Efficient Production Planning and Scheduling

1. Front Matter

   Pages I-XIV

   PDF

2. Introduction

   • Patricia Jay Shiroma

   Pages 1-31

3. The Nature of Evolutionary Algorithms

   • Patricia Jay Shiroma

   Pages 33-42

4. Theoretical Foundations of Genetic Algorithms

   • Patricia Jay Shiroma

   Pages 43-61

5. Methodology

   • Patricia Jay Shiroma

   Pages 63-95

6. Feasibility Study: A Hybrid Genetic Algorithm Embedded in AMTOS

   • Patricia Jay Shiroma

   Pages 97-103

7. Case Study: Implementation of a Hybrid Genetic Algorithm for Production Planning in a Large Pharmaceutical Company

   • Patricia Jay Shiroma

   Pages 105-136

8. Summary and Plans for Future Research

   • Patricia Jay Shiroma

   Pages 137-142

9. Back Matter

   Pages 143-155

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Genetic algorithms refer to a class of optimization methods based on principles of natural selection and evolution. Although there have been a number of successful implementations in scientific and engineering applications, up until now there have been relatively few applications in the business world. Patricia Shiroma explores the possibility of combining genetic algorithms with simulation studies in order to generate efficient production schedules for parallel manufacturing processes. The author takes advantage of the synergistic effects between the two methods. The result is a flexible, highly effective production scheduling system which is tested in a case study.

• Fertigung
• Information Engineering und IV-Controlling
• Scheduling
• genetische Algorithmen
• production planning

Dr. Patricia Jay Shiroma ist seit Dezember 1992 als Assistentin am Lehrstuhl von Prof. G. Niemeyer an der Universität Regensburg im Bereich Wirtschaftsinformatik tätig.

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