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Burduk, Anna; Musiał, Kamil; Kochańska, Joanna; Górnicka, Dagmara; Стеценко, А. М.

doi:10.17270/j.log.2019.315

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…We noticed that the issue related to production programs has been approached from several perspectives, as is a complex optimization problem that has been analyzed from different perspectives by different researchers. For example, models such as backpack modelling [23][24][25], the PSO-GA hybrid algorithm [26][27][28], the tabu search algorithm [29][30][31][32], the improved cuckoo search (ICS) [33][34][35], Lagrangian heuristic algorithm and other heuristic algorithms [36][37][38][39][40], the mixed model [36][37][38][39][40][41][42][43], and so on, are other types of models used in the literature.…”

Section: Dao and Marianmentioning

confidence: 99%

Application Research of Soft Computing Based on Machine Learning Production Scheduling

Fülöp

Gubán

et al. 2022

Processes

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An efficient and flexible production system can contribute to production solutions. These advantages of flexibility and efficiency are a benefit for small series productions or for individual articles. The aim of this research was to produce a genetic production system schedule similar to the sustainable production scheduling problem of a discrete product assembly plant, with more heterogeneous production lines, and controlled by one-time orders. First, we present a detailed mathematical model of the system under investigation. Then, we present the IT for a solution based on a soft calculation method. In connection with this model, a computer application was created that analyzed various versions of the model with several practical problems. The applicability of the method was analyzed with software specifically developed for this algorithm and was demonstrated on a practical example. The model handles the different products within an order, as well as their different versions. These were also considered in the solution. The solution of this model is applicable in practice, and offers solutions to better optimize production and reduce the costs of production and logistics. The developed software can not only be used for flexible production lines, but also for other problems in the supply chain that can be employed more widely (such as the problem of delivery scheduling) to which the elements of this model can be applied.

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Section: Dao and Marianmentioning

confidence: 99%

Application Research of Soft Computing Based on Machine Learning Production Scheduling

Fülöp

Gubán

et al. 2022

Processes

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“…In general, the implementation of production activities is carried out in two ways: in steady-state and transient conditions (Burduk et al, 2019). In the first case, it is possible to determine future production levels with high probability, and thus it is possible to develop scenarios of remedial actions for potential threats to production (Bałdowska-Witos et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Identification of the Impact of the Availability Factor on the Efficiency of Production Processes Using the Ahp and Fuzzy Ahp Methods

Wittbrodt

2022

acs

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Maintenance has a key impact on the efficiency of the production processes because the efficiency of the machines determines the ability of the system to produce in accordance with the assumed schedule. The key element of the system performance assessment remains the availability of technological equipment, which directly translates into the efficiency and effectiveness of the performed production tasks. Taking into account the dynamic nature of manufacturing processes, the proper selection of machinery and equipment for the implementation of specific production tasks becomes an issue of particular importance. The purpose of this research was to determine the impact of technical and non-technical factors on the material selection of machine tools for production tasks and to develop a method of supporting the selection of production resources using the AHP and Fuzzy AHP methods. The research was carried out in a manufacturing company from the automotive industry.

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“…Certainly, one of the common strategic business objectives is to strengthen the market position. An enterprise that aims to broaden the group of clients, as well as foster the already existing business relations, must first and foremost be reliable and deliver quality goods within contractual deadlines [1,2]. Therefore, proper planning of works becomes central to sound execution of production processes.…”

Section: Introductionmentioning

confidence: 99%

“…Production scheduling is the solution that can boost the capacity of manufacturers, hence there are numerous scientific publications in the field [3]. Researchers are still taking active efforts to optimise the effectiveness of production jobs scheduling in order to streamline the production planning process [2,4].…”

Section: Introductionmentioning

confidence: 99%

Predictive Scheduling with Markov Chains and ARIMA Models

2020

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Production scheduling is attracting considerable scientific interest. Effective scheduling of production jobs is a critical element of smooth organization of the work in an enterprise and, therefore, a key issue in production. The investigations focus on improving job scheduling effectiveness and methodology. Due to simplifying assumptions, most of the current solutions are not fit for industrial applications. Disruptions are inherent elements of the production process and yet, for reasons of simplicity, they tend to be rarely considered in the current scheduling models. This work presents the framework of a predictive job scheduling technique for application in the job-shop environment under the machine failure constraint. The prediction methods implemented in our work examine the nature of the machine failure uncertainty factor. The first section of this paper presents robust scheduling of production processes and reviews current solutions in the field of technological machine failure analysis. Next, elements of the Markov processes theory and ARIMA (auto-regressive integrated moving average) models are introduced to describe the parameters of machine failures. The effectiveness of our solutions is verified against real production data. The data derived from the strategic machine failure prediction model, employed at the preliminary stage, serve to develop the robust schedules using selected dispatching rules. The key stage of the verification process concerns the simulation testing that allows us to assess the execution of the production schedules obtained from the proposed model.

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Untitled

Cited by 13 publications

References 18 publications

Application Research of Soft Computing Based on Machine Learning Production Scheduling

Application Research of Soft Computing Based on Machine Learning Production Scheduling

Identification of the Impact of the Availability Factor on the Efficiency of Production Processes Using the Ahp and Fuzzy Ahp Methods

Predictive Scheduling with Markov Chains and ARIMA Models

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