کاهش آلودگی‌های زیست محیطی در بهینه‌سازی زنجیره تأمین حلقه بسته با استفاده از برنامه ریزی عدد صحیح مختلط فازی
محورهای موضوعی : مدیریت صنعتی
sayyed mohammad reza davoodi
1
(Assistant Professor, Department of Management, Dehaghan Branch, Islamic Azad University , Dehaghan, Iran)
homa kalani
2
(Master of Industrial Engineering,Financial Engineering tendency, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran)
کلید واژه: اعداد فازی, الودگی زیست محیطی, زنجیره تامین حلقه بسته, زنجیره تأمین,
چکیده مقاله :
با افزایش حجم گاز های گلخانه ای وآلاینده ها و توجه روز افزون به مسائل محیط زیست، مدیران سازمانها و محققان در پی طراحی و راه اندازی شبکه هایی بر آمدند که علاوه بر بهینه سازی اقتصادی بر عوامل محیط زیستی و کاهش آلاینده ها در همه بخش ها تمرکزی ویژه داشته باشند. مقاله حاضر از نظر هدف کاربردی و از نظر روش، توصیفی پژوهشی است. در این مقاله یک مدل ریاضی دو هدفه به منظور طراحی زنجیره تامین حلقه بسته ارائه می شود. مدل ریاضی بر اساس معیار هایی از جمله تعداد محصول، تعداد محصول دوباره استفاده شده و تعداد قطعات تعریف شده است. همچنین یک روش حل پیشنهادی بر مبنای برنامه ریزی فازی مطرح شده و مدل فازی دو هدفه در نظر گرفته شده است. در گام بعد با استفاده از داده های موجود، مدل با استفاده از نرم افزار بهینه سازی GAMS آزمون می‌گردد. افزایش تقاضا منجر به افزایش شدید آلودگی های محیط زیستی می شود. لذا در این شرایط می‌توان ادعا نمود که افزایش و یا کاهش تقاضا تأثیر کاملاً شفافی روی کل هزینه های زنجیره دارد.
Introduction: with increase in greenhouse gases, and pollutants and ever-increasing attention to environmental issues, the managers and researchers tried to design and put into operation some networks which in addition to economic optimization has a specific focus on environmental factors and reducing pollutants. , Supply Chain Management has become changed into one of the basic issues of the economic firms in such a way that it has left an impression on all activities of the organizations for producing the products, improving the quality, decreasing the prices and presenting the required services for the customers. The first goal is to increase the profit of the entire supply chain and the second goal is reducing the pollutions of the environment. Materials and methods: This study in terms of purpose is practical and in terms of the method is descriptive-research. In this study, a dual-objective mathematical model is presented for the closed-loop supply chain network design. The mathematical model based on some standards such as the number of products, number of reused products and number of defined parts. Also, one solution was presented based on fuzzy-planning and a dual-objective fuzzy model was considered. In the next step with the available data, the model is tested by using GAMS software. Results and discussion: increasing the amount of the first purpose function has a complete direct relationship with increasing the multiplier. Whereas changing the demand has an indirect relationship with the second purpose function. increasing demand leads to an intensive increase in environmental pollutions.
1. Aghaei, M., and Fazli, S. (2012). "Applying the DEMATEL and ANP Combined Approach to Selecting Proper Maintenance Strategy (Case Study: Automotive Industry)". Journal of Industrial Management Prespective, 2(6), 89-107.
2. Ahmad, R., and Kamaruddin, S. (2012). "An overview of time-based and condition-based maintenance in industrial application." Computers & Industrial Engineering 63(1): 135-149.
3. Al-Najjar, B. and Alsyouf, I. (2003). "Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making." International Journal of Production Economics 84(1): 85-100.
4. Bankian-Tabrizi, B., Shahanaghi, K., and Saeed Jabalameli, M. (2012). "Fuzzy multi-choice goal programming." Applied Mathematical Modelling 36(4): 1415-1420.
5. Bashiri, M., Badri, H., and Hejazi, T. H. (2011). "Selecting optimum maintenance strategy by fuzzy interactive linear assignment method." Applied Mathematical Modelling 35(1): 152-164.
6. Bevilacqua M, and Braglia M. (2000). "The analytic hierarchy process applied to maintenance strategy selection." Reliability Engineering & System Safety 70(1):71-83.
7. Chan, F. T. S., Lau, H. C. W., Ip, R. W. L., Chan, H. K., and Kong, S. (2005). "Implementation of total productive maintenance: A case study." International Journal of Production Economics 95(1): 71-94.
8. Chang, C.-T. (2007). "Multi-choice goal programming." Omega 35(4): 389-396.
9. Charnes, A., and Cooper, W. W. (1961). Management models and industrial applications of linear programming.
10. Chung, S.-H., H.I. Lee, A., and Pearn, W. L. (2005). "Analytic network process (ANP) approach for product mix planning in semiconductor fabricator." (96).
11. Dağdeviren, M., Yüksel, İ., & Kurt, M. (2008). "A fuzzy analytic network process (ANP) model to identify faulty behavior risk (FBR) in work system." Safety Science 46(5): 771-783.
12. Dargi, A., Anjomshoae, A., Galankashi, M. R., Memari, A., and Tap, M. B. M. (2014). "Supplier Selection: A Fuzzy-ANP Approach." Procedia Computer Science 31: 691-700.
13. de Almeida, A. T., & Bohoris, G. A. (1995). Decision theory in maintenance decision making. Journal of Quality in Maintenance Engineering, 1(1), 39-45.
14. Do, P., Voisin, A., Levrat, E., & Iung, B. (2015). A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions. Reliability Engineering System Safety, 133, 22-32.
15. Ebeling, C. E. (1997). An introduction to reliability and maintainability engineering. New York: Waveland Press.
16. Eti, M. C., Ogaji, S. O. T., and Probert, S. D. (2006). "Reducing the cost of preventive maintenance (PM) through adopting a proactive reliability-focused culture." Applied Energy 83(11): 1235-1248.
17. Faddoul, R., Raphael, W., & Chateauneuf, A. (2018). Maintenance optimization of series systems subject to reliability constraints. Reliability Engineering & System Safety, 180, 179–188. doi:10.1016/j.ress.2018.07.016
18. Feyzi, A., & Sadeh, E. (2017). “Prioritizing Technological Performance of Iran’s Automotive Companies using PANDA-FANP-FVIKOR Approach.” Scientific Journal Management System 12(41): 29-46.
19. Garbatov, Y., Sisci, F., & Ventura, M. (2018). Risk-based framework for ship and structural design accounting for maintenance planning. Ocean Engineering, 166, 12–25. doi:10.1016/j.oceaneng.2018.07.058
20. Gogus, O., and Boucher, T. O. (1997). "A consistency test for rational weights in multi-criterion decision analysis with fuzzy pairwise comparisons." Fuzzy Sets and Systems 86(2): 129-138.
21. Hemmati, N., Rahiminezhad Galankashi, M., Imani, D. M., & Farughi, H. (2018). "Maintenance policy selection: a fuzzy-ANP approach". Journal of Manufacturing Technology Management, 29(7), 1253-1268.
22. Hosseini, S. (1997). Systematic Maintenance Planning and Introduction to TPM. Tehran: Industrial Management Institute.
23. Jafari, M., & Faramarzi, M. (2016). Reliability Centered Maintenance. Cement Technology, 100, 123-131.
24. Jasiulewicz-Kaczmarek, M. (2016). "SWOT analysis for Planned Maintenance strategy-a case study." IFAC-PapersOnLine 49(12): 674-679.
25. Kahraman, C., Ruan, D., & Doǧan, I. (2003). "Fuzzy group decision-making for facility location selection." Information Sciences 157: 135-153.
26. Klutke, G.-A., Kiessler, P. C., and Wortman, M. A. (2003). "A Critical Look at the Bathtub Curve." IEEE TRANSACTIONS ON RELIABILITY 52(1): 125-129.
27. Kumar, G., and Maiti, J. (2012). "Modeling risk-based maintenance using fuzzy analytic network process." Expert Systems with Applications 39(11): 9946-9954.
28. Kumar, S. R., Dinesh, K., and Pradeep, K. (2005). "FLM to select suitable maintenance strategy in process industries using MISO model." Journal of Quality in Maintenance Engineering 11(4): 359-374.
29. Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., and Liao, H. (2006). "Intelligent prognostics tools and e-maintenance." Computers in Industry 57(6): 476-489.
30. Liao, C.-N. (2009). “Formulating the multi-segment goal programming.” Computers & Industrial Engineering, 56(1): 138-141.
31. Makan, M. F., Vasili, M., & Ghandehari, M. (2013). Selecting a risk-based proper maintenance strategy using the Fuzzy Analytic Hierarchy Process. 2nd National Conference on Industrial Engineering.
32. Martinod, R. M., Bistorin, O., Castañeda, L. F., & Rezg, N. (2018). Maintenance policy optimisation for multi-component systems considering degradation of components and imperfect maintenance actions. Computers & Industrial Engineering, 124, 100–112.
33. Meade, L. M., & Presley, A. (2002). "R&D project selection using the analytic network process." IEEE transactions on engineering management 49(1): 59-66.
34. Mikhailov, L., & Singh, M. G. (2003). "Fuzzy analytic network process and its application to the development of decision support systems." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 33(1): 33-41.
35. Moubray, J. (1997). Reliability-centered maintenance: Industrial Press Inc.
36. Nikabadi, M. S., Khanaposhtani, H. F., Eftekhari, H., & Sadabadi, A. A. (2016). Using hybrid approach FA, AHP and TOPSIS for selecting and ranking the appropriate maintenance strategies. Industrial Management Studies, 13(39), 35-62.
37. Orumie, U. C., and Ebong, D. (2014). "A glorious literature on linear goal programming algorithms." American Journal of Operations Research 4(02): 59.
38. Özcan, E. C., Ünlüsoy, S., and Eren, T. (2017). "A combined goal programming – AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants." Renewable and Sustainable Energy Reviews 78: 1410-1423.
39. Pariazar, M., Zaeri, M. S., & Shahrabi, J. (2007). Selection of optimum maintenance strategies with factor analysis and analytic hierarchy process. Paper presented at the Iran Data Mining Conference.
40. Partovi, F. Y. (2001). "An analytic model to quantify strategic service vision." International Journal of Service Industry Management 12(5): 476-499.
41. Pham, H., and Wang, H. (1996). "Imperfect maintenance." European Journal of Operational Research 94(3): 425-438.
42. Pourjavad, E., and Shirouyehzad, H. (2014). "Analysing maintenance strategies by FANP considering RAM criteria." Int. J. Logistics Systems and Management 18(3): 302-321.
43. Rabbani, A., Zare, H., and Behnia, F. (2014). "Providing a proper model for the implementation of maintenance system in the continuous production lines considering decision making models and fuzzy goal programming". Industrial Management Studies, 11(31), 85-100.
44. Rouyendegh, B. D., and Saputro, T. E. (2014). "Supplier Selection Using Integrated Fuzzy TOPSIS and MCGP: A Case Study." Procedia - Social and Behavioral Sciences 116: 3957-3970.
45. Safaei, N., and Jardine, A. K. S. (2018). Aircraft routing with generalized maintenance constraints. Omega, 80, 111–122.
46. Shafiee Nick Abadi, M., Farajpour Khanaposhtani, H., Eftekhari, H., and Sadadadi, A. (2016). "Using hybrid approach FA, AHP and TOPSIS for selecting and ranking the appropriate maintenance strategies". Industrial Management Studies, 13(39), 35-62.
47. Semih, Önüt, Selin Soner Kara and Elif Is_ik. (2009). "Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company", International Journal of Expert Systems with Applications 36, 3887–3895.
48. Shin, J.-H., and Jun, H.-B. (2015). "On condition-based maintenance policy." Journal of Computational Design and Engineering 2(2): 119-127.
49. Shyjith, K., Ilangkumaran, M., and Kumanan, S. (2008). "Multi-criteria decision-making approach to evaluate optimum maintenance strategy in textile industry." Quality in Maintenance Engineering 14(4): 375-386.
50. Siew-Hong, D., and Kamaruddin, S. (2012). Selection of Optimal Maintenance Policy by Using Fuzzy Multi Criteria Decision Making Method. presented at the 2012. International Conference on Industrial Engineering and Operations Management, Istanbul Turkey: 435-443.
51. Sullivan, G. P., Pugh, R., Melendez, A. P., and Hunt, W. (2004). O&M Best Practices-A Guide to Achieving Operational Efficiency (Release 2.0). U.S: Pacific Northwest National Laboratory
52. Swanson, L. (2001). "Linking maintenance strategies to performance." International Journal of Production Economics 70(3): 237-244.
53. Vishnu, C. R., and Regikumar, V. (2016). "Reliability Based Maintenance Strategy Selection in Process Plants: A Case Study." Procedia Technology 25: 1080-1087.
54. Waeyenbergh, G., and Pintelon, L. (2004). "Maintenance concept development: A case study." International Journal of Production Economics 89(3): 395-405.
55. Wang, L., Chu, J., and Wu, J. (2007). "Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process." International Journal of Production Economics 107(1): 151-163.
56. Wey, W.-M., and Wu, K.-Y. (2007). "Using ANP priorities with goal programming in resource allocation in transportation." Mathematical and Computer Modelling 46(7): 985-1000.
57. Wu, W.-W., and Lee, Y.-T. (2007). "Selecting knowledge management strategies by using the analytic network process." Expert Systems with Applications 32(3): 841-847.
58. Yam, R., Tse, P., Li, L., & Tu, P. (2001). “Intelligent predictive decision support system for condition-based maintenance.” The International Journal of Advanced Manufacturing Technology 17(5): 383-391.
59. Yen, J., & Langari, R. (1999). Fuzzy logic: intelligence, control, and information (1): Prentice Hall Upper Saddle River, NJ.
60. Yurdakul, M. (2003). "Measuring long-term performance of a manufacturing firm using the Analytic Network Process (ANP) approach." International Journal of Production Research 41(11): 2501-2529.
61. Zimmermann, H. J. (1978). "Fuzzy programming and linear programming with several objective functions." Fuzzy Sets and Systems 1(1): 45-55.
_||_1. Aghaei, M., and Fazli, S. (2012). "Applying the DEMATEL and ANP Combined Approach to Selecting Proper Maintenance Strategy (Case Study: Automotive Industry)". Journal of Industrial Management Prespective, 2(6), 89-107.
2. Ahmad, R., and Kamaruddin, S. (2012). "An overview of time-based and condition-based maintenance in industrial application." Computers & Industrial Engineering 63(1): 135-149.
3. Al-Najjar, B. and Alsyouf, I. (2003). "Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making." International Journal of Production Economics 84(1): 85-100.
4. Bankian-Tabrizi, B., Shahanaghi, K., and Saeed Jabalameli, M. (2012). "Fuzzy multi-choice goal programming." Applied Mathematical Modelling 36(4): 1415-1420.
5. Bashiri, M., Badri, H., and Hejazi, T. H. (2011). "Selecting optimum maintenance strategy by fuzzy interactive linear assignment method." Applied Mathematical Modelling 35(1): 152-164.
6. Bevilacqua M, and Braglia M. (2000). "The analytic hierarchy process applied to maintenance strategy selection." Reliability Engineering & System Safety 70(1):71-83.
7. Chan, F. T. S., Lau, H. C. W., Ip, R. W. L., Chan, H. K., and Kong, S. (2005). "Implementation of total productive maintenance: A case study." International Journal of Production Economics 95(1): 71-94.
8. Chang, C.-T. (2007). "Multi-choice goal programming." Omega 35(4): 389-396.
9. Charnes, A., and Cooper, W. W. (1961). Management models and industrial applications of linear programming.
10. Chung, S.-H., H.I. Lee, A., and Pearn, W. L. (2005). "Analytic network process (ANP) approach for product mix planning in semiconductor fabricator." (96).
11. Dağdeviren, M., Yüksel, İ., & Kurt, M. (2008). "A fuzzy analytic network process (ANP) model to identify faulty behavior risk (FBR) in work system." Safety Science 46(5): 771-783.
12. Dargi, A., Anjomshoae, A., Galankashi, M. R., Memari, A., and Tap, M. B. M. (2014). "Supplier Selection: A Fuzzy-ANP Approach." Procedia Computer Science 31: 691-700.
13. de Almeida, A. T., & Bohoris, G. A. (1995). Decision theory in maintenance decision making. Journal of Quality in Maintenance Engineering, 1(1), 39-45.
14. Do, P., Voisin, A., Levrat, E., & Iung, B. (2015). A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions. Reliability Engineering System Safety, 133, 22-32.
15. Ebeling, C. E. (1997). An introduction to reliability and maintainability engineering. New York: Waveland Press.
16. Eti, M. C., Ogaji, S. O. T., and Probert, S. D. (2006). "Reducing the cost of preventive maintenance (PM) through adopting a proactive reliability-focused culture." Applied Energy 83(11): 1235-1248.
17. Faddoul, R., Raphael, W., & Chateauneuf, A. (2018). Maintenance optimization of series systems subject to reliability constraints. Reliability Engineering & System Safety, 180, 179–188. doi:10.1016/j.ress.2018.07.016
18. Feyzi, A., & Sadeh, E. (2017). “Prioritizing Technological Performance of Iran’s Automotive Companies using PANDA-FANP-FVIKOR Approach.” Scientific Journal Management System 12(41): 29-46.
19. Garbatov, Y., Sisci, F., & Ventura, M. (2018). Risk-based framework for ship and structural design accounting for maintenance planning. Ocean Engineering, 166, 12–25. doi:10.1016/j.oceaneng.2018.07.058
20. Gogus, O., and Boucher, T. O. (1997). "A consistency test for rational weights in multi-criterion decision analysis with fuzzy pairwise comparisons." Fuzzy Sets and Systems 86(2): 129-138.
21. Hemmati, N., Rahiminezhad Galankashi, M., Imani, D. M., & Farughi, H. (2018). "Maintenance policy selection: a fuzzy-ANP approach". Journal of Manufacturing Technology Management, 29(7), 1253-1268.
22. Hosseini, S. (1997). Systematic Maintenance Planning and Introduction to TPM. Tehran: Industrial Management Institute.
23. Jafari, M., & Faramarzi, M. (2016). Reliability Centered Maintenance. Cement Technology, 100, 123-131.
24. Jasiulewicz-Kaczmarek, M. (2016). "SWOT analysis for Planned Maintenance strategy-a case study." IFAC-PapersOnLine 49(12): 674-679.
25. Kahraman, C., Ruan, D., & Doǧan, I. (2003). "Fuzzy group decision-making for facility location selection." Information Sciences 157: 135-153.
26. Klutke, G.-A., Kiessler, P. C., and Wortman, M. A. (2003). "A Critical Look at the Bathtub Curve." IEEE TRANSACTIONS ON RELIABILITY 52(1): 125-129.
27. Kumar, G., and Maiti, J. (2012). "Modeling risk-based maintenance using fuzzy analytic network process." Expert Systems with Applications 39(11): 9946-9954.
28. Kumar, S. R., Dinesh, K., and Pradeep, K. (2005). "FLM to select suitable maintenance strategy in process industries using MISO model." Journal of Quality in Maintenance Engineering 11(4): 359-374.
29. Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., and Liao, H. (2006). "Intelligent prognostics tools and e-maintenance." Computers in Industry 57(6): 476-489.
30. Liao, C.-N. (2009). “Formulating the multi-segment goal programming.” Computers & Industrial Engineering, 56(1): 138-141.
31. Makan, M. F., Vasili, M., & Ghandehari, M. (2013). Selecting a risk-based proper maintenance strategy using the Fuzzy Analytic Hierarchy Process. 2nd National Conference on Industrial Engineering.
32. Martinod, R. M., Bistorin, O., Castañeda, L. F., & Rezg, N. (2018). Maintenance policy optimisation for multi-component systems considering degradation of components and imperfect maintenance actions. Computers & Industrial Engineering, 124, 100–112.
33. Meade, L. M., & Presley, A. (2002). "R&D project selection using the analytic network process." IEEE transactions on engineering management 49(1): 59-66.
34. Mikhailov, L., & Singh, M. G. (2003). "Fuzzy analytic network process and its application to the development of decision support systems." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 33(1): 33-41.
35. Moubray, J. (1997). Reliability-centered maintenance: Industrial Press Inc.
36. Nikabadi, M. S., Khanaposhtani, H. F., Eftekhari, H., & Sadabadi, A. A. (2016). Using hybrid approach FA, AHP and TOPSIS for selecting and ranking the appropriate maintenance strategies. Industrial Management Studies, 13(39), 35-62.
37. Orumie, U. C., and Ebong, D. (2014). "A glorious literature on linear goal programming algorithms." American Journal of Operations Research 4(02): 59.
38. Özcan, E. C., Ünlüsoy, S., and Eren, T. (2017). "A combined goal programming – AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants." Renewable and Sustainable Energy Reviews 78: 1410-1423.
39. Pariazar, M., Zaeri, M. S., & Shahrabi, J. (2007). Selection of optimum maintenance strategies with factor analysis and analytic hierarchy process. Paper presented at the Iran Data Mining Conference.
40. Partovi, F. Y. (2001). "An analytic model to quantify strategic service vision." International Journal of Service Industry Management 12(5): 476-499.
41. Pham, H., and Wang, H. (1996). "Imperfect maintenance." European Journal of Operational Research 94(3): 425-438.
42. Pourjavad, E., and Shirouyehzad, H. (2014). "Analysing maintenance strategies by FANP considering RAM criteria." Int. J. Logistics Systems and Management 18(3): 302-321.
43. Rabbani, A., Zare, H., and Behnia, F. (2014). "Providing a proper model for the implementation of maintenance system in the continuous production lines considering decision making models and fuzzy goal programming". Industrial Management Studies, 11(31), 85-100.
44. Rouyendegh, B. D., and Saputro, T. E. (2014). "Supplier Selection Using Integrated Fuzzy TOPSIS and MCGP: A Case Study." Procedia - Social and Behavioral Sciences 116: 3957-3970.
45. Safaei, N., and Jardine, A. K. S. (2018). Aircraft routing with generalized maintenance constraints. Omega, 80, 111–122.
46. Shafiee Nick Abadi, M., Farajpour Khanaposhtani, H., Eftekhari, H., and Sadadadi, A. (2016). "Using hybrid approach FA, AHP and TOPSIS for selecting and ranking the appropriate maintenance strategies". Industrial Management Studies, 13(39), 35-62.
47. Semih, Önüt, Selin Soner Kara and Elif Is_ik. (2009). "Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company", International Journal of Expert Systems with Applications 36, 3887–3895.
48. Shin, J.-H., and Jun, H.-B. (2015). "On condition-based maintenance policy." Journal of Computational Design and Engineering 2(2): 119-127.
49. Shyjith, K., Ilangkumaran, M., and Kumanan, S. (2008). "Multi-criteria decision-making approach to evaluate optimum maintenance strategy in textile industry." Quality in Maintenance Engineering 14(4): 375-386.
50. Siew-Hong, D., and Kamaruddin, S. (2012). Selection of Optimal Maintenance Policy by Using Fuzzy Multi Criteria Decision Making Method. presented at the 2012. International Conference on Industrial Engineering and Operations Management, Istanbul Turkey: 435-443.
51. Sullivan, G. P., Pugh, R., Melendez, A. P., and Hunt, W. (2004). O&M Best Practices-A Guide to Achieving Operational Efficiency (Release 2.0). U.S: Pacific Northwest National Laboratory
52. Swanson, L. (2001). "Linking maintenance strategies to performance." International Journal of Production Economics 70(3): 237-244.
53. Vishnu, C. R., and Regikumar, V. (2016). "Reliability Based Maintenance Strategy Selection in Process Plants: A Case Study." Procedia Technology 25: 1080-1087.
54. Waeyenbergh, G., and Pintelon, L. (2004). "Maintenance concept development: A case study." International Journal of Production Economics 89(3): 395-405.
55. Wang, L., Chu, J., and Wu, J. (2007). "Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process." International Journal of Production Economics 107(1): 151-163.
56. Wey, W.-M., and Wu, K.-Y. (2007). "Using ANP priorities with goal programming in resource allocation in transportation." Mathematical and Computer Modelling 46(7): 985-1000.
57. Wu, W.-W., and Lee, Y.-T. (2007). "Selecting knowledge management strategies by using the analytic network process." Expert Systems with Applications 32(3): 841-847.
58. Yam, R., Tse, P., Li, L., & Tu, P. (2001). “Intelligent predictive decision support system for condition-based maintenance.” The International Journal of Advanced Manufacturing Technology 17(5): 383-391.
59. Yen, J., & Langari, R. (1999). Fuzzy logic: intelligence, control, and information (1): Prentice Hall Upper Saddle River, NJ.
60. Yurdakul, M. (2003). "Measuring long-term performance of a manufacturing firm using the Analytic Network Process (ANP) approach." International Journal of Production Research 41(11): 2501-2529.
61. Zimmermann, H. J. (1978). "Fuzzy programming and linear programming with several objective functions." Fuzzy Sets and Systems 1(1): 45-55.