Intelligent Systems Engg. Lab.

Dept. of Indust. & Inform. Systems Engg.

Graduate School of Engineering

Ashikaga Inst. of Technology

Ashikaga, 326-8558 Japan

Phone: +81(284)62-0605 ext. 376

or +81(284)62-2985 direct

Fax: +81(284)64-1071

Email: gen@ashitech.ac.jp

or gen@genlab.ashitech.ac.jp

Gursel A. Suer

Univ. of Puerto Rico - Mayaguez, P.R.

a_suer@rumac.upr.clu.edu

1. Spanning Tree-based Genetic Algorithm for Bicriteria Fixed Charge Transportation Problem

Mitsuo Gen, Yinzen Li, and Jong Ryul Kim

Dept. of Indust. & Inform. Systems Engg.

Ashikaga Inst. of Tech.

Ashikaga 326-8558, Japan

2. Genetic Algorithm for Solving Bicriteria Network Topology Design Problem

Jong Ryul Kim and Mitsuo Gen

Dept. of Indust. & Inform. Systems Engg.

Ashikaga Inst. of Tech.

Ashikaga 326-8558, Japan

3. Genetic Algorithms in Lot Sizing Decisions

William Hernandez and Gursel A. Suer

Dept. of Industrial Engg.

University of Puerto Rico

Mayaguez, PR 00681, USA

The work presented in this paper focuses on the application of genetic algorithms to obtain the order quantities for an uncapacitated, no shortages allowed, single-item, single-level lot sizing problem. The problem is tested under various conditions with different lot sizing and genetic algorithm parameters. The impact of scaling on the fitness function is also explored. The results indicate that genetic algorithms are promising for this area as well and more complex lot sizing problems can be tackled.

4. A Hybrid Approach of Genetic Algorithms and Local Optimizers in Cell Loading

Gursel A. Suer *, Ramon Vazquez +, and Miguel Cortes ++

* Dept. of Industrial Engg.

+ Depts. of Electrical Engg. & Civil Engg.

University of Puerto Rico-Mayaguez

Mayaguez, PR 00681, USA

and

++ Dept. of Science and Technology

Interamerican University-Aguadilla

Aguadilla, PR 00605, USA

In this paper, we explore the potential application of evolutionary programming to cell loading problem. The objective is to minimize the number of tardy jobs. The proposed approach is a hybrid three phase approach; 1) Evolutionary programming is used to generate a job sequence, 2) Minimum load rule is applied to assign jobs to cells and 3) Moore's Algorithm is applied to each cell independently. The experimentation results show that the inclusion of the local optimizing procedure mentioned in step 3 accelarates reaching a good solution.

5. Adaptive Penalty Methods for Reliability Optimization of Series-Parallel Systems Using an Ant System Approach

Yun-Chia Liang and Alice E. Smith

Department of Industrial Engineering,

University of Pittsburgh, Pittsburgh, PA 15261

aesmith@engrng.pitt.edu

This paper solves the redundancy allocation problem of a series-parallel system by developing and demonstrating a problem-specific Ant System. The problem is to select components and redundancy-levels to optimize some objective function, given system-level constraints on reliability, cost, and/or weight. The Ant System algorithm presented in this paper is combined with adaptive penalty methods to deal with the highly constrained problem. Experiments on well-known problems from the literature were used to test this approach.

6. A Hybrid Genetic Algorithm Approach for Backbone Design of Communication Networks

Abdullah Konak and Alice E. Smith

Department of Industrial Engineering,

University of Pittsburgh, Pittsburgh, PA 15261

aesmith@engrng.pitt.edu

This paper presents a hybrid approach of a genetic algorithm (GA) and local search algorithms for the backbone design of communication networks. The backbone network design problem is defined as finding the network topology minimizing the design/operating cost of a network under performance and survivability considerations. This problem is known to be NP-hard. In the hybrid approach, the local search algorithm efficiently improves the solutions in the population by using domain-specific information while the GA recombines good solutions in order to investigate different regions of the solution space. The results of the test problems show that the hybrid methodology improves on previous approaches.