Introduction

Abstract

One of the major challenges for researchers in the field of management science, information systems, business informatics, and computer science is to develop methods and tools that help organizations, such as companies or public institutions, to fulfill their tasks efficiently. However, during the last decade, the dynamics and size of tasks organizations are faced with has changed. Firstly, production and service processes must be reorganized in shorter time intervals and adapted dynamically to the varying demands of markets and customers. Although there is continuous change, organizations must ensure that the efficiency of their processes remains high. Therefore, optimization techniques are necessary that help organizations to reorganize themselves, to increase the performance of their processes, and to stay efficient. Secondly, with increasing organization size the complexity of problems in the context of production or service processes also increases. As a result, standard, traditional, optimization techniques are often not able to solve these problems of increased complexity with justifiable effort in an acceptable time period. Therefore, to overcome these problems, and to develop systems that solve these complex problems, researchers proposed using genetic and evolutionary algorithms (GEAs). Using these nature-inspired search methods it is possible to overcome some limitations of traditional optimization methods, and to increase the number of solvable problems. The application of GEAs to many optimization problems in organizations often results in good performance and high quality solutions.