Abstract
Recently, many methods based on artificial neural networks (ANNs) or deep neural networks (DNNs) have been proposed to accurately predict the cycle time of a job. However, the prediction mechanism of an ANN is difficult to understand and communicate for users, which limits its acceptability (or usefulness). To solve this problem, a two-stage explainable artificial intelligence (XAI) approach is proposed in this study to better explain a classification-based cycle time prediction method. In the proposed methodology, first, jobs are divided into several clusters. A scatter radar diagram is then designed to illustrate the classification result. Compared with existing XAI techniques, the scatter radar diagram meets more requirements for better interpretation. Subsequently, an ANN is constructed for each cluster to predict the cycle times of jobs in the cluster. A random forest is then constructed to approximate the prediction mechanism of the ANN. In existing practice, the random forest generates many decision rules to predict the cycle time of a job, which may cause confusion for the user. To solve this problem, a systematic procedure is established to re-organize these decision rules. In this way, the first few decision rules can provide most of the information, and the user does not have to read all the rules. The two-stage XAI approach has been applied to a real case from the literature to evaluate its effectiveness.
Similar content being viewed by others
Availability of data and materials
There is no original data associated with this paper.
Code availability
Not applicable.
References
van Dongen BF, Crooy RA, van der Aalst WM (2008) Cycle time prediction: when will this case finally be finished? OTM Confederated International Conferences on the Move to Meaningful Internet Systems 319–336
Ankenman BE, Bekki JM, Fowler J, Mackulak GT, Nelson BL, Yang F (2011) Simulation in production planning: an overview with emphasis on recent developments in cycle time estimation. Plan Prod Invent Extend Enterp 565–591
Chen T, Wang YC, Tsai HR (2009) Lot cycle time prediction in a ramping-up semiconductor manufacturing factory with a SOM–FBPN-ensemble approach with multiple buckets and partial normalization. Int J Adv Manuf Technol 42(11):1206–1216
Yang F, Ankenman B, Nelson BL (2007) Efficient generation of cycle time-throughput curves through simulation and metamodeling. Nav Res Logist 54(1):78–93
Chiu C, Chang PC, Chiu NH (2003) A case-based expert support system for due-date assignment in a wafer fabrication factory. J Intell Manuf 14(3):287–296
Cobb BR, Li L (2022) Forward cycle time distributions for returnable transport items. J Remanuf 12(1):125–151
Chen T (2013) An effective fuzzy collaborative forecasting approach for predicting the job cycle time in wafer fabrication. Comput Ind Eng 66(4):834–848
Wang J, Zhang J (2016) Big data analytics for forecasting cycle time in semiconductor wafer fabrication system. Int J Prod Res 54(23):7231–7244
Wang YC, Chen T, Hsu TC (2021) A fuzzy deep predictive analytics approach for enhancing cycle time range estimation precision in wafer fabrication. Decis Anal J 1:100010
Chen T, Wu HC (2017) A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory. J Intell Manuf 28(5):1095–1107
Chen T, Wang Y-C (2022) Hybrid big data analytics and Industry 4.0 approach for projecting cycle time ranges. Int J Adv Manuf Technol 1–17
Chen TCT, Wang YC (2021) Fuzzy dynamic-prioritization agent-based system for forecasting job cycle time in a wafer fabrication plant. Complex Intell Syst 7(4):2141–2154
Chen T, Wang YC (2017) A nonlinearly normalized back propagation network and cloud computing approach for determining cycle time allowance during wafer fabrication. Robot Comput-Integr Manuf 45:144–156
Gunning D, Stefik M, Choi J, Miller T, Stumpf S, Yang GZ (2019) XAI—explainable artificial intelligence. Sci Robot 4(37):eaay7120
Kumar D, Wong A, Taylor GW (2017) Explaining the unexplained: a class-enhanced attentive response (clear) approach to understanding deep neural networks. Proc IEEE Conf Comput Vis Pattern Recognit Workshops 36–44
Chen T, Wang YC (2010) Incorporating the FCM–BPN approach with nonlinear programming for internal due date assignment in a wafer fabrication plant. Robot Comput-Integr Manuf 26(1):83–91
Wu HC, Chen T (2015) CART–BPN approach for estimating cycle time in wafer fabrication. J Ambient Intell Humaniz Comput 6(1):57–67
Chen T (2016) Embedding a back propagation network into fuzzy c-means for estimating job cycle time: wafer fabrication as an example. J Ambient Intell Humaniz Comput 7(6):789–800
Wang J, Zhang J, Wang X (2017) Bilateral LSTM: a two-dimensional long short-term memory model with multiply memory units for short-term cycle time forecasting in re-entrant manufacturing systems. IEEE Trans Industr Inf 14(2):748–758
Schulz A, Gisbrecht A, Bunte K, Hammer B (2012) How to visualize a classifier. New Challenges Neural Comput 73–83
Zhang J, Wang H, Zhu H (2018) Increase the classification and expression ability and visualize the decision through a novel deep neural network model for the diagnosis of glaucoma. Invest Ophthalmol Vis Sci 59(9):4079–4079
Das A, Rad P (2020) Opportunities and challenges in explainable artificial intelligence (xai): A survey. arXiv preprint arXiv:2006.11371
Bai L (2021) Analysis on various approaches to visualize and interpret convolution neural network. IEEE Int Conf Front Technol Inf Comput 584–589
Hao N, He F, Hou Y, Yao Y (2022) Graph-based observability analysis for mutual localization in multi-robot systems. Syst Control Lett 161:105152
Shaojie WANG, Liang HOU, Lee J, Xiangjian BU (2017) Evaluating wheel loader operating conditions based on radar chart. Autom Constr 84:42–49
Lin YC, Chen TCT (2022) Type-II fuzzy approach with explainable artificial intelligence for nature-based leisure travel destination selection amid the COVID-19 pandemic. Digital Health 8:20552076221106320
Liu J, Huang Q, Ulishney C, Dumitrescu CE (2022) Comparison of random forest and neural network in modeling the performance and emissions of a natural gas spark ignition engine. J Energy Resour Technol 144(3)
Plattner S, Mason DM, Leshkevich GA, Schwab DJ, Rutherford ES (2006) Classifying and forecasting coastal upwellings in Lake Michigan using satellite derived temperature images and buoy data. J Great Lakes Res 32(1):63–76
Dong LJ, Peng GJ, Fu YH, Bai YF, Liu YF (2008) Unascertained measurement classifying model of goaf collapse prediction. J Coal Sci Eng (China) 14(2):221–224
Chen TCT, Honda K (2020) Fuzzy collaborative forecasting and clustering: methodology, system architecture, and applications. Springer International Publishing, Cham, Switzerland
ConvNetJS (2022) ConvnetJS demo: Toy 2d classification with 2-layer neural network. https://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html
Na S, Xumin L, Yong G (2010) Research on k-means clustering algorithm: An improved k-means clustering algorithm. Int Symp Intell Inf Technol Secur Inf 63–67
L’Yi S, Ko B, Shin D, Cho YJ, Lee J, Kim B, Seo J (2015) XCluSim: A visual analytics tool for interactively comparing multiple clustering results of bioinformatics data. BMC Bioinformatics 16(11):1–15
Bholowalia P, Kumar A (2014) EBK-means: a clustering technique based on elbow method and k-means in WSN. Int J Comput Appl 105(9):17–24
Mantri S, Bapat K (2011) Neural network based face recognition using MATLAB. Int J Comput Sci Eng Technol 1(1):6–9
Da Silva IN, Spatti DH, Flauzino RA, Liboni LHB, dos Reis Alves SF (2017) Artificial neural networks. Springer International Publishing, Cham
ConvNetJS (2022) ConvnetJS demo: Toy 1d regression. https://cs.stanford.edu/people/karpathy/convnetjs/demo/regression.html
GitHub (2022) tensorflow. https://github.com/tensorflow
Green M, Ekelund U, Edenbrandt L, Björk J, Forberg JL, Ohlsson M (2009) Exploring new possibilities for case-based explanation of artificial neural network ensembles. Neural Netw 22(1):75–81
Kenny EM, Keane MT (2019) Twin-systems to explain artificial neural networks using case-based reasoning: comparative tests of feature-weighting methods in ANN-CBR twins for XAI. Int Joint Conf Artif Intell 2708–2715
Ribeiro MT, Singh S, Guestrin C (2016) “Why should I trust you?” Explaining the predictions of any classifier. Proc ACM SIGKDD Int Conf Knowl Discov Data Min 1135–1144
Chen T (2012) A job-classifying and data-mining approach for estimating job cycle time in a wafer fabrication factory. Int J Adv Manuf Technol 62(1):317–328
Bardak S, Bardak T, Peker H, Sözen E, Çabuk Y (2021) Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining Models. Bioresources 16(3)
Loh WY (2011) Classification and regression trees. Wiley Interdiscip Rev Data Min Knowl Discov 1(1):14–23
Tiryaki S, Tan H, Bardak S, Kankal M, Nacar S, Peker H (2019) Performance evaluation of multiple adaptive regression splines, teaching–learning based optimization and conventional regression techniques in predicting mechanical properties of impregnated wood. Eur J Wood Wood Prod 77(4):645–659
Author information
Authors and Affiliations
Contributions
Both authors contributed equally to the writing of this paper. Both authors read and approved the final manuscript. Data curation, methodology, and writing original draft: Toly Chen and Yu–Cheng Wang; writing–review and editing: Toly Chen and Yu-Cheng Wang.
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, T., Wang, YC. A two-stage explainable artificial intelligence approach for classification-based job cycle time prediction. Int J Adv Manuf Technol 123, 2031–2042 (2022). https://doi.org/10.1007/s00170-022-10330-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-022-10330-z