基于案例推理的快速成本评估方法研究
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摘要
随着现代科学技术的迅猛发展和用户个性化需求的日益增强，20世纪90年代中后期，大规模定制(mass customization，MC)作为一种新的生产模式应运而生。最近几年，随着供应链管理的出现和Internet应用的增长，MC生产企业逐渐意识到改善本企业对顾客需求的快速有效的响应能力，能给企业带来巨大的利益和竞争优势。鉴于此，本文运用基于案例推理的方法研究了大规模定制生产环境下的快速产品成本评估问题，提出了基于案例推理的快速成本评估基本模型和算法。
本文在研究、继承已有国内外研究成果的基础上，(1)采用逻辑BOM作为本文研究的前提和基础，并引入了基于案例推理的方法；(2)建立了双层基于案例推理的快速成本评估模型和通用算法；(3)应用案例描述的通用形式(没有严格的属性值域)，在CBR系统中提出了一种模糊相似性评估方法，并建立了模糊案例相似性评估模型和通用算法；(4)运用上述理论与方法，进行了实例分析。
With the swift and violent development of current science and technology and the increasing customization demand, mass customization (MC) emerged as a new manufacturing paradigm in the middle and late of 1990s. In recent years, as the emerging of supply chain management and the increasing use of Internet, mass customization companies are becoming realized that improving the rapid and efficient response capacity to customers demand can lead to immense profit and competition advantage. So, this thesis studies the rapid cost estimation for mass customization with case-based reasoning and proposes corresponding model and arithmetic.
Base on studying and inheriting existing domestic and international achievement, this thesis (1) uses logic BOM as the precondition and groundwork and introduces the case-based reasoning method; (2) proposes double-level CBR model of rapid cost estimation; (3) proposes a fuzzy similarity estimation method with the common form of case representation (without rigid properties range) in the CBR system and establishes the fuzzy similarity estimation model and general arithmetic; (4) analyses examples with the theories and methods mentioned above.
本文在研究、继承已有国内外研究成果的基础上，(1)采用逻辑BOM作为本文研究的前提和基础，并引入了基于案例推理的方法；(2)建立了双层基于案例推理的快速成本评估模型和通用算法；(3)应用案例描述的通用形式(没有严格的属性值域)，在CBR系统中提出了一种模糊相似性评估方法，并建立了模糊案例相似性评估模型和通用算法；(4)运用上述理论与方法，进行了实例分析。
With the swift and violent development of current science and technology and the increasing customization demand, mass customization (MC) emerged as a new manufacturing paradigm in the middle and late of 1990s. In recent years, as the emerging of supply chain management and the increasing use of Internet, mass customization companies are becoming realized that improving the rapid and efficient response capacity to customers demand can lead to immense profit and competition advantage. So, this thesis studies the rapid cost estimation for mass customization with case-based reasoning and proposes corresponding model and arithmetic.
Base on studying and inheriting existing domestic and international achievement, this thesis (1) uses logic BOM as the precondition and groundwork and introduces the case-based reasoning method; (2) proposes double-level CBR model of rapid cost estimation; (3) proposes a fuzzy similarity estimation method with the common form of case representation (without rigid properties range) in the CBR system and establishes the fuzzy similarity estimation model and general arithmetic; (4) analyses examples with the theories and methods mentioned above.
引文
[1] S. Davis. From future perfect: Mass customization. Planning Review, 1989, 17(2), pp. 16-2
[2] Alvin.Toftler. Powershift. Arts and Licensing International Inc., 1990
[3] Jeff C. Having it their way: How to successfully customize your mass product. Kimbell Proceedings-American Production and Inventory Control Society. 1997, pp.89-91
[4] Duray, Rebecca, Ward and Peter T. Identification and categorization of mass customization configurations. Proceeding of the 1996 27th Annual Meeting of the Decision Sciences Institute. Part 3. Conference Date: 1996. 11. Proceedings-Atlanta. pp. 1307-1309
[5] Tseng, Mitchell M.; Lei, Ming and Su, Chuanjun, Collaborative control system for mass customization manufacturing. CIRP Annuals-Manufacturing Technology, 1997, 46(1), pp.373-376
[6] 大批量定制技术及其应用，祁国宁，顾新建，谭建容等著，机械工业出版社，2003．10，pp．49-52
[7] Pine B J Ⅱ and Gilmore J H. The Four Faces of Mass Customization [J]. Harvard Business Review, 1997, 75 (1), pp. 91-101
[8] Lample J and Mintzberg H.Customizing Customization [J]. Sloan Management Review, 1996, 38 (1) pp.21-30
[9] Alford D Sacker and P Nelder G.Mass Customization-an Automotive Perspective[J]. Int. J. of Production Economics, 2000, 65, pp. 99-110.
[10] 大批量定制的若干理论与方法问题研究，李仁旺，浙江大学博士论文，1999
[11] 祁国宁，顾新建，李仁旺 大批量定制及其模型的研究[J]，计算机集成制造系统 2000，6(2)pp．41-45
[12] Cullinance, T. P., S.V. Kamarthi, N. Wongvasu, and P.S.S. Chinnaiah (1997, July). A Generic IDEF0 Model of a Production System for Mass Customization. In Proceeding of the 1997 Portland International Conference on Management of Engineering and Technology(PICMET'97)
[13] Stewart, R. D., R.M. Wyskida, and J. D. Johannes (Eds.). Cost Estimator's reference manual (2nd ed.). New York: John Wiley & Sons, Inc. 1995
[14] Stockton, D.J. and J. E. Middle(1982). An approach to improving cost estimating. Internation Journal of Production Research 20 (6), pp. 741-751
[15] Veeramani, D. and P Joshi. Methodologies for rapid and effective response to requests for quotation. HE Transactions 1997, 29. pp. 825-838
[16] Melin Jr., J. B., Parametric Estimation. Cost Engineering, 1994, 36(1), pp.19-24.
[17] de la Garza, D. J. M. and K. G. Rouhana. Neural networks Versus Parameter-Based Application in Cost Estimating. Cost Engineering, 1995, February, 37(2), pp. 14-18
[18] Ostwald, P. F.. Engineering Cost Estimating(3nd ed.). Englewood Cliffs. New Jersey: Prentice Hall, 1992
[19] Cochran. E.B. Using regression techniques in cost analysis. International Journal of Production Research 1976, 14(4), pp. 489-511
[20] Sigurdsen, A CERA: An Integrated Cost Estimating Program. Cost Engineering, 1992, 34, June. pp. 25-30
[21] Smith, A. E. and A. K. Mason. Cost Estimation Predictive Modeling: Regression Versus Neural Net work. The Engineering Economist, 1997, 42(2), pp. 137-161
[22] Bourke, R. W. Configurators: Rule-based Product Definition. APICS-The Performance Advantage, 1991, December, pp.51-54
[23] Thomas, G.. Workstation Rules-Based Configuration: The Engineer's Way. In Conference Proceedings-American Production & Inventory Control Society. 1990, pp.443-445
[24] Racker, R. Reducing the Configurator Confusion. In Conference Proceedings- American Production & Inventory Control Society, 1994, pp. 558-559
[25] Turbide. D. A. Figuring Out Configurators. Systems3X/400, 1994, June, pp.96-102
[26] Wongvasu, N. and S. V. Kamarthi. Inter-Component Compatibility: A New Perspective on How to Represent Relationships Between Items in a Generic Bill-of-Material Structure. In Proceedings of the Group Technology/Cellular Manufacturing World Symposium, 2000a, March, pp.27-29
[27] Bareiss, E.R., Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning. Academic, Boston, 1989
[28] Kolodner, J.L., Simpson, R., Sycara-Cyranski, K., A process model of case-based reasoning in problem solving. In: Proceedings of the Ninth International Joint Conference on Artificial Intelligence,
Menlo Park, Calif, 1985, pp. 284-290
[29] Davis, R,, Diagnostic reasoning based on structural and behaviour. Artificial Intelligence 24, 1984, pp.347-410
[30] Calandranis, J., Stephanopoulos, G,, Nunokawa, S., DiADKit/boiler: on-line performance monitoring and diagnosis, Chem. Eng. Pro. 86, 1990, pp. 60-80
[31] Waston, I. Case-Bsed reasoning is a methodology not a technology. Knowledge Based System, 1999, 12, pp.303-308
[32] Stanfill, C., Learning to read: a memory-based model. In: Proceedings of the DARPA Workshop on Case-Based Reasoning, San Mateo, Calif, 1988, pp. 402-413
[33] Hammond K. Case-Based Planning: Viewing Planning as a Memory Task.. Academic, New York,1989
[34] Branting L. Integrating generalizations with exemplar-based reasoning. In: Proceedings of the DARPA Workshop on Case-Based Reasoning, San Matero, Calif, 1989, pp.80-93
[35] Selfridge M. and Cuthill B. Retrieving relevant out-of-context cases: a dynamic memory approach to case-based reasoning. In: Proceedings of the Workshop on Case-Based Reasoning, San Mateo, Calif, 1989, pp. 370-387
[36] Turner R. Organizing and using schematic knowledge for medical diagnosis. In: Proceedings of the DARPA Workshop on Case-Based Reasoning, San Mateo, Calif, 1988, pp.435-446
[37] Koton P. Using Experience in Learning and Problem Solving. Technical Report, 1989, MIT/LCS/TR pp.441
[38] Watson I. A case study of maintenance of a commercially fielded case-based reasoning system. Computational Intelligence: an International Journal 17(2), 2001, pp. 387-398
[39] Watson I. and Marir E Case-based reasoning: a review. The Knowledge Engineering Review, 1994, 9(5), pp. 355-381
[40] I. Watson. Case-based reasoning is a methodology not a technology. Knowledge-based systems, 1999. 12, pp. 303-308
[41] Stewart R. D. R. M. Wyskida and Johannes (Eds.) Cost Estimator's reference manual (2nd ed.). New York: John Wiley & Sons, Inc. 1995
[42] Gupta S. M. and R Veerakamolmal. A Case-based Reasoning Approach for Optimal Planning of Multi-product/Multi-manufacturer Disassembly Processes. International Journal of Environmentally Conscious Design & Manufacturing 2000, 9(1), pp. 15-25
[43] Bergmann R. and Wilke W. Towards a new formal model of transformational adaptation in case-based reasoning. In: European Conference on Artificial Intelligence 1998 (ECAI '98)
[44] Shiu S.C.K., Yeung D.S., Sun C.H. and Wang X.Z., Transferring case knowledge to adaptation knowledge: an approach for case-base maintenance. Computational Intelligence: an International Journal, 2001, 2(17), pp.295-314
[45] Yang Q. and Zhu J. A case-addition policy for case-base maintenance. Computational Intelligence: an International Journal, 2001, 2(17), pp.250-262
[46] Bing Chiang Jeng and Ting-Peng Liang Fuzzy Indexing and Retrieval in Case-Based Systems, Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan, Expert Systems With Application, 1995, 8(1), pp. 135-142
[47] J.L. Wybo E and Geffraye A. Russiel, PROFIL: a decision support tool for metallic sections design using a CBR approach, Case-Based Reasoning Research and Development- First Int., ICCBR-95, Berlin, 1995, pp.33-42
[48] R.W. Lee, R.M. Barcia and S.K. Khator, Case-Based reasoning for cash flow forecasting using fuzzy retrieval, Case-Based Reasoning Research and Development-First Int. Conf., ICCBR-95, Springer, Berlin, 1995, pp. 510-519
[49] W. Cheetham and J. Graf. Case-based reasoning in color matching. Case-Based Reasoning Research and Development-Second Int. Conf., ICCBR-97, Springer, Berlin, 1995, pp. 1-12
[50] H. Bunke and B.T. Messmer. Similarity measure for structured representations, Topics in Case-based Reasoning-First European Workshop, EWCBR-93, Spring, Berlin, 1994, pp. 106-118
[51] R.W. Lee, R.M. Barcia and S.K. Khator, Case-Based reasoning for cash flow forecasting using fuzzy retrieval, Case-Based Reasoning Research and Development-First Int. Conf., ICCBR-95, Springer, Berlin, 1995, pp. 510-519
[52] P. Myllymaki and H. Tirri, Massively parallel CBR with probabilistic similarity metric, Topics in Case-based Reasoning-First European Workshop, EWCBR-93, Spinger, Berlin, 1994, pp. 144-154
[53] D. Janetzko, S. Wess and E. Melis, Goal-driven similarity assessment, Proc. Advances in Artificial
Intelligence-16th German Conf. on Artificial Intelligence, GWAI-92, Springer, Berlin, 1993, pp. 283-298
[54] B.K. MacKellar, F. Maryanski, A knowledge base for code reuse by similarity, Proc. 13th Annual Int. Computer Software and Applications Conf. IEEE, Washington DC. 1994, pp. 634-641
[55] M. Schneider, A. Kandel, G. Langholz and G. Chew, Fuzzy Expert System Tools, Wiley, Chichester, West Sussex, UK, 1996
[2] Alvin.Toftler. Powershift. Arts and Licensing International Inc., 1990
[3] Jeff C. Having it their way: How to successfully customize your mass product. Kimbell Proceedings-American Production and Inventory Control Society. 1997, pp.89-91
[4] Duray, Rebecca, Ward and Peter T. Identification and categorization of mass customization configurations. Proceeding of the 1996 27th Annual Meeting of the Decision Sciences Institute. Part 3. Conference Date: 1996. 11. Proceedings-Atlanta. pp. 1307-1309
[5] Tseng, Mitchell M.; Lei, Ming and Su, Chuanjun, Collaborative control system for mass customization manufacturing. CIRP Annuals-Manufacturing Technology, 1997, 46(1), pp.373-376
[6] 大批量定制技术及其应用，祁国宁，顾新建，谭建容等著，机械工业出版社，2003．10，pp．49-52
[7] Pine B J Ⅱ and Gilmore J H. The Four Faces of Mass Customization [J]. Harvard Business Review, 1997, 75 (1), pp. 91-101
[8] Lample J and Mintzberg H.Customizing Customization [J]. Sloan Management Review, 1996, 38 (1) pp.21-30
[9] Alford D Sacker and P Nelder G.Mass Customization-an Automotive Perspective[J]. Int. J. of Production Economics, 2000, 65, pp. 99-110.
[10] 大批量定制的若干理论与方法问题研究，李仁旺，浙江大学博士论文，1999
[11] 祁国宁，顾新建，李仁旺 大批量定制及其模型的研究[J]，计算机集成制造系统 2000，6(2)pp．41-45
[12] Cullinance, T. P., S.V. Kamarthi, N. Wongvasu, and P.S.S. Chinnaiah (1997, July). A Generic IDEF0 Model of a Production System for Mass Customization. In Proceeding of the 1997 Portland International Conference on Management of Engineering and Technology(PICMET'97)
[13] Stewart, R. D., R.M. Wyskida, and J. D. Johannes (Eds.). Cost Estimator's reference manual (2nd ed.). New York: John Wiley & Sons, Inc. 1995
[14] Stockton, D.J. and J. E. Middle(1982). An approach to improving cost estimating. Internation Journal of Production Research 20 (6), pp. 741-751
[15] Veeramani, D. and P Joshi. Methodologies for rapid and effective response to requests for quotation. HE Transactions 1997, 29. pp. 825-838
[16] Melin Jr., J. B., Parametric Estimation. Cost Engineering, 1994, 36(1), pp.19-24.
[17] de la Garza, D. J. M. and K. G. Rouhana. Neural networks Versus Parameter-Based Application in Cost Estimating. Cost Engineering, 1995, February, 37(2), pp. 14-18
[18] Ostwald, P. F.. Engineering Cost Estimating(3nd ed.). Englewood Cliffs. New Jersey: Prentice Hall, 1992
[19] Cochran. E.B. Using regression techniques in cost analysis. International Journal of Production Research 1976, 14(4), pp. 489-511
[20] Sigurdsen, A CERA: An Integrated Cost Estimating Program. Cost Engineering, 1992, 34, June. pp. 25-30
[21] Smith, A. E. and A. K. Mason. Cost Estimation Predictive Modeling: Regression Versus Neural Net work. The Engineering Economist, 1997, 42(2), pp. 137-161
[22] Bourke, R. W. Configurators: Rule-based Product Definition. APICS-The Performance Advantage, 1991, December, pp.51-54
[23] Thomas, G.. Workstation Rules-Based Configuration: The Engineer's Way. In Conference Proceedings-American Production & Inventory Control Society. 1990, pp.443-445
[24] Racker, R. Reducing the Configurator Confusion. In Conference Proceedings- American Production & Inventory Control Society, 1994, pp. 558-559
[25] Turbide. D. A. Figuring Out Configurators. Systems3X/400, 1994, June, pp.96-102
[26] Wongvasu, N. and S. V. Kamarthi. Inter-Component Compatibility: A New Perspective on How to Represent Relationships Between Items in a Generic Bill-of-Material Structure. In Proceedings of the Group Technology/Cellular Manufacturing World Symposium, 2000a, March, pp.27-29
[27] Bareiss, E.R., Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning. Academic, Boston, 1989
[28] Kolodner, J.L., Simpson, R., Sycara-Cyranski, K., A process model of case-based reasoning in problem solving. In: Proceedings of the Ninth International Joint Conference on Artificial Intelligence,
Menlo Park, Calif, 1985, pp. 284-290
[29] Davis, R,, Diagnostic reasoning based on structural and behaviour. Artificial Intelligence 24, 1984, pp.347-410
[30] Calandranis, J., Stephanopoulos, G,, Nunokawa, S., DiADKit/boiler: on-line performance monitoring and diagnosis, Chem. Eng. Pro. 86, 1990, pp. 60-80
[31] Waston, I. Case-Bsed reasoning is a methodology not a technology. Knowledge Based System, 1999, 12, pp.303-308
[32] Stanfill, C., Learning to read: a memory-based model. In: Proceedings of the DARPA Workshop on Case-Based Reasoning, San Mateo, Calif, 1988, pp. 402-413
[33] Hammond K. Case-Based Planning: Viewing Planning as a Memory Task.. Academic, New York,1989
[34] Branting L. Integrating generalizations with exemplar-based reasoning. In: Proceedings of the DARPA Workshop on Case-Based Reasoning, San Matero, Calif, 1989, pp.80-93
[35] Selfridge M. and Cuthill B. Retrieving relevant out-of-context cases: a dynamic memory approach to case-based reasoning. In: Proceedings of the Workshop on Case-Based Reasoning, San Mateo, Calif, 1989, pp. 370-387
[36] Turner R. Organizing and using schematic knowledge for medical diagnosis. In: Proceedings of the DARPA Workshop on Case-Based Reasoning, San Mateo, Calif, 1988, pp.435-446
[37] Koton P. Using Experience in Learning and Problem Solving. Technical Report, 1989, MIT/LCS/TR pp.441
[38] Watson I. A case study of maintenance of a commercially fielded case-based reasoning system. Computational Intelligence: an International Journal 17(2), 2001, pp. 387-398
[39] Watson I. and Marir E Case-based reasoning: a review. The Knowledge Engineering Review, 1994, 9(5), pp. 355-381
[40] I. Watson. Case-based reasoning is a methodology not a technology. Knowledge-based systems, 1999. 12, pp. 303-308
[41] Stewart R. D. R. M. Wyskida and Johannes (Eds.) Cost Estimator's reference manual (2nd ed.). New York: John Wiley & Sons, Inc. 1995
[42] Gupta S. M. and R Veerakamolmal. A Case-based Reasoning Approach for Optimal Planning of Multi-product/Multi-manufacturer Disassembly Processes. International Journal of Environmentally Conscious Design & Manufacturing 2000, 9(1), pp. 15-25
[43] Bergmann R. and Wilke W. Towards a new formal model of transformational adaptation in case-based reasoning. In: European Conference on Artificial Intelligence 1998 (ECAI '98)
[44] Shiu S.C.K., Yeung D.S., Sun C.H. and Wang X.Z., Transferring case knowledge to adaptation knowledge: an approach for case-base maintenance. Computational Intelligence: an International Journal, 2001, 2(17), pp.295-314
[45] Yang Q. and Zhu J. A case-addition policy for case-base maintenance. Computational Intelligence: an International Journal, 2001, 2(17), pp.250-262
[46] Bing Chiang Jeng and Ting-Peng Liang Fuzzy Indexing and Retrieval in Case-Based Systems, Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan, Expert Systems With Application, 1995, 8(1), pp. 135-142
[47] J.L. Wybo E and Geffraye A. Russiel, PROFIL: a decision support tool for metallic sections design using a CBR approach, Case-Based Reasoning Research and Development- First Int., ICCBR-95, Berlin, 1995, pp.33-42
[48] R.W. Lee, R.M. Barcia and S.K. Khator, Case-Based reasoning for cash flow forecasting using fuzzy retrieval, Case-Based Reasoning Research and Development-First Int. Conf., ICCBR-95, Springer, Berlin, 1995, pp. 510-519
[49] W. Cheetham and J. Graf. Case-based reasoning in color matching. Case-Based Reasoning Research and Development-Second Int. Conf., ICCBR-97, Springer, Berlin, 1995, pp. 1-12
[50] H. Bunke and B.T. Messmer. Similarity measure for structured representations, Topics in Case-based Reasoning-First European Workshop, EWCBR-93, Spring, Berlin, 1994, pp. 106-118
[51] R.W. Lee, R.M. Barcia and S.K. Khator, Case-Based reasoning for cash flow forecasting using fuzzy retrieval, Case-Based Reasoning Research and Development-First Int. Conf., ICCBR-95, Springer, Berlin, 1995, pp. 510-519
[52] P. Myllymaki and H. Tirri, Massively parallel CBR with probabilistic similarity metric, Topics in Case-based Reasoning-First European Workshop, EWCBR-93, Spinger, Berlin, 1994, pp. 144-154
[53] D. Janetzko, S. Wess and E. Melis, Goal-driven similarity assessment, Proc. Advances in Artificial
Intelligence-16th German Conf. on Artificial Intelligence, GWAI-92, Springer, Berlin, 1993, pp. 283-298
[54] B.K. MacKellar, F. Maryanski, A knowledge base for code reuse by similarity, Proc. 13th Annual Int. Computer Software and Applications Conf. IEEE, Washington DC. 1994, pp. 634-641
[55] M. Schneider, A. Kandel, G. Langholz and G. Chew, Fuzzy Expert System Tools, Wiley, Chichester, West Sussex, UK, 1996