How to cite this paper
Delgoshaei, A., Delgoshaei, A., Aram, A & Ali, A. (2019). A robust optimization approach for scheduling a supply chain system considering preventive maintenance and emergency services using a hybrid ant colony optimization and simulated annealing algorithm.Uncertain Supply Chain Management, 7(2), 251-274.
Refrences
Arora, R., Haleem, A., & Farooquie, J. (2017). Impact of critical success factors on successful technology implementation in Consumer Packaged Goods (CPG) supply chain. Management Science Letters, 7(5), 213-224.
Azadegan, A., Porobic, L., Ghazinoory, S., Samouei, P., & Kheirkhah, A. S. (2011). Fuzzy logic in manufacturing: A review of literature and a specialized application. International Journal of Production Economics, 132(2), 258-270.
Azmi, F., Abdullah, A., Bakri, M., Musa, H., & Jayakrishnan, M. (2018). The adoption of halal food supply chain towards the performance of food manufacturing in Malaysia. Management Science Letters, 8(7), 755-766.
Baykasoglu, A., & Gocken, T. (2010). Multi-objective aggregate production planning with fuzzy parameters. Advances in Engineering Software, 41(9), 1124-1131.
Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299-312.
Delgoshaei, A., Ali, A., Ariffin, M. K. A., & Gomes, C. (2016a). A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty. Computers & Industrial Engineering, 100, 110-132.
Delgoshaei, A., Ariffin, M., Baharudin, B., & Leman, Z. (2016b). A new method for decreasing cell-load variation in dynamic cellular manufacturing systems. International Journal of Industrial Engineering Computations, 7(1), 83-110.
Delgoshaei, A., Ariffin, M. K. A. M., Leman, Z., Baharudin, B. H. T. B., & Gomes, C. (2016c). Review of evolution of cellular manufacturing system’s approaches: Material transferring models. International Journal of Precision Engineering and Manufacturing, 17(1), 131-149.
Delgoshaei, A., Ariffin, M. K. A., & Ali, A. (2017). A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS. International Journal of Production Research, 55(4), 997-1039.
Delgoshaei, A., & Gomes, C. (2016). A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Applied Soft Computing, 49, 27-55.
Egilmez, G., & Süer, G. (2014). The impact of risk on the integrated cellular design and control. International Journal of Production Research, 52(5), 1455-1478.
Egilmez, G., SüEr, G. A., & Huang, J. (2012). Stochastic cellular manufacturing system design subject to maximum acceptable risk level. Computers & Industrial Engineering, 63(4), 842-854.
El-Baz, M. A. (2011). Fuzzy performance measurement of a supply chain in manufacturing companies. Expert Systems with Applications, 38(6), 6681-6688.
Figueroa-GarcíA, J. C., Kalenatic, D., & Lopez-Bello, C. A. (2012). Multi-period mixed production planning with uncertain demands: fuzzy and interval fuzzy sets approach. Fuzzy Sets and Systems, 206, 21-38.
Janaki, D., Izadbakhsh, H., & Hatefi, S. (2018). The evaluation of supply chain performance in the Oil Products Distribution Company, using information technology indicators and fuzzy TOPSIS technique. Management Science Letters, 8(8), 835-848.
Jeon, G., & Leep, H. R. (2006). Forming part families by using genetic algorithm and designing machine cells under demand changes. Computers & operations research, 33(1), 263-283.
Jia, G. Z., & Bai, M. (2011). An approach for manufacturing strategy development based on fuzzy-QFD. Computers & Industrial Engineering, 60(3), 445-454.
Li, Q., Gong, J., Fung, R. Y., & Tang, J. (2012). Multi-objective optimal cross-training configuration models for an assembly cell using non-dominated sorting genetic algorithm-II. International Journal of Computer Integrated Manufacturing, 25(11), 981-995.
Liang, T. F., Cheng, H. W., Chen, P. Y., & Shen, K. H. (2011). Application of fuzzy sets to aggregate production planning with multiproducts and multitime periods. IEEE Transactions on Fuzzy Systems, 19(3), 465-477.
Miller, S., & John, R. (2010). An interval type-2 fuzzy multiple echelon supply chain model. In Research and Development in Intelligent Systems XXVI (pp. 407-420). Springer, London.
Mula, J., Peidro, D., & Poler, R. (2010). The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. International Journal of Production Economics, 128(1), 136-143.
Olugu, E. U., & Wong, K. Y. (2012). An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry. Expert Systems with Applications, 39(1), 375-384.
Peidro, D., Mula, J., Jiménez, M., & del Mar Botella, M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205(1), 65-80.
Qin, Z., Bai, M., & Ralescu, D. (2011). A fuzzy control system with application to production planning problems. Information Sciences, 181(5), 1018-1027.
Renna, P., & Ambrico, M. (2015). Design and reconfiguration models for dynamic cellular manufacturing to handle market changes. International Journal of Computer Integrated Manufacturing, 28(2), 170-186.
Safaei, N., & Tavakkoli-Moghaddam, R. (2009). Integrated multi-period cell formation and subcontracting production planning in dynamic cellular manufacturing systems. International Journal of Production Economics, 120(2), 301-314.
Süer, G. A., Huang, J., & Maddisetty, S. (2010). Design of dedicated, shared and remainder cells in a probabilistic demand environment. International journal of production research, 48(19), 5613-5646.
Tavakkoli-Moghaddam, R., Aryanezhad, M. B., Safaei, N., & Azaron, A. (2005). Solving a dynamic cell formation problem using metaheuristics. Applied Mathematics and Computation, 170(2), 761-780.
Tavakkoli-Moghaddam, R., Aryanezhad, M. B., Safaei, N., Vasei, M., & Azaron, A. (2007a). A new approach for the cellular manufacturing problem in fuzzy dynamic conditions by a genetic algorithm. Journal of Intelligent & Fuzzy Systems, 18(4), 363-376.
Tavakkoli-Moghaddam, R., Javadian, N., Javadi, B., & Safaei, N. (2007b). Design of a facility layout problem in cellular manufacturing systems with stochastic demands. Applied Mathematics and Computation, 184(2), 721-728.
Venkata Rao, R., & Patel, B. K. (2010). Decision making in the manufacturing environment using an improved PROMETHEE method. International Journal of Production Research, 48(16), 4665-4682.
Vinodh, S., & Balaji, S. R. (2011). Fuzzy logic based leanness assessment and its decision support system. International Journal of Production Research, 49(13), 4027-4041.
Wong, B. K., & Lai, V. S. (2011). A survey of the application of fuzzy set theory in production and operations management: 1998–2009. International Journal of Production Economics, 129(1), 157-168.
Zhao, F., Hong, Y., Yu, D., Yang, Y., & Zhang, Q. (2010). A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems. International Journal of Computer Integrated Manufacturing, 23(1), 20-39.
Azadegan, A., Porobic, L., Ghazinoory, S., Samouei, P., & Kheirkhah, A. S. (2011). Fuzzy logic in manufacturing: A review of literature and a specialized application. International Journal of Production Economics, 132(2), 258-270.
Azmi, F., Abdullah, A., Bakri, M., Musa, H., & Jayakrishnan, M. (2018). The adoption of halal food supply chain towards the performance of food manufacturing in Malaysia. Management Science Letters, 8(7), 755-766.
Baykasoglu, A., & Gocken, T. (2010). Multi-objective aggregate production planning with fuzzy parameters. Advances in Engineering Software, 41(9), 1124-1131.
Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299-312.
Delgoshaei, A., Ali, A., Ariffin, M. K. A., & Gomes, C. (2016a). A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty. Computers & Industrial Engineering, 100, 110-132.
Delgoshaei, A., Ariffin, M., Baharudin, B., & Leman, Z. (2016b). A new method for decreasing cell-load variation in dynamic cellular manufacturing systems. International Journal of Industrial Engineering Computations, 7(1), 83-110.
Delgoshaei, A., Ariffin, M. K. A. M., Leman, Z., Baharudin, B. H. T. B., & Gomes, C. (2016c). Review of evolution of cellular manufacturing system’s approaches: Material transferring models. International Journal of Precision Engineering and Manufacturing, 17(1), 131-149.
Delgoshaei, A., Ariffin, M. K. A., & Ali, A. (2017). A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS. International Journal of Production Research, 55(4), 997-1039.
Delgoshaei, A., & Gomes, C. (2016). A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Applied Soft Computing, 49, 27-55.
Egilmez, G., & Süer, G. (2014). The impact of risk on the integrated cellular design and control. International Journal of Production Research, 52(5), 1455-1478.
Egilmez, G., SüEr, G. A., & Huang, J. (2012). Stochastic cellular manufacturing system design subject to maximum acceptable risk level. Computers & Industrial Engineering, 63(4), 842-854.
El-Baz, M. A. (2011). Fuzzy performance measurement of a supply chain in manufacturing companies. Expert Systems with Applications, 38(6), 6681-6688.
Figueroa-GarcíA, J. C., Kalenatic, D., & Lopez-Bello, C. A. (2012). Multi-period mixed production planning with uncertain demands: fuzzy and interval fuzzy sets approach. Fuzzy Sets and Systems, 206, 21-38.
Janaki, D., Izadbakhsh, H., & Hatefi, S. (2018). The evaluation of supply chain performance in the Oil Products Distribution Company, using information technology indicators and fuzzy TOPSIS technique. Management Science Letters, 8(8), 835-848.
Jeon, G., & Leep, H. R. (2006). Forming part families by using genetic algorithm and designing machine cells under demand changes. Computers & operations research, 33(1), 263-283.
Jia, G. Z., & Bai, M. (2011). An approach for manufacturing strategy development based on fuzzy-QFD. Computers & Industrial Engineering, 60(3), 445-454.
Li, Q., Gong, J., Fung, R. Y., & Tang, J. (2012). Multi-objective optimal cross-training configuration models for an assembly cell using non-dominated sorting genetic algorithm-II. International Journal of Computer Integrated Manufacturing, 25(11), 981-995.
Liang, T. F., Cheng, H. W., Chen, P. Y., & Shen, K. H. (2011). Application of fuzzy sets to aggregate production planning with multiproducts and multitime periods. IEEE Transactions on Fuzzy Systems, 19(3), 465-477.
Miller, S., & John, R. (2010). An interval type-2 fuzzy multiple echelon supply chain model. In Research and Development in Intelligent Systems XXVI (pp. 407-420). Springer, London.
Mula, J., Peidro, D., & Poler, R. (2010). The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. International Journal of Production Economics, 128(1), 136-143.
Olugu, E. U., & Wong, K. Y. (2012). An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry. Expert Systems with Applications, 39(1), 375-384.
Peidro, D., Mula, J., Jiménez, M., & del Mar Botella, M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205(1), 65-80.
Qin, Z., Bai, M., & Ralescu, D. (2011). A fuzzy control system with application to production planning problems. Information Sciences, 181(5), 1018-1027.
Renna, P., & Ambrico, M. (2015). Design and reconfiguration models for dynamic cellular manufacturing to handle market changes. International Journal of Computer Integrated Manufacturing, 28(2), 170-186.
Safaei, N., & Tavakkoli-Moghaddam, R. (2009). Integrated multi-period cell formation and subcontracting production planning in dynamic cellular manufacturing systems. International Journal of Production Economics, 120(2), 301-314.
Süer, G. A., Huang, J., & Maddisetty, S. (2010). Design of dedicated, shared and remainder cells in a probabilistic demand environment. International journal of production research, 48(19), 5613-5646.
Tavakkoli-Moghaddam, R., Aryanezhad, M. B., Safaei, N., & Azaron, A. (2005). Solving a dynamic cell formation problem using metaheuristics. Applied Mathematics and Computation, 170(2), 761-780.
Tavakkoli-Moghaddam, R., Aryanezhad, M. B., Safaei, N., Vasei, M., & Azaron, A. (2007a). A new approach for the cellular manufacturing problem in fuzzy dynamic conditions by a genetic algorithm. Journal of Intelligent & Fuzzy Systems, 18(4), 363-376.
Tavakkoli-Moghaddam, R., Javadian, N., Javadi, B., & Safaei, N. (2007b). Design of a facility layout problem in cellular manufacturing systems with stochastic demands. Applied Mathematics and Computation, 184(2), 721-728.
Venkata Rao, R., & Patel, B. K. (2010). Decision making in the manufacturing environment using an improved PROMETHEE method. International Journal of Production Research, 48(16), 4665-4682.
Vinodh, S., & Balaji, S. R. (2011). Fuzzy logic based leanness assessment and its decision support system. International Journal of Production Research, 49(13), 4027-4041.
Wong, B. K., & Lai, V. S. (2011). A survey of the application of fuzzy set theory in production and operations management: 1998–2009. International Journal of Production Economics, 129(1), 157-168.
Zhao, F., Hong, Y., Yu, D., Yang, Y., & Zhang, Q. (2010). A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems. International Journal of Computer Integrated Manufacturing, 23(1), 20-39.