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Abstract

Improving production processes includes not only activities concerning manufacturing itself, but also all the activities that are necessary to achieve the main objectives. One such activity is transport, which, although a source of waste in terms of adding value to the product, is essential to the realization of the production process. Over the years, many methods have been developed to help manage supply and transport in such a way as to reduce it to the necessary minimum. In the paper, the problem of delivering components to a production area using trains and appropriately laid-out carriages was described. It is a milk run stop locations problem (MRSLP), whose proposed solution is based on the use of heuristic algorithms. Intelligent solutions are getting more and more popular in the industry because of the possible advantages they offer, especially those that include the possibility of finding an optimum local solution in a relatively short time and the prevention of human errors. In this paper, the applicability of three algorithms – tabu search, genetic algorithm, and simulated annealing – was explored.
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Authors and Affiliations

Joanna Kochańska
1
Anna Burduk
1
ORCID: ORCID
Dagmara Łapczyńska
1
Kamil Musiał
1

  1. Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
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Abstract

The paper deals with the problem of production material flow management. The proper way of logistic tasks management has an impact on the production process effectiveness and the cycle time, which is a very important factor in manufacturing. Reducing the production process cycle time results not only in the ability to provide more customers with orders but also in increasing the level of resources usage (machines, operators etc.). In order to reach the aim of improving production effectiveness, the simulation modeling was used. It is a computer method that supports a decision-making process and allows to perform experiments on production without interfering with the real process. The paper also includes a risk analysis performed to evaluate the imperfections of simulation modeling, based on the rules of fuzzy logic.
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Authors and Affiliations

Anna Burduk
ORCID: ORCID
Dagmara Łapczynska
ORCID: ORCID
Piotr Popiel
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Abstract

The paper deals with the issue of production scheduling for various types of employees in a large manufacturing company where the decision-making process was based on a human factor and the foreman’s know-how, which was error-prone. Modern production processes are getting more and more complex. A company that wants to be competitive on the market must consider many factors. Relying only on human factors is not efficient at all. The presented work has the objective of developing a new employee scheduling system that might be considered a particular case of the job shop problem from the set of the employee scheduling problems. The Neuro-Tabu Search algorithm and the data gathered by manufacturing sensors and process controls are used to remotely inspect machine condition and sustainability as well as for preventive maintenance. They were used to build production schedules. The construction of the Neuro-Tabu Search algorithm combines the Tabu Search algorithm, one of the most effective methods of constructing heuristic algorithms for scheduling problems, and a self-organizing neural network that further improves the prohibition mechanism of the Tabu Search algorithm. Additionally, in the paper, sustainability with the use of Industry 4.0 is considered. That would make it possible to minimize the costs of employees’ work and the cost of the overall production process. Solving the optimization problem offered by Neuro-Tabu Search algorithm and real-time data shows a new way of production management.
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Authors and Affiliations

Anna Burduk
1
ORCID: ORCID
Kamil Musiał
1
Artem Balashov
1
Andre Batako
2
Andrii Safonyk
3
ORCID: ORCID

  1. Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
  2. Liverpool John Moores University, Faculty of Engineering and Technology,70 Mount Pleasant Liverpool L3 3AF, UK
  3. National University of Water and Environmental Engineering, Department of Automation, Electrical Engineering and Computer-Integrated Technologies, Rivne 33000, Ukraine

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