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Number of results: 8
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Abstract

The Decision Makers in the production organizations, which produce multiple different products

at the same time, set the priorities for what the organization desires to produce. This

priority is sorting the products in order to schedule the production based on these priorities.

The production organizations receive a huge number of orders from different customers, each

order contains many products with close delivery dates. The organization aims to produce

multiple different products at the same time, in order to satisfy all customers by delivering

all orders at the right time. This study will propose a method to prioritize the production

to produce a multiple different products at the same time, the production lines will produce

multiple different products. This method will prioritize the products using Multi Criteria

Decision Making technique, and prioritize the production operations using a new algorithm

called Algorithm for Prioritization of Production Operations. In addition, the study will provide

an algorithm for production scheduling using the production priority calculated based

on the proposed method. The study will also compare the scheduling based on the priority

rules and based on the proposed method through total production time and the variety of

products produced.

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Authors and Affiliations

Rami Mokao
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Abstract

Optimization in mine planning could improve the economic benefit for mining companies. The main optimization contents in an underground mine includes stope layout, access layout and production scheduling. It is common to optimize each part sequentially, where optimal results from one phase are treated as the input for the next phase. The production schedule is based on the mining design. Access layout plays an important role in determining the connection relationships between stopes. This paper proposes a shortest-path search algorithm to design a network that automatically connects each stope. Access layout optimization is treated as a network flow problem. Stopes are viewed as nodes, and the roads between the stopes are regarded as edges. Moreover, the decline location influences the ore transport paths and haul distances. Tree diagrams of the ore transportation path are analyzed when each stope location is treated as an alternative decline location. The optimal decline location is chosen by an enumeration method. Then, Integer Programming (IP) is used to optimize the production scheduling process and maximize the Net Present Value (NPV). The extension sequence of access excavation and stope extraction is taken into account in the optimization model to balance access development and stope mining. These optimization models are validated in an application involving a hypothetical gold deposit, and the results demonstrate that the new approach can provide a more realistic solution compared with those of traditional approaches.

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Authors and Affiliations

Jie Hou
Guoqing Li
Nailian Hu
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Abstract

Unrelated Parallel Machines Scheduling Problem (U-PMSP) is a category of discrete optimization problems in which various manufacturing jobs are assigned to identical parallel machines at particular times. In this paper, a specific production scheduling task the U-PMSP with Machine and Job Dependent Setup Times, Availability Constraint, Time Windows and Maintenance Times is introduced. Machines with different capacity limits and maintenance times are available to perform the tasks. After that our problem, the U-PMSP with Machine and Job Dependent Setup Times, Availability Constraints, Time Windows and Maintenance Times is detailed. After that, the applied optimization algorithm and their operators are introduced. The proposed algorithm is the genetic algorithm (GA), and proposed operators are the order crossover, partially matched crossover, cycle crossover and the 2-opt as a mutation operator. Then we prove the efficiency of our algorithm with test results. We also prove the efficiency of the algorithm on our own data set and benchmark data set. The authors conclude that this GA is effective for solving high complexity parallel machine problems.
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Authors and Affiliations

Anita Agárdi
Károly Nehéz
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Abstract

The effective implementation of new market strategies presents the mining enterprises with new challenges which require precise assessment instruments of the carried out business to be met at the level of mines, preparation plants, coking plants, and steelworks. These instruments include deposit, technological, and economic parameters, which together with a safety margin, determining the percentage reserve level of each parameter, shape the profitability of undertaken projects. The paper raises the issue of designing an IT architecture of the system for deposit modelling and mining production scheduling, implemented in the JSW SA. The development and application of the system was important with regard to the overriding objective of the Quality ProgramProgram of the JSW Capital Group, which is increasing the effectiveness of deposit and commercial product quality management. The paper also presents the required specification of the technical architecture necessary to implement systems and the actions required to integrate them with other IT systems of the JSW Group. The heuristic technical architecture of the JSW SA production line management system presented in the paper enables an analysis of the production process profitability in a carried account system in the area of mines, preparation plants, and coking plants of the mining group of the biggest European coal producer for metallurgical purposes.
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Authors and Affiliations

Artur Dyczko
1
ORCID: ORCID

  1. Mineral and Energy Economy Research Institute, Polish Academy of Sciences, Kraków, Poland
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Abstract

A classical algorithm Tabu Search was compared with Q Learning (named learning) with regards to the scheduling problems in the Austempered Ductile Iron (ADI) manufacturing process. The first part comprised of a review of the literature concerning scheduling problems, machine learning and the ADI manufacturing process. Based on this, a simplified scheme of ADI production line was created, which a scheduling problem was described for. Moreover, a classic and training algorithm that is best suited to solve this scheduling problem was selected. In the second part, was made an implementation of chosen algorithms in Python programming language and the results were discussed. The most optimal algorithm to solve this problem was identified. In the end, all tests and their results for this project were presented.
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Bibliography

[1] Yang, L., Jiang, G., Chen, X., Li, G., Li, T. & Chen, X. (2019). Design of integrated steel production scheduling knowledge network system. Claster Comput. 10197-10206.
[2] Żurada, J. Barski, M., Jędruch, W. (1996). Artificial Neural Networks. Fundamentals of theory and application. Warszawa: PWN. (in Polish).
[3] Janiak, A. (2006). Scheduling in computer and manufacturing systems. Warszawa: Wydawnictwa Komunikacji i Łączności.
[4] Smutnicki, C. (2002). Scheduling algorithms. Warszawa: Akademicka Oficyna Wydawnicza EXIT. (in Polish).
[5] Coffman, E.G. (1980). Task scheduling theory. Warszawa: Wydawnictwa Naukowo-Techniczne. (in Polish).
[6] Janczarek, M. (2011). Managing production processes in the enterprise. Lublin: Lubelskie Towarzystwo Naukowe. (in Polish).
[7] Szeliga, M. (2019) Practical machine learning. Warszawa: PWN. (in Polish).
[8] Raschka, S. (2018) Python machine learning. Gliwice: Helion. (in Polish).
[9] Choi, H-S, Kim, J-S. & Lee, D-H. (2011). Real-time scheduling for reentrant hybrid flow shops: A decision tree based mechanism and its application to a TFT-LCD line. Expert System with Application. 38, 3514-3521.
[10] Agarwal, A., Pirkul, H. & Jacob, V.S. (2003). Augmented neutral network for task scheduling. European Journal of Operational Research. 151, 481-502.
[11] Jain, A.S. & Meeran, S. (1998). Jop-shop scheduling using neutral networks. International Journal of Production Research. 36(5), 1249-1272
[12] Fonseca-Reyna, Y.C., Martinez-Jimenez, Y. & Nowe, A. (2017). Q-Learning algorithm performance for m-machine, n-jobs flow shop scheduling problems to minimize makespan, Revista Investigacion Operacional. 38(3), 281-290.
[13] Dewi, Andriansyah, & Syahriza, (2019). Optimization of flow shop scheduling problem using classic algorithm: case study, IOP Conf. Series: Materials Science and Engineering 506.
[14] Putatunda, K. (2001) Development of austempered ductile cast iron (ADI) with simultaneous high yield strength and fracture toughness by a novel two-step austempering process. Material Science and Engineering A. 315, 70-80.
[15] Dayong Han, Hubei Key, Qiuhua Tang; Zikai Zhang; Jun Cao, (2020). Energy-efficient integration optimization of production scheduling and ladle dispatching in steelmaking plants. IEEE Access. 8, 176170-176187.
[16] Perzyk, M. (2017). The use of production data mining methods in the diagnosis of the causes of product defects and disruptions in the production process. Utrzymanie Ruchu. 4, 45-47. (in Polish).
[17] Perzyk, M., Dybowski, B. & Kozłowski, J. (2019). Introducing advanced data analytics in perspective of industry 4.0 in a die casting foundry. Archives of Foundry Engineering. 19(1), 53-57.
[18] Yescas, M. (2003). Prediction of the Vickers hardness in austempered ductile irons using neural networks. International Journal of Cast Metals Research. 15(5), 513-521.
[19] Report on the contract no. U / 227/2014 implemented at the Foundry Research Institute. (in Polish).
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Authors and Affiliations

D. Wilk-Kołodziejczyk
1 2
ORCID: ORCID
K. Chrzan
2
ORCID: ORCID
K. Jaśkowiec
2
ORCID: ORCID
Z. Pirowski
2
ORCID: ORCID
R. Żuczek
2
ORCID: ORCID
A. Bitka
2
ORCID: ORCID
D. Machulec
3
ORCID: ORCID

  1. AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland
  2. Łukasiewicz Research Network – Krakow Institute of Technology, 73 Zakopiańska Str., 30-418 Kraków, Poland
  3. AGH University of Science and Technology, Kraków, Poland
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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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Abstract

The article is to present the application of genetic algorithm in production scheduling in a production company. In the research work the assumptions of the methodology were described and the operation of the proposed genetic algorithm was presented in details. Genetic algorithms are useful in complex large scale combinatorial optimisation tasks and in the engineering tasks with numerous limitations in the production engineering. Moreover, they are more reliable than the existing direct search algorithms. The research is focused on the effectivity improvement and on the methodology of scheduling of a manufacturing cell work. The genetic algorithm used in the work appeared to be robust and fast in finding accurate solutions. It was shown by experiment that using this method enables obtaining schedules suitable for a model. It
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Authors and Affiliations

Marcin Matuszny
1
ORCID: ORCID

  1. University of Bielsko-Biala, Bielsko-Biała, Poland
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Abstract

Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied.
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Authors and Affiliations

Mateo DEL GALLO
Filippo Emanuele CIARAPICA
Giovanni MAZZUTO
Maurizio BEVILACQUA

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