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Bio-inspired and evolutionary algorithms applied to a bi-objective network design problem

Published: 06 July 2013 Publication History

Abstract

Logistics network design is one of the principal parts of strategic decisions in the planning and control of production systems. It deals with determining the warehouses locations and the definition of product flow between facilities and clients. This work is focused in finding an approximation of the Pareto-optimal front for two conflicting objective functions in logistic networks design: minimize costs and maximize coverage. Since the establishing of which warehouses must be opened constitute a combinatorial optimization problem, two metaheuristic techniques, namely Improved Strength Pareto Evolutionary Algorithm - SPEA2 and a novel binary version of Bacterial Chemotaxis Multi-objective Optimization Algorithm - BCMOA, were applied. With the aim of finding the optimal flow between clients and warehouses, network flow algorithms were also used.
The performances of the above techniques were evaluated by comparative analysis of the results obtained in the solution of eight randomly generated problems by means of the dominated hypervolume metric (S-metric). The hybrid methodology here developed to solve the logistics network design problem - which combines metaheuristic techniques with a network flow algorithms - showed to be competitive regarding the Pareto Optimal Front approximation, and also displayed high efficiency in execution times.

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  • (2017)Network Defense Strategy Selection with Reinforcement Learning and Pareto OptimizationApplied Sciences10.3390/app71111387:11(1138)Online publication date: 6-Nov-2017

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cover image ACM Conferences
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
July 2013
1798 pages
ISBN:9781450319645
DOI:10.1145/2464576
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Publication History

Published: 06 July 2013

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Author Tags

  1. metaheuristics
  2. multi-objective
  3. network design

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GECCO '13
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GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2017)Network Defense Strategy Selection with Reinforcement Learning and Pareto OptimizationApplied Sciences10.3390/app71111387:11(1138)Online publication date: 6-Nov-2017

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