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

Inventory and routing are the two most important elements to company’s survival in supply chain environments. Hence, solution approaches of inventory routing problem (IRP) should assure adequate in...

[1]  Tuncer Ören,et al.  Artificial intelligence in simulation , 1987, MMB.

[2]  R. A. Kilmer,et al.  Applications of Artificial Neural Networks to combat simulations , 1996 .

[3]  T. N. Desai,et al.  A systematic review and meta-analysis of recent developments in sustainable supply chain management , 2018, International Journal of Logistics Research and Applications.

[4]  Martin W. P. Savelsbergh,et al.  The Stochastic Inventory Routing Problem with Direct Deliveries , 2002, Transp. Sci..

[5]  Kenneth A. Loparo,et al.  Artificial Intelligence, Simulation, and Modeling , 1990 .

[6]  George Ioannou,et al.  Lead time estimation in MRP/ERP for make-to-order manufacturing systems , 2012 .

[7]  Jin-Young Park,et al.  Lead Time Estimation Method for Complex Product Development Process , 2006, Concurr. Eng. Res. Appl..

[8]  László Monostori,et al.  Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer , 2018 .

[9]  Dimitris Mourtzis,et al.  Knowledge-based Estimation of Manufacturing Lead Time for Complex Engineered-to-order Products , 2014 .

[10]  Michael Affenzeller,et al.  Simulation-based evolution of resupply and routing policies in rich vendor-managed inventory scenarios , 2013, Central Eur. J. Oper. Res..

[11]  Mustafa Göçken,et al.  Characterizing continuous (s, S) policy with supplier selection using Simulation Optimization , 2017, Simul..

[12]  Mehdi Hosseinabadi Farahani,et al.  A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment , 2017, Comput. Ind. Eng..

[13]  Thomas K. Keyser,et al.  Intelligent simulation: Integration of SIMIO and MATLAB to deploy decision support systems to simulation environment , 2017, Simul. Model. Pract. Theory.

[14]  Martijn Mes,et al.  Inventory routing for dynamic waste collection. , 2014, Waste management.

[15]  Georgios I. Doukidis,et al.  A framework for Integrating Artificial Intelligence and Simulation , 2004, Artificial Intelligence Review.

[16]  László Monostori,et al.  Lead time prediction in a flow-shop environment with analytical and machine learning approaches , 2018 .

[17]  Luca Bertazzi,et al.  Inventory routing problems: an introduction , 2012, EURO J. Transp. Logist..

[18]  Leandro C. Coelho,et al.  The exact solution of several classes of inventory-routing problems , 2013, Comput. Oper. Res..

[19]  Aschkan Omidvar,et al.  A simulation optimization approach to apply value at risk analysis on the inventory routing problem with backlogged demand , 2014 .

[20]  Nur Evin Özdemirel,et al.  Manufacturing lead time estimation using data mining , 2006, Eur. J. Oper. Res..

[21]  Angel A. Juan,et al.  A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands , 2020, Int. Trans. Oper. Res..

[22]  Gilbert Laporte,et al.  The inventory-routing problem with transshipment , 2012, Comput. Oper. Res..

[23]  Matloub Hussain,et al.  Analysis of capacity constraints on the backlog bullwhip effect in the two-tier supply chain: a Taguchi approach , 2016 .

[24]  Ashish Sharma,et al.  An application of artificial neural networks for rainfall forecasting , 2001 .

[25]  Hokey Min,et al.  Artificial intelligence in supply chain management: theory and applications , 2010 .

[26]  Ayşe Tuğba Dosdoğru.  Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market , 2019 .

[27]  Angel A. Juan,et al.  A simheuristic algorithm for the Single-Period Stochastic Inventory-Routing Problem with stock-outs , 2014, Simul. Model. Pract. Theory.

[28]  Angel A. Juan,et al.  A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems , 2015 .