The low price of coffee in the international markets has forced the Federación Nacional de Cafeteros de Colombia (FNCC) to look for cost-cutting opportunities. An alternative that has been considered is the reduction of the operating infrastructure by closing some of the FNCC-owned depots. This new proposal of the coffee supplier network is supported by (uncapacitated and capacitated) facility location models that minimize operating costs while maximizing service level (coverage). These bi-objective optimization models are solved by means of NSGA II, a multi-objective evolutionary algorithm (MOEA). From a computational perspective, this chapter presents the multi-objective Java Genetic Algorithm (MO-JGA) framework, a new tool for the rapid development of MOEAs built on top of the Java Genetic Algorithm (JGA). We illustrate MO-JGA by implementing NSGA II-based solutions for the bi-objective location models.
Key Terms in this Chapter
Multi-Objective Evolutionary Algorithm (MOEA): The extension of evolutionary algorithms to solve the multi-objective optimization problem.
Efficient Frontier: Let X E be the set of efficient solutions. The Efficient Frontier (or Pareto Optimal Front) is the set of images (in the criteria space) of the solutions in X E . Let PF E be the efficient frontier defined by .
Facility Location Problem (or Facility Siting Problem): Facility location problems (FLPs) deal with choosing from among some candidate facilities a subset from which an organization will serve its customers. The FLP is also concerned with determining how these customers will be served by the open facilities.
Efficient Solution (Non-Dominated Solution): A feasible solution x ? X is called efficient if there does not exist x ’? X such that for all k =1,?, p and for some k .
JGA: Acronym for Java Genetic Algorithm; a flexible and extensible computational object-oriented framework for rapid development of evolutionary algorithms for solving complex optimization problems.
Multi-Objective Optimization Problem (MOP): The MOP is defined as: where X ? R n is the feasible set and f : R n ? R p is a vector valued objective function.
Evolutionary (Genetic) Algorithm: An evolutionary algorithm (EA) is an stochastic search procedure inspired by the evolution process in nature. In this process, a population of individuals evolve, and the fitter ones have a better chance of reproduction and survival. The reproduction mechanisms favor the characteristics of the stronger parents and hopefully produce better children, guaranteeing the presence of those characteristics in future generations.
Complete Chapter List
P. Collet, J. Rennard
I. Naveh, R. Sun
J. Barr, F. Saraceno
H. Kwasnicka, W. Kwasnicki
A. Berro, I. leroux
N. J. Saam, W. Kerber
A. Brabazon, A. Silva, T. F.S. Sousa, R. Matthews, M. O’Neill
G. D.M. Serugendo
K. Taveter, G. Wagner
L. Shan, R. Shen, J. Wang
M. Klein, P. Faratin, H. Sayama
A. Mochon, Y. Saez
R. Marks, D. Midgley, L. Cooper
T. Erez, S. Moldovan, Soloman
M. Ciprian, M. Kaucic
S. Lavigne, S. Sanchez