Bi-objective load balancing multiple allocation hub location: a compromise programming approach

In this paper we address unbalanced spatial distribution of hub-level flows in an optimal hub-and-spoke network structure of median-type models. Our study is based on a rather general variant of the multiple allocation hub location problems with fixed setup costs for hub nodes and hub edges in both...

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Bibliographic Details
Published in:Annals of operations research 2021-01, Vol.296 (1-2), p.363-406
Main Authors: Monemi, Rahimeh Neamatian, Gelareh, Shahin, Nagih, Anass, Jones, Dylan
Format: Article
Language:English
Subjects:
Algorithms
Business and Management
Combinatorics
Computer Science
Linear programming
Mathematical optimization
Mathematics
Methods
Mixed integer
Nodes
Nonlinear programming
Operating costs
Operations research
Operations Research/Decision Theory
Resource allocation
S.i.: Mopgp 2017
Spatial distribution
Theory of Computation
Transportation
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Summary:In this paper we address unbalanced spatial distribution of hub-level flows in an optimal hub-and-spoke network structure of median-type models. Our study is based on a rather general variant of the multiple allocation hub location problems with fixed setup costs for hub nodes and hub edges in both capacitated and uncapacitated variants wherein the number of hub nodes traversed along origin-destination pairs is not constrained to one or two as in the classical models.. From the perspective of an infrastructure owner, we want to make sure that there exists a choice of design for the hub-level sub-network (hubs and hub edges) that considers both objectives of minimizing cost of transportation and balancing spatial distribution of flow across the hub-level network. We propose a bi-objective (transportation cost and hub-level flow variance) mixed integer non-linear programming formulation and handle the bi-objective model via a compromise programming framework. We exploit the structure of the problem and propose a second-order conic reformulation of the model along with a very efficient matheuristics algorithm for larger size instances.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-019-03421-w