ORIGINAL_ARTICLE
Investigation into skill leveled operators in a multi-period cellular manufacturing system with the existence of multi-functional machines
Many works published in the area of cellular manufacturing system are based on the assumption that machines are reliable in the whole production horizon without any break down. As such assumptions often are not realistic, to contribute to closing this gap to reality, the model has been modified by additionally including machine reliability, alternative process routings and workforce assignment in a dynamic environment. In this research to integrate this aspects, the modified problem has been defined and formulated and an extended mixed integer multi-period mathematical model has been proposed.In order to evaluate the effectiveness and capability of the extended model, some hypothetical numerical instances are generated and computational experiment are carried out using Gams optimization package. Experimental results demonstrate that the demand value can affect the machine breakdown rate, and a machine with a minimum breakdown rate is implemented more often than others. Moreover, the observed trade-off between the workforce-related costs and the cell-formation costs indicates that workforce-related issues have a significant impact on the total efficiency of the system. The proposed model can be implemented in medium- and large-scale manufacturing companies.
http://scientiairanica.sharif.edu/article_21513_0cb8b3180eb6e874de511ae629ccd74f.pdf
2020-12-01
3219
3232
10.24200/sci.2019.21513
multi-period cellular manufacturing system
machine reliability
workforce learning-forgetting effect
alternative process routing
M.
Rafiee
rafiee@sharif.edu
1
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
LEAD_AUTHOR
A.
Mohamaditalab
2
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
Liu, C., Wang, J., and Leung, J.Y.T. Integrated bacteria foraging algorithm for cellular manufacturing in supply chain considering facility transfer and production planning", Applied Soft Computing, 62, pp. 602{618 (2018). 2. Askin, R.G. Contributions to the design and analysis of cellular manufacturing systems", International Journal of Production Research, 51, pp. 6778{6787 (2013). 3. Ameli, M.S.J. and Arkat, J. Cell formation with alternative process routings and machine reliability consideration", The International Journal of Advanced Manufacturing Technology, 35, pp. 761{768 (2008). 4. Bulgak, A.A. and Bektas T. Integrated cellular manufacturing systems design with production planning and 3232 M. Ra_ee and A. Mohamaditalab/Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3219{3232 dynamic system recon_guration", European Journal of Operational Research, 192, pp. 414{428 (2009). 5. Mehdizadeh, E., Niaki S.V.D., and Rahimi, V. A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training", Computers & Industrial Engineering, 101, pp. 35{52 (2016). 6. Mahdavi, I., Aalaei, A., Paydar, M.M., and Solimanpur, M. Designing a mathematical model for dynamic cellular manufacturing systems considering production planning and worker assignment", Computers & Mathematics with Applications, 60, pp. 1014{1025 (2010). 7. Aryanezhad, M.B., Deljoo, V., and Mirzapour Al-e- Hashem, S. Dynamic cell formation and the worker assignment problem: a new model", The International Journal of Advanced Manufacturing Technology, 41, pp. 329{342 (2009). 8. Bagheri, M. and Bashiri M. A new mathematical model towards the integration of cell formation with workforce assignment and inter-cell layout problems in a dynamic environment", Applied Mathematical Modelling, 38, pp. 1237{1254 (2014). 9. Javadi, B., Jolai, F., Slomp, J., Rabbani, M., and Tavakkoli- Moghaddam, R. An integrated approach for the cell formation and layout design in cellular manufacturing systems", International Journal of Production Research, 51, pp. 6017{6044 (2013). 10. Bagheri, M., Sadeghi, S., and Saidi-Mehrabad, M. A benders' decomposition approach for dynamic cellular manufacturing system in the presence of unreliable machines", Journal of Optimization in Industrial Engineering, 17, pp. 37{49 (2015). 11. Bayram, H. and S_ahin, R. A comprehensive mathematical model for dynamic cellular manufacturing system design and linear programming embedded hybrid solution techniques", Computers & Industrial Engineering, 91, pp. 10{29 (2016). 12. Azadeh, A., Ravanbakhsh, M., Rezaei-Malek, M., Sheikhalishahi, M., and Taheri-Moghaddam, A. Unique NSGA-II and MOPSO algorithms for improved dynamic cellular manufacturing systems considering human factors", Applied Mathematical Modelling, 48, pp. 655{672 (2017). 13. Liu, C., Wang, J., and Leung, J.Y.T. Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm", Computers & Industrial Engineering, 96, pp. 162{179 (2016). 14. Mehdizadeh, E. and Rahimi, V. An integrated mathematical model for solving dynamic cell formation problem considering workforce assignment and inter/intra cell layouts", Applied Soft Computing, 42, pp. 325{341 (2016). 15. Ra_ei, H. and Ghodsi, R. A bi-objective mathematical model toward dynamic cell formation considering labor utilization", Applied Mathematical Modelling, 37, pp. 2308{2316 (2013). 16. Sakhaii, M., Tavakkoli-Moghaddam, R., Bagheri, M., and Vatani, B. A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines", Applied Mathematical Modelling, 40, pp. 169{191 (2016). 17. Nouri, H. Development of a comprehensive model and BFO algorithm for a dynamic cellular manufacturing system", Applied Mathematical Modelling, 40, pp. 1514{1531 (2016).
1
ORIGINAL_ARTICLE
Integration of machine learning techniques and control charts in multivariate processes
Using multivariate control chart instead of establishing univariate control chart for all variables in processes provides time and labor advantage. In addition, it is considered in the relations between variables. However, the statistical calculation of the measured values of all variables is seen as a single value in the control chart. Therefore, it is necessary to determine which variable(s) is the cause of the out of control signal. Effective corrective measures can only be developed when the causes of the fault(s) are determined correctly. The aim of the study is to determine the machine learning techniques that will accurately estimate the type of fault. With the Hotelling T2 chart, out of control signals are identified and the types of faults affected by the variables are defined. Various machine learning techniques are used to compare classification performances. The developed model was applied in the evaluation of the paint quality in a painting process. ANN was determined as the most successful techniques according to performance criteria. The novelty of the study is to classify the fault according to the types of faults, not the variables. Defining the faults according to its types will enable to take effective corrective actions quickly.
http://scientiairanica.sharif.edu/article_21503_2df725c24f3b36bfb07bc4309a69a5af.pdf
2020-12-01
3233
3241
10.24200/sci.2019.50377.1667
Multivariate Control Chart
Naive Bayes - Kernel
K - Nearest Neighbor
Decision Tree
artificial neural network
Multi-Layer Perpectron
Deep Learning
D.
Demircioglu Diren
ddemircioglu@sakarya.edu.tr
1
Department of Industrial Engineering, Sakarya University, Sakarya, 54050, Turkey
LEAD_AUTHOR
S.
Boran
boran@sakarya.edu.tr
2
Department of Industrial Engineering, Sakarya University, Sakarya, 54050, Turkey
AUTHOR
I.
Cil
icil@sakarya.edu.tr
3
Department of Industrial Engineering, Sakarya University, Sakarya, 54050, Turkey
AUTHOR
Hotelling, H. Multivariate quality control illustrated by air testing of sample bombsights", Techniques of Statistical Analysis, 2(5), pp. 111{152 (1947). 2. Woodall, W.H. and Ncube, M.M. Multivariate cusum quality control procedures", Technometrics, 27(3), pp. 285{292 (1985). 3. Lowry, A.A., Woodall, W.H., Champ, C.W., et al. A multivariate exponentially weighted moving average control chart", Technometrics, 34(1), pp. 46{53 (1992). 4. Murphy, B.J. Selecting out of control variables with the T2 multivariate quality control procedure", The Statistician, 36, pp. 571{583 (1987). 5. Mason, R.L., Tracy, N.D., and Young, C.H. Decomposition of T2 for multivariate control chart interpretation", Journal of Quality Technology, 27(2), pp. 99{ 108 (1995). 6. Kourti, T. and MacGregor J.F. Multivariate SPC methods for process and product monitoring", Journal of Quality Technology, 28(4), pp. 409{428 (1996). 7. Li, J., Jin, J., and Shi, J. Causation-based T2 decomposition for multivariate process monitoring and diagnosis", Journal of Quality Technology, 40(1), pp. 46{58 (2008). 8. Midany, T.E., Baz, A.A.E., and Elwahed, M.S.A. A proposed framework for control chart pattern recognition in multivariate process using arti_cial neural networks", Expert Systems with Applications, 37(2), pp. 1035{1042 (2010). 9. Addeh, A., Khormali, A., and Golilarz, N.A. Control chart pattern recognition using RBF neural network with new training algorithm and practical features", ISA Transactions, 79, pp. 202{216 (2018). 10. Wang, X. Hybrid abnormal patterns recognition of control chart using support vector machining", International Conference on Computational Intelligence and Security, pp. 238{241 (2008). 11. Li, T.L., Hu, S., Wei, Z., and Liao, Z. A framework for diagnosis the out of control signals in multivariate process using optimized support vector machine", Mathematical Problems in Engineering, 2013(2), pp. 1{9 (2013). 12. Xanthopoulos, P. and Razzaghi, T. A weighted support vector machine method for control chart pattern recognition", Computers & Industrial Engineering, 70, pp. 134{149 (2014). 13. Wang, C.H., Dong, T.P., and Kuo, W. A hybrid approach for identi_cation of concurrent control chart patterns", Journal of Intelligent Manufacturing, 20(4), pp. 409{419 (2009). 14. Lu, C.J., Shao, Y.E., and Li, P.H. Mixture control chart patterns recognition using independent component analysis and support vector machine", Neurocomputing, 74(11), pp. 1908{1914 (2011). 15. Zhang, M. and Cheng, W. Recognition of mixture control chart pattern using multiclass support vector machine and genetic algorithm based on statistical and shape features", Mathematical Problems in Engineering, 2015(5), pp. 1{10 (2015). 3240 D. Demircio_glu Diren et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3233{3241 16. Chen, L.H. andWang, T.Y. Arti_cial neural networks to classify mean shifts from multivariate _2 chart signals", Computers and Industrial Engineering, 47(2{ 3), pp. 195{205 (2004). 17. Niaki, S.T.A. and Abbasi, B. Fault diagnosis in multivariate control charts using arti_cial neural networks", Quality and Reliability Engineering International, 21, pp. 825{840 (2005). 18. Aparisi, F. and Sanz, J. Interpreting the out-ofcontrol signals of multivariate control charts employing neural networks", International Journal of Computer and Information Engineering, 4(1), pp. 24{28 (2010). 19. Atashger, K. and Noorossana, R. An integrating approach to root cause analysis of a bivariate mean vector with a linear trend disturbance", The International Journal of Advanced Manufacturing Technology, 52(1{ 4), pp. 407{420 (2011). 20. Masood, I. and Hassan, A. Pattern recognition for bivariate process mean shifts using feature-based arti- _cial neural network", The International Journal of Advanced Manufacturing Technology, 66(9{12), pp. 1201{1218 (2013). 21. Du, S., Lv, J., and Xi, L. On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines", International Journal of Production Research, 50(22), pp. 6288{6310 (2012). 22. Guh, R-S. and Shiue, Y-R. An e_ective application of decision tree learning for on-line detection of mean shifts in multivariate control charts", Computers and Industrial Engineering, 55(2), pp. 475{493 (2008). 23. He, S., Wang, G.A., Zhang, M., et al. Multivariate process monitoring and fault identi_cation using multiple decision tree classi_ers", International Journal of Production Research, 51(11), pp. 3355{3371 (2013). 24. Jiang, J. and Song, H.M. Diagnosis of out-of-control signals in multivariate statistical process control based on bagging and decision tree", Asian Business & Management, 2(2), pp. 1{6 (2017). 25. Jadhav, S.D. and Channe, H.P. Comparative study of K-NN, Naive Bayes and decision tree classi_cation techniques", International Journal of Science and Research, 5(1), pp. 1842{1845 (2016). 26. Cheng, C.S. and Cheng, H.P. Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines", Expert Systems with Applications, 35(1{2), pp. 198{206 (2008). 27. Salehi, M., Bahreininejad, A., and Nakhai, I. Online analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model", Neurocomputing, 74(12{13), pp. 2083{2095 (2011). 28. Ceylan, Y., Usta, K., Yumurtac_, H., et al. An ESR study on 22, 4 diaminotoluene exposed to gamma rays and application of machine learning", Acta Physica Polonica A, 130(1), pp. 184{187 (2016). 29. Moore, A. and Zuev, D. Internet tra_c classi_cation using Bayesian analysis techniques", Sigmetrics, 33(1), pp. 50{60 (2005). 30. Murakami, Y. and Mizuguchi, K. Applying the naive Bayes classi_er with kernel density estimation to the prediction of protein-protein interaction sites", Bioinformatics, 26(15), pp. 1841{1848 (2010). 31. Kuter, S., Usul, N., and Kuter, N. Bandwidth determination for kernel density analysis of wild_re events at forest sub-district scale", Ecological Modelling, 222(17), pp. 3033{3040 (2011). 32. Fix, E. and Hodges, J. Discriminatory analysis. nonparametric discrimination: consistency properties", Technical Report 4, USAF School of Aviation Medicine, Randolph Field, Texas, USA (1951). 33. Williams, J.W. and Li, Y. Comparative study of distance functions for nearest neigbor", Advanced Techniques in Computing Sciences and Software Engineering, Elleithy, Khaled, Ed., pp. 79{84 (2010). 34. Kataria, A. and Singh, M. A review of data classi_cation using K-nearest neigbor algorithm", International Journal of Emerging Technology and Advanced Engineering, 3(6), pp. 354{360 (2013). 35. Yuksel, A.S, C_ ankaya, S.F. and Uncu, _I.S. Design of a machine learning based predictive analytics system for spam problem", Acta Physica Polonica A, 132(1), pp. 500{504 (2017). 36. Quinlan, J.R. Improved use of continuous attributes in C4.5", Journal of Arti_cial Intelligence Research, 4, pp. 77{90 (1996). 37. Mitchell, T.M., Machine Learning, McGraw-Hill Science/ Engineering/Math, 81 (1997). 38. Tsai, K-M. and Luo, H-J. An inverse model for injection molding of optical lens using arti_cial neural network coupled with genetic algorithm", Journal of Intelligent Manufacturing, 28(2), pp. 473{487 (2017). 39. Alpayd_n, E., Introduction to Machine Learning, Second Edn., The MIT Press, England, pp. 203{247 (2010). 40. Hinton, G.E., Osindero, S., and Teh, Y-W. A fast learning algorithm for deep belief nets", Neural Computation, 18, pp. 1527{1554 (2006). 41. Vargas, R., Mosavi, A., and Ruiz, R. Deep learning: A review", Advances in Intelligent Systems and Computing, 5(2), pp. 53040{53065 (2017). 42. LeCun, Y., Bengio, Y., and Hinton, G. Deep learning", Nature, 521, pp. 436{444 (2015). 43. Seker, S.E. and Ocak, I. Performance prediction of roadheaders using ensemble machine learning techniques", Neural Computing and Applications, 31(4), pp. 1103{116 (2019). 44. Rodrigues, E.O., Pinheiro, V.H.A., Liatsis, P., et al. Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes", Computers Biology and Medicine, 89, pp. 520{529 (2017). D. Demircio_glu Diren et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3233{3241 3241 45. Haji, M.M. and Katebi, S.D. Machine learning approaches to text segmentation", Scientia Iranica, A, 13(4), pp. 395{403 (2006). 46. Rabiei, A., Naja-Jilani, A., and Zakeri-Niri. M. Application of neural network models to improve prediction accuracy of wave run-up on antifer covered breakwater", Scientia Iranica, A, 24(2), pp. 567{575 (2017). 47. Peng, X. and Dai, J. A bibliometric analysis of neutrosophic set: two decades review from 1998 to 2017", Arti_cial Intelligence Review, 53, pp. 199{255 (2020). 48. Peng, X. and Selvachandran, G. Pythagorean fuzzy set: State of the art and future directions", Arti_cial Intelligence Review, 52, pp. 1873{1927 (2019).
1
ORIGINAL_ARTICLE
Weight determination and ranking priority in interval group MCDM
In this study, we propose a method to determine the weight of decision makers (DMs) in group multiple criteria decision making (GMCDM) problems with interval data .Here, we obtain an interval weight for each DM and the relative closeness of each decision from the negative ideal solution (NIS) and the positive ideal solution (PIS) is then computed. In the proposed method, after weighting the decision matrix of each DM, the alternatives are ranked using interval arithmetic. A comparative example together with a real world problem on air quality assessment is given to illustrate our method. Our findings show that the proposed approach is a suitable tool to solve GMCDM problems.
http://scientiairanica.sharif.edu/article_21403_5ff5619396a91a08759ddbb2449ee83f.pdf
2020-12-01
3242
3252
10.24200/sci.2019.51133.2022
Decision Analysis
GMCDM
TOPSIS
interval matrix
weight of criteria
S.
Saffarzadeh
s.saffarzadeh@gmail.com
1
Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
AUTHOR
A.
Hadi-Vencheh
abdh12345@yahoo.com
2
Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
LEAD_AUTHOR
A.
Jamshidi
ali.jamshidi@khuisf.ac.ir
3
Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
AUTHOR
Ishizaka, A. and Nemery P., Multi-Criteria Decision Analysis: Methods and Software, John Wiley and Sons (2013). 2. Keeney, R. and Rai_a, H., Decisions With Multiple Objectives: Preferences and Value Tradeo_s, New York: Wiley (1976). 3. Koksalan, M., Wallenius, J., and Zoints, S., Multiple Criteria Decision Making: From Early History to the 21st Century, World Scienti_c Publishing, New Jersey (2011). 4. Jacquet-Lagr_eze, E. and Siskos, J. Assessing a set of additive utility functions for multiple criteria decision making", European Journal of Operational Research, 10, pp. 151{164 (1982). 5. Solymosi, T. and Dombi, J. A method for determining the weights of criteria: the centralized weights", European Journal of Operational Research, 26, pp. 35{41 (1986). 6. Guitouni, A. and Martel, J.M. Tentative guidelines to help choosing an appropriate MCDA method", European Journal of Operational Research, 109, pp. 501{521 (1998). 7. Figueira, J., Salvatore, G., and Ehrgott, M., Eds, Multiple Criteria Decision Analysis: State of the Art Surveys, New York: Springer Science & Business Media (2005). 8. Wallenius, J., Dyer, J.S., Fishburn, P.C., Steuer, R.E., Zionts, S., and Deb, K. Multiple criteria decision making, multiattribute utility theory: recent accomplishments and what lies ahead", Management Science, 54, pp. 1336{1349 (2008). 9. Hwang, C.L. and Yoon, K., Multiple Attributes Decision Making Methods and Applications, Berlin, Heidelberg: Springer (1981). 10. Opricovic, S. and Tzeng, G.-H. Compromise solution by MCDM methods", European Journal of Operational Research, 156(2), pp. 445{455 (2004). 11. Saaty, T.L., A scaling method for priorities in hierarchical structures", Journal of Mathematical Psychology, 15, pp. 234{281 (1977). 12. Edwards, W. How to use multi attribute utility measurement for social decision making", IEEE Transactions on Systems, Man and Cybernetics, 7, pp. 326{ 340 (1977). 13. Banae Costa, C. and Vansnick, J.-C. MACBETH - An interactive path towards the construction of cardinal value functions", International Transactions in Operational Research, 1, pp. 489{500 (1994). 14. Govindan, K. and Jespen, MB. ELECTRE: A comprehensive literature review on methodologies and applications", European Journal of Operational Research, 250, pp. 1{29 (2016). 15. Ishizaka, A. and Siraj, S. Are multi-criteria decisionmaking tools useful? An experimental comparative study of three methods", European Journal of Operational Research, 264(2), pp. 462{471 (2017). 16. Nassereddine, M. and Eskandari, H. An integrated MCDM approach to evaluate public transportation systems in Tehran", Transportation Research Part A: Policy and Practice, 106, pp. 427{439 (2017). 17. Abdollahi, A., Pour-Moallem, N., and Abdollahi, A. Dynamic negawatt demand response resource modeling and prioritizing in power markets", Scientia Iranica, 27(3), pp. 1361{1372 (2020). S. Sa_arzadeh et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3242{3252 3251 18. Gou, X., Xu, Z., and Liao, H. Hesitant fuzzy linguistic entropy and cross-entropy measures and alternative queuing method for multiple criteria decision making", Information Sciences, 388, pp. 225{246 (2017). 19. Ren, Z., Xu, Z., and Wang, H. Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method", Information Sciences, 388, pp. 1{16 (2017). 20. Zavadskas, E.K., Antucheviciene, J., Turskis, Z., and Adeli, H. Hybrid multiple-criteria decision-making methods: A review of applications in engineering", Scientia Iranica., Transactions A, Civil Engineering, 23(1), p. 1 (2016). 21. Chen, L. and Xu, Z. A new prioritized multi-criteria outranking method: The prioritized PROMETHEE", Journal of Intelligent & Fuzzy Systems, 29(5), pp. 2099{2110 (2015). 22. Yu, X., Xu, Z., and Ma, Y. Prioritized multi-criteria decision making based on the idea of PROMETHEE", Procedia Computer Science, 17, pp. 449{456 (2013). 23. Keeney, R.L. and Kirkwood, C.W. Group decision making using cardinal social welfare functions", Management Science, 22, pp. 430{437 (1975). 24. Keeney, R.L. A group preference axiomatization with cardinal utility", Management Science, 23, pp. 140{ 145 (1976). 25. Brock, H.W. The problem of 'utility weights' in group preference aggregation", Operations Research, 28, pp. 176{187 (1980). 26. Ramanathan, R. and Ganesh, L.S. Group preference aggregation methods employed in AHP: an evaluation and an intrinsic process for deriving members' weightages", European Journal of Operational Research, 79, pp. 249{265 (1994). 27. Van den Honert, R.C. Decisional power in group decision making: a note on the allocation of group members weights in the multiplicative AHP and SMART", Group Decision and Negotiation, 10, pp. 275{286 (2001). 28. Xu, Z.S. Group decision making based on multiple types of linguistic preference relations", Information Sciences, 178, pp. 452{467 (2008). 29. Abootalebi, S., Hadi-Vencheh, A., and Jamshidi, A. An Improvement to determining expert weights in group multiple attribute decision making problem", Group Decision and Negotiation, 27(2), pp. 215{221 (2018). 30. Wang, H.-F., Multicriteria Decision Analysis - From Certainty to Uncertainty, Ting Lung Book Co, Taipei (2004). 31. Mahmoudi, A., Sadi-Nezhad, S., and Makui, A. An extended fuzzy VIKOR for group decisionmaking based on fuzzy distance to supplier selection", Scientia Iranica., Transactions E, Industrial Engineering, 23(4), p. 1879 (2016). 32. Hu, J., Chen, P., and Chen, X. Intuitionistic random multi-criteria decision-making approach based on prospect theory with multiple reference intervals", Scientia Iranica., Transactions E, Industrial Engineering, 21(6), p. 2347 (2014). 33. Minatour, Y., Khazaie, J., Ataei, M., and Javadi, A.A. An integrated decision support system for dam site selection", Scientia Iranica., Transactions A, Civil Engineering, 22(2), p. 319 (2015). 34. Yu, X., Zhang, S., Liao, X., and Qi, X. ELECTRE methods in prioritized MCDM environment", Information Sciences, 424, pp. 301{316 (2018). 35. Yue, Z. An extended TOPSIS for determining weights of decision makers with interval numbers", Knowledge- Based Systems, 24, pp. 146{153 (2011). 36. Jahanshahloo, G.R., Hosseinzadeh Lot_, F., and Izadikhah, M. An algorithmic method to extend TOPSIS for decision-making problems with interval data", Applied Mathematics and Computation, 175, pp. 1375{1384 (2006). 37. Sayadi, M.K., Heydari, M., and Shahanaghi, K. Extension of VIKOR method for decision making problem with interval numbers", Applied Mathematical Modelling, 33, pp. 2257{2262 (2009). 38. Rezaei, J. and Salimi, N. Optimal ABC inventory classi_cation using interval Programming", International Journal of Systems Science, 46(11), pp. 1944{ 1952 (2015). 39. Dymova, L., Sevastjanov, P., and Tikhonenko, A. A direct interval extension of TOPSIS method", Expert Systems with Applications, 40, pp. 4841{4847 (2013). 40. Hafezalkotob, A., Hafezalkotob, A., and Sayadi, M.K. Extension of MULTIMOORA method with interval numbers: An application in materials selection", Applied Mathematical Modelling, 40, pp. 1372{1386 (2016). 41. Liu, W. and Li, L. An approach to determining the integrated weights of decision makers based on interval number group decision matrices", Knowledge-Based Systems, 90, pp. 92{98 (2015). 42. Pamu_car, D., Stevi_c, _Z., and Zavadskas, E.K. Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages", Applied Soft Computing, 67, pp. 141{163 (2018). 43. Feng, Y., Hong, Z., Tian, G., Li, Z., Tan, J., and Hu, H. Environmentally friendly MCDM of reliabilitybased product optimisation combining DEMATELbased ANP, interval uncertainty and Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR)", Information Sciences, 442, pp. 128{144 (2018). 44. Wanke, P., Kalam Azad, M.A., Barros, C.P., and Hadi-Vencheh, A. Predicting performance in ASEAN banks: an integrated fuzzy MCDM-neural network approach", Expert Systems, 33(3), pp. 213{229 (2016). 3252 S. Sa_arzadeh et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3242{3252 45. Hajek, P. and Froelich, W. Integrating TOPSIS with interval-valued intuitionistic fuzzy cognitive maps for e_ective group decision making", Information Sciences, 485, pp. 394{412 (2019). 46. Frini, A. and Amor, S.B. MUPOM: A multi-criteria multi-period outranking method for decision-making in sustainable development context", Environmental Impact Assessment Review, 76, pp. 10{25 (2019). 47. Alefeld, G. and Herzberger, J., Introduction to Interval Computations, New York: Academic Press (1983). 48. Xu, Z.S. On method for uncertain multiple attribute decision making problems with uncertain multiplicative preference information on alternatives", Fuzzy Optimization and Decision Making, 4, pp. 131{139 (2005). 49. Xu, Z.S. Dependent uncertain ordered weighted aggregation operators", Information Fusion, 9, pp. 310{ 316 (2008). 50. Li, D.-F. Relative ratio method for multiple attribute decision making problems", International Journal of Information Technology & Decision Making, 8(2) pp. 289{311 (2009).
1
ORIGINAL_ARTICLE
Extended economic production quantity models with preventive maintenance
This paper generalizes the standard economic production quantity (EPQ) model in process manufacturing industry by incorporating regular preventive maintenance (PM) activities into classic EPQ model. The PM program improves the condition of the production to an acceptable level, and avoids potential stoppages and disruptions, hence, it is a vital task in every production process. However, the standard EPQ model does not consider PM activities and then is not applicable to real-world situations. We consider manufacturer which produces a product under EPQ setting with a defective production process, in which every production cycle involves a number of sub-production cycles. Two models are proposed, based on the disposal time of defective items, to determine optimal number of sub-production cycles. In model I, the disposal of defective items is performed once per cycle at the end of each production cycle, while in model II, the disposal of defective items is performed multiple times per cycle, at the end of each sub-production cycle. The total cost functions are derived for each model separately, and then simple solution algorithms are designed. A numerical example is presented and discussed to evaluate proposed models. The results illustrate that model II is more cost effective than model I.
http://scientiairanica.sharif.edu/article_21394_3f60af9af08b638edc11e412d69af2dc.pdf
2020-12-01
3253
3264
10.24200/sci.2019.51199.2055
Preventive maintenance
Production-Inventory System
Defective Process
Manufacturing Planning
Defective items
H.
Mokhtari
mokhtari_ie@kashanu.ac.ir
1
Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, P.O. Box 8731753153, Iran
LEAD_AUTHOR
J.
Asadkhani
javad.asadkhani@gmail.com
2
Department of Management and Entrepreneurship, Faculty of Humanities, University of Kashan, Kashan, P.O. Box 1471835191, Iran
AUTHOR
Jain, M. and Rathore, S. Economic production quantity models with Shortage, price and stock-dependent Demand for deteriorating items", IJE Transactions, 20(2), pp. 159{166 (2007). 2. Pan, E., Jin, Y., Wang, S., and Cang, T. An integrated EPQ model based on a control chart for an imperfect production process", International Journal of Production Research, 50(23), pp. 6999{7011 (2012). 3. Khedlekar, U.K. A disruption production model with exponential demand", International Journal of Industrial Engineering Computations, 3(4), pp. 607{616 (2012). 4. Wee, H.M., Wang, W.T., and Yang, P.C. A production quantity model for imperfect quality items with shortage and screening constraint", International Journal of Production Research, 51(6), pp. 1869{1884 (2013). 5. Dash, B., Pattnaik, M., and Pattnaik, H. Deteriorated Economic Production Quantity (EPQ) model for declined quadratic demand with time value of money and shortages", Applied Mathematical Sciences, 8(73), pp. 3607{3618 (2014). 6. Karimi-Nasab, M. and Sabri-Laghaie, K. Developing approximate algorithms for EPQ problem with process compressibility and random error in producH. Mokhtari and J. Asadkhani/Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3253{3264 3263 tion/inspection", International Journal of Production Research, 52(8), pp. 2388{2421 (2014). 7. Nasr, W.W., Salameh, M.K., and Moussawi-Haidar, L. Integrating the economic production model with deteriorating raw material over multi-production cycles", International Journal of Production Research, 52(8), pp. 2477{2489 (2014). 8. Pacheco-Velazquez, E.A. and C_ardenas-Barr_on, L.E. An economic production quantity inventory model with backorders considering the raw material costs", Scientia Iranica, 23(2), pp. 736{746 (2016). 9. Jawad, H., Jaber, M.Y., Bonney, M., and Rosen, M.A. Deriving an exergetic economic production quantity model for better sustainability", Applied Mathematical Modelling, 40(11{12), pp. 6026{6039 (2016). 10. Sadeghi, J., Akhavan Niaki, S.T., Malekian, M.R., and Sadeghi, S. Optimizing multi-item economic production quantity model with trapezoidal fuzzy demand and backordering: two tuned meta-heuristics", European Journal of Industrial Engineering, 10(2), pp. 170{195 (2016). 11. Al-Salamah, M. Economic production quantity in batch manufacturing with imperfect quality, imperfect inspection, and destructive and non-destructive acceptance sampling in a two-tier market", Computers & Industrial Engineering, 93, pp. 275{285 (2016). 12. Mokhtari, H., Naimi-Sadigh, A., and Salmasnia, A. A computational approach to economic production quantity model for perishable products with backordering shortage and stock-dependent demand", Scientia Iranica, 24(4), pp. 2138{2151 (2017). 13. Mokhtari, H. and Rezvan, M.T. A single-supplier, multi-buyer, multi-product VMI production-inventory system under partial backordering", Operational Research, 20(1), pp. 1{21 (2020). DOI: 10.1007/s12351- 017-0311-z 14. Nasr, W.W., Salameh, M., and Moussawi-Haidar, L. Economic production quantity with maintenance interruptions under random and correlated yields", International Journal of Production Research, 55(16), pp. 4544{4556 (2017). 15. Karmakar, S., Kumar De, S., and Goswami, A. A pollution sensitive dense fuzzy economic production quantity model with cycle time dependent production rate", Journal of Cleaner Production, 154, pp. 139{ 150 (2017). 16. Nobil, A.H., Sedigh, A.H.A., and C_ardenas-Barr_on, L.E. Multi-machine economic production quantity for items with scrapped and rework with shortages and allocation decisions", Scientia Iranica, Transaction E, Industrial Engineering, 25(4), pp. 2331{2346 (2018). 17. Wee, H.M., Fu, K., Chen, Z., and Zhang, Y. Optimal production inventory decision with learning and fatigue behavioral e_ect in labor intensive manufacturing", Scientia Iranica, 27(2), pp. 1{42 (2020). DOI: 10.24200/SCI.2018.50614.1788 18. Rezg, N., Dellagi, S., and Chelbi, A. Joint optimal inventory control and preventive maintenance policy", International Journal of Production Research, 46(19), pp. 5349{5365 (2008). 19. Radhoui, M., Rezg, N., and Chelbi, A. Integrated model of preventive maintenance, quality control and bu_er sizing for unreliable and imperfect production systems", International Journal of Production Research, 47(2), pp. 389{402 (2009). 20. Liao, G.L. and Sheu, S.H. Economic production quantity model for randomly failing production process with minimal repair and imperfect maintenance", International Journal of Production Economics, 130(1), pp. 118{124 (2011). 21. Sana, S.S. Preventive maintenance and optimal bu_er inventory for products sold with warranty in an imperfect production system", International Journal of Production Research, 50(23), pp. 6763{6774 (2012). 22. Wee, H.M. and Widyadana, G.A. Economic production quantity models for deteriorating items with rework and stochastic preventive maintenance time", International Journal of Production Research, 50(11), pp. 2940{2952 (2012). 23. Pal, B., Sana, S.S., and Chaudhuri, K. A mathematical model on EPQ for stochastic demand in an imperfect production system", Journal of Manufacturing Systems, 32(1), pp. 260{270 (2013). 24. Chen, Y.C. An optimal production and inspection strategy with preventive maintenance error and rework", Journal of Manufacturing Systems, 32(1), pp. 99{106 (2013). 25. Jafari, L. and Makis, V. Optimal lot-sizing and maintenance policy for a partially observable production system", Computers & Industrial Engineering, 93, pp. 88{98 (2016). 26. Sett, B.K., Sarkar, S., and Sarkar, B. Optimal bu_er inventory and inspection errors in an imperfect production system with preventive maintenance", The International Journal of Advanced Manufacturing Technology, 90(1{4), pp. 545{560 (2017). 27. La Fata, C.M. and Passannanti, G. A simulated annealing-based approach for the joint optimization of production/inventory and preventive maintenance policies", The International Journal of Advanced Manufacturing Technology, 91(9{12), pp. 3899{3909 (2017). 28. Lai, X., Chen, Z., and Bidanda, B. Optimal decision of an economic production quantity model for imperfect manufacturing under hybrid maintenance policy with shortages and partial backlogging", International Journal of Production Research, 57(19), pp. 1{25 (2019). DOI: 10.1080/00207543.2018.1562249
1
ORIGINAL_ARTICLE
Investigating the effect of learning on setup cost in imperfect production systems using two-way inspection plan for rework under screening constraints
In the modern industrial environment, there is a continuous need for the advancement and improvement of the organization’s operations. Learning is an inherent property which is time-dependent and comes with experience. In view of this, the present framework considers the process of learning for an imperfect production system which aids in reducing the setup cost with the level of maturity gained, hence, providing positive results for the organization. Because of machine disturbances/ malfunctions, defectives are manufactured with a known probability density function. To satisfy the demand with good products only, the manufacturer invests in a two-way inspection process with multiple screening constraints. The first inspection misclassifies some of the items and delivers inaccuracies, viz., Type-I and Type–II. The loss due to inspection at the first stage is managed efficiently through a second inspection which is presumed to be free from errors. The study mutually optimizes the production and backordering quantities in order to maximize the expected total profit per unit time. Numerical analysis and detailed sensitivity analysis is carried out to validate the hypothesis and further cater to some valuable implications.
http://scientiairanica.sharif.edu/article_21444_36f4e5aea20605232e6564d458757fcb.pdf
2020-12-01
3265
3288
10.24200/sci.2019.51333.2120
Inventory
Imperfect-production
Two-way inspection
Sales-returns
Learning
Screening-constraints
A.
Kishore
kishore.aakanksha@gmail.com
1
Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi, India
AUTHOR
P.
Gautam
prerna3080@gmail.com
2
Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi, India
AUTHOR
A.
Khanna
dr.aditikhanna.or@gmail.com
3
Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi, India
LEAD_AUTHOR
C.K.
Jaggi
ckjaggi@yahoo.com
4
Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi, India
AUTHOR
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A manufacturingoriented supply chain model for imperfect quality with inspection errors, stochastic demand under rework and shortages", Comput. Ind. Eng., 106, pp. 299{314 (2017). 24. Sett, B.K., Sarkar, S., and Sarkar, B. Optimal bu_er inventory and inspection errors in an imperfect production system with preventive maintenance", T. Int. J. Adv. Manuf. Tech., 90(1{4), pp. 545{560 (2017). 25. Hayek, P.A. and Salameh, M.K. Production lot sizing with the reworking of imperfect quality items produced", Prod. Plan. Control, 12(6), pp. 584{590 (2001). 26. Chiu, Y.P. Determining the optimal lot size for the _nite production model with random defective rate, the rework process, and backlogging", Eng. Optim., 35(4), pp. 427{437 (2003). 27. Chiu, S.W., Gong, D.C., and Wee, H.M. E_ects of random defective rate and imperfect rework process on economic production quantity model", Jap. J. Ind. Appl. Math., 21(3), pp. 375{389 (2004). 28. Chiu, Y.S.P., Lin, H.D., and Cheng, F.T. Optimal production lot sizing with backlogging, random defective rate, and rework derived without derivatives", P. I. Mech. Eng. B-J. Eng., 220(9), pp. 1559{1563 (2006). A. Kishore et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3265{3288 3283 29. Chiu, S.W., Ting, C.K., and Chiu, Y.S.P. Optimal production lot sizing with rework, scrap rate, and service level constraint", Math. Comput. Model., 46(3), pp. 535{549 (2007). 30. Sana, S.S. A production-inventory model in an imperfect production process", Eur. J. Oper. Res., 200(2), pp. 451{464 (2010). 31. Sarkar, B., Sana, S.S., and Chaudhuri, K. Optimal reliability, production lot size and safety stock in an imperfect production system", Int. J. Math. Oper. Res., 2(4), pp. 467{490 (2010). 32. Sarkar, B., Sana, S.S., and Chaudhuri, K. An economic production quantity model with stochastic demand in an imperfect production system", Int. J. Serv. Oper. Manag., 9(3), pp. 259{283 (2011). 33. Dey, O. and Giri, B.C. Optimal vendor investment for reducing defect rate in a vendor-buyer integrated system with imperfect production process", Int. J. Prod. Econ., 155, pp. 222{228 (2014). 34. Chiu, S.W. Production lot size problem with failure in repair and backlogging derived without derivatives", Eur. J. Oper. Res., 188(2), pp. 610{615 (2008). 35. Lin, T.Y. Optimal policy for a simple supply chain system with defective items and returned cost under screening errors", J. Oper. Res. Soc. Jpn., 52(3), pp. 307{320 (2009). 36. Yoo, S.H., Kim, D., and Park, M.S. Economic production quantity model with imperfect-quality items, two-way imperfect inspection and sales return", Int. J. Prod. Econ., 121(1), pp. 255{265 (2009). 37. Hsu, J.T. and Hsu, L.F. Two EPQ models with imperfect production processes, inspection errors, planned backorders, and sales returns", Comput. Ind. Eng., 64(1), pp. 389{402 (2013). 38. Wee, H.M., Wang, W.T., and Yang, P.C. A production quantity model for imperfect quality items with shortage and screening constraint", Int. J. Prod. Res., 51(6), pp. 1869{1884 (2013). 39. C_ardenas-Barr_on, L.E., Chung, K.J., and Trevi~no- Garza, G. Celebrating a century of the economic order quantity model in honor of Ford Whitman Harris", Int. J. Prod. Econ., 155, pp. 1{7 (2014). 40. Taleizadeh, A.A., Khanbaglo, M.P.S., and C_ardenas- Barr_on, L.E. An EOQ inventory model with partial backordering and reparation of imperfect products", Int. J. Prod. Econ., 182, pp. 418{434 (2016). 41. Wang, W.T., Wee, H.M. Cheng, Y.L., Wen, C.L., and C_ardenas-Barr_on, L.E. EOQ model for imperfect quality items with partial backorders and screening constraint", Eur. J. Ind. Eng., 9(6), pp. 744{773 (2015). 42. Jaggi, C.K., Khanna, A., and Kishore, A. Production inventory policies for defective items with inspection errors, sales return, imperfect rework process and backorders", In K. Singh, M. Pandey, L. Solanki, S. B. Dandin, & P. S. Bhatnagar (Eds.), AIP Conf. Proc., 1715(1), p. 020062 (2016). 43. Moussawi-Haidar, L., Salameh, M., and Nasr, W. Production lot sizing with quality screening and rework", Appl. Math. Model., 40(4), pp. 3242{3256 (2016). 44. Liao, G.L. Production and maintenance policies for an EPQ model with perfect repair, rework, free-repair warranty, and preventive maintenance", IEEE T. Syst. Man CY.-S., 46(8), pp. 1129{1139 (2016). 45. Pal, S., Mahapatra, G.S., and Samanta, G.P. A threelayer supply chain EPQ model for price-and stockdependent stochastic demand with imperfect item under rework", J. Uncert. Anal. Appl., 4(1), p. 10 (2016). 46. Shah, N.H., Patel, D.G., and Shah, D.B. EPQ model for returned/reworked inventories during imperfect production process under price-sensitive stockdependent demand", Oper. Res., 18(2), pp. 1{17 (2016). 47. Sekar, T. and Uthayakumar, R. A multi-production inventory model for deteriorating items considering penalty and environmental pollution cost with failure rework", Uncert. Sup. Chain Manag., 5(3), pp. 229{ 242 (2017). 48. Benkherouf, L., Skouri, K., and Konstantaras, I. Optimal batch production with rework process for products with time-varying demand over _nite planning horizon", Oper. Res. Eng. Cyb. Sec., 113, pp. 57{68 (2017). 49. Chen, T.H. Optimizing pricing, replenishment and rework decision for imperfect and deteriorating items in a manufacturer-retailer channel", Int. J. Prod. Econ., 183, pp. 539{550 (2017). 50. Sha_ee-Gol, S., Nasiri, M.M., and Taleizadeh, A.A. Pricing and production decisions in multi-product single machine manufacturing system with discrete delivery and rework", Opsearch, 53(4), pp. 873{888 (2016). 51. Jawla, P. and Singh, S. Multi-item economic production quantity model for imperfect items with multiple production setups and rework under the e_ect of preservation technology and learning environment", Int. J. Ind. Eng. Comput., 7(4), pp. 703{716 (2016). 52. C_ardenas-Barr_on, L.E., Trevi~no-Garza, G., Taleizadeh, A.A., and Vasant, P. Determining replenishment lot size and shipment policy for an EPQ inventory model with delivery and rework", Math. Prob. Eng., 2015 (2015). 3284 A. Kishore et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3265{3288 53. Nobil, A.H., Sedigh, A.H.A., and C_ardenas-Barr_on, L.E. Multi-machine economic production quantity for items with scrapped and rework with shortages and allocation decisions", Scient. Iran., Transactions E, 25(4), pp. 2331{2346 (2018). 54. Chung, K.J., Ting, P.S., and C_ardenas-Barr_on, L.E. A simple solution procedure to solve the multidelivery policy into economic production lot size problem with partial rework", Scient. Iran., Transactions E, 24(5), pp. 2640{2644 (2017). 55. Nobil, A.H., Afshar Sedigh, A.H., Tiwari, S., and Wee, H.M. An imperfect multi-item single machine production system with shortage, rework, and scrapped considering inspection, dissimilar de_ciency levels, and non-zero setup times", Scient Iran, 26(1), pp. 557{570 (2019). 56. Adler, G.L. and Nanda, R. The e_ects of learning on optimal lot size determination multiple product case", AIIE T., 6(1), pp. 21{27 (1974). 57. Sule, D.R. A note on production time variation in determining EMQ under inuence of learning and forgetting", AIIE T., 13(1), pp. 91{95 (1981). 58. Urban, T.L. Analysis of production systems when run length inuences product quality", Int. J. Prod. Res., 36(11), pp. 3085{3094 (1998). 59. Jaber, M.Y. and Bonney, M. The economic manufacture/ order quantity (EMQ/EOQ) and the learning curve: past, present, and future", Int. J. Prod. Econ., 59(1), pp. 93{102 (1999). 60. Jaber, M.Y. and Bonney, M. Lot sizing with learning and forgetting in set-ups and in product quality", Int. J. Prod. Econ., 83(1), pp. 95{111 (2003). 61. Jaber, M.Y. Lot sizing for an imperfect production process with quality corrective interruptions and improvements, and reduction in setups", Comput. Ind. Eng., 51(4), pp. 781{790 (2006). 62. Darwish, M.A. EPQ models with varying setup cost", Int. J. Prod. Econ., 113(1), pp. 297{306 (2008). 63. Khan, M., Jaber, M.Y., Gui_rida, A.L., and Zolfaghari, S. A review of the extensions of a modi_ed EOQ model for imperfect quality items", Int. J. Prod. Econ., 132(1), pp. 1{12 (2011). 64. Konstantaras, I., Skouri, K., and Jaber, M.Y. Inventory models for imperfect quality items with shortages and learning in inspection", Appl. Math. Model., 36(11), pp. 5334{5343 (2012). 65. Mukhopadhyay, A. and Goswami, A. Economic Production Quantity (EPQ) model for three type imperfect items with rework and learning in setup", An Int. J. Optim. Control: Th. Appl. (IJOCTA), 4(1), pp. 57{ 65 (2013). 66. Gautam, P. and Khanna, A. An imperfect production inventory model with setup cost reduction and carbon emission for an integrated supply chain", Uncert. Sup. Chain Manag, 6(3), pp. 271{286 (2018).
1
ORIGINAL_ARTICLE
A game theoretic approach to coordination of pricing, ordering, and co-op advertising in supply chains with stochastic demand
This paper combines the newsboy problem with the cooperative advertisement problem in the presence of uncertain demand which depends on retail price as well as both local and national advertising expenditures to coordinate pricing, ordering, and advertising decisions in a manufacturer-retailer supply chain. A game theoretic approach is adopted to determine the equilibrium values of the decisions. Three different game scenarios based on the newsboy problem model are developed and analyzed: 1) Stackelberg manufacturer game in which manufacturer as the dominant power plays the role of leader in the market and the follower retailer makes its own best decisions after observing the leader decisions, 2) Nash game wherein both manufacturer and retailer have equal power in the market and make their decisions simultaneously to find their own best strategies and 3) centralized scenario in which retailer and manufacturer make the best decisions by information sharing and joint cooperation. The equilibrium decisions are obtained exactly in the three scenarios. Some corollaries are also presented and theoretically proved to show the relationships among the variables in centralized vs. decentralized supply chain. Finally, some numerical examples are randomly generated and a sensitivity analysis is carried out to show capabilities of the proposed models.
http://scientiairanica.sharif.edu/article_21404_4c3778f3329a454a32efcf6892e44959.pdf
2020-12-01
3289
3304
10.24200/sci.2019.51588.2264
supply chain coordination
newsboy problem
Pricing
cooperative advertising
ordering
uncertainty
Game theory
H.
Ghashghaei
hghashghae@gmail.com
1
Department of Industrial Engineering, Electronic Branch, Islamic Azad University, Tehran, P.O. Box 14335-499, Iran.
AUTHOR
M.
Mozafari
m_mozafari@iauec.ac.ir
2
Department of Industrial Engineering, Electronic Branch, Islamic Azad University, Tehran, P.O. Box 14335-499, Iran.
LEAD_AUTHOR
Dridi, D. and Ben Youssef, S. A game theoretic framework for competing/cooperating retailers under price and advertising dependent demand", Munich Personal RePEc Archive, available at: https://mpra.ub.unimuenchen. de/62705/ (2015). 2. Berger, P. Vertical cooperative advertising ventures", J. of Mark. Res., 9(3), pp. 309{312 (1972). 3. Aust, G. and Buscher, U. Cooperative advertising H. Ghashghaei and M. Mozafari/Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3289{3304 3301 models in supply chain management: A review", Eur. J. of Oper. Res., 234(1), pp. 1{14 (2014). 4. Huang, Z. and Li, S.X. Co-op advertising models of manufacturer-retailer supply chains: A game theoretic approach", Eur. J. of Oper. Res., 135, pp. 527{544 (2001). 5. Xie, J. and Wei, J.C. Coordinating advertising and pricing in a manufacturer-retailer channel", Eur. J. of Oper. Res., 197, pp. 785{791 (2009). 6. Karray, S. and Amin, S.H. Cooperative advertising in a supply chain with retail competition", Int. J. of Prod. Res., 53(1), pp. 88{105 (2015). 7. Yan, R., Cao, Z., and Pei, Z. Manufacturer's cooperative advertising, demand uncertainty, and information sharing", J. of Bus. Res., 69(2), pp. 709{717 (2016). 8. J_rgensen, S. and Zaccour, G. A survey of gametheoretic models of cooperative advertising", Eur. J. of Oper. Res., 237(1), pp. 1{14 (2014). 9. Alirezaei, A. and Khoshalhan, F. Coordination of pricing and co-op advertising models in supply chain: A game theoretic approach", Int. J. of Ind. Eng. Comput., 5, pp. 23{40 (2014). 10. He, X., Prasad, A., and Sethi, S.P. Cooperative advertising and pricing in a dynamic stochastic supply chain: Feedback Stackelberg strategies", Prod. and Oper. Manag., 18, pp. 78{94 (2009). 11. Szmerekovsky, J.G. and Zhang, J. Pricing and twotier advertising with one manufacturer and one retailer", Eur. J. of Oper. Res., 192(3), pp. 904{917 (2009). 12. Xie, J. and Neyret, A. Computers & industrial engineering co-op advertising and pricing models in manufacturer - retailer supply chains", Comput. & Ind. Eng., 56(4), pp. 1375{1385 (2009). 13. Chen, T.H. E_ects of the pricing and cooperative advertising policies in a two-echelon dual-channel supply chain", Comput. & Ind. Eng., 87, pp. 250{259 (2015). 14. Seyed Esfahani, M.M., Biazaran, M., and Gharakhani, M. A game theoretic approach to coordinate pricing and vertical co-op advertising in manufacturer-retailer supply chains", Eur. J. of Oper. Res., 211, pp. 263{ 273 (2011). 15. Naimi Sadigh, A., Chaharsooghi, S.K., and Sheikhmohammady, M. Game-theoretic analysis of coordinating pricing and marketing decisions in a multi-product multi-echelon supply chain", Sci. Iran, 23(3), pp. 1459{1473 (2016). 16. Choi, S. Price competition in a channel structure with a common retailer", Mark. Sci., 10(4), pp. 271{296 (1991). 17. Aust, G. Vertical cooperative advertising and pricing decisions in a manufacturer-retailer supply chain: a game-theoretic approach", In Vertical Cooperative Advertising in Supply Chain Management, pp. 65{99, Springer International Publishing (2015). 18. Ke, H., Wu, Y., Huang, H., and Chen, Z. Pricing decision in a two-echelon supply chain with competing retailers under uncertain environment", J. of Uncertain. Anal. and Appl., 5(1), p. 5 (2017). DOI: 10.1186/s40467-017-0059-2 19. Naimi Sadigh, A., Karimi, B., and Zangirani Farahani, R. A game theoretic approach for two echelon supply chains with continuous depletion", Int. J. of Manag. Sci. and Eng. Manag., 6(6), pp. 408{412 (2011). 20. Amirtaheri, O., Zandieh, M., and Dorri, B. A bi-level programming model for decentralized manufacturerdistributer supply chain considering cooperative advertising", Sci. Iran., 25(2), pp. 891{910 (2017). 21. Zhou, Y-Wu, Li, J., and Zhong, Y. Cooperative advertising and ordering policies in a two-echelon supply chain with risk-averse agents", Omega, 75, pp. 97{117 (2018). 22. Petruzzi, N.C. and Dada, M. Pricing and the newsvendor problem: a review with extensions", Oper. Res., 47(2), pp. 183{194 (1999).
1
ORIGINAL_ARTICLE
Bi-objective optimization of non-periodic preventive maintenance strategy by considering time value of money
Recently, design of preventive maintenance (PM) policies during the warranty period has attracted the attention of researchers. The methods mainly design warranty servicing strategies in a way that reduce the cost imposed on the manufacturer without considering the impact of customer dissatisfaction. While dissatisfaction with a product is an important issue which may result in the loss of potential buyers and switching existing buyers to competitors. Therefore, this study develops a bi-objective model which simultaneously minimizes the manufacturer and the buyer cost under a Non-homogeneous Poisson Process framework. Also, a non-periodic preventive maintenance strategy is presented in which PM actions are performed at discrete time instants in a way that the expected number of failures remains a constant value over all PM intervals. Furthermore, it is a known fact that the value of money reduces over time due to different reasons and has a significant impact on long-term contracts. Since PMs and repairs are conducted at different times, the time value of money is considered to estimate the cost more accurately. A comparative study is conducted to support this claim that the presented non-periodic reliability-based PM policy has a better performance in comparison with a periodic PM policy.
http://scientiairanica.sharif.edu/article_21384_0bb84f78cffebf6a3477529b80d578c9.pdf
2020-12-01
3305
3321
10.24200/sci.2019.51791.2365
warranty
non-periodic preventive maintenance
time value of money
bi-objective optimization
A.
Salmasnia
a.salmasnia@qom.ac.ir
1
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
LEAD_AUTHOR
A.
Shahidian
shahidian.ali@gmail.com
2
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
AUTHOR
M.
Seivandian
mesbah.sivandian@ut.ac.ir
3
Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
AUTHOR
B.
Abdzadeh
behnamabdzadeh@gmail.com
4
Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran
AUTHOR
Chen, C.-K., Lo, C.-C., and Weng, T.-C. Optimal production run length and warranty period for an imperfect production system under selling price dependent on warranty period", Eur. J. Oper. Res., 259(2), pp. 401{412 (2017). 2. Tian, Z., Jia Y., Li, X., Van Doan, C., and Revhaug, I. Modelling upgrading maintenance policy in the onedimensional renewing warranty period", J. Shanghai Jiaotong Univ., 21(6), pp. 737{743 (2016). 3. Taleizadeh, A.A., Khaligh, P.P., and Moon, I. Hybrid NSGA-II for an imperfect production system considering product quality and returns under two warranty policies", Appl. Soft Comput., 75, pp. 333{348 (2019). 4. Mahmoudi, A. and Shavandi, H. Analyzing price, warranty length, and service capacity under a fuzzy environment: Genetic algorithm and fuzzy system", Sci. Iran., 20(3), pp. 975{982 (2013). 5. Murthy, D.N.P. and Djamaludin, I. New product warranty: A literature review", Int. J. Prod. Econ., 79(3), pp. 231{260 (2002). 6. Darghouth, M.N., Chelbi, A., and Ait-kadi, D. Investigating reliability improvement of second-hand production equipment considering warranty and preventive maintenance strategies", Int. J. Prod. Res., 55(16), pp. 4643{4661 (2017). 7. Faridimehr, S. and Niaki, S.T.A. Optimal strategies for price, warranty length, and production rate of a new product with learning production cost", Sci. Iran., Trans. E, Ind. Eng., 20(6), pp. 2247{2258 (2013). 8. Darghouth, M.N., A_t-Kadi, D., and Chelbi, A. Joint optimization of design, warranty and price for products sold with maintenance service contracts", Reliab. Eng. Syst. Saf., 165, pp. 197{208 (2017). 3320 A. Salmasnia et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3305{3321 9. Marshall, S., Arnold, R., Chukova, S., et al. Warranty cost analysis: Increasing warranty repair times", Appl. Stoch. Model. Bus. Ind., 34(4), pp. 544{561 (2018). 10. Wang, X. and Su, C. A two-dimensional preventive maintenance strategy for items sold with warranty", Int. J. Prod. Res., 54(19), pp. 5901{5915 (2016). 11. Murthy, D.N.P., Solem, O., and Roren, T. Product warranty logistics: Issues and challenges", Eur. J. Oper. Res., 156(1), pp. 110{126 (2004). 12. Murthy, D.N.P. Product warranty and reliability", Ann. Oper. Res., 143(1), pp. 133{146 (2006). 13. Djamaludin, I., Murthy, D.N.P., and Kim, C.S. Warranty and preventive maintenance", Int. J. Reliab. Qual. Saf. Eng., 8(02), pp. 89{107 (2001). 14. Kim, C.S. Djamaludin, I., and Murthy, D.N.P. Warranty and discrete preventive maintenance", Reliab. Eng. Syst. Saf., 84(3), pp. 301{309 (2004). 15. Sha_ee, M. and Chukova, S. Maintenance models in warranty: A literature review", Eur. J. Oper. Res., 229(3), pp. 561{572 (2013). 16. Wang, X. and Xie, W. Two-dimensional warranty: A literature review", Proc. Inst. Mech. Eng. Part O J. Risk Reliab., p. 1748006X17742776 (2017). 17. Wu, J., Xie, M., and Ng, T.S.A. On a general periodic preventive maintenance policy incorporating warranty contracts and system ageing losses", Int. J. Prod. Econ., 129(1), pp. 102{110 (2011). 18. Park, M., Jung, K.M., and Park, D.H. Optimal maintenance strategy under renewable warranty with repair time threshold", Appl. Math. Model., 43, pp. 498{508 (2017). 19. Su, C. and Wang, X. Optimizing upgrade level and preventive maintenance policy for second-hand products sold with warranty", Proc. Inst. Mech. Eng. Part O J. Risk Reliab., 228(5), pp. 518{528 (2014). 20. Huang, Y.-S., Huang, C.-D., and Ho, J.-W. A customized two-dimensional extended warranty with preventive maintenance", Eur. J. Oper. Res., 257(3), pp. 971{978 (2017). 21. Park, M. and Pham, H. Cost models for age replacement policies and block replacement policies under warranty", Appl. Math. Model., 40(9), pp. 5689{5702 (2016). 22. Bouguerra, S., Chelbi, A., and Rezg, N. A decision model for adopting an extended warranty under di_erent maintenance policies", Int. J. Prod. Econ., 135(2), pp. 840{849 (2012). 23. Salmasnia, A. and Yazdekhasti, A. A bi-objective model to optimize periodic preventive maintenance strategy during warranty period by considering customer satisfaction", Int. J. Syst. Assur. Eng. Manag., 8(4), pp. 770{781 (2017). 24. Teng, H.-M. Extended warranty pricing considering the time of money", J. Inf. Optim. Sci., 27(2), pp. 401{409 (2006). 25. Fang, C.-C. and Huang, Y.-S. A study on decisions of warranty, pricing, and production with insu_cient information", Comput. Ind. Eng., 59(2), pp. 241{250 (2010). 26. Shahanaghi, K., Noorossana, R., Jalali-Naini, S.G., and Heydari, M. Failure modeling and optimizing preventive maintenance strategy during two-dimensional extended warranty contracts", Eng. Fail. Anal., 28, pp. 90{102 (2013). 27. Ambad, P.M. and Kulkarni, M.S. A methodology for design for warranty with focus on reliability and warranty policies", J. Adv. Manag. Res., 10(1), pp. 139{155 (2013). 28. Su, C. and Wang, X. A study of availability-based warranty policy", in Industrial Engineering and Engineering Management (IEEM), 2016 IEEE International Conference on, pp. 1655{1659 (2016). 29. Moeini, A., Foumani, M., and Jenab, K. Utilisation of pruned Pareto-optimal solutions in the multi objective optimisation: an application to system redundancy allocation problems", Int. J. Appl. Decis. Sci., 6(1), pp. 50{65 (2013). 30. Moeini, A., Jenab, K., Mohammadi, M., and Foumani, M. Fitting the three-parameter Weibull distribution with cross entropy", Appl. Math. Model., 37(9), pp. 6354{6363 (2013). 31. Kijima, M. Some results for repairable systems with general repair", J. Appl. Probab., 26(01), pp. 89{102 (1989). 32. Salmasnia, A., Bashiri, M., and Salehi, M. A robust interactive approach to optimize correlated multiple responses", Int. J. Adv. Manuf. Technol., 67(5{8), pp. 1923{1935 (2013). 33. Roozitalab, A. and Asgharizadeh, E. Optimizing the warranty period by cuckoo meta-heuristic algorithm in heterogeneous customers' population", J. Ind. Eng. Int., 9(1), pp. 1{6 (2013). 34. Talbi, E.-G., Metaheuristics: From Design to Implementation, 74, John Wiley & Sons (2009). 35. Mahmoodian, V., Jabbarzadeh, A., and Rezazadeh, H. A novel intelligent particle swarm optimization algorithm for solving cell formation problem", Neural Comput. Appl., 31(2), pp. 801{815 (2017). 36. Bui, K.-T.T., Bui, D.T., Zou, J., et al. A novel hybrid arti_cial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam", Neural Comput. Appl., 29(12), pp. 1495{1506 (2016). 37. Salmasnia, A., Abdzadeh, B., and Namdar, M. A joint design of production run length, maintenance policy and control chart with multiple assignable causes", J. Manuf. 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1
ORIGINAL_ARTICLE
Technology valuation of NTBFs in the field of cleaner production in terms of investors' exibility and uncertainty in public policy
Technology valuation, especially in the early stages of new technology-based firms (NTBFs) growth is one of the most critical challenges, which most often hinders the investor and entrepreneur's deals during the venture capital (VC) financing process. It is clear that uncertainties arising from the likelihood of implementing public policies could significantly affect the volatility of NTBFs cash flows in the field of cleaner production. Commonly, these kinds of technologies require public supportive policies for achieving success. Consequently, their technology valuation is more challenging and traditional valuation methods are not suitable anymore because of the definitive assumption of cash flow and ignoring the investors’ flexibilities and uncertainties. Therefore, this paper proposes a method by introducing a framework based on the decision tree and the real options analysis which is tailored to meet the technology valuation of such firms during all stages of their growth. Furthermore, unlike previous papers that have utilized the compound options, option to choose has been used to apply investors’ flexibilities. Then, the proposed framework is supported by a case study, which has been conducted to verify and validate it. Finally, the conclusion section discusses the contributions and limitations of the study and provides directions for future research.
http://scientiairanica.sharif.edu/article_21365_f5efe19dd6c26590990426ae6751719a.pdf
2020-12-01
3322
3337
10.24200/sci.2019.52078.2523
Technology valuation
New technology-based firms (NTBFs)
Real options analysis (ROA)
Option to choose
Decision tree analysis (DTA)
Cleaner production
K.
Fattahi
komeil_fattahi@yahoo.com
1
Department of Progress Engineering at Iran University of Science and Technology (IUST), Narmak, Tehran, Iran
AUTHOR
A.
Bonyadi Naeini
bonyadi@iust.ac.ir
2
Department of Progress Engineering at Iran University of Science and Technology (IUST), Narmak, Tehran, Iran
LEAD_AUTHOR
S.J.
Sadjadi
sjsadjadi@iust.ac.ir
3
Department of Progress Engineering at Iran University of Science and Technology (IUST), Narmak, Tehran, Iran
AUTHOR
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1
ORIGINAL_ARTICLE
An evaluation of inventory systems via an evidence theory for deteriorating items under uncertain conditions and advanced payment
The inventory model for deteriorating items, which is developed by The Evidential Reasoning Algorithm (ERA) and the imprecise inventory costs, is one of the most important factors in complex systems which plays a vital role in Payment. The ERA is able to strengthen the precision of the model and give the perfect interval-valued utility. In this model, during lead-time and reorder level two different cases can be happened which the mathematical model turns into an imposed nonlinear mixed integer problem with interval objective for each case. Placement of an order, which is overlooked by many researchers till now, is normally connected with the advance payment (AP) in business. Specifying the optimal profit and the optimal number of cycles in the finite time horizon and lot-sizing in each cycle, are our goals so. In order to solve this model, we apply the real-coded genetic algorithm (RCGA) with ranking selection. By the model, we represent some numerical examples and also a sensitivity analysis with the variation of different inventory parameters.
http://scientiairanica.sharif.edu/article_21478_b87332cdc38fb15baa1573b7e329e32d.pdf
2020-12-01
3338
3351
10.24200/sci.2019.52116.2543
Evidence theory
Inventory
advance payment
Deterioration
Genetic Algorithm
interval order relations
M.
Soleimani Amiri
mth.soleimany@gmail.com
1
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
AUTHOR
A.
Mirzazadeh
ind.eng.res@gmail.com
2
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Ira
LEAD_AUTHOR
M.M.
Seyed Esfahani
msesfahani@aut.ac.ir
3
Department of Industrial Engineering and Management Systems, Amirkabir University, Tehran, Iran.
AUTHOR
Sh.
Ghasemi
shiva.s.ghasemi@gmail.com
4
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
AUTHOR
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Modelling multi tour inventory routing problem for deteriorating items with time windows", International Journal of Science and Technology, pp. 932{941 (2019). 6. Li, Ruihai, Skouri, K., Teng, J.T., and Yang, W.G. Seller's optimal replenishment policy and payment term among advance, cash, and credit payments", International Journal of Production Economics, Elsevier, 197(C), pp. 35{42 (2018). 7. Feng, L., Chan, Y.L., and C_ardenas-Barr_on, L.E. Pricing and lot-sizing polices for perishable goods when the demand depends on selling price, displayed stocks, and expiration date", International Journal of Production Economics, 185, pp. 11{20 (2017). 8. Chih-Te Yang, Cha-Huei Ho, Hsiu-Mei Lee and Liang- Yuh Ouyang. Supplier-retailer production and inventory models with defective items and inspection errors in non-cooperative and cooperative environments", RAIRO Operations Research, 52(2), pp. 453{ 471 (2018). 9. Maiti, A.A., Maiti, M.K., and Maiti, M. Inventory model with stochastic lead-time and price dependent demand incorporating advance payment", Applied Mathematical Modelling, 33(5), pp. 2433{2443 (2009). 10. Taleizadeh, A.A. An EOQ model with partial backordering and advance payments for an evaporating item", International Journal of Production Economics, 155, pp. 185{193 (2014). 11. Nodoust, S., Mirzazadeh, A., and Mohammadi, M. A genetic algorithm for an inventory system under belief structure inationary condition", RAIRO Operations Research, 50, pp. 1027{1041 (2016). 12. Tiwari, S., Jaggi, C.K., Gupta, M., and C_ardenas- Barr_on, L.E. Optimal pricing and lot-sizing policy for supply chain system with deteriorating items under limited storage capacity", International Journal of Production Economics, 200, pp. 278{290 (2018). 13. Zhang, Q., Tsao, Y.C., and Chen, T.H. Economic order quantity under advance payment", Applied Mathematical Modelling, 38(24), pp. 5910{5921 (2014). 14. 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Managing an integrated production and inventory system selling to a dual market: Long-term and walk-in", European Journal of Operational Research, 268(1), pp. 215{230 (2018). 19. Dempster, A.P. Upper and lower probabilities induced by a multi valued mapping", The Annals of Mathematical Statistics, 38(2), pp. 325{339 (1967). 20. Glenn, Sh., A Mathematical Theory of Evidence, Princeton University Press, ISBN 0-608-02508-9 (1976). 21. Kari, S., and Ferson, S., Combination of evidence in dempster-shafer theory, Sandia National Laboratories SAND, 0835 (2002). 22. Park, Y.B., Yoo, J.S., and Park, H.S. A genetic algorithm for the vendor-managed inventory routing problem with lost sales", Expert Systems with Applications, 53, pp. 149{159 (2016). M. Soleimani Amiri et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 3338{3351 3351 23. Hiassat, A., Diabat, A., and Rahwan, L. A genetic algorithm approach for location-inventory-routing problem with perishable products", Journal of Manufacturing Systems, 42, pp. 93{103 (2017). 24. Azadeh, A., Elahi, S., Hosseinabadi Farahani, M., and Nasirian, B. A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment", Computers & Industrial Engineering, 104, pp. 124{133 (2017). 25. Taleizadeh, A.A., Akhavan Niaki, S.T., Aryanezhad, M.B., and Sha_i, N. A hybrid method of fuzzy simulation and genetic algorithm to optimize constrained inventory control systems with stochastic replenishments and fuzzy demand", Information Sciences, 220, pp. 425{441 (2013). 26. Saracoglu, I., Topaloglu, S., and Keshkinturk, T. A genetic algorithm approach for multi-product multiperiod continuous review inventory models", Expert Systems with Applications, 41(18), pp. 8189{8202 (2014). 27. O'Neill, B. and Sanni, S. Pro_t optimisation for deterministic inventory systems with linear cost", Computers & Industrial Engineering, 122, pp. 303{ 317 (2018). 28. Souri M.E., Sheikh, R., and Sanad, Sh.S. Grey SERVQUAL method to measure consumers' attitudes towards green products - A case study of Iranian consumers of LED bulbs", Journal of Cleaner Production, 177, pp. 187{196 (2018). 29. Ishibuchi, H. and Tanaka, H. Multi objective programming in optimization of the interval objective function", European Journal of Operational Research, 48, pp. 219{225 (1990). 30. Chanas, S. and Kutcha, D. Multiobjective, programming in the optimization of interval objective functions - A generalized approach", European Journal of Operational Research, 94(3), pp. 594{598 (1996). 31. Sengupta, A. and Pal, T.K. Theory and methodology on comparing interval numbers", European Journal of Operational Research, 127, pp. 28{43 (2000). 32. Mahato, S.K. and Bhunia, A.K. Interval-arithmeticoriented interval computing technique for global optimization", Applied Mathematics Research Express, 2006, p. 69642 (2006). 33. Gupta, R.K, Bhunia, A.K., and Goyal, S.K. An application of genetic algorithm in a marketing oriented inventory model with interval valued inventory costs and three-component demand rate dependent on displayed stock level", Applied Mathematics and Computation, 192(2), pp. 466{478 (2007). 34. Ahmadzadeh, F. Multi criteria decision making with evidential reasoning under uncertainty", 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (2016). 35. Gupta, R.K., Bhunia, A.K., and Roy, D. A GA based penalty function technique for solving constrained redundancy allocation problem of series system with interval valued reliability of components", Journal of Computational and Applied Mathematics, 232(2), pp. 275{284 (2009). 36. Sahoo, L., Bhunia, A.K., and Kapur, P.K. Genetic algorithm base multi-objective reliability optimization in interval environment", Applied Mathematics and Computation, 62(1), pp. 152{160 (2007).
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