Multi-period hub network design from a dual perspective: An integrated approach considering congestion, demand uncertainty, and service quality optimization

This study introduces a hub network design problem that considers three key factors: congestion, demand uncertainty, and multi-periodicity. Unlike classical models, which tend to address these factors separately, our model considers them simultaneously, providing a more realistic representation of h...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:European journal of operational research Jg. 326; H. 1; S. 78 - 95
Hauptverfasser: Bayram, Vedat, Aydoğan, Çiya, Kargar, Kamyar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.10.2025
Schlagworte:
Benders decomposition
Column generation
Congestion
Location
Multi-period
Second-order cone programming
Service level
Stochastic programming
Service level
Column generation
Benders decomposition
Congestion
Multi-period
Second-order cone programming
Location
Stochastic programming
ISSN:0377-2217
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This study introduces a hub network design problem that considers three key factors: congestion, demand uncertainty, and multi-periodicity. Unlike classical models, which tend to address these factors separately, our model considers them simultaneously, providing a more realistic representation of hub network design challenges. Our model also incorporates service level considerations of network users, extending beyond the focus on transportation costs. Service quality is evaluated using two measures: travel time and the number of hubs visited during travel. Moreover, our model allows for adjustments in capacity levels and network structure throughout the planning horizon, adding a dynamic and realistic aspect to the problem setting. The inherent nonlinear nonconvex integer programming problem is reformulated into a mixed-integer second-order cone programming (SOCP) problem. To manage the model’s complexity, we propose an exact solution algorithm based on Benders decomposition, where the sub-problems are solved using a column generation technique. The efficacy of the solution approach is demonstrated through extensive computational experiments. Additionally, we discuss the benefits of each considered feature in terms of transportation costs and their impact on network structure, providing insights for the field. •A hub network design model addressing congestion, uncertainty, and multi-periodicity.•Service level considerations balance cost minimization with user restrictions.•A multi-period framework for dynamic hub location, capacity, and assignment changes.•Formulated as MISOCP; solved via Benders decomposition with column generation.•Experiments reveal trade-offs between cost, congestion, and network evolution.
ISSN:0377-2217
DOI:10.1016/j.ejor.2025.04.011