The process of IPGA.

<div><p>Pickup and delivery problem (PDP) and dynamic vehicle routing problem (DVRP) are two key components of crowdsourced freight delivery services. Although previous research has focused predominantly on static vehicle routing problems, this study formally defines the dynamic problem...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Jingxian Zhang (83932) (author)
منشور في: 2025
الموضوعات:
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
_version_ 1852022496683884544
author Jingxian Zhang (83932)
author_facet Jingxian Zhang (83932)
author_role author
dc.creator.none.fl_str_mv Jingxian Zhang (83932)
dc.date.none.fl_str_mv 2025-02-24T18:50:01Z
dc.identifier.none.fl_str_mv 10.1371/journal.pone.0318432.g004
dc.relation.none.fl_str_mv https://figshare.com/articles/figure/The_process_of_IPGA_/28476331
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Medicine
Science Policy
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
two key components
study formally defines
simulated annealing algorithm
numerical experiments demonstrate
although previous research
dynamic problem specific
combinatorial optimization problem
total service costs
crowdsourced freight delivery
xlink "> pickup
delivery problem
transportation costs
penalty costs
delivery planning
solid foundation
practical applicability
planning routes
horizon framework
focused predominantly
dc.title.none.fl_str_mv The process of IPGA.
dc.type.none.fl_str_mv Image
Figure
info:eu-repo/semantics/publishedVersion
image
description <div><p>Pickup and delivery problem (PDP) and dynamic vehicle routing problem (DVRP) are two key components of crowdsourced freight delivery services. Although previous research has focused predominantly on static vehicle routing problems, this study formally defines the dynamic problem specific to crowdsourced freight delivery and presents a mixed-integer linear programming model based on a rolling-horizon framework. The objective is to minimize total service costs, including fixed vehicle costs, transportation costs, and penalty costs for delays, while planning routes that cover all orders. To solve this combinatorial optimization problem, we propose an improved partheno genetic algorithm (IPGA) and a simulated annealing algorithm (SA). Numerical experiments demonstrate that the IPGA outperforms the SA, reducing the total service costs by over 10% on average. In addition, a real-world case study illustrates the practical applicability of our model and algorithms, providing a solid foundation for real-world implementation.</p></div>
eu_rights_str_mv openAccess
id Manara_759d6c47ed8497f033697c551bdfcdee
identifier_str_mv 10.1371/journal.pone.0318432.g004
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/28476331
publishDate 2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling The process of IPGA.Jingxian Zhang (83932)MedicineScience PolicyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedtwo key componentsstudy formally definessimulated annealing algorithmnumerical experiments demonstratealthough previous researchdynamic problem specificcombinatorial optimization problemtotal service costscrowdsourced freight deliveryxlink "> pickupdelivery problemtransportation costspenalty costsdelivery planningsolid foundationpractical applicabilityplanning routeshorizon frameworkfocused predominantly<div><p>Pickup and delivery problem (PDP) and dynamic vehicle routing problem (DVRP) are two key components of crowdsourced freight delivery services. Although previous research has focused predominantly on static vehicle routing problems, this study formally defines the dynamic problem specific to crowdsourced freight delivery and presents a mixed-integer linear programming model based on a rolling-horizon framework. The objective is to minimize total service costs, including fixed vehicle costs, transportation costs, and penalty costs for delays, while planning routes that cover all orders. To solve this combinatorial optimization problem, we propose an improved partheno genetic algorithm (IPGA) and a simulated annealing algorithm (SA). Numerical experiments demonstrate that the IPGA outperforms the SA, reducing the total service costs by over 10% on average. In addition, a real-world case study illustrates the practical applicability of our model and algorithms, providing a solid foundation for real-world implementation.</p></div>2025-02-24T18:50:01ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0318432.g004https://figshare.com/articles/figure/The_process_of_IPGA_/28476331CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284763312025-02-24T18:50:01Z
spellingShingle The process of IPGA.
Jingxian Zhang (83932)
Medicine
Science Policy
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
two key components
study formally defines
simulated annealing algorithm
numerical experiments demonstrate
although previous research
dynamic problem specific
combinatorial optimization problem
total service costs
crowdsourced freight delivery
xlink "> pickup
delivery problem
transportation costs
penalty costs
delivery planning
solid foundation
practical applicability
planning routes
horizon framework
focused predominantly
status_str publishedVersion
title The process of IPGA.
title_full The process of IPGA.
title_fullStr The process of IPGA.
title_full_unstemmed The process of IPGA.
title_short The process of IPGA.
title_sort The process of IPGA.
topic Medicine
Science Policy
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
two key components
study formally defines
simulated annealing algorithm
numerical experiments demonstrate
although previous research
dynamic problem specific
combinatorial optimization problem
total service costs
crowdsourced freight delivery
xlink "> pickup
delivery problem
transportation costs
penalty costs
delivery planning
solid foundation
practical applicability
planning routes
horizon framework
focused predominantly