RESIDUAL BASED FLOW TIME ESTIMATION ALGORITHM FOR LABOR INTENSIVE PROJECT TYPE PRODUCTION SYSTEMS

Regresyon tabanlı metodlar, müşteri siparişlerinin akış zamanının hesaplanmasında yaygın olarak kullanılmaktadır. Ancak emek yoğun proje tipi üretim yapan işletmelerde, ilk defa yeni tasarım parametreleri ile üretilecek olan ürünlerin akış zamanını, üretime başlamadan önce müşteriye fiyat teklifi verme aşamasında tahmin etmek zor bir problemdir. Bu durum, üretim sisteminin matematiksel modellerde yer verilemeyen pek çok kontrol edilemeyen değişken içermesinden kaynaklanır. Bu kontrol edilemeyen değişkenler ise beklenen akış zamanı ile regresyon tabanlı denklemlerle tahmin edilen zamanlar arasında hatırı sayılır bir tahmin hatasını ortaya çıkarır. Bu çalışmada, regresyon denkleminin tahmin hatasını minimize etmek üzere, yapay sinir ağları ile regresyon analizini birleştiren bir algoritma önerilmiştir. Bu yolla regresyon denkleminin tahmin performansı arttırılmış ve tahmin hataları minimize edilmiştir

Anahtar Kelimeler:

regresyon analizi, yapay sinir ağları, akış zamanı, proje tipi üretim

EMEK YOĞUN ÇALIŞILAN PROJE TİPİ ÜRETİM SİSTEMLERİ İÇİN ARTIK TERİM TABANLI AKIŞ ZAMANI TAHMİN ALGORİTMASI

Regression-based methods are widely used for flow time estimation of customer orders. However, for the customer orders that will be produced for the first time in a labor intensive project type production system with new design parameters, it is hard to make thoroughly accurate flow time prediction at the quotation stage. This is caused by having so many uncontrollable factors in a production system, that are not placed in the mathematical models. These uncontrollable factors cause high differences between the observed and expected flow time. In this study, a new algorithm - that combines the regression analysis and the artificial neural networks - is proposed. By this way, the prediction performance of fitted regression model is improved and the lack-of-fit is decreased

Keywords:

Regression analysis, neural network applications, flow time, project type production,

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Ragatz GL, Mabert VA. A Simulation Analysis of Due Date Assignment Rules, Journal of Operations Management, Cilt. 5, 1984, s. 27–39.
Vig MM, Dooley KJ. Mixing Static and Dynamic Flow Time Estimates for Due-Date Assignment, Journal of Operations Management, Cilt. 11, 1993, s. 67–79.
Arizono I, Yamamoto A, Ohta H. Scheduling for Minimizing Total Actual Flow Time by Neural Networks, International Journal of Production Research, Cilt. 30, 1992, s. 503–511.
Enns ST. A Dynamic Forecasting Model for Job Shop Flowtime Prediction and Tardiness Control, International Journal of Production Research, Cilt. 33, No. 5, 1995, s. 1295–1312.
Harris CR. Modelling the Impact of Design, Tactical, and Operational Factors on Manufacturing System Performance, International Journal of Production Research, Cilt. 35, No. 2, 1997, s. 479-499.
Veral EA. Computer Simulation of Due-Date Setting in Multi-Machine Job Shops, Computers & Industrial Engineering, Cilt. 41, No. 1, 2001, s. 77-94.
Govind N, Roeder TM. Estimating Expected Completion Times with Probabilistic Job Routing, Proceedings of the 2006 Winter Simulation Conference, Cilt. 1, No. 5, Winter Simulation Conference, Monterey, CA, Dec 03-06, 2006, s. 1804-1810.
Li S, Li Y, Liu Y et al. A GA-Based NN Approach for Makespan Estimation, Applied Mathematics and Computation, Cilt. 185, No. 2, 2007, s. 1003-1014.
Alenezi A., Moses SA, Trafalis TB. Real-Time Prediction of Order Flow Times Using Support Vector Regression, Computers & Operations Research, Cilt. 35, No. 11, 2008, s. 3489-3503.
Asadzadeh SM, Azadeh A, Ziaeifar AA. Neuro-Fuzzy-Regression Algorithm for Improved Prediction of Manufacturing Lead Time with Machine Breakdowns, Concurrent Engineering-Research and Applications, Cilt. 19, No. 4, 2011, s. 269-281.
Chen T, and Wang YC. An Iterative Procedure for Optimizing the Performance of the Fuzzy-Neural Job Cycle Time Estimation Approach in a Wafer Fabrication Factory, Mathematical Problems in Engineering, Article 2013, Article Number: 740478.
Kumru M, Kumru PY. Using Artificial Neural Networks to Forecast Operation Times in Metal Industry, International Journal of Computer Integrated Manufacturing, Cilt. 27, No. 1, 2014, s. 48-59.
Pan Q, and Dong Y. An improved migrating birds optimisation for a hybrid flowshop scheduling with total flowtime minimisation, Information Sciences, Cilt. 277, 2014, s. 643-655.
Ribas I, Companys R, Tort-Martorell X. An efficient Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with total flowtime minimization, Expert Systems with Applications, Cilt. 42, No. 15-16, 2015, s. 6155-6167
Harlow JH. Electric Power Transformer Engineering, 3rd Edn. CRS Press, Taylor & Francis Group, USA, 2012.
Karaoglan AD, Karademir O. Flow time and product cost estimation by using an artificial neural network (ANN): A case study for transformer orders, The Engineering Economist, 2016, (INPRESS). (DOI:10.1080/0013791X.2016.1185808)
Montgomery DC. Design and Analysis of Experiments, 5th Edn. John Wiley &Sons, Inc., New York, 2001.
Giesbrecht FG, Gumpertz ML. Planning, Construction, and Statistical Analysis of Comparative Experiments. John Wiley &Sons, Inc., New Jersey, 2004.
Mason RL, Gunst RF, Hess JL. Statistical Design and Analysis of Experiments, 2nd Edn. John Wiley &Sons, Inc., New Jersey, 2003.
Castillo ED. Process Optimization - A Statistical Approach. Springer, New York, 2007.
Ham FM, Kostanic I. Principles of Neurocomputing for Science and Engineering, 1st Edn. McGraw-Hill, New York, 2001.
Karaoglan AD. An Integrated Neural Network Structure for Recognizing Autocorrelated and Trending Processes, Mathematical & Computational Applications, Cilt. 16, No. 2, 2011, s.514-523.