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Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems: A Survey

Author

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  • Georgios Dounias

    (Management and Decision Engineering Lab, Department of Financial and Management Engineering, University of the Aegean, Chios, Greece)

  • Vassilios Vassiliadis

    (Management and Decision Engineering Lab, Department of Financial and Management Engineering, University of the Aegean, Chios, Greece)

Abstract

The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related approaches are used either as stand-alone algorithms, or as hybrid schemes i.e. in combination to other AI techniques. Ant Colony Optimization (ACO), Particle Swarm Optimization, Artificial Bee Colonies, Artificial Immune Systems and DNA Computing are some of the most popular approaches belonging to nature inspired intelligence. On the other hand, supply chain management represents an interesting domain of OR applications, including a variety of hard optimization problems such as vehicle routing (VRP), travelling salesman (TSP), team orienteering, inventory, knapsack, supply network problems, etc. Nature inspired intelligent algorithms prove capable of identifying near optimal solutions for instances of those problems with high degree of complexity in a reasonable amount of time. Survey findings indicate that NII can cope successfully with almost any kind of supply chain optimization problem and tends to become a standard in related scientific literature during the last five years.

Suggested Citation

  • Georgios Dounias & Vassilios Vassiliadis, 2014. "Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems: A Survey," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 4(3), pages 26-51, July.
    • Handle: RePEc:igg:jncr00:v:4:y:2014:i:3:p:26-51
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