Heuristics for a hub location‐routing problem

Abstract

We investigate a variant of the many‐to‐many hub location‐routing problem which consists in partitioning the set of nodes of a graph into routes containing exactly one hub each, and determining an extra route interconnecting all hubs. A variable neighborhood descent with neighborhood structures based on remove/add, swap and exchange moves nested with routing and location operations is used as a local search procedure in a multistart algorithm. We also consider a sequential version of this local search in the multistart. In addition, a biased random‐key genetic algorithm working with a local search routine, which also considers routing and location operations, is applied to the problem. To compare the heuristic solutions, we develop an integer programming formulation which is solved with a branch‐and‐cut algorithm. Capacity and path elimination constraints are added in a cutting plane fashion. The separation algorithms are based on the computation of min‐cut trees and on the connected components of a support graph. Computational experiments were conducted on several benchmark instances of routing problems and show that the heuristics are effective on medium to large‐sized instances, while the branch‐and‐cut algorithm solves small to medium sized problems to optimality. These algorithms were also compared with a commercial hybrid solver showing that the heuristics are quite competitive. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 68(1), 54–90 2016

Hub Location, Routing, and Route Dimensioning: Strategic and Tactical Intermodal Transportation Hub Network Design

“…In both cases, the traffic from a node to another node passes through at least one hub and no other non-hub nodes. Consistent with the literature utilizing more realistic access networks with stopovers (Yaman et al, 2007) or ring topologies (Nagy and Salhi, 1998;Wasner and Zäpfel, 2004;Rodríguez-Martín et al, 2014;Lopes et al, 2016;Kartal et al, 2017Kartal et al, , 2019, we also benefit from the consolidation of flows on our access-routes carrying traffic of several non-hub nodes to their designated hub and back. These access-routes may start and end at the hub as in the hub location and routing problem (C ¸etiner et al, 2010;de Camargo et al, 2013) or can start at a non-hub node and end at a hub node as in the hub location problem with stopovers.…”

A BRKGA with Implicit Path-Relinking for the Vehicle Routing Problem with Occasional Drivers and Time Windows

“…BRKGA has been firstly proposed by with the goal of developing a sophisticated general search metaheuristic that can be enhanced with intensification and diversification strategies to address several types of problems. The approach has been applied successfully to several combinatorial optimization problems, including the VRP (see, e.g., , Andrade et al (2013), Lopes et al (2016), Higino et al (2018), and). See Londe et al (2024) for a recent review of the literature of BRKGA.…”

The Multi-Objective Model of the Hub Covering Location-Routing Problem with Simultaneous Delivery and Pickup Under Uncertainty

“…According to the focus of this paper on the hub covering location-routing problem, the research conducted has been reviewed in the following. Lopes et al (2016) solved the hub location-routing model using GA with random search. They also used the branch and cut algorithm to check the efficiency of their solution method.…”