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.

Modelling and Optimization of Personalized Scenic Tourism Routes Based on Urgency

“…It is a computational model of the biological evolution process that simulates the natural selection and genetic mechanism of Darwinian biological evolution. It thus searches for the optimal solution by simulating the natural evolution process [27][28][29][30].…”

Production Planning Forecasting System Based on M5P Algorithms and Master Data in Manufacturing Processes

“…Production planning and scheduling are at the core of SCM. These are important problems in various industries and require efficient scheduling methods to improve productivity and reduce costs [3]. Researchers have been working on solving workshop scheduling problems using machine learning algorithms, and their contributions can be seen.…”

“…A method to achieve this is to use machine-learning to predict and provide effective data. Machinelearning algorithms are utilized to improve production planning and address scheduling challenges [25]. It is challenging for conventional job-planning techniques to obtain effective optimal solutions to complex distributed resource-planning problems using individual numerical analysis methods [26].…”