2026-05-27T20:44:39Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/69132024-05-20T07:52:24Zcom_10259.4_104com_10259_2604col_10259_6848
Repositorio Institucional de la Universidad de Burgos
author
Angarita Monroy, Andrés Guillermo
author
Lamos Díaz, Henry
2022-09-19T10:00:51Z
2022-09-19T10:00:51Z
2021-07
978-84-18465-12-3
http://hdl.handle.net/10259/6913
10.36443/10259/6913
Disasters around the world are becoming more frequent, diverse, complex and extremely
challenging, causing millions of casualties and affecting both human development and
available resources. Consequently, the present study addresses a multi-objective location,
inventory and multi-scale routing (2E-LIRP) problem, which supports comprehensive
decision making, so that the logistics network designer and manager can obtain adequate
strategic planning in the face of uncertainty and the negative impact that an adverse event
can generate.
Moreover, the problem is formulated as an integer linear programming model, having as
main objectives to minimize private logistics costs and maximize the welfare of the
affected areas, considering dynamic demand, multiple products and heterogeneous fleet.
Due to the computational complexity associated with the model, a new solution approach
is proposed, based on the design of evolutionary metaheuristic algorithms; the first one,
known as Non-dominated Sorting Genetic Algorithm version II (NSGA-II), the second
one, Strength Pareto Evolutionary Algorithm version II (SPEA-II) and the third one, called
Genetic Algorithm (GA), programmed in parallel and executed individually under a
cooperative environment. Finally, the experimentation carried out on a test set, composed
of twenty instances of varying complexity, allows inferring that the parallel-cooperative
and purely parallel approach applied to NSGA-II, substantially improves the processing
times and the number of non-dominated solutions, if compared to the results obtained by
SPEA-II, designed under identical conditions. Moreover, by building a GA with these
same characteristics, it improves up to 50% of the solutions, in terms of social costs
(logistic and humanitarian costs), with computation times similar to its sequential
counterpart.
eng
Logística
Operaciones
Logistics
Operations
A parallel programming approach to the solution of the location-inventory and multi-echelon routing problem in the humanitarian supply chain
info:eu-repo/semantics/conferenceObject
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URL
https://riubu.ubu.es/bitstream/10259/6913/1/Angarita_CIT2021_1115-1138.pdf
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Angarita_CIT2021_1115-1138.pdf