Next Article in Journal
Effect of Different Dried Vegetable Powders on Physicochemical, Organoleptic, and Antioxidative Properties of Fat-Free Dairy Desserts
Next Article in Special Issue
Task Complexity and the Skills Dilemma in the Programming and Control of Collaborative Robots for Manufacturing
Previous Article in Journal
Analysis of Rewetting Characteristics and Process Parameters in Tobacco Strip Redrying Stage
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2022, 12(22), 11512; https://doi.org/10.3390/app122211512
Submission received: 4 October 2022 / Revised: 28 October 2022 / Accepted: 11 November 2022 / Published: 12 November 2022
(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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.
Keywords: hybrid flow shop; batch size; scheduling; buffer configuration; optimization; steady-state genetic algorithm hybrid flow shop; batch size; scheduling; buffer configuration; optimization; steady-state genetic algorithm

Share and Cite

MDPI and ACS Style

Janeš, G.; Ištoković, D.; Jurković, Z.; Perinić, M. Application of Modified Steady-State Genetic Algorithm for Batch Sizing and Scheduling Problem with Limited Buffers. Appl. Sci. 2022, 12, 11512. https://doi.org/10.3390/app122211512

AMA Style

Janeš G, Ištoković D, Jurković Z, Perinić M. Application of Modified Steady-State Genetic Algorithm for Batch Sizing and Scheduling Problem with Limited Buffers. Applied Sciences. 2022; 12(22):11512. https://doi.org/10.3390/app122211512

Chicago/Turabian Style

Janeš, Gordan, David Ištoković, Zoran Jurković, and Mladen Perinić. 2022. "Application of Modified Steady-State Genetic Algorithm for Batch Sizing and Scheduling Problem with Limited Buffers" Applied Sciences 12, no. 22: 11512. https://doi.org/10.3390/app122211512

APA Style

Janeš, G., Ištoković, D., Jurković, Z., & Perinić, M. (2022). Application of Modified Steady-State Genetic Algorithm for Batch Sizing and Scheduling Problem with Limited Buffers. Applied Sciences, 12(22), 11512. https://doi.org/10.3390/app122211512

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop