A novel hybrid artificial intelligence-based decision support framework to predict lead time

References

• Abdollahi, M., M. Arvan, A. Omidvar, and F. Ameri. 2014. “A Simulation Optimization Approach to Apply Value at Risk Analysis on the Inventory Routing Problem with Backlogged Demand.” International Journal of Industrial Engineering Computations 5: 603–620. doi: 10.5267/j.ijiec.2014.6.003
   Google Scholar
• Al-Hindi, H. A. 2004. “Approximation of a Discrete Event Stochastic Simulation Using an Evolutionary Artificial Neural Network.” Journal of King Saud University: Engineering Sciences 15 (1): 125–138.
   Google Scholar
• Axsäter, S. 2000. Inventory Control. Boston: Springer Science+Business Media, LLC.
   Google Scholar
• Azadeh, A., S. Elahi, M. H. Farahani, and B. Nasirian. 2017. “A Genetic Algorithm-Taguchi Based Approach to Inventory Routing Problem of a Single Perishable Product with Transshipment.” Computers & Industrial Engineering 104: 124–133. doi: 10.1016/j.cie.2016.12.019
   Web of Science ®Google Scholar
• Bertazzi, L., and M. G. Speranza. 2012. “Inventory Routing Problems: An Introduction.” EURO Journal on Transportation and Logistics 1: 307–326. doi: 10.1007/s13676-012-0016-7
   Google Scholar
• Brunette, E. S., R. C. Flemmer, and C. L. Flemmer. 2009. “A Review of Artificial Intelligence.” Proceedings of the 4th International Conference on Autonomous Robots and Agents. Wellington, New Zealand, 385–392.
   Google Scholar
• Chen, C.-H., and L. H. Lee. 2011. Stochastic Simulation Optimization: An Optimal Computing Budget Allocation. Singapore: World Scientific Publishing.
   Google Scholar
• Coelho, L. C., J.-F. Cordeau, and G. Laporte. 2012. “The Inventory-Routing Problem with Transshipment.” Computers & Operations Research 39: 2537–2548. doi: 10.1016/j.cor.2011.12.020
   Web of Science ®Google Scholar
• Coelho, L. C., and G. Laporte. 2013. “The Exact Solution of Several Classes of Inventory-Routing Problems.” Computers & Operations Research 40: 558–565. doi: 10.1016/j.cor.2012.08.012
   Web of Science ®Google Scholar
• Dehghanimohammadabadi, M., and T. K. Keyser. 2017. “Intelligent Simulation: Integration of SIMIO and MATLAB to Deploy Decision Support Systems to Simulation Environment.” Simulation Modelling Practice and Theory 71: 45–60. doi: 10.1016/j.simpat.2016.08.007
   Web of Science ®Google Scholar
• Dosdoğru, A. T. 2019. “Comparative Study of Hybrid Artificial Neural Network Methods under Stationary and Nonstationary Data in Stock Market.” Managerial and Decision Economics 40 (4): 460–471. doi: 10.1002/mde.3016
   Web of Science ®Google Scholar
• Doukidis, G. I., and M. C. Angelides. 1994. “A Framework for Integrating Artificial Intelligence and Simulation.” Artificial Intelligence Review 8 (1): 55–85. doi: 10.1007/BF00851350
   Web of Science ®Google Scholar
• Dubedout, H., P. Dejax, N. Neagu, and T. Yeung. 2012. “A GRASP for Real Life Inventory Routing Problem: Application to Bulk Gas Distribution.” 9th International Conference on Modeling, Optimization & Simulation, June 2012, Bordeaux, France, 1–10.
   Google Scholar
• Göçken, M., A. T. Dosdoğru, A. Boru, and F. Geyik. 2015. “(R, s, S) Inventory Control Policy and Supplier Selection in a two-Echelon Supply Chain: An Optimization via Simulation Approach.” Proceedings of the 2015 Winter simulation Conference, 2057–2067.
   Google Scholar
• Göçken, M., A. T. Dosdoğru, A. Boru, and F. Geyik. 2017. “Characterizing Continuous (s, S) Policy with Supplier Selection Using Simulation Optimization.” Simulation 93 (5): 379–396. doi: 10.1177/0037549716687044
   Google Scholar
• Gruler, A., J. Panadero, J. de Armas, J. A. M. Pérez, and A. A. Juan. 2018. “ A Variable Neighborhood Search Simheuristic for the Multiperiod Inventory Routing Problem with Stochastic Demands.” International Transactions in Operational Research 27: 314–335. doi: 10.1111/itor.12540
   Web of Science ®Google Scholar
• Gyulai, D., A. Pfeiffer, G. Nick, V. Gallina, W. Sihn, and L. Monostori. 2018. “Lead Time Prediction in a Flow-Shop Environment with Analytical and Machine Learning Approaches.” IFAC-PapersOnLine 51 (11): 1029–1034. doi: 10.1016/j.ifacol.2018.08.472
   Google Scholar
• Hussain, M., M. Khan, and H. Sabir. 2016. “Analysis of Capacity Constraints on the Backlog Bullwhip Effect in the Two-tier Supply Chain: A Taguchi Approach.” International Journal of Logistics Research and Applications 19 (1): 41–61. doi: 10.1080/13675567.2015.1015510
   Web of Science ®Google Scholar
• Ioannou, G., and S. Dimitriou. 2012. “Lead Time Estimation in MRP/ERP for Make-to-Order Manufacturing Systems.” International Journal of Production Economics 139 (2): 551–563. doi: 10.1016/j.ijpe.2012.05.029
   Web of Science ®Google Scholar
• Jarugumilli, S., S. E. Grasman, and S. Ramakrishnan. 2006. “A Simulation Framework for Real-Time Management and Control of Inventory Routing Decisions.” Proceedings of the 2006 Winter simulation Conference, 1485–1492.
   Google Scholar
• Juan, A. A., J. Faulin, S. E. Grasman, M. Rabe, and G. Figueira. 2015. “A Review of Simheuristics: Extending Metaheuristics to Deal with Stochastic Combinatorial Optimization Problems.” Operations Research Perspectives 2: 62–72. doi: 10.1016/j.orp.2015.03.001
   Web of Science ®Google Scholar
• Juan, A. A., S. E. Grasman, J. Caceres-Cruz, and T. Bektas. 2014. “A Simheuristic Algorithm for the Single-Period Stochastic Inventory-Routing Problem with Stock-outs.” Simulation Modelling Practice and Theory 46: 40–52. doi: 10.1016/j.simpat.2013.11.008
   Web of Science ®Google Scholar
• Jun, H.-B., J.-Y. Park, and H.-W. Suh. 2006. “Lead Time Estimation Method for Complex Product Development Process.” Concurrent Engineering-Research and Applications 14 (4): 313–328. doi: 10.1177/1063293X06073302
   Web of Science ®Google Scholar
• Kilmer, R. A. 1996. “Applications of Artificial Neural Networks to Combat Simulations.” Mathematical and Computer Modelling 23 (1–2): 91–99. doi: 10.1016/0895-7177(95)00220-0
   Google Scholar
• Kleywegt, A. J., V. S. Nori, and M. W. P. Savelsbergh. 2002. “The Stochastic Inventory Routing Problem with Direct Deliveries.” Transportation Science 36 (1): 94–118. doi: 10.1287/trsc.36.1.94.574
   Web of Science ®Google Scholar
• Lingitz, L., V. Gallina, F. Ansari, D. Gyulai, A. Pfeiffer, W. Shin, and L. Monostori. 2018. “Lead Time Prediction Using Machine Learning Algorithms: A Case Study by a Semiconductor Manufacturer.” Procedia CIRP 72: 1051–1056. doi: 10.1016/j.procir.2018.03.148
   Google Scholar
• Luk, K. C., J. E. Ball, and A. Sharma. 2001. “An Application of Artificial Neural Networks for Rainfall Forecasting.” Mathematical and Computer Modelling 33 (6–7): 683–693. doi: 10.1016/S0895-7177(00)00272-7
   Google Scholar
• Mes, M., M. Schutten, and A. P. Rivera. 2014. “Inventory Routing for Dynamic Waste Collection.” Waste Management 34: 1564–1576. doi: 10.1016/j.wasman.2014.05.011
   PubMed Web of Science ®Google Scholar
• Min, H. 2010. “Artificial Intelligence in Supply Chain Management: Theory and Applications.” International Journal of Logistics Research and Applications 13 (1): 13–39. doi: 10.1080/13675560902736537
   Web of Science ®Google Scholar
• Mohamed, A.-A. M. 2015. “Lead-time Estimation Approach Using the Process Capability Index.” International Journal of Supply Chain Managementt 4 (3): 7–14.
   Google Scholar
• Mourtzis, D., M. Doukas, K. Fragou, K. Efthymiou, and V. Matzorou. 2014. “Knowledge-based Estimation of Manufacturing Lead Time for Complex Engineered-to-Order Products.” Procedia CIRP 17: 499–504. doi: 10.1016/j.procir.2014.01.087
   Google Scholar
• Ören, T. I. 1994. “Artificial Intelligence in Simulation.” Annals of Operations Research 53 (1): 287–319. doi: 10.1007/BF02136832
   Google Scholar
• Öztürk, A., S. Kayalıgil, and N. E. Özdemirel. 2006. “Manufacturing Lead Time Estimation Using Data Mining.” European Journal of Operational Research 173 (2): 683–700. doi: 10.1016/j.ejor.2005.03.015
   Web of Science ®Google Scholar
• Patel, A. B., and T. N. Desai. 2019. “A Systematic Review and Meta-Analysis of Recent Developments in Sustainable Supply Chain Management.” International Journal of Logistics Research and Applications 22 (4): 349–370. doi: 10.1080/13675567.2018.1534946
   Web of Science ®Google Scholar
• Rothenberg, J. 1991. “Tutorial: Artificial Intelligence and Simulation.” Proceedings of the 1991 Winter Simulation Conference, 218–222.
   Google Scholar
• Solomon, H., K. Jilcha, and E. Berhan. 2015. “Lead Time Prediction Using Simulation in Leather Shoe Manufacturing.” Afro-European Conf. for Ind. Advancement, 283–292.
   Google Scholar
• Taylor, B. W. III. 2013. Introduction to Management Science. London, UK: Pearson Education, Inc.
   Google Scholar
• Upadhyaya, S., and S. Mohanty. 2015. “Power Quality Disturbance Localization Using Maximal Overlap Discrete Wavelet Transform.” Annual IEEE India Conference (INDICON), New Delhi, 1–6.
   Google Scholar
• Vonolfen, S., M. Affenzeller, A. Beham, E. Lengauer, and S. Wagner. 2013. “Simulation-based Evolution of Resupply and Routing Policies in Rich Vendor-Managed Inventory Scenarios.” CEJOR 21: 379–400. doi: 10.1007/s10100-011-0232-5
   Google Scholar
• Widman, L. E., and K. A. Loparo. 1990. “Artificial Intelligence, Simulation, and Modeling.” Interfaces 20 (2): 48–66. doi: 10.1287/inte.20.2.48
   Web of Science ®Google Scholar