B. Ferenczi

57210111963

Publications - 3

Fuzzy Signature Based Model in Material Handling Management

Ferenc Lilik B. Ferenczi L. T. Koczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2023-01-01

Volume: 1040

Issue: Unknown

Page Range: 169-179

Description:

Scheduling and management of material handling in functional production system are among the biggest challenges of logistics. Among several methods, linear programming gives exact solution to these kinds of problems, however, linear programming is rigid and requires specially trained personnel to operate. Fuzzy logic based systems—besides they work similarly to human thinking—seems to be easily implementable in such problems. In this paper we present a fuzzy signature based approach constructed on expert knowledge. Its results are compared to the results of linear programming in the same situations.

Open Access: Yes

DOI: 10.1007/978-3-031-07707-4_21

Scheduling a forge with due dates and die deterioration

Máté Hegyháti O. Ooz B. Ferenczi

Publication Name: Annals of Operations Research

Publication Date: 2020-02-01

Volume: 285

Issue: 1-2

Page Range: 353-367

Description:

In this paper a new scheduling problem is presented, which originates from the steel processing industry. The optimal scheduling of a steel forge is investigated with the goal of minimizing setup and storage costs under strict deadlines and special resource constraints. The main distinctive feature of the problem is the deterioration of some equipment, in this case, the so-called forging dies. While the aging effect has been widely investigated in scheduling approaches, where production speed decreases through time, durability deterioration caused by equipment setup has not been addressed yet. In this paper a mixed-integer linear programming model is proposed for solving the problem. The model uses a uniform discrete time representation and resource-balance constraints based on the resource–task network model formulation method. The proposed method was tested on 3-week long schedules based on real industrial scenarios. Computational results show that the approach is able to provide optimal short-term schedules in reasonable time.

Open Access: Yes

DOI: 10.1007/s10479-019-03336-6

Use of Artificial Neural Networks in the production control of small batch production

T. B. Ladinig B. Ferenczi Péter Németh

Publication Name: Proceedings of the 2016 International Conference on Artificial Intelligence Icai 2016 Worldcomp 2016

Publication Date: 2016-01-01

Volume: Unknown

Issue: Unknown

Page Range: 237-240

Description:

Our aim with this paper is to test a new performance measurement and control system for small batch production in the automotive industry with the help of Artificial Neural Networks. After the introduction of small batch production at an automotive company a possible use of this method for production control is presented.

Open Access: Yes

DOI: DOI not available