A multi-criteria decision-making approach for capacity allocation problem in semiconductor fabrication

International Journal of Production Research

Volumn 49, Issue 19, 2011, Pages 5893-5916

  Kang, He Yau ^a  

^a NATIONAL CHIN YI UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

capacity allocation; constraint programming; fuzzy analytic network process; fuzzy Delphi method; semiconductor

Indexed keywords

CAPACITY ALLOCATION; CONSTRAINT PROGRAMMING; FUZZY ANALYTIC NETWORK PROCESS; FUZZY DELPHI METHOD; SEMICONDUCTOR;

COMPETITION; COMPUTER PROGRAMMING; CONSTRAINT THEORY; DECISION THEORY; FUZZY LOGIC; MANAGEMENT SCIENCE; PROFITABILITY; PUBLIC RELATIONS;

DECISION MAKING;

EID: 80051776580     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00207543.2010.526151     Document Type: Article

References (42)

1. Anderson, D.R., Sweeney, D.J., and Williams, T.A., 1998. Quantitative methods for business. 7th ed. Ohio, US: International Thomson Publishing.
2. Ayaǧ , Z. andÖzdemir, R.G., 2007. An intelligent approach to ERP software selection through fuzzy ANP. International Journal of Production Research, 45 (10), 2169-2194. (Pubitemid 46673849)
3. Banaszak, Z.A., Zaremba, M.B., and Muszyński, W., 2009. Constraint programming for projectdriven manufacturing. International Journal of Production Economics, 120 (2), 463-475.
4. Börner, F., et al., 2009. The complexity of constraint satisfaction games and QCSP. Information and Computation, 207 (9), 923-944.
5. Brandolese, A., Brun, A., and Portioli-Staudacher, A., 2000. A multi-agent approach for the capacity allocation problem. International Journal of Production Economics, 66 (3), 269-285.
6. Bulatov, A.A. and Dalmau, V., 2007. Towards a dichotomy theorem for the counting constraint satisfaction problem. Information and Computation, 205 (5), 651-678. (Pubitemid 46529466)
7. Chamodrakas, I., Alexopoulou, N., and Martakos, D., 2009. Customer evaluation for order acceptance using a novel class of fuzzy methods based on TOPSIS. Expert Systems with Applications, 36 (4), 7409-7415.
8. Chang, I.S., et al., 1995. An efficient approach for large scale project planning based on fuzzy Delphi method. Fuzzy Sets and Systems, 76 (3), 277-288.
9. Chang, P.C. and Wang, Y.W., 2006. Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry. Expert Systems with Applications, 30 (4), 715-726.
10. Chang, C.W., Wu, C.R., and Chen, H.C., 2007. Applying a fuzzy analytic network process to construct a purchase project: A case for the purchase of a slicing diamond cutting machine. Production Planning and Control, 18 (8), 628-640.
11. Chen, T., 2009. Enhancing the yield competitiveness of a semiconductor fabrication factory with dynamic capacity re-allocation. Computers and Industrial Engineering, 57 (3), 931-936.
12. Cheng, C.H. and Lin, Y., 2002. Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European Journal of Operational Research, 142 (1), 174-186. (Pubitemid 35171685)
13. Chung, S.-H. and Hsieh, M.H., 2008. Long-term tool elimination planning for a wafer fab. Computers and Industrial Engineering, 54 (3), 589-601. (Pubitemid 351199807)
14. Chung, S.-H., Lee, A.H.I., and Pearn, W.L., 2005. Analytic network process (ANP) approach for product mix planning in semiconductor fabricator. International Journal of Production Economics, 96 (1), 15-36. (Pubitemid 40135608)
15. Frederix, F., 1996. Planning and scheduling multi-site semiconductor production chains: A survey of needs, current practices and integration issues. The conference on integration in manufacturing, Galway, Ireland, 107-116.
16. H'Mida, F. and Vernadat, F., 2009. A constraint approach (flexible CSP) for alternative cost estimation of a mechanical product. International Journal of Production Research, 47 (2), 305-320.
17. Hsiao, T.Y., 2006. Establish standards of standard costing with the application of convergent gray zone test. European Journal of Operational Research, 168 (2), 593-611.
18. Hsu, C.-I. and Li, H.-C., 2009. An integrated plant capacity and production planning model for high-tech manufacturing firms with economies of scale. International Journal of Production Economics, 118 (2), 486-500.
19. ILOG, 2000. ILOG OPL studio 3.7 User's Manual and Reference Manual. Paris, France: ILOG S.A.
20. Ishikawa, A., et al., 1993. The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets and Systems, 55 (3), 241-253.
21. Karakoc, E. and Senkul, P., 2009. Composing semantic Web services under constraints. Expert Systems with Applications, 36 (8), 11021-11029.
22. Kaufmann, A. and Gupta, M.M., 1988. Fuzzy mathematical models in engineering and management science. New York: Elsevier Science.
23. Korpela, J., et al., 2002. An analytic approach to production capacity allocation and supply chain design. International Journal of Production Economics, 78 (2), 187-195. (Pubitemid 34532514)
24. Kumar, V., 1992. Algorithms for constraint-satisfaction problems: a survey. AI Magazine, 13 (1), 32-44.
25. Lee, A.H.I., 2009. A fuzzy AHP evaluation model for buyer-supplier relationships with the consideration of benefits, opportunities, costs and risks. International Journal of Production Research, 47 (15), 4255-4280.
26. Lee, A.H.I., Wang, W.-M., and Lin, T.-Y., 2010a. An evaluation framework for technology transfer of new equipment in high technology industry. Technological Forecasting and Social Change, 77 (1), 135-150.
27. Lee, A.H.I., et al., 2010b. An evaluation framework for product planning using FANP, QFD and multi-choice goal programming. International Journal of Production Research, 48 (13), 3977-3997.
28. Leenen, L., Meyer, T., and Ghose, A., 2007. Relaxations of semiring constraint satisfaction problems. Information Processing Letters, 103 (5), 177-182. (Pubitemid 46898377)
29. Lin, H.-T. and Chang, W.-L., 2008. Order selection and pricing methods using flexible quantity and fuzzy approach for buyer evaluation. European Journal of Operational Research, 187 (2), 415-428. (Pubitemid 350102851)
30. Lin, L.Z. and Hsu, T.H., 2008. The qualitative and quantitative models for performance measurement systems: the agile service development. Quality and Quantity, 42 (2), 445-476.
31. Maher, M.J., 2009. Local consistency for extended CSPs. Theoretical Computer Science, 410 (46), 4769-4783.
32. Mallik, S. and Harker, P.T., 2004. Coordinating supply chains with competition: Capacity allocation in semiconductor manufacturing. European Journal of Operational Research, 159 (2), 330-347.
33. Rupp, T.M. and Ristic, M., 2000. Fine planning for supply chains in semiconductor manufacture. Journal of Materials Processing Technology, 107 (1-3), 390-397. (Pubitemid 32072630)
34. Russell, S. and Norvig, P., 2002. Artificial intelligence: a modern approach. 2nd ed. New Jersey: Prentice Hall.
35. Saaty, T.L., 1996. Decision making with dependence and feedback: the analytic network process. Pittsburgh: RWS Publications.
36. Saaty, R.W., 2003a. Decision making in complex environment: the Analytic Hierarchy Process (AHP) for decision making and the Analytic Network Process (ANP) for decision making with dependence and feedback. Pittsburgh: Super Decisions.
37. Saaty, T.L., 2003b. Negative priorities in the analytic hierarchy process. Mathematical and Computer Modelling, 37 (9-10), 1063-1075. (Pubitemid 36708109)
38. Saaty, T.L., 2004. Fundamentals of the analytic network process-multiple networks with benefits, opportunities, costs and risks. Journal of Systems Science and Systems Engineering, 13 (3), 348-379. (Pubitemid 40031869)
39. Shen, Y. and Leachman, R.C., 2003. Stochastic wafer fabrication scheduling. IEEE Transactions on Semiconductor Manufacturing, 16 (1), 2-13.
40. Tsang, E.P.K., 1993. Foundations of constraint satisfaction. London: Academic Press.
41. Uzsoy, R., Lee, C.Y., and Martin-Vega, L.A., 1992. A review of production planning and scheduling models in the semiconductor industry, Part I: system characteristics, performance evaluation and production planning. IIE Transactions on Scheduling Logistics, 24 (4), 47-61.
42. Yager, R.R., 1980. On a general class of fuzzy connective. Fuzzy Sets and Systems, 4 (3), 235-242.