scholarly journals Efficient simulated annealing based solution approaches to the competitive single and multiple allocation hub location problems

2018 ◽  
Vol 90 ◽  
pp. 173-192 ◽  
Author(s):  
Nader Ghaffarinasab ◽  
Alireza Motallebzadeh ◽  
Younis Jabarzadeh ◽  
Bahar Y. Kara
Keyword(s):  
Simulated Annealing ◽  
Location Problems ◽  
Hub Location ◽  
Multiple Allocation

Robust optimization approach to capacitated single and multiple allocation hub location problems

2014 ◽  
Vol 35 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Fereidoon Habibzadeh Boukani ◽  
Babak Farhang Moghaddam ◽  
Mir Saman Pishvaee
Keyword(s):  
Robust Optimization ◽  
Location Problems ◽  
Optimization Approach ◽  
Hub Location ◽  
Multiple Allocation

Diversification-based learning simulated annealing algorithm for hub location problems

2019 ◽  
Vol 26 (6) ◽  
pp. 1995-2016
Author(s):  
Himanshu Rathore ◽  
Shirsendu Nandi ◽  
Peeyush Pandey ◽  
Surya Prakash Singh
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Convergence Rates ◽  
Location Problems ◽  
Computational Time ◽  
Data Sets ◽  
Learning Mechanisms ◽  
Hub Location ◽  
Content Type ◽  
Annealing Algorithm

Purpose The purpose of this paper is to examine the efficacy of diversification-based learning (DBL) in expediting the performance of simulated annealing (SA) in hub location problems. Design/methodology/approach This study proposes a novel diversification-based learning simulated annealing (DBLSA) algorithm for solving p-hub median problems. It is executed on MATLAB 11.0. Experiments are conducted on CAB and AP data sets. Findings This study finds that in hub location models, DBLSA algorithm equipped with social learning operator outperforms the vanilla version of SA algorithm in terms of accuracy and convergence rates. Practical implications Hub location problems are relevant in aviation and telecommunication industry. This study proposes a novel application of a DBLSA algorithm to solve larger instances of hub location problems effectively in reasonable computational time. Originality/value To the best of the author’s knowledge, this is the first application of DBL in optimisation. By demonstrating its efficacy, this study steers research in the direction of learning mechanisms-based metaheuristic applications.

Preprocessing and cutting for multiple allocation hub location problems

2004 ◽  
Vol 155 (3) ◽  
pp. 638-653 ◽  
Author(s):  
Natashia Boland ◽  
Mohan Krishnamoorthy ◽  
Andreas T. Ernst ◽  
Jamie Ebery
Keyword(s):  
Location Problems ◽  
Hub Location ◽  
Multiple Allocation

An efficient and scalable approach to hub location problems based on contraction

2020 ◽  
pp. 106955
Author(s):  
Sebastian Wandelt ◽  
Weibin Dai ◽  
Jun Zhang ◽  
Qiuhong Zhao ◽  
Xiaoqian Sun
Keyword(s):  
Location Problems ◽  
Hub Location

scholarly journals Location of Urban Logistic Terminals as Hub Location Problem

1970 ◽  
Vol 24 (5) ◽  
pp. 433-440 ◽  
Author(s):  
Jasmina Pašagić Škrinjar ◽  
Kristijan Rogić ◽  
Ratko Stanković
Keyword(s):  
Location Problem ◽  
Location Problems ◽  
Heuristic Methods ◽  
Hub Location ◽  
Hub Location Problem ◽  
Telecommunication Systems ◽  
Big Cities ◽  
Np Problems ◽  
Delivery Networks ◽  
Single Allocation

In this paper the problems of locating urban logistic terminals are studied as hub location problems that due to a large number of potential nodes in big cities belong to hard non-polynomial problems, the so-called NP-problems. The hub location problems have found wide application in physical planning of transport and telecommunication systems, especially systems of fast delivery, networks of logistic and distribution centres and cargo traffic terminals of the big cities, etc. The paper defines single and multiple allocations and studies the numerical examples. The capacitated single allocation hub location problems have been studied, with the provision of a mathematical model of selecting the location for the hubs on the network. The paper also presents the differences in the possibilities of implementing the exact and heuristic methods to solve the actual location problems of big dimensions i.e. hub problems of the big cities.

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