. An adaptive large neighborhood search heuristic embedded into sample average approximation method is used to generate good-quality solutions. Computational results on instances with up to 40 requests (i.e., 80 locations) reveal that the integrated transportation networks can lead to operational cost savings of up to 16% compared with classical pickup and delivery systems.",

keywords = "Freight transportation, Heuristic algorithm, Pickup and delivery problem, Scheduled lines, Stochastic demands",

author = "Veaceslav Ghilas and Emrah Demir and Woensel, {Tom Van}",

note = "Funding Information: The authors gratefully acknowledge funds provided by the Dutch Institute for Advanced Logistics (DINALOG) , under the grant titled “Cargo Hitching” , # 2011 4 086R. Thanks are due to the Editor-in-chief, Area Editor, and three anonymous reviewers for their useful comments and for raising interesting points for discussion. Publisher Copyright: {\textcopyright} 2016 Elsevier Ltd.",

year = "2016",

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doi = "10.1016/j.trb.2016.04.015",

language = "British English",

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journal = "Transportation Research Part B: Methodological",

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AU - Ghilas, Veaceslav

AU - Demir, Emrah

AU - Woensel, Tom Van

N1 - Funding Information: The authors gratefully acknowledge funds provided by the Dutch Institute for Advanced Logistics (DINALOG) , under the grant titled “Cargo Hitching” , # 2011 4 086R. Thanks are due to the Editor-in-chief, Area Editor, and three anonymous reviewers for their useful comments and for raising interesting points for discussion. Publisher Copyright: © 2016 Elsevier Ltd.

PY - 2016/9/1

Y1 - 2016/9/1

N2 - The Pickup and Delivery Problem with Time Windows, Scheduled Lines and Stochastic Demands (PDPTW-SLSD) concerns scheduling a set of vehicles to serve a set of requests, whose expected demands are known in distribution when planning, but are only revealed with certainty upon the vehicles' arrival. In addition, a part of the transportation plan can be carried out on limited-capacity scheduled public transportation line services. This paper proposes a scenario-based sample average approximation approach for the PDPTW-SLSD. An adaptive large neighborhood search heuristic embedded into sample average approximation method is used to generate good-quality solutions. Computational results on instances with up to 40 requests (i.e., 80 locations) reveal that the integrated transportation networks can lead to operational cost savings of up to 16% compared with classical pickup and delivery systems.

AB - The Pickup and Delivery Problem with Time Windows, Scheduled Lines and Stochastic Demands (PDPTW-SLSD) concerns scheduling a set of vehicles to serve a set of requests, whose expected demands are known in distribution when planning, but are only revealed with certainty upon the vehicles' arrival. In addition, a part of the transportation plan can be carried out on limited-capacity scheduled public transportation line services. This paper proposes a scenario-based sample average approximation approach for the PDPTW-SLSD. An adaptive large neighborhood search heuristic embedded into sample average approximation method is used to generate good-quality solutions. Computational results on instances with up to 40 requests (i.e., 80 locations) reveal that the integrated transportation networks can lead to operational cost savings of up to 16% compared with classical pickup and delivery systems.

KW - Freight transportation

KW - Heuristic algorithm

KW - Pickup and delivery problem

KW - Scheduled lines

KW - Stochastic demands

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VL - 91

SP - 34

EP - 51

JO - Transportation Research Part B: Methodological

JF - Transportation Research Part B: Methodological

ER -

A scenario-based planning for the pickup and delivery problem with time windows, scheduled lines and stochastic demands

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Keywords

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