Approaches for solving production planning and scheduling problems using genetic algorithms

Abstract

Manufacturers are today faced with increasing market competition. Acknowledging considerable pressure by competition, efficient planning and scheduling of production therefore become essential requirements. Satisfying customers' needs and, moreover, benefitting economically, is thus owed to detailed plans of rational production, sales and distribution. Understanding planning, allocating resources and controlling processes of production are indispensable for success in business activities. In conditions of fast changes, when increasingly less time is available for optimal organization of production, it is a must to implement methods (algorithms) utilized towards finding an appropriate solution with relatively low number of attempts (evolutions). Genetic algorithms, as part of a wider field of artificial intelligence, are being increasingly employed and are indeed successful in dealing with hard-solving issues (NP-hard).