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Journal articles on the topic 'Production management model'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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1

Kosieradzka, Anna. "Maturity Model for Production Management." Procedia Engineering 182 (2017): 342–49. http://dx.doi.org/10.1016/j.proeng.2017.03.109.

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2

Danilovic, Dusan, Vesna Karovic-Maricic, and Branko Lekovic. "Small oilfield integrated production management model." Podzemni radovi, no. 28 (2016): 19–27. http://dx.doi.org/10.5937/podrad1628019d.

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3

Likhacheva, L. B., L. I. Nazina, A. V. Lomanova, and N. A. Chernykh. "Information model of production system management." Proceedings of the Voronezh State University of Engineering Technologies 80, no. 4 (March 21, 2019): 128–32. http://dx.doi.org/10.20914/2310-1202-2018-4-128-132.

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Improvement of production systems of the organization is carried out through the introduction of quality management systems, changes in the model of production organization, the use of modern approaches to improve product quality and customer satisfaction. Measurement, evaluation and analysis of the production system allows to set the direction of activity to improve production processes and to develop activities aimed at ensuring the effectiveness of the whole system. The production system is an open system, it is connected and exchanged with the external environment information, resources, etc. the production system is Called the operating system, which consists of three subsystems: processing subsystem, directly related to the technological processes of raw materials and semi-finished products conversion into finished products; support subsystem, which performs auxiliary functions necessary for the implementation of the main technological processes; planning and control subsystems that receive and process information from the internal and external environment of the organization. The task of building an automated information system is connected with the need to integrate with the subsystem of data collection and analysis, visual representation of information for decision-making at all levels. Building an information management system of the production system is impossible without a powerful infrastructure, without a single information system support and process control. The proposed information system will help to automate the processes of management of the organization, quickly form the strategic and tactical goals of the organization. Within a given period of time, data will be collected and analyzed from the internal and external environment, timely analysis of deviations of the values of indicators from the planned values. The results of information processing will be timely visualized both for each employee and the production system of the organization as a whole.
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Muniz, Jorge, Edgard Dias Batista, and Geilson Loureiro. "Knowledge‐based integrated production management model." Journal of Knowledge Management 14, no. 6 (October 26, 2010): 858–71. http://dx.doi.org/10.1108/13673271011084907.

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5

Kristiadi Harun, Marinus, and Hariyatno Dwiprabowo. "MODEL RESOLUSI KONFLIK LAHAN DI KESATUAN PEMANGKUAN HUTAN PRODUKSI MODEL BANJAR." Jurnal Penelitian Sosial dan Ekonomi Kehutanan 11, no. 4 (December 31, 2014): 265–80. http://dx.doi.org/10.20886/jpsek.2014.11.4.265-280.

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6

Kamath, Nagaraj, and Lewlyn L. R. Rodrigues. "The new conceptual model for print management: Total Production Management." International Journal of Productivity and Quality Management 1, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijpqm.2019.10022010.

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7

Kamath, H. Nagaraj, and Lewlyn L. R. Rodrigues. "The new conceptual model for print management: total production management." International Journal of Productivity and Quality Management 29, no. 3 (2020): 285. http://dx.doi.org/10.1504/ijpqm.2020.105985.

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8

Syrový, O., and V. Podpěra. "Simulation mathematical model of expert system for working processes management." Research in Agricultural Engineering 55, No. 1 (February 11, 2009): 1–9. http://dx.doi.org/10.17221/12/2008-rae.

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The elementary simulation mathematical models presented in this article are related with the sub-system Crop production of the expert system for the decision support in technological and working processes management and their optimisation. Along with this sub-system, the expert system also involves the sub-systems Livestock production and Material handling which is further divided into the parts Transport and Storage. The boundary between the individual parts of the expert system is usually a short-term or long-term material storage. The relative individual sub-systems are mutually connected through the information flow. For each of the sub-systems, specific simulation models are created. The simulation models in the expert system investigated replace the complex of general standards and norms used in other expert systems. The simulation models allow to take into consideration the concrete natural and production conditions (area, plots shape and inclination, soil type, transport routes length and surface, fertilisers doses, crops yields etc.) and also the technological systems utilised during the realisation of operations in working processes (technical, exploitation, energy, economical or energy means, attached vehicles, machines and equipment and method of their work) and the calculation of the parameters utilised. The simulation models also allow the creation of suitable working, and transport sets to choose their optimal variants for the given conditions. In comparison with the utilised standards and norms, the parameters computed through the simulation models significantly improve the data which represent the output from the expert system.
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9

Polyanskov, Yuriy, Igor Lutoshkin, Margarita Yardaeva, and Svetlana Lipatova. "Model of production schedule modification assessment for digital production management systems." IOP Conference Series: Materials Science and Engineering 497 (April 2, 2019): 012082. http://dx.doi.org/10.1088/1757-899x/497/1/012082.

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10

Scanlon, Michael, Vikram Sharma, and Georg Zangl. "Model centric production management systemsy—opportunity and challenges." APPEA Journal 50, no. 2 (2010): 690. http://dx.doi.org/10.1071/aj09054.

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While modelling technology is routinely used in oil and gas to control and optimise process facilities and downstream plants, the upstream sector has been slow to adopt model-based control systems, or the models are very coarse and mono-dimensional, reducing confidence in their predictive capability. Further, the models are discipline specific, and the interdependence between the subsurface and the surface is poorly evaluated. This is particularly relevant to coal seam gas developments, where well productivity planning and management needs to be considered in the context of the complete production value chain, and the dependencies between the gathering system and the processing plants considered in optimising the contract nominations. A key breakthrough technology will be a model-based control system that can assimilate all the available data, put the data into context, represent the full value chain, assess the relative impact of the components of the production train, and then assess the decision alternatives in the context of deliverability, processing cost, HSE and market opportunity in sufficient time to have an impact. Advances in data acquisition, transmission and storage, coupled with advances in computational efficiency and software engineering, mean that integrated modelling systems are realisable today.Collaboration between technology providers has delivered software tools that offer a unified perspective across the subsurface, surface gathering systems and facility worlds. The presentation will explore the technology capability, the application opportunity and organisational requirement to fully realise the potential of integrated asset models.
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11

Leonov, A. V., and A. Yu Pronin. "The dynamic model for high-tech production management." National Interests: Priorities and Security 15, no. 5 (May 16, 2019): 798–816. http://dx.doi.org/10.24891/ni.15.5.798.

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12

ZHU, Yan, Wei-Xing CAO, Ting-Bo DAI, Yong-Chao TIAN, and Xia YAO. "A Knowledge Model System for Wheat Production Management." Pedosphere 17, no. 2 (April 2007): 172–81. http://dx.doi.org/10.1016/s1002-0160(07)60023-x.

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13

Romo-González, Dra Ana Eugenia, and Dra María de los Ángeles Villalobos. "Knowledge Management Model for Scientific and Technological Production." IOSR Journal of Business and Management 18, no. 10 (October 2016): 114–24. http://dx.doi.org/10.9790/487x-181003114124.

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14

Mehli-Qaissi, Jamila, Amadou Coulibaly, and Bernard Mutel. "Product data model for production management and logistics." Computers & Industrial Engineering 37, no. 1-2 (October 1999): 27–30. http://dx.doi.org/10.1016/s0360-8352(99)00016-9.

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15

RAY, S. R., and S. WALLACE. "A production management information model for discrete manufacturing." Production Planning & Control 6, no. 1 (January 1995): 65–79. http://dx.doi.org/10.1080/09537289508930254.

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16

Ferneda, Edilson, Hercules Antonio Do Prado, and Ricardo Coelho De Faria. "Applying combinatorial neural model for vegetable production management." International Journal of Reasoning-based Intelligent Systems 3, no. 2 (2011): 132. http://dx.doi.org/10.1504/ijris.2011.042267.

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17

Kukartsev, V. V., V. S. Tynchenko, A. I. Cherepanov, N. N. Dzhioeva, and V. E. Petrenko. "Model of production resource management for manufacturing enterprise." Journal of Physics: Conference Series 1661 (November 2020): 012178. http://dx.doi.org/10.1088/1742-6596/1661/1/012178.

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18

Punt, A. E. "The performance of a production-model management procedure." Fisheries Research 21, no. 3-4 (January 1995): 349–74. http://dx.doi.org/10.1016/0165-7836(94)00302-d.

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19

Ben-Daya, Mohamed, Moncer Hariga, and Syed Naveed Khursheed. "Economic production quantity model with a shifting production rate." International Transactions in Operational Research 15, no. 1 (January 2, 2008): 87–101. http://dx.doi.org/10.1111/j.1475-3995.2007.00620.x.

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20

Meier, Klaus, and Jean-Marie Proth. "Scheduling in large scale production systems: A medium term production management model." Engineering Costs and Production Economics 14, no. 1 (May 1988): 67–74. http://dx.doi.org/10.1016/0167-188x(88)90054-7.

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21

Alot, Zbigniew. "The Model of the Production Process for the Quality Management." Foundations of Management 9, no. 1 (February 23, 2017): 43–60. http://dx.doi.org/10.1515/fman-2017-0004.

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Abstract This article is a result of the research on the models of the production processes for the quality management and their identification. It discusses the classical model and the indicators for evaluating the capabilities by taking as its starting point the assumption of the normal distribution of the process characteristics. The division of the process types proposed by ISO 21747:2006 standard introducing models for non-stationary processes is presented. A general process model that allows in any real case to precisely describe the statistical characteristics of the process is proposed. It gives the opportunity for more detailed description, in comparison to the model proposed by ISO 21747:2006 standard, of the process characteristics and determining its capability. This model contains the type of process, statistical distribution, and the method for determining the capability and performance (long-term capability) of the process. One of the model elements is proposed, own classification and resulting set of process types. The classification follows the recommendations of ISO 21747:2006 introducing models for the non-stationary processes. However, the set of the process types allows, beyond a more precise description of the process characteristics, its usage to monitor the process.
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22

Horstman, Erik M., Karin R. Bryan, Julia C. Mullarney, and Conrad A. Pilditch. "MODEL VERSUS NATURE: HYDRODYNAMICS IN MANGROVE PNEUMATOPHORES." Coastal Engineering Proceedings, no. 35 (June 23, 2017): 19. http://dx.doi.org/10.9753/icce.v35.management.19.

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Water flows through submerged and emergent vegetation control the transport and deposition of sediment in coastal wetlands. Many past studies into the hydrodynamics of vegetation fields have used idealized vegetation mimics, mostly rigid dowels of uniform height. In this study, a canopy of real mangrove pneumatophores was reconstructed in a flume to quantify flow and turbulence within and above this canopy. At a constant flow forcing, an increase in pneumatophore density, from 71 m-2 to 268 m-2, was found to cause a reduction of the within-canopy flow velocities, whereas the over-canopy flows increased. Within-canopy velocities reduced to 46% and 27% of the free-stream velocities for the lowest and highest pneumatophore densities, respectively, resulting in stronger vertical shear and hence greater turbulence production around the top of the denser pneumatophore canopies. The maximum Reynolds stress was observed at 1.5 times the average pneumatophore height, in contrast to uniform-height canopies, in which the maximum occurs at approximately the height of the vegetation. The ratios of the within-canopy velocity to the free-stream velocity for the pneumatophores were found to be similar to previous observations with uniform-height vegetation mimics for the same vegetation densities. However, maxima of the scaled friction velocity were two times smaller over the real pneumatophore canopies than for idealized dowel canopies, due to the reduced velocity gradients over the variable-height pneumatophores compared to uniform-height dowels. These findings imply that results from previous studies with idealized and uniform vegetation mimics may have limited application when considering sediment transport and deposition in real vegetation, as the observed turbulence characteristics in non-uniform canopies deviate significantly from those in dowel canopies.
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23

Schuh, Günther, Thomas Gartzen, and Felix Basse. "Scientific Management 2.0 – A Design Model for Experimental Research in Production Management." Advanced Materials Research 1018 (September 2014): 571–79. http://dx.doi.org/10.4028/www.scientific.net/amr.1018.571.

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Reliable and accurate predictions on future states of production systems are the objective of production theories. In this paper, the authors determined shortcomings of current deterministic models and traced them back to the poor theoretical basis of scientific research in the area. The observations resulted in the development of the conceptScientific Management 2.0as an appropriate research methodology for production management. This new empirical approach takes into account three requirements to scientifically precise investigations: It expands existing theory by socio-technical aspects, uses embedded experiments as a profound basis for investigation and provides a design that warrants the methodical exactness required. RWTH Aachen’sDemonstration Factoryrepresents an adequate infrastructure to prove feasibility and performance of the new approach.
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24

Krauth, J., and J. Warschat. "An optimal production-mix model." European Journal of Operational Research 42, no. 2 (September 1989): 142–51. http://dx.doi.org/10.1016/0377-2217(89)90317-2.

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25

Cyplik, P., L. Hadas, and M. Fertsch. "Production planning model with simultaneous production of spare parts." International Journal of Production Research 47, no. 8 (February 27, 2009): 2091–108. http://dx.doi.org/10.1080/00207540802644837.

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26

Nakai, Teruhisa. "Production management model for goods produced through many intermediates." Journal of Information and Optimization Sciences 17, no. 1 (January 1996): 85–96. http://dx.doi.org/10.1080/02522667.1996.10699264.

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27

Javied, Tallal, Marius Deutsch, and Jörg Franke. "A model for integrating energy management in lean production." Procedia CIRP 84 (2019): 357–61. http://dx.doi.org/10.1016/j.procir.2019.04.252.

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28

Bretthauer, Kurt, Bala Shetty, Siddhartha Syam, and Susan White. "A Model for Resource Constrained Production and Inventory Management." Decision Sciences 25, no. 4 (July 1994): 561–77. http://dx.doi.org/10.1111/j.1540-5915.1994.tb01860.x.

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29

Bourrières, J. P., and T. Lecompte-Alix. "A linear model for production management—optimal solving policies." International Journal of Production Research 48, no. 18 (September 14, 2009): 5415–32. http://dx.doi.org/10.1080/00207540903067193.

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30

Biryukova, V. V. "Production System Management Based on a Balanced Development Model." IOP Conference Series: Materials Science and Engineering 753 (March 7, 2020): 062014. http://dx.doi.org/10.1088/1757-899x/753/6/062014.

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31

Anastácio, P. M., S. N. Nielsen, J. C. Marques, and S. E. Jørgensen. "Integrated production of crayfish and rice: a management model." Ecological Engineering 4, no. 3 (April 1995): 199–210. http://dx.doi.org/10.1016/0925-8574(94)00059-e.

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32

Speranza, M. G., and W. Ukovich. "Applying an optimization model to production management and logistics." Computer Integrated Manufacturing Systems 5, no. 3 (August 1992): 239–44. http://dx.doi.org/10.1016/0951-5240(92)90035-b.

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33

Sladoljev, Želimir. "Search for a Model of Effective Ship Production Management." Journal of Ship Production 12, no. 04 (November 1, 1996): 220–29. http://dx.doi.org/10.5957/jsp.1996.12.4.220.

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The objective of the paper is to explain problems and offer a simple model of the production management in shipyards with an uneven production. Shipyards with flexible and different building programs frequently meet disturbances in their production caused by changes in designs and workshop drawings; these changes are due to owners' demands, overloading of capacity, and many other reasons. Such shipyards are constrained to consider it as their normal way of building ships. In order to stay competitive they have to organize the work-preparing process with integrated production engineering activities and an effective coordination of all participants in the building process. Essential scopes as well as the basic structure of the production management of a flexible shipyard are explained.
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34

Bonamigo, Andrei, Helio Aisenberg Ferenhof, Rafael Tezza, and Fernando Antonio Forcellini. "Management Model for Dairy Production Based on a Business Ecosystem Concept." Journal of Business Ecosystems 1, no. 1 (January 2020): 38–62. http://dx.doi.org/10.4018/jbe.2020010103.

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This article proposes and evaluates a management model to boost the dairy production development from the perspective of a business ecosystem concept. This study proposes a management model based on two bibliographic systematic reviews of the dairy production. To analyze the resulting portfolio, the authors used a prior content analysis proposed by another researcher, which served as the basis for analysis and discussion of the elements for the model construction. Then, the model was tested with dairy experts. This research identified a total of 13 models which apply to dairy production and 2 models from the business ecosystem concept perspective. Consulting with 450 experts in the dairy sector, the authors empirically evaluated the model. The authors found that the results demonstrated an important validity indicator and model reliability in practice. Based on the finding, this article proposes a management model for dairy production from the business ecosystem concept.
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35

Fedotova, I. V., and S. A. Mikhaylova. "Justifi cation of occupational risk management model in polyurethane foam production." Russian Journal of Occupational Health and Industrial Ecology 1, no. 10 (November 13, 2019): 844–49. http://dx.doi.org/10.31089/1026-9428-2019-59-10-844-849.

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Introduction. Employees of production of polyurethane foams (PUF) are exposed to various harmful occupational factors that cause an increased risk of respiratory disorders, nervous system, cancer.The aim of the study was to substantiate the occupational risk management system aimed at its reduction on the basis of the analysis of working conditions and health of employees of modern production facilities of PUF.Materials and methods. The characteristics of working conditions at 5 modern PUF productions are given on the basis of the analysis of a large amount of laboratory and instrumental research of occupational factors; the state of workers’ health in the production of PUF was assessed by the results of periodic medical examination over a number of years. For risk assessment, indicators of odds ratio, relative risk, etiological fraction of occupational factors in a group of 5 years of work experience or more compared to a group of 0–4 years of work experience for men and women in nosological forms were calculated with an assessment of differences in 95% confidence intervals and the magnitude of chi-square (c2 ).Results. Working conditions in the production of PUF are characterized by exposure to a complex of chemicals, of which the most significant are isocyanates and amines. Workers are also affected by factors such as increased noise, the severity of work. Working conditions in the production of polyurethane foam are evaluated as harmful to the first to third degrees. The increased risk of development of diseases of endocrine, nervous, respiratory systems and musculoskeletal system in workers was revealed, the level of which increases with the increase of work experience in the production of PUF. The requirements to the organization of the production process, monitoring of working conditions, improvement of medical care aimed at the prevention of violations of the health status of employees are substantiated.Conclusions. Modern production of PUF is characterized by a complex of harmful occupational factors that have an adverse impact on the health of employees, which requires the introduction of a unified model of occupational risk management aimed at reducing it.
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36

Ivanenko, Tetiana, Viktor Hrushko, and Anatolii Frantsuz. "Optimal investment decision making on the model of production enterprise with limited resources." Investment Management and Financial Innovations 15, no. 4 (October 23, 2018): 61–68. http://dx.doi.org/10.21511/imfi.15(4).2018.05.

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Investments are among the most important factors of national economic growth. Selection of optimal investment project is the first priority for any enterprise with limited financial resources. This study is dedicated to a choice among mutually exclusive projects, which are impossible to complete partially, so, one project must be chosen and all others must be rejected. An investor must find among all possible projects the one that allows to better achieve all investor’s aims. A mathematical model of multi-purpose multi-criteria investor decision making is proposed for investment project selection problem. Efficiency and riskiness of studied projects are evaluated using such indicators as profit, rate of return, payback period, marginal cost of capital, also taking into account subjective characteristics, namely the investor’s attitude towards financial risks, importance assessment of decision making criteria, etc. Decision making assessment methods for the situations of risk and uncertainty are applied to resolve the problem of optimal project selection, such as Wald’s pessimistic criterion, maximax optimistic criterion, as well as Hurwicz’s, Laplace’s, Bayes- Laplace, Hodges-Lehmann criteria, and Savage’s minimax risk criterion. Calculations carried out and results obtained indicate that the best investment project chosen that way will provide the highest absolute profit, despite certain disadvantages such as lower rate of return, longer payback period and higher risk than other projects.
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37

Ritchie, Mark E. "Grazing Management, Forage Production and Soil Carbon Dynamics." Resources 9, no. 4 (April 23, 2020): 49. http://dx.doi.org/10.3390/resources9040049.

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Soil carbon pools remain a target for sequestering greenhouse gases, but appropriate land management options to achieve such sequestration remain uncertain. Livestock grazing can have profound positive or negative effects on soil carbon. Different models for assessing the influences of grazing are available, but few explicitly account for different management options on soil organic carbon (SOC). Here, I link a previous simple SOC dynamic model (SNAP) to a recent model of episodic grazing and its effects on primary production. The resulting combined model, called SNAPGRAZE, assesses the potential effects of grazing management on SOC across a range of climates with only eight climate, soil, and management input variables. SNAPGRAZE predicts that, at high stocking densities relative to those sustainable under continuous grazing and at higher mean annual temperature and precipitation, short-duration, high stocking density (SDHSD) grazing schemes can enhance forage production and increase stocks of soil organic carbon. Model predictions for current SOC, given a known 50 year grazing history, agrees well with data from nine private ranches in the North American Great Plains. SNAPGRAZE may provide a framework for exploring the consequences of grazing management for forage production and soil carbon dynamics.
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38

He, Yong, and Ju He. "A Production Model for Deteriorating Inventory Items with Production Disruptions." Discrete Dynamics in Nature and Society 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/189017.

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Disruption management has recently become an active area of research. In this study, an extension is made to consider the fact that some products may deteriorate during storage. A production-inventory model for deteriorating items with production disruptions is developed. Then the optimal production and inventory plans are provided, so that the manufacturer can reduce the loss caused by disruptions. Finally, a numerical example is used to illustrate the model.
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39

Kowalski, Zygmunt, and Kinga Krupa-Żuczek. "A model of the meat waste management." Polish Journal of Chemical Technology 9, no. 4 (December 1, 2007): 91–97. http://dx.doi.org/10.2478/v10026-007-0098-4.

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A model of the meat waste management The European Union produces about 18 million tons of waste from meat industry per year. The real danger of the BSE disease caused a necessity of looking for a new alternative solution of meat waste management. The proposed solution of meat industry waste management would create meat production waste free with the use of the cleaner production method. Cleaner production includes: pollution prevention, reduction of the source, recovery of materials and energy (for example: the recovery of blood plasma and protein hydrolisate from bone sludge) and their recycling. The thermal processing of meat industrial waste (bone sludge, meat-bone meal and odour) is anticipated, too. Ashes from meat calcining have the phosphorus content close to its concentration, of the typical phosphoric raw materials. That confirmed the possibility of using such ashes as the substitute of phosphoric raw materials. The target model of waste free meat waste management included the results of the implemented and current research.
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40

Goyal, S. K., S. G. Deshmukh, and A. Subash Babu. "A Model for Integrated Procurement-Production Systems." Journal of the Operational Research Society 41, no. 11 (November 1990): 1029. http://dx.doi.org/10.2307/2582898.

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41

Goyal, S. K., S. G. Deshmukh, and A. Subash Babu. "A Model for Integrated Procurement-Production Systems." Journal of the Operational Research Society 41, no. 11 (November 1990): 1029–35. http://dx.doi.org/10.1057/jors.1990.161.

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42

Alkhedher, M. J., M. A. Darwish, and Abdulrahman R. Alenezi. "Stochastic inventory model for imperfect production processes." International Journal of Logistics Systems and Management 15, no. 1 (2013): 32. http://dx.doi.org/10.1504/ijlsm.2013.053237.

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43

Khouja, Moutaz, and Abraham Mehrez. "Economic Production Lot Size Model with Variable Production Rate and Imperfect Quality." Journal of the Operational Research Society 45, no. 12 (December 1994): 1405. http://dx.doi.org/10.2307/2583934.

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44

Khouja, Moutaz, and Abraham Mehrez. "Economic Production Lot Size Model with Variable Production Rate and Imperfect Quality." Journal of the Operational Research Society 45, no. 12 (December 1994): 1405–17. http://dx.doi.org/10.1057/jors.1994.217.

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45

Bowers, Melissa R., and James P. Jarvis. "A Hierarchical Production Planning and Scheduling Model." Decision Sciences 23, no. 1 (January 1992): 144–59. http://dx.doi.org/10.1111/j.1540-5915.1992.tb00381.x.

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46

Mishra, Vinod Kumar, and Lal Sahab Singh. "Production inventory model for time dependent deteriorating items with production disruptions." International Journal of Management Science and Engineering Management 6, no. 4 (January 2011): 256–59. http://dx.doi.org/10.1080/17509653.2011.10671170.

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47

Kaufman, Allen, and Ernie Englander. "A team production model of corporate governance." Academy of Management Perspectives 19, no. 3 (August 2005): 9–22. http://dx.doi.org/10.5465/ame.2005.18733212.

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48

Titov, V. V., and D. A. Bezmelnitsyn. "ORGANIZATION OF COMPLEX MANAGEMENT MODEL-BASED PRODUCTION OPERATIONAL-CALENDAR AND NETWORK PLANNING." Economics Profession Business, no. 2 (June 10, 2020): 93–100. http://dx.doi.org/10.14258/epb201977.

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Abstract:
The paper considers a methodological approach to the system coordination of operational-calendar, network and tactical planning in the conditions of individual and serial production of complex high-tech products. It is proposed to organize an operational management system with this type of production based on the use of optimization tasks for volume-calendar, operational-calendar and network planning. Planning in such productions is usually organized on the basis of network planning, determining the critical path. However, this does not solve the main problem — resource usage restrictions are not taken into account, and an effective algorithm for solving it is not developed. Here you can create a set of options for leading operations to perform them over time, which allows you to shift work schedules in one direction or another, and take into account the use of resources over time. It is also important that the problem of setting the duration of work (operations) is not approximately solved in integers. The optimization criterion is a minimum of work completion time and a minimum of work in progress. In General, the problem of network production planning, taking into account resource constraints, is reduced to the problem of linear integer programming. Operational production management is organized as an iterative, rolling process (reducing production risks), implemented by a single task with tactical management. In General, the developed system of models is undoubtedly of interest for the theory and practice of industrial enterprise management.
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49

Bellinger, William K. "A utility-production model of union behavior." Journal of Labor Research 10, no. 1 (March 1989): 135–45. http://dx.doi.org/10.1007/bf02685522.

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50

Rashford, Benjamin S., Bruce D. Dugger, and Richard M. Adams. "Application of a Bioeconomic Production Model to Improve Wildlife Management." Journal of Wildlife Management 72, no. 2 (February 2008): 510–17. http://dx.doi.org/10.2193/2006-464.

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