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Solving Facility Location Problems with a Tol for Rapid Development of Multi-Objective Evolutionary Algorithms (MOEAs)

Solving Facility Location Problems with a Tol for Rapid Development of Multi-Objective Evolutionary Algorithms (MOEAs)

A. L. Medaglia
Copyright: © 2007 |Pages: 19
ISBN13: 9781591409847|ISBN10: 1591409845|EISBN13: 9781591409854
DOI: 10.4018/978-1-59140-984-7.ch042
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MLA

Medaglia, A. L. "Solving Facility Location Problems with a Tol for Rapid Development of Multi-Objective Evolutionary Algorithms (MOEAs)." Handbook of Research on Nature-Inspired Computing for Economics and Management, edited by Jean-Philippe Rennard, IGI Global, 2007, pp. 642-660. https://doi.org/10.4018/978-1-59140-984-7.ch042

APA

Medaglia, A. L. (2007). Solving Facility Location Problems with a Tol for Rapid Development of Multi-Objective Evolutionary Algorithms (MOEAs). In J. Rennard (Ed.), Handbook of Research on Nature-Inspired Computing for Economics and Management (pp. 642-660). IGI Global. https://doi.org/10.4018/978-1-59140-984-7.ch042

Chicago

Medaglia, A. L. "Solving Facility Location Problems with a Tol for Rapid Development of Multi-Objective Evolutionary Algorithms (MOEAs)." In Handbook of Research on Nature-Inspired Computing for Economics and Management, edited by Jean-Philippe Rennard, 642-660. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59140-984-7.ch042

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Abstract

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.

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