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

Georgios Dounias and Vassilios Vassiliadis
Additional contact information
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

International Journal of Natural Computing Research (IJNCR), 2014, vol. 4, issue 3, 26-51

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.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijncr.2014070102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jncr00:v:4:y:2014:i:3:p:26-51

Access Statistics for this article

International Journal of Natural Computing Research (IJNCR) is currently edited by Xuewen Xia

More articles in International Journal of Natural Computing Research (IJNCR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().