How to cite this paper
Parnianifard, A., Zemouche, A., Imran, M & Wuttisittikulkij, L. (2020). Robust simulation-optimization of dynamic-stochastic production/inventory control system under uncertainty using computational intelligence.Uncertain Supply Chain Management, 8(4), 633-648.
Refrences
Ab Wahab, M. N., Nefti-Meziani, S., & Atyabi, A. (2015). A Comprehensive Review of Swarm Optimization Algorithms. PLOS ONE, 10(5), e0122827. 7
Agrell, P. J., & Wikner, J. (1996). An MCDM framework for dynamic systems. International Journal of Production Economics, 45(1–3), 279–292.
Ali Asghar, B. (2019). Computational Intelligence and Its Applications in Uncertainty-Based Design Optimization. In Bridge Optimization-Inspection and Condition Monitoring. IntechOpen.
Ang, K. H., Chong, G., & Li, Y. (2005). PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology, 13(4), 559–576.
Ansari, N., & Hou, E. (2012). Computational intelligence for optimization. Springer Science & Business Media.
Astrom, K. J., & Hagglund, T. (1995). PID controllers: theory, design, and tuning (Vol. 2). Isa Research Triangle Park, NC.
Åström, K. J., Hägglund, T., Hang, C. C., & Ho, W. K. (1993). Automatic tuning and adaptation for PID controllers-a survey. Control Engineering Practice, 1(4), 699–714.
Azarskov, V. N., Skurikhin, V. I., Zhiteckii, L. S., & Lypoi, R. O. (2013). Modern control theory applied to inventory control for a manufacturing system. IFAC Proceedings, 46(9), 1200–1205.
Azarskov, V. N., Zhiteckii, L. S., Solovchuk, K. Y., Sushchenko, O. A., & Lupoi, R. O. (2017). Inventory Control for a Manufacturing System under Uncertainty: Adaptive Approach. IFAC-PapersOnLine, 50(1), 10154–10159.
Barros, M. F. M., Guilherme, J. M. C., & Horta, N. C. G. (2010). Analog circuits and systems optimization based on evolutionary computation techniques (Vol. 294). Springer.
Camcıoğlu, Ş., Özyurt, B., Doğan, İ. C., & Hapoğlu, H. (2017). Application of response surface methodology as a New PID tuning method in an electrocoagulation process control case. Water Science and Technology, 76(12), 3410–3427.
Chen, S., & Kuo, C. (2017). Design and Implement of the Recurrent Radial Basis Function Neural Network Control for Brushless DC Motor. Applied System Innovation (ICASI), 2017 International Conference IEEE, 562–565.
Chiha, I., Liouane, N., & Borne, P. (2012). Tuning PID Controller Using Multiobjective Ant Colony Optimization. Applied Computational Intelligence and Soft Computing, 2012(1), 1–7.
Cohen, Gh. (1953). Theoretical consideration of retarded control. Trans. Asme, 75, 827–834.
Dejonckheere, J., Disney, S. M., Lambrecht, M. R., & Towill, D. R. (2003). Measuring and avoiding the bullwhip effect: A control theoretic approach. European Journal of Operational Research, 147(3), 567–590.
Del Castillo, E., & Montgomery, D. C. (1993). A nonlinear programming solution to the dual response problem. Journal of Quality Technology, 25, 199–204.
Dellino, G., Lino, P., Meloni, C., & Rizzo, A. (2009). Kriging metamodel management in the design optimization of a CNG injection system. Mathematics and Computers in Simulation, 79(8), 2345–2360.
Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942–1948.
Faruq, A., Shah, M. F. N., & Abdullah, S. S. (2018). Multi-objective optimization of PID controller using pareto-based surrogate modeling algorithm for MIMO evaporator system. International Journal of Electrical and Computer Engineering, 8(1), 556.
Figueira, G., & Almada-Lobo, B. (2014). Hybrid simulation optimization methods a taxonomy and discussion. Simulation Modelling Practice and Theory, 46, 118–134.
Garcia Salcedo, C. A., Ibeas Hernandez, A., Vilanova, R., & Herrera Cuartas, J. (2013). Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized Internal Model Control approaches. European Journal of Operational Research, 224(2), 261–272.
Grubbström, R. W., & Wikner, J. (1996). Inventory trigger control policies developed in terms of control theory. International Journal of Production Economics, 25(1–3), 397–406.
Hang, C. C., Åström, K. J., & Ho, W. K. (1991). Refinements of the Ziegler–Nichols tuning formula. IEE Proceedings D (Control Theory and Applications), 138(2), 111–118.
Hasani, A., Eskandarpour, M., & Fattahi, M. (2018). A simulation-based optimisation approach for multi-objective inventory control of perishable products in closed-loop supply chains under uncertainty. International Journal of Advanced Operations Management, 10(4), 324–344.
Havinga, J., van den Boogaard, A. H., & Klaseboer, G. (2017). Sequential improvement for robust optimization using an uncertainty measure for radial basis functions. Structural and Multidisciplinary Optimization, 55(4), 1345–1363.
Ho, T., Chen, Y., Chen, P., & Hu, P. (2017). The Design of a Motor Drive Based on Neural Network. Applied System Innovation (ICASI), 2017 International Conference IEEE, 337–340.
Hu, F., Lu, Y., Vasilakos, A. V., Hao, Q., Ma, R., Patil, Y., Zhang, T., Lu, J., Li, X., & Xiong, N. N. (2016). Robust Cyber-Physical Systems: Concept, models, and implementation. Future Generation Computer Systems, 56, 449–475.
Hwang, K.-Y., Rhee, S.-B., Yang, B.-Y., & Kwon, B.-I. (2007). Rotor Pole Design in Spoke-Type Brushless DC Motor by Response Surface Method. IEEE Transactions on Magnetics, 43(4), 1833–1836.
Jin, R., Chen, W., & Simpson, T. W. (2001). Comparative studies of metamodelling techniques under multiple modelling criteria. Structural and Multidisciplinary Optimization, 23(1), 1–13.
Jones, K. O., & Hengue, W. (2009). Limitations of multivariable controller tuning using genetic algorithms. Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing, 46.
Jurecka, F, Ganser, M., & Bletzinger, K. U. (2007). Update scheme for sequential spatial correlation approximations in robust design optimisation. Computers and Structures, 85(10), 606–614.
Jurecka, Florian. (2007). Robust Design Optimization Based on Metamodeling Techniques. PhD Thesis.
Liu, X., Li, M., & Xu, M. (2016). Kriging assisted on-line torque calculation for brushless DC motors used in electric vehicles. International Journal of Automotive Technology, 17(1), 153–164.
Liu, Xiang, Ma, C., Li, M., & Xu, M. (2011). A kriging assisted direct torque control of brushless DC motor for electric vehicles. Natural Computation (ICNC), 2011 Seventh International Conference on, IEEE, 3(July), 1705–1710.
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239–245.
Meshram, P. M., & Kanojiya, R. G. (2012). Tuning of PID Controller using Ziegler-Nichols Method for Speed Control of DC Motor. 2013 IEEE International Conference on Control Applications (CCA), 117–122.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.
Myers, R., C.Montgomery, D., & Anderson-Cook, M, C. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments-Fourth Edittion. John Wiley & Sons.
Nagaraj, B., & Murugananth, N. (2010). A comparative study of PID controller tuning using GA, EP, PSO and ACO. Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference, October, 305–313.
Nakayama, H., Arakawa, M., & Sasaki, R. (2002). Simulation-based optimization using computational intelligence. Optimization and Engineering, 3(2), 201–214.
Neck, R. (1984). Stochastic control theory and operational research. European Journal of Operational Research, 17(3), 283–301.
Ortega, M., & Lin, L. (2004). Control theory applications to the production-inventory problem: A review. International Journal of Production Research, 42(11), 2303–2322.
Parnianifard, A, Azfanizam, A., Ariffin, M., Ismail, M., & Ebrahim, N. (2019). Recent developments in metamodel based robust black-box simulation optimization: An overview. Decision Science Letters, 8(1), 17–44.
Parnianifard, A, Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2018). Kriging-Assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty. IEEE Transactions on Magnetics, 54(7), 1–10.
Parnianifard, Amir, Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2018a). An overview on robust design hybrid metamodeling : Advanced methodology in process optimization under uncertainty. International Journal of Industrial Engineering Computations, 9(1), 1–32.
Parnianifard, Amir, Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2018b). Design and analysis of computer experiments using polynomial regression and latin hypercube sampling in optimal design of PID controller. Journal of Applied Research on Industrial Engineering, 5(2), 156–168.
Parnianifard, Amir, Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2019a). Crossing weighted uncertainty scenarios assisted distribution-free metamodel-based robust simulation optimization. Engineering with Computers, 36(1), 139–150.
Parnianifard, Amir, Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2019b). Comparative study of metamodeling and sampling design for expensive and semi-expensive simulation models under uncertainty. SIMULATION, 96(1), 89–110.
Parnianifard, Amir, Azfanizam, A. S., Ariffin, M. K., Ismail, M. I., Maghami, M. R., & Gomes, C. (2018). Kriging and Latin hypercube sampling assisted aimulation optimization in optimal design of PID controller for speed control of DC motor. Journal of Computational and Theoretical Nanoscience, 15(5), 1471–1479.
Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall PTR.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649.
Rivera, D. E., & Pew, M. D. (2005). Evaluating PID control for supply chain management: A freshman design project. Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC ’05, Dece, 3415–3419.
Rutten, K. (2015). Methods For Online Sequential Process Improvement. PhD Thesis.
Ryabchikov, M. Y., & Ryabchikova, E. S. (2017). Optimizing control system based on integration of competing search optimization algorithms. 2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 1–6.
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science, 4, 409–435.
Schwartz, J. D., & Rivera, D. E. (2010). A process control approach to tactical inventory management in production-inventory systems. International Journal of Production Economics, 125(1), 111–124.
Sethi, S. P. (2019). Optimal Control Theory-Applications to Management Science and Economics-3th Edittion. In Optimal Control Theory. Springer International Publishing.
Shah, M. F. N., Zainal, M. A., Faruq, A., & Abdullah, S. S. (2011). Metamodeling approach for PID controller optimization in an evaporator process. 2011 4th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2011, 5–8.
Sóbester, A., Leary, S. J., & Keane, A. J. (2004). A parallel updating scheme for approximating and optimizing high fidelity computer simulations. Structural and Multidisciplinary Optimization, 27(5), 371–383.
Sourirajan, K., Ramachandran, B., & An, L. (2008). Application of control theoretic principles to manage inventory replenishment in a supply chain. International Journal of Production Research, 46(21), 6163–6188.
Tang, L., Ma, Y., Wang, J., Ouyang, L., & Byun, J.-H. (2019). Robust parameter design of supply chain inventory policy considering the uncertainty of demand and lead time. Scientia Iranica, 26(5), 2971–2987.
Tao, Y., Lee, L. H., Chew, E. P., Sun, G., & Charles, V. (2017). Inventory control policy for a periodic review system with expediting. Applied Mathematical Modelling, 49, 375–393.
Tenne, Y., & Goh, C.-K. (2010). Computational intelligence in expensive optimization problems (Vol. 2, Issue March). Springer Science & Business Media.
Thomas, N., & Poongodi, P. (2009). Position control of DC motor using genetic algorithm based PID controller. Proceedings of the World Congress on Engineering, 2, 1–3.
Towill, D. R., & Yoon, S. S. (1982). Some features common to inventory system and process controller design. Engineering Costs and Production Economics, 6(April), 225–236.
Van Landeghem, H., & Vanmaele, H. (2002). Robust Planning: A New Paradigm for Demand Chain Planning. Journal of Operations Management, 20(6), 769–783.
Viana, F. A. C. (2016). A tutorial on latin hypercube design of experiments. Quality and Reliability Engineering International, 32(5), 1975–1985.
Wang, G., & Shan, S. (2007). Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design, 129(4), 370–380.
Wang, Q.-G., Lee, T.-H., Fung, H.-W., Bi, Q., & Zhang, Y. (1999). PID tuning for improved performance. IEEE Transactions on Control Systems Technology, 7(4), 457–465.
Wang, W., Di Maio, F., & Zio, E. (2018). Hybrid fuzzy-PID control of a nuclear Cyber-Physical System working under varying environmental conditions. Nuclear Engineering and Design, 331(December 2017), 54–67.
White, A S. (1999). Management of inventory using control theory. International Journal of Technology Management, 17(7–8), 847–860.
White, Anthony S, & Censlive, M. (2016). Inventory Control Systems Model for Strategic Capacity Acquisition. Journal of Industrial Engineering.
Wikner, J. (1994). Dynamic modelling and analysis of information flows in production-inventory and supply chain systems. Profil, Linköping,.
Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controllers. Trans. ASME, 64(11).
Agrell, P. J., & Wikner, J. (1996). An MCDM framework for dynamic systems. International Journal of Production Economics, 45(1–3), 279–292.
Ali Asghar, B. (2019). Computational Intelligence and Its Applications in Uncertainty-Based Design Optimization. In Bridge Optimization-Inspection and Condition Monitoring. IntechOpen.
Ang, K. H., Chong, G., & Li, Y. (2005). PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology, 13(4), 559–576.
Ansari, N., & Hou, E. (2012). Computational intelligence for optimization. Springer Science & Business Media.
Astrom, K. J., & Hagglund, T. (1995). PID controllers: theory, design, and tuning (Vol. 2). Isa Research Triangle Park, NC.
Åström, K. J., Hägglund, T., Hang, C. C., & Ho, W. K. (1993). Automatic tuning and adaptation for PID controllers-a survey. Control Engineering Practice, 1(4), 699–714.
Azarskov, V. N., Skurikhin, V. I., Zhiteckii, L. S., & Lypoi, R. O. (2013). Modern control theory applied to inventory control for a manufacturing system. IFAC Proceedings, 46(9), 1200–1205.
Azarskov, V. N., Zhiteckii, L. S., Solovchuk, K. Y., Sushchenko, O. A., & Lupoi, R. O. (2017). Inventory Control for a Manufacturing System under Uncertainty: Adaptive Approach. IFAC-PapersOnLine, 50(1), 10154–10159.
Barros, M. F. M., Guilherme, J. M. C., & Horta, N. C. G. (2010). Analog circuits and systems optimization based on evolutionary computation techniques (Vol. 294). Springer.
Camcıoğlu, Ş., Özyurt, B., Doğan, İ. C., & Hapoğlu, H. (2017). Application of response surface methodology as a New PID tuning method in an electrocoagulation process control case. Water Science and Technology, 76(12), 3410–3427.
Chen, S., & Kuo, C. (2017). Design and Implement of the Recurrent Radial Basis Function Neural Network Control for Brushless DC Motor. Applied System Innovation (ICASI), 2017 International Conference IEEE, 562–565.
Chiha, I., Liouane, N., & Borne, P. (2012). Tuning PID Controller Using Multiobjective Ant Colony Optimization. Applied Computational Intelligence and Soft Computing, 2012(1), 1–7.
Cohen, Gh. (1953). Theoretical consideration of retarded control. Trans. Asme, 75, 827–834.
Dejonckheere, J., Disney, S. M., Lambrecht, M. R., & Towill, D. R. (2003). Measuring and avoiding the bullwhip effect: A control theoretic approach. European Journal of Operational Research, 147(3), 567–590.
Del Castillo, E., & Montgomery, D. C. (1993). A nonlinear programming solution to the dual response problem. Journal of Quality Technology, 25, 199–204.
Dellino, G., Lino, P., Meloni, C., & Rizzo, A. (2009). Kriging metamodel management in the design optimization of a CNG injection system. Mathematics and Computers in Simulation, 79(8), 2345–2360.
Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942–1948.
Faruq, A., Shah, M. F. N., & Abdullah, S. S. (2018). Multi-objective optimization of PID controller using pareto-based surrogate modeling algorithm for MIMO evaporator system. International Journal of Electrical and Computer Engineering, 8(1), 556.
Figueira, G., & Almada-Lobo, B. (2014). Hybrid simulation optimization methods a taxonomy and discussion. Simulation Modelling Practice and Theory, 46, 118–134.
Garcia Salcedo, C. A., Ibeas Hernandez, A., Vilanova, R., & Herrera Cuartas, J. (2013). Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized Internal Model Control approaches. European Journal of Operational Research, 224(2), 261–272.
Grubbström, R. W., & Wikner, J. (1996). Inventory trigger control policies developed in terms of control theory. International Journal of Production Economics, 25(1–3), 397–406.
Hang, C. C., Åström, K. J., & Ho, W. K. (1991). Refinements of the Ziegler–Nichols tuning formula. IEE Proceedings D (Control Theory and Applications), 138(2), 111–118.
Hasani, A., Eskandarpour, M., & Fattahi, M. (2018). A simulation-based optimisation approach for multi-objective inventory control of perishable products in closed-loop supply chains under uncertainty. International Journal of Advanced Operations Management, 10(4), 324–344.
Havinga, J., van den Boogaard, A. H., & Klaseboer, G. (2017). Sequential improvement for robust optimization using an uncertainty measure for radial basis functions. Structural and Multidisciplinary Optimization, 55(4), 1345–1363.
Ho, T., Chen, Y., Chen, P., & Hu, P. (2017). The Design of a Motor Drive Based on Neural Network. Applied System Innovation (ICASI), 2017 International Conference IEEE, 337–340.
Hu, F., Lu, Y., Vasilakos, A. V., Hao, Q., Ma, R., Patil, Y., Zhang, T., Lu, J., Li, X., & Xiong, N. N. (2016). Robust Cyber-Physical Systems: Concept, models, and implementation. Future Generation Computer Systems, 56, 449–475.
Hwang, K.-Y., Rhee, S.-B., Yang, B.-Y., & Kwon, B.-I. (2007). Rotor Pole Design in Spoke-Type Brushless DC Motor by Response Surface Method. IEEE Transactions on Magnetics, 43(4), 1833–1836.
Jin, R., Chen, W., & Simpson, T. W. (2001). Comparative studies of metamodelling techniques under multiple modelling criteria. Structural and Multidisciplinary Optimization, 23(1), 1–13.
Jones, K. O., & Hengue, W. (2009). Limitations of multivariable controller tuning using genetic algorithms. Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing, 46.
Jurecka, F, Ganser, M., & Bletzinger, K. U. (2007). Update scheme for sequential spatial correlation approximations in robust design optimisation. Computers and Structures, 85(10), 606–614.
Jurecka, Florian. (2007). Robust Design Optimization Based on Metamodeling Techniques. PhD Thesis.
Liu, X., Li, M., & Xu, M. (2016). Kriging assisted on-line torque calculation for brushless DC motors used in electric vehicles. International Journal of Automotive Technology, 17(1), 153–164.
Liu, Xiang, Ma, C., Li, M., & Xu, M. (2011). A kriging assisted direct torque control of brushless DC motor for electric vehicles. Natural Computation (ICNC), 2011 Seventh International Conference on, IEEE, 3(July), 1705–1710.
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239–245.
Meshram, P. M., & Kanojiya, R. G. (2012). Tuning of PID Controller using Ziegler-Nichols Method for Speed Control of DC Motor. 2013 IEEE International Conference on Control Applications (CCA), 117–122.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.
Myers, R., C.Montgomery, D., & Anderson-Cook, M, C. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments-Fourth Edittion. John Wiley & Sons.
Nagaraj, B., & Murugananth, N. (2010). A comparative study of PID controller tuning using GA, EP, PSO and ACO. Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference, October, 305–313.
Nakayama, H., Arakawa, M., & Sasaki, R. (2002). Simulation-based optimization using computational intelligence. Optimization and Engineering, 3(2), 201–214.
Neck, R. (1984). Stochastic control theory and operational research. European Journal of Operational Research, 17(3), 283–301.
Ortega, M., & Lin, L. (2004). Control theory applications to the production-inventory problem: A review. International Journal of Production Research, 42(11), 2303–2322.
Parnianifard, A, Azfanizam, A., Ariffin, M., Ismail, M., & Ebrahim, N. (2019). Recent developments in metamodel based robust black-box simulation optimization: An overview. Decision Science Letters, 8(1), 17–44.
Parnianifard, A, Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2018). Kriging-Assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty. IEEE Transactions on Magnetics, 54(7), 1–10.
Parnianifard, Amir, Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2018a). An overview on robust design hybrid metamodeling : Advanced methodology in process optimization under uncertainty. International Journal of Industrial Engineering Computations, 9(1), 1–32.
Parnianifard, Amir, Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2018b). Design and analysis of computer experiments using polynomial regression and latin hypercube sampling in optimal design of PID controller. Journal of Applied Research on Industrial Engineering, 5(2), 156–168.
Parnianifard, Amir, Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2019a). Crossing weighted uncertainty scenarios assisted distribution-free metamodel-based robust simulation optimization. Engineering with Computers, 36(1), 139–150.
Parnianifard, Amir, Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2019b). Comparative study of metamodeling and sampling design for expensive and semi-expensive simulation models under uncertainty. SIMULATION, 96(1), 89–110.
Parnianifard, Amir, Azfanizam, A. S., Ariffin, M. K., Ismail, M. I., Maghami, M. R., & Gomes, C. (2018). Kriging and Latin hypercube sampling assisted aimulation optimization in optimal design of PID controller for speed control of DC motor. Journal of Computational and Theoretical Nanoscience, 15(5), 1471–1479.
Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall PTR.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649.
Rivera, D. E., & Pew, M. D. (2005). Evaluating PID control for supply chain management: A freshman design project. Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC ’05, Dece, 3415–3419.
Rutten, K. (2015). Methods For Online Sequential Process Improvement. PhD Thesis.
Ryabchikov, M. Y., & Ryabchikova, E. S. (2017). Optimizing control system based on integration of competing search optimization algorithms. 2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 1–6.
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science, 4, 409–435.
Schwartz, J. D., & Rivera, D. E. (2010). A process control approach to tactical inventory management in production-inventory systems. International Journal of Production Economics, 125(1), 111–124.
Sethi, S. P. (2019). Optimal Control Theory-Applications to Management Science and Economics-3th Edittion. In Optimal Control Theory. Springer International Publishing.
Shah, M. F. N., Zainal, M. A., Faruq, A., & Abdullah, S. S. (2011). Metamodeling approach for PID controller optimization in an evaporator process. 2011 4th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2011, 5–8.
Sóbester, A., Leary, S. J., & Keane, A. J. (2004). A parallel updating scheme for approximating and optimizing high fidelity computer simulations. Structural and Multidisciplinary Optimization, 27(5), 371–383.
Sourirajan, K., Ramachandran, B., & An, L. (2008). Application of control theoretic principles to manage inventory replenishment in a supply chain. International Journal of Production Research, 46(21), 6163–6188.
Tang, L., Ma, Y., Wang, J., Ouyang, L., & Byun, J.-H. (2019). Robust parameter design of supply chain inventory policy considering the uncertainty of demand and lead time. Scientia Iranica, 26(5), 2971–2987.
Tao, Y., Lee, L. H., Chew, E. P., Sun, G., & Charles, V. (2017). Inventory control policy for a periodic review system with expediting. Applied Mathematical Modelling, 49, 375–393.
Tenne, Y., & Goh, C.-K. (2010). Computational intelligence in expensive optimization problems (Vol. 2, Issue March). Springer Science & Business Media.
Thomas, N., & Poongodi, P. (2009). Position control of DC motor using genetic algorithm based PID controller. Proceedings of the World Congress on Engineering, 2, 1–3.
Towill, D. R., & Yoon, S. S. (1982). Some features common to inventory system and process controller design. Engineering Costs and Production Economics, 6(April), 225–236.
Van Landeghem, H., & Vanmaele, H. (2002). Robust Planning: A New Paradigm for Demand Chain Planning. Journal of Operations Management, 20(6), 769–783.
Viana, F. A. C. (2016). A tutorial on latin hypercube design of experiments. Quality and Reliability Engineering International, 32(5), 1975–1985.
Wang, G., & Shan, S. (2007). Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design, 129(4), 370–380.
Wang, Q.-G., Lee, T.-H., Fung, H.-W., Bi, Q., & Zhang, Y. (1999). PID tuning for improved performance. IEEE Transactions on Control Systems Technology, 7(4), 457–465.
Wang, W., Di Maio, F., & Zio, E. (2018). Hybrid fuzzy-PID control of a nuclear Cyber-Physical System working under varying environmental conditions. Nuclear Engineering and Design, 331(December 2017), 54–67.
White, A S. (1999). Management of inventory using control theory. International Journal of Technology Management, 17(7–8), 847–860.
White, Anthony S, & Censlive, M. (2016). Inventory Control Systems Model for Strategic Capacity Acquisition. Journal of Industrial Engineering.
Wikner, J. (1994). Dynamic modelling and analysis of information flows in production-inventory and supply chain systems. Profil, Linköping,.
Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controllers. Trans. ASME, 64(11).