Application of Modified Steady-State Genetic Algorithm for Batch Sizing and Scheduling Problem with Limited Buffers

Abstract

Batch sizing and scheduling problems are usually tough to solve because they seek solutions in a vast combinatorial space of possible solutions. This research aimed to test and further develop a scheduling method based on a modified steady-state genetic algorithm and test its performance, in both the speed (low computational time) and quality of the final results as low makespan values. This paper explores the problem of determining the order and size of the product batches in a hybrid flow shop with a limited buffer according to the problem that is faced in real-life. Another goal of this research was to develop a new reliable software/computer program tool in c# that can also be used in production, and as result, obtain a flexible software solution for further research. In all of the optimizations, the initial population of the genetic algorithm was randomly generated. The quality of the obtained results, and the short computation time, together with the flexibility of the genetic paradigm prove the effectiveness of the proposed algorithm and method to solve this problem.