A possibility of Business Rules application in production planning

• Autorzy: Duda J. , Stawowy A.
• Języki publikacji: EN

Abstrakty

EN

The complexity of scheduling problems in production systems and their impact on production functioning (in the area of technology and economics) cause that it is necessary to search for and develop new methods and algorithms solving such problems. One of the latest approach to computer support of business activities is Business Rules Management (BRM). This approach can be used for quantitative as well as qualitative decisions support, among them for production planning. The paper describes Business Rules application in scheduling and planning problem for manufacturing iron castings. Our research confirm that BRM can be employed as a heuristic module in production planning systems.

Słowa kluczowe

EN

application of information technology; foundry industry; production planning

PL

zastosowanie technologii informatycznych; przemysł odlewniczy; planowanie produkcji

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2010
• Tom: Vol. 10, iss. 2
• Strony: 27--32
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Duda J.

• Faculty of Management, AGH University of Science and Technology, Gramatyka 10, 30-067 Krakow, Poland

autor

Stawowy A.

• Faculty of Management, AGH University of Science and Technology, Gramatyka 10, 30-067 Krakow, Poland

Bibliografia

• [1] D. Waters, Operations Management: Producing Goods and Servicess, FT Prentice Hall (2001).
• [2] M. van Eck, Advanced Planning and Scheduling. Is logistic everything? BWI paper, Universiteit Amsterdam (2003).
• [3] E. Hackmack, VAI-PPC – An Integrated Production Planning and Control System for Steel Industry, Proceedings of CPC’93, Seul (1993) 218-228.
• [4] R.G. Ross, Principles of the Business Rule Approach, Addison Wesley (2003).
• [5] B. von Halle, L. Goldberg (ed.), The Business Rule Revolution. Running Business the Right Way, Happy About, Silicon Valley (2006).
• [6] The Business Rules Manifesto, www.business-rulesgroup.org/brmanifesto.htm
• [7] The role of a rules architect, www.ibm.com/ developerworks/library/ar-busrules2
• [8] ILOG Business Rules Management System, www.ilog.com/products/businessrules/index.cfm
• [9] A. Günter, C. Kühn, Knowledge-Based Configuration. Survey and Future Directions, Lecture Notes in Computer Science, vol. 1570 (1999) 47-66.
• [10] C. Huyck, Lectures on Knowledge Based Systems for Business, Middlesex University (2008).
• [11] M.S. Fox., S. Smith, ISIS: A Knowledge-Based System for Factory Scheduling, Expert Systems Journal, vol. 1, No. 1 (1984) 25-49.
• [12] S.F. Smith, M.S. Fox, P.S. Ow, Constructing and Maintaining Detailed Production Plans: Investigations into the Development of Knowledge-based Factory-scheduling Systems, The AI Magazine, vol. 7, No. 4 (1986) 45-61.
• [13] S.J. Noronha, V.V.S. Sarma, Knowledge-Based Approaches for Scheduling Problems: A Survey, IEEE Transactions on Knowledge and Data Engineering, vol. 3, No. 2 (1991) 160-171.
• [14] S. Adiga, W.T. Lin, An object-oriented architecture for knowledge-based production scheduling systems, Journal of Intelligent Manufacturing, vol. 4, No. 2 (1993) 139-150.
• [15] S. Smith, Knowledge-based production management approaches, results and prospects, Production Planning & Control, vol. 3, No. 4 (1992) 350-380.
• [16] K. Metaxiotis, D. Askounis, J. Psarras, Expert systems in production planning and scheduling: a state-of-the-art survey, Journal of Intelligent Manufacturing, vol. 13, No. 4 (2001) 253-260.
• [17] G. Sullivan, K. Fordyce, IBM Burlington’s Logistics Management System, Interfaces, vol. 20, No. 1 (1990) 43-64.
• [18] L. Custodio, J. Sentieiro, C. Bispo, Production planning and scheduling using a fuzzy decision system, IEEE Transactions on Robotics and Automation, vol. 10, No. 2 (1994) 160-168.
• [19] N.C. Tsourveloudis, E. Dretoulakis, S. Ioannidis, Fuzzy work-in-process inventory control of unreliable manufacturing systems, Information Sciences, vol. 127, No. 1-2 (2000) 69-83.
• [20] V. Subramaniam, T. Ramesh, G.K. Lee, Y.S. Wongand, G.S. Hong, Job Shop Scheduling with Dynamic Fuzzy Selection of Dispatching Rules, The International Journal of Advanced Manufacturing Technology, vol. 16, No. 10 (2000) 759-764.
• [21] S.C. Feng, Preliminary design and manufacturing planning integration using web-based intelligent agents, Journal of Intelligent Manufacturing, vol. 16, No. 4-5 (2005) 423-437.