A Lagrangian heuristic and GRASP for the hub-and-spoke network system with economies-of-scale and congestion

Faisal Alkaabneh, Ali Diabat, Samir Elhedhli

Research output: Contribution to journalArticle

Abstract

We consider a hub-and-spoke network design problem with inter-hub economies-of-scale and hub congestion. We explicitly model the economies-of-scale as a concave piece-wise linear function and congestion as a convex function. The problem is modeled as a nonlinear mixed integer program that is difficult to solve directly since the objective function has both convex and concave nonlinear terms and hence finding an optimal solution may not be easy. A Lagrangian approach is proposed to obtain tight upper and lower bounds. The Lagrangian decomposition exploits the structure of the problem and decomposes it to convex and concave subproblems. Furthermore, we add some valid inequalities to accelerate the convergence rate of the Lagrangian heuristic. To tackle large instances, a Greedy Randomized Adaptive Search Procedure (GRASP) is developed. Both the Lagrangian heuristic and GRASP provide high-quality solutions within reasonable computational time. The optimal designs of hub-and-spoke networks with nonlinear inter-hub economies-of-scale and congestion at hub locations are analyzed in comparison with other models in the literature to demonstrate the significant impact of modeling nonlinearity in economies-of-scale and congestion simultaneously rather than independently.

Original languageEnglish (US)
Pages (from-to)249-273
Number of pages25
JournalTransportation Research Part C: Emerging Technologies
Volume102
DOIs
StatePublished - May 1 2019

Fingerprint

heuristics
economy
Decomposition
Optimal design

Keywords

  • Congestion
  • Economies-of-scale
  • GRASP
  • Hub-and-Spoke design network
  • Lagrangian relaxation
  • Mixed Integer Nonlinear programming

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

Cite this

A Lagrangian heuristic and GRASP for the hub-and-spoke network system with economies-of-scale and congestion. / Alkaabneh, Faisal; Diabat, Ali; Elhedhli, Samir.

In: Transportation Research Part C: Emerging Technologies, Vol. 102, 01.05.2019, p. 249-273.

Research output: Contribution to journalArticle

@article{6bbb7e7d66904117b0e7c25d57a51e79,
title = "A Lagrangian heuristic and GRASP for the hub-and-spoke network system with economies-of-scale and congestion",
abstract = "We consider a hub-and-spoke network design problem with inter-hub economies-of-scale and hub congestion. We explicitly model the economies-of-scale as a concave piece-wise linear function and congestion as a convex function. The problem is modeled as a nonlinear mixed integer program that is difficult to solve directly since the objective function has both convex and concave nonlinear terms and hence finding an optimal solution may not be easy. A Lagrangian approach is proposed to obtain tight upper and lower bounds. The Lagrangian decomposition exploits the structure of the problem and decomposes it to convex and concave subproblems. Furthermore, we add some valid inequalities to accelerate the convergence rate of the Lagrangian heuristic. To tackle large instances, a Greedy Randomized Adaptive Search Procedure (GRASP) is developed. Both the Lagrangian heuristic and GRASP provide high-quality solutions within reasonable computational time. The optimal designs of hub-and-spoke networks with nonlinear inter-hub economies-of-scale and congestion at hub locations are analyzed in comparison with other models in the literature to demonstrate the significant impact of modeling nonlinearity in economies-of-scale and congestion simultaneously rather than independently.",
keywords = "Congestion, Economies-of-scale, GRASP, Hub-and-Spoke design network, Lagrangian relaxation, Mixed Integer Nonlinear programming",
author = "Faisal Alkaabneh and Ali Diabat and Samir Elhedhli",
year = "2019",
month = "5",
day = "1",
doi = "10.1016/j.trc.2018.12.011",
language = "English (US)",
volume = "102",
pages = "249--273",
journal = "Transportation Research Part C: Emerging Technologies",
issn = "0968-090X",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - A Lagrangian heuristic and GRASP for the hub-and-spoke network system with economies-of-scale and congestion

AU - Alkaabneh, Faisal

AU - Diabat, Ali

AU - Elhedhli, Samir

PY - 2019/5/1

Y1 - 2019/5/1

N2 - We consider a hub-and-spoke network design problem with inter-hub economies-of-scale and hub congestion. We explicitly model the economies-of-scale as a concave piece-wise linear function and congestion as a convex function. The problem is modeled as a nonlinear mixed integer program that is difficult to solve directly since the objective function has both convex and concave nonlinear terms and hence finding an optimal solution may not be easy. A Lagrangian approach is proposed to obtain tight upper and lower bounds. The Lagrangian decomposition exploits the structure of the problem and decomposes it to convex and concave subproblems. Furthermore, we add some valid inequalities to accelerate the convergence rate of the Lagrangian heuristic. To tackle large instances, a Greedy Randomized Adaptive Search Procedure (GRASP) is developed. Both the Lagrangian heuristic and GRASP provide high-quality solutions within reasonable computational time. The optimal designs of hub-and-spoke networks with nonlinear inter-hub economies-of-scale and congestion at hub locations are analyzed in comparison with other models in the literature to demonstrate the significant impact of modeling nonlinearity in economies-of-scale and congestion simultaneously rather than independently.

AB - We consider a hub-and-spoke network design problem with inter-hub economies-of-scale and hub congestion. We explicitly model the economies-of-scale as a concave piece-wise linear function and congestion as a convex function. The problem is modeled as a nonlinear mixed integer program that is difficult to solve directly since the objective function has both convex and concave nonlinear terms and hence finding an optimal solution may not be easy. A Lagrangian approach is proposed to obtain tight upper and lower bounds. The Lagrangian decomposition exploits the structure of the problem and decomposes it to convex and concave subproblems. Furthermore, we add some valid inequalities to accelerate the convergence rate of the Lagrangian heuristic. To tackle large instances, a Greedy Randomized Adaptive Search Procedure (GRASP) is developed. Both the Lagrangian heuristic and GRASP provide high-quality solutions within reasonable computational time. The optimal designs of hub-and-spoke networks with nonlinear inter-hub economies-of-scale and congestion at hub locations are analyzed in comparison with other models in the literature to demonstrate the significant impact of modeling nonlinearity in economies-of-scale and congestion simultaneously rather than independently.

KW - Congestion

KW - Economies-of-scale

KW - GRASP

KW - Hub-and-Spoke design network

KW - Lagrangian relaxation

KW - Mixed Integer Nonlinear programming

UR - http://www.scopus.com/inward/record.url?scp=85063077528&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063077528&partnerID=8YFLogxK

U2 - 10.1016/j.trc.2018.12.011

DO - 10.1016/j.trc.2018.12.011

M3 - Article

AN - SCOPUS:85063077528

VL - 102

SP - 249

EP - 273

JO - Transportation Research Part C: Emerging Technologies

JF - Transportation Research Part C: Emerging Technologies

SN - 0968-090X

ER -