Preprocessing and cutting for multiple allocation hub location problems

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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Multimodal Capacitated Hub Location Problems with Multi-Commodities: An Application in Freight Transport

Journal of Advanced Transportation ◽

10.1155/2020/2431763 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Alan Osorio-Mora ◽

Francisco Núñez-Cerda ◽

Gustavo Gatica ◽

Rodrigo Linfati

Keyword(s):

Latin American ◽

Location Problems ◽

Transport Cost ◽

Mixed Integer ◽

Hub Location ◽

Proposed Model ◽

Multimodal Transport ◽

Two Factors ◽

Commercial Solver

Hub location problems (HLPs) support decision making on multimodal transport strategic planning. It is related to the location of hubs and the allocation of origin/destination (O/D) flow in a system. Classical formulations assume that these flows are predefined paths and direct delivery is not available. This applied research presents a mixed integer linear programming (MILP) model for a capacitated multimodal, multi-commodity HLP. Furthermore, an application on the export process in a Latin American country is detailed. The new proposed model, unlike the traditional HLP, allows direct shipment, and its O/D flows are part of the decision model. Situations with up to 100 nodes, six products, and two transport modes are used, working with initial and projected flows. All instances can be solved optimally using the commercial solver, Gurobi 7.5.0, in computational times less than a minute. Results indicate that only one hub is profitable for the case study, both for the initial and projected scenarios. The installation of a hub generates transport savings over 1% per year. Two factors affect the location decision: low concentration and distance between the hubs and destinations. Long distances involve an exhaustive use of trains instead of trucks, which leads to lower transport cost per unit.

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

Annals of Operations Research ◽

10.1007/s10479-019-03421-w ◽

2020 ◽

Vol 296 (1-2) ◽

pp. 363-406 ◽

Cited By ~ 1

Author(s):

Rahimeh Neamatian Monemi ◽

Shahin Gelareh ◽

Anass Nagih ◽

Dylan Jones

Keyword(s):

Spatial Distribution ◽

Transportation Cost ◽

Compromise Programming ◽

Mixed Integer ◽

Programming Approach ◽

Hub Location ◽

Hub And Spoke ◽

Classical Models ◽

Linear Programming Formulation ◽

Multiple Allocation

AbstractIn 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.

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