Duygu analizi ve yapay sinir ağı kullanılarak envanter rotalama problemi için talep tahmini

Year 2021, Volume: 11 Issue: 1, 126 - 134, 15.01.2021

Aslı Boru İpek Ayse Tugba Dosdogru , Rızvan Erol

https://doi.org/10.17714/gumusfenbil.691428

Abstract

Sosyal medya kullanıcılarının sayısı her geçen gün katlanarak artmaktadır. Bu gelişme, tedarik zincirinde iş zekasının ilerletilmesinde önemli fırsatlar sunduğu için araştırmacıları ve yöneticileri sosyal medya ve müşteri duygularını analiz etmeye teşvik etmektedir. Ancak, tedarik zinciri üyeleri günümüz iş dünyasında genel duyguları anlamakta zorlanmaktadır. Bu nedenle, tedarik zincirine değerli bilgiler sağlamak için çeşitli yöntemler kullanılmaktadır. Bu çalışmada, SentiStrength tek bir ürün ile ilgili müşteri yorumlarını analiz etmek için kullanılmaktadır. Daha sonra, SentiStrength'in çıktısı ve ürün talepleri, müşteri taleplerini tahmin etmek için Yapay Sinir Ağına beslenmiştir. Ardından, tahmin edilen müşteri talepleri kullanılarak envanter rotalama problemini çözmek için Baskılanamayan Sıralamalı Genetik Algoritma II (NSGA-II) tabanlı simülasyon optimizasyonu kullanılmıştır. Çalışmanın sonuçları, duygu analizi içeren melez metodolojinin kullanımının envanter rotalama problemini başarılı bir şekilde analiz edebileceğini göstermiştir.

Keywords

Duygu analizi, Envanter rotalama problemi, Simülasyon optimizasyonu

Thanks

Aslı Boru Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) 2211-C Yurt İçi Öncelikli Alanlar Doktora Burs Programına teşekkür etmektedir.

Demand forecasting for inventory routing problem using artificial neural network and sentiment analysis

Abstract

Social media users have been growing exponentially in recent years. This growth has evoked researchers and manager to analyze the social media and customer sentiment because it offers significant opportunities to advance business intelligence in supply chain. However, supply chain members are struggling in understanding the general sentiments in today’s business world. Therefore, various methods are used to provide valuable insights in supply chain. In this paper, SentiStrength is used to analyze customer reviews related to one type of product. The output of SentiStrength and demands of the product are then fed into Artificial Neural Network to forecast the customer demands. After, nondominated sorting genetic algorithm II (NSGA-II) based simulation optimization is employed to solve the inventory routing problem using forecasted customer demands. The results of the study demonstrated that the use of hybrid methodology containing sentiment analysis can successfully analyze the inventory routing problem.

Keywords

Sentiment analysis, Inventory routing problem, Simulation optimization

References

• Ahn, H.-I. and Spangler, W. S. (2014). Sales prediction with social media analysis. In 2014 Annual Service Research and Innovation Institute Global Conference (ss. 213−222). https://doi.org/10.1109/SRII.2014.37
• Avci, M. G. ve Selim, H. (2017). A multi-objective, simulation-based optimization framework for supply chains with premium freights. Expert Systems With Applications, 67, 95–106. https://doi.org/10.1016/j.eswa.2016.09.034
• Balaji, S. M. E. (2015). Predicting sentiment analysis from online reviews. International Research Journal of Engineering and Technology, 2(9), 528−531.
• Boru, A. Dosdoğru, A.T. Göçken, M. ve Erol, R. (2019). A novel hybrid artificial intelligence based methodology for the inventory routing problem. Symmetry, 11, 717. https://doi.org/10.3390/sym11050717
• Chen, D. Sain S. L. ve Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing & Customer Strategy Management, 19, 197–208. https://doi.org/10.1057/dbm.2012.17
• Deb, K. Pratap, A. Agarwal, S. ve Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. DOI: 10.1109/4235.996017
• Deniz, A. ve Kiziloz, H. E. (2017). Effects of various preprocessing techniques to Turkish text categorization using n-gram features. In 2017 International Conference on Computer Science and Engineering (ss. 655−660). https://doi.org/10.1109/UBMK.2017.8093491
• Dey, L. Chakraborty, S. Biswas, A. Bose, B. ve Tiwari, S. (2016). Sentiment analysis of review datasets using Naive Bayes and K-NN classifier. International Journal of Information Engineering and Electronic Business, 4, 54–62. https://doi.org/10.5815/ijieeb.2016.04.07
• Fan, Z.-P. Che, Y.-J. ve Chen, Z.-Y. (2017). Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, 74, 90−100. https://doi.org/10.1016/j.jbusres.2017.01.010
• Gaikar, D. ve Marakarkandy, B. (2015). Product sales prediction based on sentiment analysis using twitter data. International Journal of Computer Science and Information Technologies, 6(3), 2303−2313.
• Gezici, G. ve Yanıkoglu B. (2018). Sentiment analysis in Turkish. Turkish Natural Language Processing. Theory and Applications of Natural Language Processing (ss. 255−271). Springer.
• Jadav, B. M. ve Vaghela, V. B. (2016). Sentiment analysis using support vector machine based on feature selection and semantic analysis. International Journal of Computer Applications, 146(13), 26−30.
• Konak, A. Coit, D. W. ve Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992−1007. https://doi.org/10.1016/j.ress.2005.11.018
• Kumar, K. L. S. Desai, J. ve Majumdar, J. (2016). Opinion mining and sentiment analysis on online customer review. In 2016 IEEE International Conference on Computational Intelligence and Computing Research (ss. 1−4). Chennaihttps://doi.org/10.1109/ICCIC.2016.7919584
• Lau, R. Y. K. Zhang, W. ve Xu, W. (2018). Parallel aspect-oriented sentiment analysis for sales forecasting with big data. Production and Operations Management, 27(10), 1775−1794. https://doi.org/10.1111/poms.12737
• Ozturk, Z. K. Cicek, Z. İ. E. ve Ergül, Z. (2017). Sentiment Analysis: an application to Anadolu University, Acta Physica Polonica A, 132 (3), 753−755. https://doi.org/10.12693/APhysPolA.132.753
• Pai, P.-F. ve Liu, C.-H. (2018). Predicting vehicle sales by sentiment analysis of Twitter data and stock market values. IEEE Access, 6, 57655−57662. https://doi.org/10.1109/ACCESS.2018.2873730
• Popović, D. Vidović, M. ve Radivojević, G. (2012.9 Variable neighborhood search heuristic for the inventory routing problem in fuel delivery. Expert Systems with Applications, 39(18), 13390−13398. https://doi.org/10.1016/j.eswa.2012.05.064
• Rani, S. ve Singh, J. (2017). Sentiment analysis of tweets using support vector machine. International Journal of Computer Science and Mobile Applications, 5(10), 83−91.
• Sarkar, D. ve Modak, J. M. (2005). Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm. Chemical Engineering Science, 60, 481–492. https://doi.org/10.1016/j.ces.2004.07.130
• Schneider, M. J. ve Gupta, S. (2016). Forecasting sales of new and existing products using consumer reviews: A random projections approach. International Journal of Forecasting, 32(2), 243−256. https://doi.org/10.1016/j.ijforecast.2015.08.005
• Swain, A. K. ve Cao, R. Q. (2019). Using sentiment analysis to improve supply chain intelligence. Information Systems Frontiers, 21(2), 469–484. https://doi.org/10.1007/s10796-017-9762-2
• Thelwall, M. Buckley, K. ve Paltoglou, G. (2012). Sentiment strength detection for the social Web. Journal of the American Society for Information Science and Technology, 63(1), 163−173. https://doi.org/10.1002/asi.21662
• Yu, X. Liu, Y. Huang, J. X. ve An, A. (2012). Mining online reviews for predicting sales performance: A case study in the movie domain. IEEE Transactions on Knowledge and Data Engineering, 24(4), 720−734. https://doi.org/10.1109/TKDE.2010.269
• Yusoff, Y. Ngadiman, M. S. ve Syntetos, A. M. (2011). Overview of NSGA-II for optimizing machining process parameters. Procedia Engineering, 15, 3978–3983. https://doi.org/10.1016/j.proeng.2011.08.745