DAMP: Decision-Making with the Combination of Analytical Hierarchy Process and Deep Learning (Case study: Car Sales Forecasting)

Document Type : Original Article

Authors

1 Department of Computer Engineering, Birjand University of Technology, Birjand, Iran.

2 Department of Statistics, Allameh Tabataba'i University, Tehran, Iran.

3 Department of Computer Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran.

4 Department of Information and Communication Technology (ICT), Technical and Vocational University (TVU), Tehran, Iran.

Abstract

Nowadays, prediction and decision making are two inseparable principles in the management and two distinct roles of managers. The organizations spend a large part of their budgets on predictions from past data. They will lose their money if they are neglected. On the other hand, decision-making is the most critical step in problem-solving. Moreover, it is considered the main task of a manager as a problem solver. Making decision becomes more complicated when we are faced with multi-criteria decision-making issues. Combining prediction and decision-making approaches helps researchers to make a better choice utilizing prior knowledge. One of the most essential and comprehensive systems designed for multi-criteria decision-making is Analytical Hierarchy Process (AHP) process. Deep learning as a valuable extension of artificial neural networks has been the focus of many researchers. In this paper, AHP is used to classify, compare, and determine the weights of a deep learning approach. In order to evaluate the efficiency of the proposed method, the prediction of vehicle price application is chosen, and the results are compared with neural networks. The data set is related to the sale of Hyundai and Kia Motors cars in the United States and Canada. It is emphasized that the data are used only to evaluate the proposed method and can be generalized to solve all similar issues. The sales forecasting data of two car companies showed that the proposed method is superior to other regression methods. To extend the proposed methos as our future work, the aim will be to develop a comprehensive decision-making and forecasting system by combining these two approaches.

Keywords

Main Subjects


  • F.C. Goldstein, and H. S. Levin, “Disorders of reasoning and problem-solving ability,” Guilford Press, 1987.
  • R. Yager, “Bidirectional possibilistic dominance in uncertain decision making,” Knowledge-Based Systems, (133): 269–277, 2017.
  • Simon, “Making Management Decisions: The Role of Intuition and Emotion,” The Academy of Management Executive, 1(1), 1987.
  • Lee, P. Newman and R. Price, “Decision making in organisations,” Financial Times/Pitman Pub, 1999.
  • Zopounidis and M. Doumpos, “Multiple Criteria Decision Making,” Springer International Publishing, 2017.
  • L. Saaty, “The analytic hierarchy process : planning, priority setting, resource allocation,” McGraw-Hill International Book Co., 1980.
  • Dharani Kumari, B. Shylaja, “AMGRP: AHP-based Multimetric Geographical Routing Protocol for Urban environment of VANETs,” Journal of King Saud University - Computer and Information Sciences, 31(1):72–81, 2019.
  • Salgado, C. Hernandez, V. Molina, F. Beltran-Molina, “Intelligent Algorithm for Spectrum Mobility in Cognitive Wireless Networks,” Procedia Computer Science, 83, 278–283, 2016.
  • Nayak, C. Tripathy, “Deadline sensitive lease scheduling in cloud computing environment using AHP,” Journal of King Saud University - Computer and Information Sciences, 30(2): 152–163, 2018.
  • Moshref Javadi, Z. Azmoon, Z. (2011). “Ranking branches of System Group company in Terms of acceptance preparation of electronic Customer Relationship Management using AHP method,”Procedia Computer Science, 3: 1243–1248, 2011.
  • Chang, H. Ishii, “Fuzzy Multiple Criteria Decision-Making Approach to Assess the Project Quality Management in Project,” Procedia Computer Science, 22: 928–936, 2013.
  • Byun, R. Chang, M. Park, H. Son, C.Kim, “Prioritizing Community-Based Intervention Programs for Improving Treatment Compliance of Patients with Chronic Diseases: Applying an Analytic Hierarchy Process,” Int. J. Environ. Res. Public Health, 18: 455, 2021.
  • Sudaryono, U. Rahardja, Masaeni, “Decision Support System for Ranking of Students in Learning Management System (LMS) Activities using Analytical Hierarchy Process (AHP) Method,” Phys.: Conf. Ser. 1477: 022022, 2020.
  • Kim, Y. Kim, H. Yi, “Fuzzy Analytic Hierarchy Process-Based Mobile Robot Path Planning,” Electronics 9:290, 2020.
  • Meyer, V. Noblet, C. Mazzara, C., A. Lallement, “Survey on deep learning for radiotherapy. Computers in Biology and Medicine,” 98:126–146, 2018.
  • Jin, X. Yu, X. Wang,Y. Bai, T. Su, J. Kong, “Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System,” Sustainability,12:1433, 2020.
  • Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz and D. Terzopoulos, "Image Segmentation Using Deep Learning: A Survey," in IEEE Transactions on Pattern Analysis and Machine Intelligence,2021.
  • Diro, N. Chilamkurti, “Distributed attack detection scheme using deep learning approach for Internet of Things,” Future Generation Computer Systems, 82:761–768, 2018.
  • Kuo, S. Chi, S. Kao, “A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network,” Computers in Industry, 47(2): 199–214,2002.
  • Tang, N. Hakim, W. Khaksar, M. Ariffin, S. Sulaiman, P. Pah, “A Hybrid Method using Analytic Hierarchical Process and Artificial Neural Network for Supplier Selection,” International Journal of Innovation, Management and Technology, 4(1): 109–111, 2013.
  • Farahani, M. Momeni, N. Sayyed Amiri, “Car Sales Forecasting Using Artificial Neural Networks and Analytical Hierarchy Process,” The Fifth International Conference on Data Analytics, 57–62, 2016.
  • Kabir, M. Hasin, “Multi-criteria inventory classification through integration of fuzzy analytic hierarchy process and artificial neural network,” International Journal of Industrial and Systems Engineering, 14(1): 74, 2013.
  • Kar, “A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network,” Journal of Computational Science, 6:23–33, 2015.
  • Ilunga, “Analytic Hierarchy Process (AHP) in selecting rainfall forecasting models,” Proceedings of the 20th World Multi-Conference on Systemics, Cybernetics and Informatics, 225–229, 2016.
  • Bengio, O. Delalleau, and N. Roux, “The Curse of Highly Variable Functions for Local Kernel Machines,” Proceedings of the 18th International Conference on Neural Information Processing Systems, 107–114, 2007.
  • Zou, X. Mi, P. Tighe, G. Koch, F. Zou, “On kernel machine learning for propensity score estimation under complex confounding structures,”. Pharmaceutical Statistics. 1– 13, 2021.
  • Liu, P. Sun, N.Wergeles, Y. Shang, “A survey and performance evaluation of deep learning methods for small object detection,” Expert Systems with Applications, 172, 114602, 2021.
  • Wang, Q. Lai, H. Fu, J. Shen, H. Ling and R. Yang, "Salient Object Detection in the Deep Learning Era: An In-depth Survey," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
  • Dhillon, G. Verma, “Convolutional neural network: a review of models, methodologies and applications to object detection,” Prog Artif Intell, 9: 85–112, 2020.
  • Glorot, Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249–256, 2010.
  • Cireşan, U. Meier, L. Gambardella, J. Schmidhuber, “Deep, Big, Simple Neural Nets for Handwritten Digit Recognition,” Neural Computation, 22 (12): 3207–3220, 2010.
  • Sabzekar and S.M.H. Hasheminejad, “Robust regression using support vector regressions,” Chaos, Solitons and Fractals, 14, 110738, 2021.
  • Wilcoxon, “Individual Comparisons by Ranking Methods,” pp. 196–202, 1992, doi: 10.1007/978-1-4612-4380-9_16.
CAPTCHA Image