ORIGINAL_ARTICLE
Optimal pricing of a new generation product when customers are strategic
In this research, we develop a mathematical model for new generation products in the presence of strategic customers. A firm that produces and sells a two-generation product is considered. It is assumed that potential customers of the first generation are strategic and may delay their purchase to the next generation. The firm aims to determine the optimal price by maximizing total profit. The optimal control theory is used for analyzing the proposed model. The results reveal that strategic behavior influences pricing strategy and reduces optimal price and firm’s profit.
https://www.jise.ir/article_143883_a72f4925805bf914446c67602f931fe9.pdf
2022-01-27
1
10
New generation product
pricing
Strategic Customers
optimal control theory
Somayeh
Najafi Ghobadi
s.najafi2010@gmail.com
1
Department of Industrial Engineering, Factually of Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
LEAD_AUTHOR
Anderson TR, Daim TU, Kim J (2008) Technology forecasting for wireless communication. Technovation 28(9): 602-614
1
Cenamor J, Usero B, Fernández Z (2013) The role of complementary products on platform adoption: Evidence from the video console market. Technovation 33(12): 405-416
2
Chen M, Chen ZL (2015) Recent developments in dynamic pricing research: multiple products, competition, and limited demand information. Prod. Oper. Manag. 24(5): 704-731
3
Chevalier J, Goolsbee A (2009) Are durable goods consumers forward-looking? Evidence from college textbooks. Q. J. Econom. 124(4): 1853-1884
4
Du J, Zhang J, Hua G (2015) Pricing and inventory management in the presence of strategic customers with risk preference and decreasing value. Int. J. prod. Econ. 164160-166
5
Gönsch J, Klein R, Neugebauer M, Steinhardt C (2013) Dynamic pricing with strategic customers. J. Bus. Econ. 83(5): 505-549
6
Guo Z, Chen J (2018) Multigeneration Product Diffusion in the Presence of Strategic Consumers. Inf. Sys. Res. 29(1): 206-224
7
Hendel I, Nevo A (2013) Intertemporal price discrimination in storable goods markets. Am. Econ. Rev. 103(7): 2722-2751
8
Jiang Z, Jain DC (2012) A generalized Norton–Bass model for multigeneration diffusion. Manage. Sci. 58(10): 1887-1897
9
Kalish S (1983) Monopolist pricing with dynamic demand and production cost. Mark. Sci. 2(2): 135-159
10
Krishnan TV, Bass FM, Jain DC (1999) Optimal pricing strategy for new products. Manage. Sci. 45(12): 1650-1663
11
Levin Y, McGill J, Nediak M (2009) Dynamic pricing in the presence of strategic consumers and oligopolistic competition. Manage. Sci. 55(1): 32-46
12
Li J, Granados N, Netessine S (2014) Are consumers strategic? Structural estimation from the air-travel industry. Manage. Sci. 60(9): 2114-2137
13
Liang C, Çakanyıldırım M, Sethi SP (2014) Analysis of product rollover strategies in the presence of strategic customers. Manage. Sci. 60(4): 1033-1056
14
Nair H (2007) Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games. Quant. Mark. Econ. 5(3): 239-292
15
Netessine S, Tang CS (2009) Consumer-driven demand and operations management models: a systematic study of information-technology-enabled sales mechanisms vol 131. (Springer Science & Business Media,
16
Norton JA, Bass FM (1987) A diffusion theory model of adoption and substitution for successive generations of high-technology products. Manage. Sci. 33(9): 1069-1086
17
Osadchiy N, Bendoly E (2015) Are consumers really strategic? Implications from an experimental study.
18
Özer Ö, Ozer O, Phillips R (2012) The Oxford handbook of pricing management. (Oxford University Press,
19
Şeref MM, Carrillo JE, Yenipazarli A (2016) Multi-generation pricing and timing decisions in new product development. Int. J. Prod. Econ. Res. 54(7): 1919-1937
20
Sethi SP, Thompson GL (2000) Optimal control theory: Applications to management science and economics. (Springer US, Boston, MA: Kluwer Academic)
21
Shen ZJM, Su X (2007) Customer behavior modeling in revenue management and auctions: A review and new research opportunities. Prod. Oper. Manag. 16(6): 713-728
22
Shi X, Fernandes K, Chumnumpan P (2014) Diffusion of multi-generational high-technology products. Technovation 34(3): 162-176
23
Tang CS (2010) A review of marketing–operations interface models: From co-existence to coordination and collaboration. Int. J. prod. Econ. 125(1): 22-40
24
Zhou C, Wu Y (2011) Study on revenue management considering strategic customer behavior. J. Serv. Sci. Manag. 4(04): 507
25
ORIGINAL_ARTICLE
An integrated fuzzy AHP- fuzzy DEA approach for location optimization of renewable energy plants
This study presents an integrated approach for optimizing the location of renewable energy plants. The proposed approach is composed of fuzzy analytic hierarchy process (FAHP) and fuzzy data envelopment analysis (FDEA). The FDEA and FAHP methods are used to select the preferred location. The results of FDEA are validated by DEA, and then it is employed for ranking of location of renewable energy plants and the best α-cut is selected based on the test of Normality. Also, FAHP that is a method based on expert opinion is used for ranking. Five kinds of renewable energies including solar, wind, geothermal, biofuel and hydrogen and fuel cell are considered. The most related criteria are identified from the literature. The intelligent approach of this study is applied to an actual location optimization of renewable energy plants in Iran. In the proposed case study, in some cases FDEA and FAHP select the same alternatives, and for some other cases different alternatives are preferred by these two methods. According to the obtained results, the proposed approach of this study is ideal for renewable energy plant location optimization with possible ambiguity and uncertainty. The aim of this study is helping managers to select optimal locations for renewable energy plants when experts’ opinions are available or not.
https://www.jise.ir/article_143884_c3439c46bb5aa6d7f011f29c6f995f7d.pdf
2022-01-27
11
18
Fuzzy Data Envelopment Analysis (FDEA)
Fuzzy Analytic Hierarchy Process (FAHP)
location optimization
renewable energy unit
Uncertainty
Mohammad
Sheikhalishahi
m.alishahi@ut.ac.ir
1
School of Industrial and Systems Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Alireza
Hakimi
a.hakimi@ut.ac.ir
2
School of Industrial and Systems Engineering, University of Tehran, Tehran, Iran
AUTHOR
Mehdi
Hakimi
m.hakimi@ut.ac.ir
3
School of Industrial and Systems Engineering, University of Tehran, Tehran, Iran
AUTHOR
Anane, M., L. Bouziri, A. Limam and S. Jellali (2012). "Ranking suitable sites for irrigation with reclaimed water in the Nabeul-Hammamet region (Tunisia) using GIS and AHP-multicriteria decision analysis." Resources, Conservation and Recycling 65: 36-46.
1
Arocena, P. (2008). "Cost and quality gains from diversification and vertical integration in the electricity industry: A DEA approach." Energy Economics 30(1): 39–58.
2
Azadeh, A., S. Ghaderi and A. Maghsoudi (2008). "Location optimization of solar plants by an integrated hierarchical DEA PCA approach." Energy Policy 36(10): 3993-4004.
3
Azadeh, A., S. F. Ghaderi and M. R. Nasrollahi (2011). "Location optimization of wind plants in Iran by an integrated hierarchical Data Envelopment Analysis." Renewable Energy 36 (2011) 36(5): 1621–1631.
4
Azadeh, A., M. Sheikhalishahi and S. Asadzadeh (2011). "A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity." Renewable Energy 36(12): 3394-3401.
5
Beccali, M., M. Cellura and M. Mistretta (2003). "Decision-making in energy planning. Application of the Electre method at regional level for the diffusion of renewable energy technology." Renewable Energy 28(13): 2063-2087.
6
Cavallaro, F. and L. Ciraolo (2005). "A multicriteria approach to evaluate wind energy plants on an Italian island." Energy Policy 33(2): 235-244.
7
Chang, D.-Y. (1996). "Applications of the extent analysis method on fuzzy AHP." European journal of operational research 95(3): 649-655.
8
Hiremath, R., S. Shikha and N. Ravindranath (2007). "Decentralized energy planning; modeling and application—a review." Renewable and Sustainable Energy Reviews 11(5): 729-752.
9
Jahanshahloo, G. R., M. Soleimani-Damaneh and E. Nasrabadi (2004). "Measure of efficiency in DEA with fuzzy input–output levels: a methodology for assessing, ranking and imposing of weights restrictions." Applied Mathematics and Computation 156(1): 175-187.
10
Kahraman, C. and İ. Kaya (2010). "A fuzzy multicriteria methodology for selection among energy alternatives." Expert Systems with Applications 37(9): 6270-6281.
11
Kaya, T. and C. Kahraman (2010). "Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul." Energy 35(6): 2517-2527.
12
Lee, S. K., G. Mogi and K. S. Hui (2013). "A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: In the case of energy technologies against high oil prices." Renewable and Sustainable Energy Reviews 21: 347–355.
13
Lertworasirikul, S., S.-C. Fang, J. A Joines and H. LW Nuttle (2003). "Fuzzy data envelopment analysis (DEA): a possibility approach." Fuzzy sets and systems 139(2): 379-394.
14
Mourmouris, J. and C. Potolias (2013). "A multi-criteria methodology for energy planning and developing renewable energy sources at a regional level: A case study Thassos, Greece." Energy Policy 52: 522-530.
15
Stein, E. W. (2013). "A comprehensive multi-criteria model to rank electric energy production technologies." Renewable and Sustainable Energy Reviews 22: 640–654.
16
Sueyoshi, T. and M. Goto (2012). "Efficiency-based rank assessment for electric power industry: A combined use of Data Envelopment Analysis (DEA) and DEA-Discriminant Analysis (DA)." Energy Economics 34(3): 634-644.
17
Wang, J.-J., Y.-Y. Jing, C.-F. Zhang and J.-H. Zhao (2009). "Review on multi-criteria decision analysis aid in sustainable energy decision-making." Renewable and Sustainable Energy Reviews 13(9): 2263-2278.
18
Wen, M. and H. Li (2009). "Fuzzy data envelopment analysis (DEA): Model and ranking method." Journal of Computational and Applied Mathematics 223(2): 872-878.
19
Yang, M.S., Wu, K.L., Hsieh, J.N. and Yu, J (2008) “Alpha-Cut Implemented Fuzzy Clustering Algorithms and Switching Regressions”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 38(3): 588-603.
20
ORIGINAL_ARTICLE
Diagnosis the dependence of revenue sources of communication service companies on specific services using machine learning
Nowadays, Telecommunication has a vital role in both developed and emerging economies countries. Especially after coronavirus epidemic, the importance of telecommunication service like internet in education, research, economy and other areas is evident. Due to the alluring market of providing internet services to the main customers of IT industry and its significant profit, the demand of the other services has decreased sharply. Hence, a large part of the revenues of the IT industry be related to internet services. In this study, balancing of revenue sources has investigated as one of the important diagnosis facing the IT industry. In order to overcome this problem, introducing low-demand services along with internet service in the form of a package to the main customers is analyzed with a best-known machine learning algorithm, Generalized Linear Model. In order to validate the applicability of our study, a case study of a company providing telecommunication infrastructure and internet network bandwidth in Iran, is presented.
https://www.jise.ir/article_143902_cf07e7c60083c67687ca50e7ce636595.pdf
2022-01-27
19
30
IT industry
internet services
Telecommunication
Machine Learning
Mahmoud
Tajik Jangali
mahmoud_tajik@ind.iust.ac.ir
1
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Ahmad
Makui
amakui@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Narges
Taheri
ntaheri@tic.ir
3
Strategic & business development, Telecommunication Infrastructure Company, Tehran, Iran
AUTHOR
Ehsan
Dehghani
ehsan_dehghani@mail.iust.ac.ir
4
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Somaye
Kazemi
s.kazemy@tic.ir
5
Tehran University, Tehran, Iran
AUTHOR
Agresti, A. (2015) Foundations of linear and generalized linear models. John Wiley \& Sons.
1
Badri Ahmadi, H., Hashemi Petrudi, S. and Wang, X. (2017) ‘Integrating sustainability into supplier selection with analytical hierarchy process and improved grey relational analysis: A case of telecom industry.’, International Journal of Advanced Manufacturing Technology, 90.
2
Bandyoapdhyay, P. S. et al. (2011) ‘The logic of Simpson’s paradox’, Synthese, 181(2), pp. 185–208.
3
Büyüközkan, G. and şakir Ersoy, M. (2009) ‘Applying fuzzy decision making approach to IT outsourcing supplier selection’, system, 2, p. 2.
4
Carrera, Á. et al. (2014) ‘A real-life application of multi-agent systems for fault diagnosis in the provision of an Internet business service’, Journal of Network and Computer Applications, 37, pp. 146–154.
5
Chen, X. et al. (2020) ‘A Novel Fault Diagnosis Method for High-Speed Railway Turnout Based On DCAE-Logistic Regression’, in 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 318–323.
6
Covas, M. T., Silva, C. A. and Dias, L. C. (2013) ‘Multicriteria decision analysis for sustainable data centers location’, International Transactions in Operational Research, 20(3), pp. 269–299. doi: 10.1111/j.1475-3995.2012.00874.x.
7
Daim, T. U., Bhatla, A. and Mansour, M. (2013) ‘Site selection for a data centre--a multi-criteria decision-making model’, International Journal of Sustainable Engineering, 6(1), pp. 10–22.
8
Echraibi, A. et al. (2020) ‘An Infinite Multivariate Categorical Mixture Model for Self-Diagnosis of Telecommunication Networks’, in 2020 23rd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), pp. 258–265.
9
Emami, M. et al. (2020) ‘Generalization error of generalized linear models in high dimensions’, in International Conference on Machine Learning, pp. 2892–2901.
10
Félix, A., Garc\’\ia, N. and Vera, R. (2020) ‘Participatory diagnosis of the tourism sector in managing the crisis caused by the pandemic (COVID-19)’, Revista Interamericana de Ambiente y Turismo, 16(1), pp. 66–78.
11
Hossain, M. L., Abu-Siada, A. and Muyeen, S. M. (2018) ‘Methods for advanced wind turbine condition monitoring and early diagnosis: A literature review’, Energies, 11(5), p. 1309.
12
Khatib, E. J. et al. (2015) ‘Data mining for fuzzy diagnosis systems in LTE networks’, Expert Systems with Applications, 42(21), pp. 7549–7559.
13
Kim, J. S. et al. (2018) ‘Development of data-driven in-situ monitoring and diagnosis system of fused deposition modeling (FDM) process based on support vector machine algorithm’, International Journal of Precision Engineering and Manufacturing-Green Technology, 5(4), pp. 479–486.
14
Ko, R. K. L., Lee, S. S. G. and Lee, E. W. (2009) ‘Business process management (BPM) standards: a survey’, Business Process Management Journal.
15
Kohlbacher, M. (2010) ‘The effects of process orientation: a literature review’, Business process management journal.
16
Lei, Y. et al. (2020) ‘Applications of machine learning to machine fault diagnosis: A review and roadmap’, Mechanical Systems and Signal Processing, 138, p. 106587.
17
Liu, P. et al. (2020) ‘Optimization of Edge-PLC-Based Fault Diagnosis With Random Forest in Industrial Internet of Things’, IEEE Internet of Things Journal, 7(10), pp. 9664–9674.
18
Nelder, J. A. and Wedderburn, R. W. M. (1972) ‘Generalized linear models’, Journal of the Royal Statistical Society: Series A (General), 135(3), pp. 370–384.
19
Quiñones-Grueiro, M., Llanes-Santiago, O. and Neto, A. J. S. (2021) ‘Fault Diagnosis in Industrial Systems’, in Monitoring Multimode Continuous Processes. Springer, pp. 1–14.
20
De Ramon Fernandez, A., Ruiz Fernandez, D. and Sabuco Garcia, Y. (2020) ‘Business Process Management for optimizing clinical processes: A systematic literature review’, Health informatics journal, 26(2), pp. 1305–1320.
21
Shahabi, H. et al. (2020) ‘Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier’, Remote Sensing, 12(2), p. 266.
22
Simpson, E. H. (1949) ‘Measurement of diversity’, nature, 163(4148), p. 688.
23
Sorrentino, M. et al. (2019) ‘A Novel Energy Efficiency Metric for Model-Based Fault Diagnosis of Telecommunication Central Offices’, Energy Procedia, 158, pp. 3901–3907.
24
Soualhi, A. and Razik, H. (2020) ‘Diagnostic Methods for the Health Monitoring of Gearboxes’, Electrical Systems 1: From Diagnosis to Prognosis, pp. 1–43.
25
Sun, Y. et al. (2020) ‘A new convolutional neural network with random forest method for hydrogen sensor fault diagnosis’, IEEE Access, 8, pp. 85421–85430.
26
Tetteh, V. K. (2012) Organisational Diagnosis--A Management Tool for Change in the Telecommunication Industry.
27
Varela-Vaca, Á. J. et al. (2019) ‘Automatic verification and diagnosis of security risk assessments in business process models’, IEEE Access, 7, pp. 26448–26465.
28
Wang, F. et al. (2020) ‘Neural cognitive diagnosis for intelligent education systems’, in Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6153–6161.
29
Wardani, S., Sihombing, P. and others (2020) ‘Hybrid of Support Vector Machine Algorithm and K-Nearest Neighbor Algorithm to Optimize the Diagnosis of Eye Disease’, in 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), pp. 321–326.
30
Wu, J.-Y. and Hsiao, H.-I. (2021) ‘Food quality and safety risk diagnosis in the food cold chain through failure mode and effect analysis’, Food Control, 120, p. 107501.
31
Yao, J. and Ye, Y. (2020) ‘The effect of image recognition traffic prediction method under deep learning and naive Bayes algorithm on freeway traffic safety’, Image and Vision Computing, 103, p. 103971.
32
ORIGINAL_ARTICLE
Using grey relational analysis for dynamic portfolio selection in Tehran Stock Exchange
In this study, first, a brief survey of various portfolio selection problems is presented to explore the related methodologies, hypotheses, and constraints that are considered in these problems. Among these methods, the grey relational analysis approach is employed to deal with poor information and uncertainties in portfolio selection problems. Return, risk, skewness, and kurtosis are used at the same time as selecting criteria in the portfolio construction. To evaluate the effectiveness of the proposed method, an empirical analysis has done. Therefore, fourteen stocks of various industries like metal, banks, financial institutions, car manufactures, transportation, and petroleum from the thirty largest active companies’ index in Tehran Stock Exchange have been randomly selected and all above mention moments have been calculated for each stocks. In this study, the portfolio is restructured dynamically each week based on the ranking of previous week. The result from the analysis indicates that the selected approach has better performance in comparison with the benchmarks in terms of return, standard deviation, and Sharpe ratio.
https://www.jise.ir/article_143903_c90222ac6cabfe59adadd4ec16531ed5.pdf
2022-01-27
31
39
Portfolio selection
Grey Relational Analysis
Tehran Stock Exchange
Nasrin
Ramezani
n.ramezani@khatam.ac.ir
1
Department of Industrial Engineering, Khatam University, Tehran, Iran
AUTHOR
Farimah
mokhatab
f.mokhatab@modares.ac.ir
2
Department of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Abdelaziz, F. B., & Masmoudi, M. (2014). A multiple objective stochastic portfolio selection problem with random Beta. International Transactions in Operational Research, 21(6), 919-933.
1
Aggarwal, R., Rao, R. P., & Hiraki, T. (1989). Skewness and kurtosis in Japanese equity returns: empirical evidence. Journal of Financial Research, 12(3), 253-260.
2
Amin, G. R., & Hajjami, M. (2021). Improving DEA cross-efficiency optimization in portfolio selection. Expert Systems with Applications, 168, 114280.
3
Bayramoglu, M. F., & Hamzacebi, C. (2016). Stock selection based on fundamental analysis approach by grey relational analysis: a case of Turkey. International Journal of Economics and Finance, 8(7), 178-184.
4
Beedles, W. L. (1984). The Anomalous and Asymmetric Nature of Equity Returns: An Empirical Synthesis. Journal of Financial Research, 7(2), 151-160.
5
Beshkooh, M., & Afshari, M. A. (2012). Selection of the optimal portfolio investment in stock market with a hybrid approach of hierarchical analysis (AHP) and grey theory analysis (GRA). Journal of Basic and Applied Scientific Research, 2(11), 11218-11225.
6
Chan, L. K., Karceski, J., & Lakonishok, J. (1999). On portfolio optimization: Forecasting covariances and choosing the risk model. The review of Financial studies, 12(5), 937-974.
7
Dai, Y., & Qin, Z. (2021). Multi-period uncertain portfolio optimization model with minimum transaction lots and dynamic risk preference. Applied Soft Computing, 107519.
8
Galankashi, M. R., Rafiei, F. M., & Ghezelbash, M. (2020). Portfolio selection: a fuzzy-ANP approach. Financial Innovation, 6(1), 1-34.
9
Ghahtarani, A., & Najafi, A. A. (2013). Robust goal programming for multi-objective portfolio selection problem. Economic Modelling, 33, 588-592.
10
Guo, S., Ching, W. K., Li, W. K., Siu, T. K., & Zhang, Z. (2020). Fuzzy hidden Markov-switching portfolio selection with capital gain tax. Expert Systems with Applications, 149, 113304.
11
Hsu, C. M. (2014). An integrated portfolio optimization procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming. International Journal of Systems Science, 45(12), 2645-2664.
12
Huang, K. Y., & Jane, C. J. (2008, December). An automatic stock market forecasting and portfolio selection mechanism based on VPRS, ARX and grey system. In 2008 IEEE Asia-Pacific Services Computing Conference (pp. 1430-1435).
13
Huang, K. Y., Jane, C. J., & Chang, T. C. (2010, December). A hybrid model for portfolio selection based on grey relational analysis and RS theories. In 2010 International Computer Symposium (ICS2010) (pp. 1014-1019).
14
Huang, K. Y., Jane, C. J., & Chang, C. (2011). An enhanced approach to optimizing the stock portfolio selection based on Modified Markowitz MV Method. Journal of Convergence Information Technology, 6(2), 226-239.
15
Huang, C. F. (2012). A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing, 12(2), 807-818.
16
Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert systems with applications, 37(2), 1784-1789.
17
Khedmati, M., & Azin, P. (2020). An online portfolio selection algorithm using clustering approaches and considering transaction costs. Expert Systems with Applications, 159, 113546.
18
Liu, S., Forrest, J., & Yang, Y. (2013, November). A summary of the progress in grey system research. In Proceedings of 2013 IEEE international conference on grey systems and intelligent services (GSIS) (pp. 1-10).
19
Liu, Y. J., Zhang, W. G., & Zhang, P. (2013). A multi-period portfolio selection optimization model by using interval analysis. Economic Modelling, 33, 113-119.
20
Lwin, K. T., Qu, R., & MacCarthy, B. L. (2017). Mean-VaR portfolio optimization: A nonparametric approach. European Journal of Operational Research, 260(2), 751-766.
21
Markowitz, H. M. (1968). Portfolio selection. Yale university press.
22
Mashayekhi, Z., & Omrani, H. (2016). An integrated multi-objective Markowitz-DEA cross-efficiency model with fuzzy returns for portfolio selection problem. Applied Soft Computing, 38, 1-9.
23
Mills, E. F. E. A., Baafi, M. A., Amowine, N., & Zeng, K. (2020). A hybrid grey MCDM approach for asset allocation: evidence from China’s Shanghai Stock Exchange. Journal of Business Economics and Management, 21(2), 446-472.
24
Naqvi, B., Mirza, N., Naqvi, W. A., & Rizvi, S. K. A. (2017). Portfolio optimization with higher moments of risk at the Pakistan Stock Exchange. Economic research-Ekonomska istraživanja, 30(1), 1594-1610.
25
Raei, R., & Jahromi, M. (2012). Portfolio optimization using a hybrid of fuzzy ANP, VIKOR and TOPSIS. Management Science Letters, 2(7), 2473-2484.
26
Safitri, I. N. N., Sudradjat, S., & Lesmana, E. (2020). Stock portfolio analysis using Markowitz model. International Journal of Quantitative Research and Modeling, 1(1), 47-58.
27
Škrinjarić, T., & Šego, B. (2019). Using grey incidence analysis approach in portfolio selection. International Journal of Financial Studies, 7(1), 1.
28
Škrinjarić, T. (2020). Dynamic portfolio optimization based on grey relational analysis approach. Expert systems with applications, 147, 113207.
29
Steuer, R. E., Wimmer, M., & Hirschberger, M. (2013). Overviewing the transition of Markowitz bi-criterion portfolio selection to tri-criterion portfolio selection. Journal of Business Economics, 83(1), 61-85.
30
Sun, Q., & Yan, Y. (2003). Skewness persistence with optimal portfolio selection. Journal of Banking & Finance, 27(6), 1111-1121.
31
Vezmelai, A., Lashgari, Z., & Keyghobadi, A. (2015). Portfolio selection using ELECTRE III: evidence from Tehran Stock Exchange. Decision Science Letters, 4(2), 227-236.
32
Wang, B., Wang, S., & Watada, J. (2011). Fuzzy-portfolio-selection models with value-at-risk. IEEE Transactions on Fuzzy Systems, 19(4), 758-769.
33
Xia, Y., Liu, B., Wang, S., & Lai, K. K. (2000). A model for portfolio selection with order of expected returns. Computers & Operations Research, 27(5), 409-422.
34
Xu, G., Guo, P., Li, X., & Jia, Y. (2014). Grey relational analysis and its application based on the angle perspective in time series. Journal of Applied Mathematics, 2014.
35
Yin, M. S. (2013). Fifteen years of grey system theory research: a historical review and bibliometric analysis. Expert systems with Applications, 40(7), 2767-2775.
36
Zhang, W. G., Liu, Y. J., & Xu, W. J. (2012). A possibilistic mean-semi variance-entropy model for multi-period portfolio selection with transaction costs. European Journal of Operational Research, 222(2), 341-349.
37
Zhu, M. (2013). Return distribution predictability and its implications for portfolio selection. International Review of Economics & Finance, 27, 209-223.
38
Zhou, W., & Xu, Z. (2018). Portfolio selection and risk investment under the hesitant fuzzy environment. Knowledge-Based Systems, 144, 21-31.
39
ORIGINAL_ARTICLE
Predicting coronary artery diseases using effective features selected by Harris Hawks optimization algorithm and support vector machine
With 17 million annual deaths, cardiovascular diseases are the leading cause of mortality across the world with coronary artery disease (CAD) as the most prevalent one. CAD is the leading cause of death in industrial countries and at the same time is rapidly spreading in the developing world. Thus, the development and introduction of machine learning methods for the accurate diagnosis of heart diseases, especially CAD, have been an important debate in recent years in order to overcome relevant problems. The aim of this paper was to propose a model for enhancing CAD prediction accuracy. It sought a framework for predicting and diagnosing CAD using the features selection of Harris Hawks Optimization algorithm (HHO) and Support Vector Machine (SVM). The heart disease data set of Cleveland hospital available in the University of California Irvine (UCI) was used as the studied data set. It included 303 cases. Each case had 14 features with the final medical status of cases (CAD or normal case) as one of the features where 165 and 138 cases were diagnosed as CAD and normal, respectively. The results of this study revealed that HHO could enhance CAD diagnosis accuracy.
https://www.jise.ir/article_143910_2f554e7b25ddfcfdf68e4cee065983f1.pdf
2022-01-27
40
47
CORONARY ARTERY DISEASES
Feature selection
Harris Hawk optimization algorithm
Support Vector Machine
Sarina
Maleki
sarinamaleki1398@gmail.com
1
Department of Industrial Engineering, Technical Engineering Faculty, Yazd University, Yazd, Iran
AUTHOR
Yahia
Zare Mehrjerdi
mehrjerdyazd@gmail.com
2
Department of Industrial Engineering, Technical Engineering Faculty, Yazd University, Yazd, Iran
LEAD_AUTHOR
Davood
shishebori
shishebori@yazd.ac.ir
3
Department of Industrial Engineering, Technical Engineering Faculty, Yazd University, Yazd, Iran
AUTHOR
Masoud
Mirzaei
masoud_mirzaei@hotmail.com
4
Disease Modeling Center of Shahid Sadoughi University of Medical Sciences, Yazd, Iran
AUTHOR
Abdar, M., Książek, W., Acharya, U. R., Tan, R. S., Makarenkov, V., & Pławiak, P. (2019). A new machine learning technique for an accurate diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 179, 104992.
1
DezhAloud, N. (2020). Diagnosis of Heart Disease Using Binary Grasshopper Optimization Algorithm and K-Nearest Neighbors. Journal of Health Administration, 23(3), 42-54.
2
Engelbrecht, A. P. (2007). Computational intelligence: an introduction. John Wiley & Sons.
3
Glaros, A. G., & Kline, R. B. (1988). Understanding the accuracy of tests with cutting scores: The sensitivity, specificity, and predictive value model. Journal of clinical psychology, 44(6), 1013-1023.
4
Giri, D., Acharya, U. R., Martis, R. J., Sree, S. V., Lim, T. C., VI, T. A., & Suri, J. S. (2013). Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowledge-Based Systems, 37, 274-282.
5
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
6
Khosravanian, A., & Ayat, S. S. (2015). Presenting an intelligent system for diagnosis of coronary heart disease by using Probabilistic Neural Network.
7
Nahar, J., Imam, T., Tickle, K. S., & Chen, Y. P. P. (2013). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40(4), 1086-1093.
8
Nasarian, E., Abdar, M., Fahami, M. A., Alizadehsani, R., Hussain, S., Basiri, M. E., ... & Sarrafzadegan, N. (2020). Association between work-related features and coronary artery disease: A heterogeneous hybrid feature selection integrated with balancing approach. Pattern Recognition Letters, 133, 33-40.
9
Ndindjock, R., Gedeon, J., Mendis, S., Paccaud, F., & Bovet, P. (2011). Potential impact of single-risk-factor versus total risk management for the prevention of cardiovascular events in Seychelles. Bulletin of the World Health Organization, 89, 286-295.
10
Negahbani, M., Joulazadeh, S., Marateb, H. R., & Mansourian, M. (2015). Coronary artery disease diagnosis using supervised fuzzy c-means with differential search algorithm-based generalized Minkowski metrics. Peertechz Journal of Biomedical Engineering, 1(1), 006-014.
11
Rani, K. U. (2011). Analysis of heart diseases dataset using neural network approach. arXiv preprint arXiv:1110.2626.
12
Reddy, K. S. (2002). Cardiovascular diseases in the developing countries: dimensions, determinants, dynamics and directions for public health action. Public health nutrition, 5(1a), 231-237.
13
Vila-Francés, J., Sanchis, J., Soria-Olivas, E., Serrano, A. J., Martinez-Sober, M., Bonanad, C., & Ventura, S. (2013). Expert system for predicting unstable angina based on Bayesian networks. Expert systems with applications, 40(12), 5004-5010.
14
ORIGINAL_ARTICLE
Prediction of marketing strategies performance based on clickstream data
Today, Internet-based businesses are one of the most useful tools to make gain in the economies of developing and developed countries. It can even said that the expansion of the World Wide Web caused other businesses to seek customers in the virtual advertising and online world to increase their sales. This study presents a data-driven approach to predict the success of the marketing strategies performance of an online shopping store. The data has been collected by a Poland online shopping website in the year 2008, which has extracted in the UCI datasets. In the data preparation phase, a decision tree (DT) is developed and 13 features of customers are selected for modeling phase. In the proposed method in this research, the rminer package of R software is used. In which three classification models including neural network(NN), support vector machine (SVM), and logistic regression(LR) are developed. Then, two criteria of AUC and ROC curves are used to compare these three models. By comparing the models, it is determined that the NN technique works better than the other three models in prediction. This result can be helpful for marketing managers to plan effectively in website design to attract new visitors and shoppers.
https://www.jise.ir/article_143911_49611bc8747c9aa1625eed8837b4022a.pdf
1999-11-30
48
56
Classification
Sales forecasting
Machine Learning
clickstream Data
marketing plan
Neural Network
Maryam
Afrasiabi
mafrasiabi@aut.ac.ir
1
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Akbar
Esfahanipour
esfahaa@aut.ac.ir
2
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Ali Mohammad
Kimiagari
kimiagar@aut.ac.ir
3
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Babcock, B., Babu, S., Datar, M., Motwani, R., & Widom, J. (2002). Models and Issues in Data Stream Systems" Proceedings of the twenty-first symposium on Principles of database systems, 1–16, https://doi.org/10.1145/543613.54361.
1
Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification And Regression Trees (Routledge Ed), 1st ed.
2
Bucklin, R. E., & Sismeiro, C. (2009). Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing. Journal of Interactive Marketing, 23(1), 35-48. doi:https://doi.org/10.1016/j.intmar.2008.10.004.
3
Chompaisal, S., Amphawan, K., & Surarerks, A.(2014). Mining N-most Interesting Multi-level Frequent Itemsets without Support Threshold. Paper presented at the Recent Advances in Information and Communication Technology, Cham.
4
Cleger-Tamayo, S., Fernández-Luna, J. M., & Huete, J. F. (2012). Top-N news recommendations in digital newspapers. Knowledge-Based Systems, 27, 180-189. doi:https://doi.org/10.1016/j.knosys.2011.11.017.
5
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/BF00994018.
6
Cortez, P. (2010). Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool. Paper presented at the Advances in Data Mining. Applications and Theoretical Aspects, Berlin, Heidelberg.
7
Hastie, T., Tibshirani, R., Friedman, J. (2008). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.): Springer-Verlag, NY, USA.
8
Huynha, H. M., Nguyenb, L. T. T., Voc, B., Nguyend. A., Tseng, V. S. (2020), Efficient methods for mining weighted clickstream patterns. Expert Systems with Applications, 142, 112993. doi: https://doi.org/10.1016/j.eswa.2019.112993.
9
Kawaf, F., & Istanbulluoglu, D. (2019). Online fashion shopping paradox: The role of customer reviews and facebook marketing. Journal of Retailing and Consumer Services, 48, 144-153. doi:https://doi.org/10.1016/j.jretconser.2019.02.017.
10
Kelly, G. A. (1955). The psychology of personal constructs, (Vol. 1).
11
Koehn, D., Lessmann, S., & Schaal, M. (2020). Predicting online shopping behaviour from clickstream data using deep learning. Expert Systems with Applications, 150, 113-342. doi:https://doi.org/10.1016/j.eswa.2020.113342
12
Li, H.-F. (2009). A sliding window method for finding top-k path traversal patterns over streaming Web click-sequences. Expert Systems with Applications, 36(3, Part 1), 4382-4386. doi:https://doi.org/10.1016/j.eswa.2008.05.025.
13
Møller, M. F. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6(4), 525-533. doi:https://doi.org/10.1016/S0893-6080(05)80056-5.
14
Nasraoui, O., Cardona, C., Rojas, C., & Gonz'alez, F. (2003). Mining Evolving User Profiles in Noisy Web Clickstream Data with a Scalable Immune System Clustering Algorithm.
15
Platt, J. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Advances in Kernel Methods-Support Vector Learning, 208.
16
Venables, W. N., Ripley, B.D. (2003). Modern Applied Statistics with S (4th ed.).
17
Xia, Y., Liu, C., Da, B., Xie, F. (2018). A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Systems With Applications, 93, 182-199. doi: 10.1016/j.eswa.2017.10.022
18
Zeng, J., Zhang, S., & Wu, C. (2008). A framework for WWW user activity analysis based on user interest. Knowledge-Based Systems, 21(8), 905-910. doi:https://doi.org/10.1016/j.knosys.2008.03.049.
19
Zhao, X., Niu, Z., & Chen, W. (2013). Interest before liking: Two-step recommendation approaches. Knowledge-Based Systems, 48, 46-56. doi:https://doi.org/10.1016/j.knosys.2013.04.009.
20
ORIGINAL_ARTICLE
Technical and economic evaluation of hydraulic reliability of urban high pressure gas loop network
Scientific analysis and providing a way to improve the reliability of high-pressure urban gas networks for sustainable and safe gas supply by suppliers is essential. A reasonable forecast for the achievable flow for each subscriber at the time of failure is the critical network hydraulic reliability in quantitative analysis and is not easy in loop networks. In this article, based on hydraulic indicators and the principles of engineering economics, the degree of reliability and availability of the city gas network has been analyzed. The proposed method relies on the data and findings of the hydraulic analysis and hydraulic regime of node flow in the network and the reliability in different situations with the utilization coefficient of the pressure drop, based on actual flow is analyzed, and provide a solution in determining the cost of the gas supplier company. The results show that the hydraulic reliability of the network has a high impact on the stability of the gas network and for improve of it, the gas companies have to pay attention to design and implementation costs as well as repair and operation costs in network service time.
https://www.jise.ir/article_143912_cf74498d61d3d12ccebd1f9d9b2b1cc7.pdf
2022-01-27
57
63
Network hydraulic reliability
utilization coefficient of the pressure drop
current value
Structural Reliability
network hydraulic regime
Behzad
Khosravi
behzadkho@gmail.com
1
Faculty of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Mahmoud
Shahrokhi
m.shahrokhi@uok.ac.ir
2
Faculty of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
LEAD_AUTHOR
Abunada, M., Trifunović, N., Kennedy, M., & Babel, M. (2014). Optimization and reliability assessment of water distribution networks incorporating demand balancing tanks. Procedia Engineering, 70, 4-13.
1
Gheisi, A., & Naser, G. (2014). Simultaneous multi-pipe failure impact on reliability of water distribution systems. Procedia Engineering, 89, 326-332.
2
Kansal, M. L., & Devi, S. (2007). An improved algorithm for connectivity analysis of distribution networks. Reliability Engineering & System Safety, 92(10), 1295-1302.
3
Li, J. (2005). Seismic Basic Theory and Application of Lifeline Engineering. In: Science Press, Beijing.
4
Li, J., Qin, C., Yan, M., Ma, J., & Yu, J. (2016). Hydraulic reliability analysis of an urban loop high-pressure gas network. Journal of Natural Gas Science and Engineering, 28, 372-378.
5
Li, J., YAn, M., & Yu, J. (2018). Evaluation on gas supply reliability of urban gas pipeline network. Eksploatacja i Niezawodność, 20(3).
6
Majid, Z., Mohsin, R., & Yusof, M. (2012). Experimental and computational failure analysis of natural gas pipe. Engineering Failure Analysis, 19, 32-42.
7
Zhuang, B., Lansey, K., & Kang, D. (2011). Reliability/availability analysis of water distribution systems considering adaptive pump operation. Paper presented at the World Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability.
8
ORIGINAL_ARTICLE
Customer experience analysis and customer journey management in Iran’s retail sector (Case study: Clothing retailers)
With the ever-growing importance of Customer Experience (CX) in today’s digital marketplace, more and more industries who are in a process of digital transformation have been focused on improving their customer’s brand experience. Focusing on CX has been named as a key factor in maintaining competitive advantage as well as improving customer acquisition and retention rates. Mapping and analyzing customer journeys through their interactions with a business have been identified as one of the most prominent ways to define, evaluate and improve CX. That is why the use of customer journey mapping has been on the rise in recent years, being named as one of the key enablers of successful digital transformation. The use of such methods must take into account differences in various industries and also the cultural context of customers, be it region, buying power, mindset, etc. For the Iranian market, not much work has been done in this area which can provide practical, data driven insights for businesses. In this research, the subject of customer experience and customer journey management in the retail sector, in particular the clothing retailers, has been addressed. To achieve our results, data has been gathered using surveys from customers of the clothing sector, and appropriate analytical methods used to identify main customer touchpoints and analyze the customer journeys.
https://www.jise.ir/article_143913_72f2e47e389290b729ce2cbf50f02ddc.pdf
2022-01-27
64
73
Customer experience
customer journey mapping
touchpoints
Digital marketing
digital transformation
Maede
Ashoori
maedeashoori31@gmail.com
1
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
Moslem
Habibi
mhabibi@sharif.edu
2
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
LEAD_AUTHOR
Berry, L. L., Carbone, L. P., & Haeckel, S. H. (2002). Managing the Total Customer Experience. MIT Sloan Management Review. https://www.researchgate.net/publication/266277275B.
1
Joseph Pine II and James H. Gilmore. (n.d.). Welcome to the Experience Economy. Harvard Business Review. Retrieved May 29, 2021, from https://hbr.org/1998/07/welcome-to-the-experience-economy
2
Blythe, J., & Sethna, Z. (2013). Consumer Behavior. SAGE Publications Ltd.Cordewener, M. (2016). Customer journey identification through temporal patterns and Markov clustering. [M.S. thesis, Eindhoven University of Technology].
3
Duncan, E., Jones, C., & Rawson, A. (2013). The Lean Management Enterprise A system for daily progress, meaningful purpose, and lasting value.
4
Gentile, C., Spiller, N., & Noci, G. (2007). How to Sustain the Customer Experience: An Overview of Experience Components that Co-create Value with the Customer. European Management Journal, 25(5), 395–410. https://doi.org/10.1016/j.emj.2007.08.005
5
Grewal, D., & Roggeveen, A. L. (2020). Understanding Retail Experiences and Customer Journey Management. Journal of Retailing, 96(1), 3–8. https://doi.org/10.1016/j.jretai.2020.02.002
6
Huang, Z. (1998). Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery, 12, 283–304.
7
Ismail, A. R. (2011). Experience marketing: An empirical investigation. Journal of Relationship Marketing, 10(3), 167–201. https://doi.org/10.1080/15332667.2011.599703
8
Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience throughout the Customer Journey.
9
Metsola, T. (2018). A Framework for Understanding the Usage of the Customer Journey in Marketing Automation. [M.S. thesis, Eindhoven University of Technology]
10
Meyer, C., & Schwager, A. (2007). Understanding Customer Experience. www.hbr.org
11
Mosquera, A., Olarte Pascual, C., & Juaneda Ayensa, E. (2017). Understanding the customer experience in the age of omni-channel shopping. ICONO14, 15(2), 166–185. https://doi.org/10.7195/ri14.v15i2.1070
12
Norton, D. W., & Pine, B. J. (2013). Using the customer journey to road test and refine the business model. Strategy and Leadership, 41(2), 12–17. https://doi.org/10.1108/10878571311318196
13
Roggeveen, A. L., Grewal, D., & Schweiger, E. B. (2020). The DAST Framework for Retail Atmospherics: The Impact of In- and Out-of-Store Retail Journey Touchpoints on the Customer Experience. Journal of Retailing, 96(1), 128–137. https://doi.org/10.1016/j.jretai.2019.11.002
14
Rosenbaum, M. S., Otalora, M. L., & Ramírez, G. C. (2017). How to create a realistic customer journey map. Business Horizons, 60(1), 143–150. https://doi.org/10.1016/j.bushor.2016.09.010
15
Stein, A., & Ramaseshan, B. (2016). Towards the identification of customer experience touch point elements. Journal of Retailing and Consumer Services, 30, 8–19. https://doi.org/10.1016/j.jretconser.2015.12.001
16
Tyrväinen, O., Karjaluoto, H., & Saarijärvi, H. (2020). Personalization and hedonic motivation in creating customer experiences and loyalty in omnichannel retail. Journal of Retailing and Consumer Services, 57. https://doi.org/10.1016/j.jretconser.2020.102233
17
Zhao, W. Y., & Deng, N. (2020). Examining the channel choice of experience-oriented customers in omni-channel retailing. International Journal of Information Systems in the Service Sector, 12(1), 16–27. https://doi.org/10.4018/IJISSS.2020010102
18
Zomerdijk, L. G., & Voss, C. A. (2010). Service design for experience-centric services. Journal of Service Research, 13(1), 67–82. https://doi.org/10.1177/1094670509351960
19
ORIGINAL_ARTICLE
A developed nonlinear model for cross-docking supply chain network design with possibility of linking between cross-docks
This paper studies location-allocation and transportation problem in cross-docking distribution networks that consists of suppliers, cross-docks and plants. A developed mixed-integer nonlinear model is proposed for a post-distribution cross-docking strategy with multi cross-docks and products that cross-docks can be connected. The objective function is to minimize the total cost comprising the cost of established cross-docks and transportation cost. For obtaining this model, at first two models are introduced and compared with each other by solving five short simulated problems (basic nonlinear model 1 and nonlinear model 2 with the possibility of connections between cross-docks). Results indicate that the total cost is decreased when the connection between cross-docks exists. So, model 2 is more efficient and suitable than the basic model. Then, in the following, consolidation of plant orders is added to model 2 and the developed model is formulated. Finally, some problems with different sizes are generated randomly and solved by GAMS software. Computational results show that the developed model is suitable to solve the location-allocation and transportation problem in cross-docking distribution networks.
https://www.jise.ir/article_143914_262da6f35189edf86dfe3034a278a2bb.pdf
2022-01-27
74
85
cross-docking
location-allocation
Transportation problem
Consolidation
Saeid
Nasrollahi
saeid_nasrollahi62@yahoo.com
1
Department of Industrial Engineering, Yazd University, Yazd, Iran
AUTHOR
Hassan
Hosseini Nasab
hhn@yazd.ac.ir
2
Department of Industrial Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
Mohammad Bagher
Fakhrzad
mfakhrzad@yazd.ac.ir
3
Department of Industrial Engineering, Yazd University, Yazd, Iran
AUTHOR
Mahboubeh
Honarvar
mhonarvar@yazd.ac.ir
4
Department of Industrial Engineering, Yazd University, Yazd, Iran
AUTHOR
Agustina, D, Lee, C. K. M, & Piplani, R. (2010). A review: mathematical models for cross dock planning. International Journal of Engineering Business Management, 2(2), 47–54.
1
Bachlaus, M, Pendey, M. K, Mahajan, C, Shankar, R, & Tiwari, M. K. (2008). Designing an Integrated multi-echelon agile supply chain network: a hybrid taguchi-particle swarm optimization approach. Journal of Intelligent Manufacturing, 19, 747–761.
2
Behnamian, J, Fatemi Ghomi, S. M. T, Jolai, F, & Heidari, P. (2018). Location-allocation and scheduling of inbound and outbound trucks in multiple cross-dockings considering breakdown trucks. Journal of Optimization in Industrial Engineering, 11(1), 51-65.
3
Boysen, N, Fliedner, M, Sassetti, R. J , & Consolo, J. (2010). Cross dock scheduling: classification, literature review and research agenda. Omega, 38, 413–422.
4
Buijs, P, Vis, I. F. A, & Carlo, H. J. (2014). Synchronization in cross-docking networks: A research classification and framework. European Journal of Operational research, 239(3), 593–608.
5
Goodarzi, A. H, & Zegordi, S. H. (2017). A new model for location and transportation problem of cross-docks in distribution networks. International Journal of Modeling and Optimization, 7(6), 51-65.
6
Gumus, M, & Bookbinder, J. H. (2004). Cross-docking and its implications in location-distribution systems. Journal of Business Logistics, 25(2), 199–228.
7
Hosseini, S. D. Shirazi, M. A. & Karimi, B. (2014). Cross-docking and milk run logistics in a consolidation network: A hybrid of harmony search and simulated annealing approach. Journal of Manufacturing Systems, 33(4), 567-577.
8
Javanmard, S. Vahdani, B, & Tavakkoli-moghaddam, R. (2014). Solving a multi-product distribution planning problem in cross docking networks: An imperialist competitive algorithm. International Journal of Advanced Manufacturing Technology, 70, 1709-1720.
9
Jayaraman, V, & Ross, A. (2003). A simulated annealing methodology to distribution network design and management. European Journal of Operational research, 144, 629–645.
10
Ladier, A-L, & Alpan, G. (2016). Cross-docking operations: Current research versus industry practice. Omega, 62, 145–162.
11
Ma, H. Miao, Z. Lim, A. & Rodrigues, B. (2011). Crossdocking distribution networks with setup cost and time window constraint. Omega, 39, 64-72.
12
Marjani, M. R. Husseini, S. M. M. & Karimi, B. (2011). Bi-objective heuristics for multi-item freights distribution planning problem in crossdocking networks. International Journal of Advanced Manufacturing Technology, 58, 1201-1216.
13
Musa, R, Arnaout, J-P, & Jung, H. (2010). Ant colony optimization algorithm to solve for the transportation problem of cross-docking network. Computers and Industrial Engineering, 59, 85–92.
14
Ross, A. & Jayaraman, V. (2008). An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design. Computers and Industrial Engineering, 55, 64–79.
15
Seyedhoseini, S. M, Rashid, R, & Teimouri, E. (2015). Developing a cross-socking network design model under uncertain environment. Journal of Industrial Engineering International, 11, 225–236.
16
Sheikholeslam, M. N. & Emamian, S. (2016). A review and classification of cross-docking concept. International Journal of Learning Management Systems, 4(1), 25-33.
17
Sung, C. S, & Song, S. H. (2003). Integrated service network design for a cross-docking supply chain network. Journal of the Operational Research Society, 54,1283–1295.
18
Sung, C. S, & Yang, W. (2008). An exact algorithm for a cross-docking supply chain network design problem. Journal of the Operational Research Society,59,119–136.
19
Van Belle, J., Valckenaers, P, & Cattrysse, D. (2012). Cross-docking: state of the art. Omega, 40, 827–846.
20
Yan, H, & Tang, S. L. (2009). Pre-distribution and post-distribution cross-docking operation. Transportation Research Part E: Logistics and Transportation Review, 45(6), 843-859.
21
Yu, V. F, Jewpanya, P, & Kachitvichyanukul, V. (2016). Particle swarm optimization for the multi-period cross-docking distribution problem with time windows. International Journal of Production Research, 54 (2), 509-525.
22
ORIGINAL_ARTICLE
Data-driven optimization model: Digikala case study
Increasing software as a service (SaaS) requires the provision of more updated models for services, so trying to develop a model customized for the customer is important. We used the linear Knapsack problem model proposed by Mike Hewitt and Emma Frejinger in 2020. Then historical data of Digikala was applied and shown that how the model works on it.
https://www.jise.ir/article_143915_fda0cb40e314fda3dc96c7b5c519c8d0.pdf
2022-01-27
86
90
Optimization modeling
statistical learning
mixed integer linear programming
Third-party Logistics
Sanaz
Hamidi
sanazh3@aut.ac.ir
1
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Seyed Mohammad Taghi
Fatemi Ghomi
fatemi@aut.ac.ir
2
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Hewitt, M., & Frejinger, E. (2020). Data-driven optimization model customization. European Journal of Operational Research, 287, 438-451.
1
KIM, J.-H. (2019). Studies on Total Logistics Management in Physical Distribution Process. East Asian Journal of Business Economics, 7(4), 15-26. doi:http://dx.doi.org/10.20498/eajbe.2019.7.4.15
2
Kolb, s. (2016). Learning Constraints and Optimization Criteria. Technical Report . The Workshops of the Thirtieth AAAI Conference on Artificial Intelligence (pp. 403-409). Phoenix, Arizona, USA: AAAI Press.
3
Kolb, S., Teso, S., Passerini, A., & De Raedt. (2018). Learning SMT(LRA) Constraints using SMT Solvers. In Proceedings of the twenty-seventh international joint conference on artificial intelligence (pp. 2333-2340). Stockholm Sweden: AAAI Press. doi:https://doi.org/10.24963/ijcai.2018/323
4
Lombardi, M., Milano, M., & Bartolini, A. (2017). Empirical decision model learning. Artificial Intelligence, 224, 343-367. doi:https://doi.org/10.1016/j.artint.2016.01.005
5
Pawlak, T., & Krawiec, K. (2017). Automatic synthesis of constraints from examples using mixed integer linear programming. European Journal of Operational Research, 261(3), 1141-1157. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S037722171730156X
6