Page 137 - Data Science Algorithms in a Week

In: Artificial Intelligence ISBN: 978-1-53612-677-8
Editors: L. Rabelo, S. Bhide and E. Gutierrez © 2018 Nova Science Publishers, Inc.

Chapter 6

SIMULATION OPTIMIZATION USING A HYBRID

SCHEME WITH PARTICLE SWARM OPTIMIZATION

FOR A MANUFACTURING SUPPLY CHAIN

1,*
2
Alfonso T. Sarmiento and Edgar Gutierrez
1 Program of Industrial Engineering,
University of La Sabana, Chía, Colombia
2 Center for Latin-American Logistics Innovation,
Bogota, Colombia

ABSTRACT

This chapter proposes the solution of an optimization problem based on the concept
of the accumulated deviations from equilibrium (ADE) to eliminate instability in the
supply chain. The optimization algorithm combines the advantage of particle swarm
optimization (PSO) to determine good regions of the search space to find the optimal
point within those regions. The local search uses a Powell hill-climbing (PHC) algorithm
as an improved procedure to the solution obtained from the PSO algorithm, which assures
a fast convergence of the ADE. The applicability of the method is demonstrated by using
a case study in the manufacturing supply chain. The experiments showed that solutions
generated by this hybrid optimization algorithm were robust.

Keywords: particle swarm optimization, instability, hybrid optimization

* Corresponding Author Email: alfonsosava@unisabana.edu.co.