Efficient Production Planning and Scheduling: An Integrated Approach with Genetic Algorithms and Simulation: Information Engineering und IV-Controlling

1 Introduction.- 2 The Nature of Evolutionary Algorithms.- 3 Theoretical Foundations of Genetic Algorithms.- 4 Methodology.- 5 Feasibility Study: A Hybrid Genetic Algorithm Embedded in Amtos.- 6 Case Study: Implementation of a Hybrid Genetic Algorithm for Production Planning in a Large Pharmaceutical Company.- 7 Summary and Plans for Future Research.

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

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.