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Specify of bottlenecks in conditions of unit and small-batch production

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The manufacturing industry has evolved over the past several decades in response to changing customer needs. Customers have become more demanding and want products that can meet their specific individual requirements. The standard products previously produced in large batches are not sufficient to meet this variety demanded. Given the increased competition, both locally and globally, companies must also now respond faster to get and keep customers. Enterprises were forced to unit and small-batch production. Currently, advanced planning systems are coming into use, however their cost exceeds the possibilities of small and medium enterprises and algorithms used often require great customization to industries' needs and conditions of unit and small-batch production. The paper has been drawn on the basis of research on overloads of moving bottlenecks in conditions of unit and small batch production in real conditions having a big number of resources and tasks. The methods used so far are not capable of finding the global optimum of such big data ranges. The author took on building a heuristic algorithm, which could find solution good enough and based on TOC (Theory of Constraints) assumptions and verification of assumptions using tests in real production systems. The above method found application to the industrial scale, as extension of the ERP class system.
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Bibliografia
  • [1] Corbett T.,Throughput Accounting, North River Press Publishing Corporation, 1999.
  • [2] Goldratt E., Cox J.,The Goal - A Process of Ongoing Improvement, 2nd Rev. Ed., North River Press Publishing Corporation: Great Barrington MA, 1992.
  • [3] Gupta M., Kohli A., “Enterprise resource planning systems and its implications for operations function”,Technovation, no. 26, 2006, pp. 687-696.
  • [4] Jones T.C., Dugdale D., “Theory of Constraints: Transforming Ideas?”, British Accounting Review, no. 30,1998, pp. 73-91.
  • [5] Singiresu R.S.,Engineering Optimization - Theory and Practice, 3 Edition, John Wiley & Sons, 1996.
  • [6] Robertson N., Perera T., “Automated data collection for simulation?”,Simulation Practice and Theory, no. 9, 2002, pp. 349-364.
  • [7] Schmidt G., “Modelling production scheduling systems”,International Journal of Production Economics ,no. 46-47, 1996, pp. 109-118.
  • [8] Smutnicki C., Algorithms of Arranging Tasks, Akademicka Oficyna Wydawnicza EXIT: Warszawa, 2002, (In Polish).
  • [9] Zulch G., Stowasser S., “The Digital Factory: An instrument of the present and the future”, Computers in Industry, no. 56, 2005, pp. 323-324.
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bwmeta1.element.baztech-article-BUJ5-0025-0014
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