How to cite this paper
Toncovich, A., Rossit, D., Frutos, M & Rossit, D. (2019). Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure.International Journal of Industrial Engineering Computations , 10(1), 1-16.
Refrences
Amiri, M., Sadjadi, S. J., Tavakkoli-Moghaddam, R., & Jabbarzadeh, A. (2018). An integrated approach for facility location and supply vessel planning with time windows. Journal of Optimization in Industrial Engineering.
Błażewicz, J., Ecker, K. H., Pesch, E., Schmidt, G., & Weglarz, J. (2007). Handbook on scheduling: from theory to applications. Berlin Heidelberg: Springer Science & Business Media.
Centobelli, P., Converso, G., Murino, T., & Santillo, L. (2016). Flow shop scheduling algorithm to optimize warehouse activities. International Journal of Industrial Engineering Computations, 7(1), 49-66.
Defersha, F. M., & Chen, M. (2012). Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem. International Journal of Advanced Manufacturing Technology, 62(1), 249-265.
Engrand, P., & Mouney, X. (1998). Une méthode originale d’optimisation multiobjectif. Tech. Rep. HT-14/97/035/A (98NJ00005), EDF-DER Electricité de France, Direction des Études et Recherches, Clamart, France.
Fichera, S., Costa, A., & Cappadonna, F. (2017). Heterogeneous workers with learning ability assignment in a cellular manufacturing system. International Journal of Industrial Engineering Computations, 8(4), 427-440.
Framiñán, J. M., Leisten, R., & García, R. R. (2014). Manufacturing scheduling systems. Berlin: Springer.
França, P. M., Gupta, J. N., Mendes, A. S., Moscato, P., & Veltink, K. J. (2005). Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups. Computers and Industrial Engineering, 48(3), 491-506.
Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research, 1(2), 117-129.
Ghosh, T., Sengupta, S., Chattopadhyay, M., & Dan, P. (2011). Meta-heuristics in cellular manufacturing: A state-of-the-art review. International Journal of Industrial Engineering Computations, 2(1), 87-122.
Haimes, Y. Y. (1971). On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE transactions on Systems, Man, and Cybernetics, 1(3), 296-297.
Han, Y., Gong, D., Jin, Y., & Pan, Q. (2017). Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns. IEEE Transactions on Cybernetics.
Hart, W. E., Laird, C., Watson, J. P., & Woodruff, D. L. (2012). Pyomo–optimization modeling in python. Vol. 67. Springer Science & Business Media.
Hart, W. E., Watson, J. P., & Woodruff, D. L. (2011). Pyomo: modeling and solving mathematical programs in Python. Mathematical Programming Computation, 3(3), 219-260.
Joines, J. A., Culbreth, C. T., & King, R. E. (1996). Manufacturing cell design: an integer programming model employing genetic algorithms. IIE Transactions, 28(1), 69-85.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
Li, Y., Li, X., & Gupta, J. N. (2015). Solving the multi-objective flowline manufacturing cell scheduling problem by hybrid harmony search. Expert Systems with Applications, 42(3), 1409-1417.
Low, C. (2005). Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines. Computers and Operations Research, 32(8), 2013-2025.
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213(2), 455-465.
Mavrotas, G., & Florios, K. (2013). An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652-9669.
Minella, G., Ruiz, R., & Ciavotta, M. (2011). Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems. Computers and Operations Research, 38(11), 1521-1533.
Osman, I. H., & Potts, C. N. (1989). Simulated annealing for permutation flow-shop scheduling. Omega, 17(6), 551-557.
Pan, Q. K., Tasgetiren, M. F., Suganthan, P. N., & Chua, T. J. (2011). A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences, 181(12), 2455-2468.
Pinedo, M. (2012). Scheduling: theory, algorithms, and systems. New Jersey: Prentice-Hall.
Rossit, D. G., Tohmé, F. A., Frutos, M., & Broz, D. R. (2017). An application of the augmented ε-constraint method to design a municipal sorted waste collection system. Decision Science Letters, 6(4), 323-336.
Ruiz, F., Şerifoğlu, R. S., & Urlings, T. (2008). Modeling realistic hybrid flexible flowshop scheduling problems. Computers and Operations Research, 35(4), 1151-1175.
Ruiz, R., & Stützle, T. (2008). An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. European Journal of Operational Research, 187(3), 1143-1159.
Ruiz-Torres, A. J., Paletta, G., Mahmoodi, F., & Ablanedo-Rosas, J. H. (2018). Scheduling assemble-to-order systems with multiple cells to minimize costs and tardy deliveries. Computers & Industrial Engineering, 115, 290-303.
Sayadi, M., Ramezanian, R., & Ghaffari-Nasab, N. (2010). A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computations, 1(1), 1-10.
Shoaardebili, N., & Fattahi, P. (2015). Multi-objective meta-heuristics to solve three-stage assembly flow shop scheduling problem with machine availability constraints. International Journal of Production Research, 53(3), 944-968.
Tirkolaee, E., Alinaghian, M., Bakhshi Sasi, M., & Seyyed Esfahani, M. M. (2016). Solving a robust capacitated arc routing problem using a hybrid simulated annealing algorithm: a waste collection application. Journal of Industrial Engineering and Management Studies, 3(1), 61-76.
Tirkolaee, E. B., Alinaghian, A., Hosseinabadi, A. A. R., Sasi, M. B., Sangaiah, A. K (2018a). An improved ant colony optimization for the multi-trip Capacitated Arc Routing Problem, Computers & Electrical Engineering, Available online 13 February 2018.
Tirkolaee, E. B., Goli, A., Bakhsi, M., & Mahdavi, I. (2017). A robust multi-trip vehicle routing problem of perishable products with intermediate depots and time windows. Numerical Algebra, Control & Optimization, 7(4), 417-433.
Tirkolaee, E. B., Hosseinabadi, A. A. R., Soltani, M., Sangaiah, A. K., & Wang, J. (2018b). A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services. Sustainability, 10(5), 1-21
Tirkolaee, E. B., Mahdavi, I., & Esfahani, M. M. S (2018c). A robust periodic capacitated arc routing problem for urban waste collection considering drivers and crew’s working time. Waste Management, Available online 26 March 2018.
Vahedi Nouri, B., Fattahi, P., & Ramezanian, R. (2013). Hybrid firefly-simulated annealing algorithm for the flow shop problem with learning effects and flexible maintenance activities. International Journal of Production Research, 51(12), 3501-3515.
Vallada, E., Ruiz, R., & Framinan, J. M. (2015). New hard benchmark for flowshop scheduling problems minimising makespan. European Journal of Operational Research, 240(3), 666-677.
Wang, X., & Tang, L. (2017). A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem. Computers & Operations Research, 79, 60-77.
Wu, X., Shen, X., & Cui, Q. (2018). Multi-Objective Flexible Flow Shop Scheduling Problem Considering Variable Processing Time due to Renewable Energy. Sustainability, 10(3), 841.
Zokaee, S., Jabbarzadeh, A., Fahimnia, B., & Sadjadi, S. J. (2017). Robust supply chain network design: an optimization model with real world application. Annals of Operations Research, 257(1-2), 15-44.
Błażewicz, J., Ecker, K. H., Pesch, E., Schmidt, G., & Weglarz, J. (2007). Handbook on scheduling: from theory to applications. Berlin Heidelberg: Springer Science & Business Media.
Centobelli, P., Converso, G., Murino, T., & Santillo, L. (2016). Flow shop scheduling algorithm to optimize warehouse activities. International Journal of Industrial Engineering Computations, 7(1), 49-66.
Defersha, F. M., & Chen, M. (2012). Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem. International Journal of Advanced Manufacturing Technology, 62(1), 249-265.
Engrand, P., & Mouney, X. (1998). Une méthode originale d’optimisation multiobjectif. Tech. Rep. HT-14/97/035/A (98NJ00005), EDF-DER Electricité de France, Direction des Études et Recherches, Clamart, France.
Fichera, S., Costa, A., & Cappadonna, F. (2017). Heterogeneous workers with learning ability assignment in a cellular manufacturing system. International Journal of Industrial Engineering Computations, 8(4), 427-440.
Framiñán, J. M., Leisten, R., & García, R. R. (2014). Manufacturing scheduling systems. Berlin: Springer.
França, P. M., Gupta, J. N., Mendes, A. S., Moscato, P., & Veltink, K. J. (2005). Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups. Computers and Industrial Engineering, 48(3), 491-506.
Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research, 1(2), 117-129.
Ghosh, T., Sengupta, S., Chattopadhyay, M., & Dan, P. (2011). Meta-heuristics in cellular manufacturing: A state-of-the-art review. International Journal of Industrial Engineering Computations, 2(1), 87-122.
Haimes, Y. Y. (1971). On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE transactions on Systems, Man, and Cybernetics, 1(3), 296-297.
Han, Y., Gong, D., Jin, Y., & Pan, Q. (2017). Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns. IEEE Transactions on Cybernetics.
Hart, W. E., Laird, C., Watson, J. P., & Woodruff, D. L. (2012). Pyomo–optimization modeling in python. Vol. 67. Springer Science & Business Media.
Hart, W. E., Watson, J. P., & Woodruff, D. L. (2011). Pyomo: modeling and solving mathematical programs in Python. Mathematical Programming Computation, 3(3), 219-260.
Joines, J. A., Culbreth, C. T., & King, R. E. (1996). Manufacturing cell design: an integer programming model employing genetic algorithms. IIE Transactions, 28(1), 69-85.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
Li, Y., Li, X., & Gupta, J. N. (2015). Solving the multi-objective flowline manufacturing cell scheduling problem by hybrid harmony search. Expert Systems with Applications, 42(3), 1409-1417.
Low, C. (2005). Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines. Computers and Operations Research, 32(8), 2013-2025.
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213(2), 455-465.
Mavrotas, G., & Florios, K. (2013). An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652-9669.
Minella, G., Ruiz, R., & Ciavotta, M. (2011). Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems. Computers and Operations Research, 38(11), 1521-1533.
Osman, I. H., & Potts, C. N. (1989). Simulated annealing for permutation flow-shop scheduling. Omega, 17(6), 551-557.
Pan, Q. K., Tasgetiren, M. F., Suganthan, P. N., & Chua, T. J. (2011). A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences, 181(12), 2455-2468.
Pinedo, M. (2012). Scheduling: theory, algorithms, and systems. New Jersey: Prentice-Hall.
Rossit, D. G., Tohmé, F. A., Frutos, M., & Broz, D. R. (2017). An application of the augmented ε-constraint method to design a municipal sorted waste collection system. Decision Science Letters, 6(4), 323-336.
Ruiz, F., Şerifoğlu, R. S., & Urlings, T. (2008). Modeling realistic hybrid flexible flowshop scheduling problems. Computers and Operations Research, 35(4), 1151-1175.
Ruiz, R., & Stützle, T. (2008). An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. European Journal of Operational Research, 187(3), 1143-1159.
Ruiz-Torres, A. J., Paletta, G., Mahmoodi, F., & Ablanedo-Rosas, J. H. (2018). Scheduling assemble-to-order systems with multiple cells to minimize costs and tardy deliveries. Computers & Industrial Engineering, 115, 290-303.
Sayadi, M., Ramezanian, R., & Ghaffari-Nasab, N. (2010). A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computations, 1(1), 1-10.
Shoaardebili, N., & Fattahi, P. (2015). Multi-objective meta-heuristics to solve three-stage assembly flow shop scheduling problem with machine availability constraints. International Journal of Production Research, 53(3), 944-968.
Tirkolaee, E., Alinaghian, M., Bakhshi Sasi, M., & Seyyed Esfahani, M. M. (2016). Solving a robust capacitated arc routing problem using a hybrid simulated annealing algorithm: a waste collection application. Journal of Industrial Engineering and Management Studies, 3(1), 61-76.
Tirkolaee, E. B., Alinaghian, A., Hosseinabadi, A. A. R., Sasi, M. B., Sangaiah, A. K (2018a). An improved ant colony optimization for the multi-trip Capacitated Arc Routing Problem, Computers & Electrical Engineering, Available online 13 February 2018.
Tirkolaee, E. B., Goli, A., Bakhsi, M., & Mahdavi, I. (2017). A robust multi-trip vehicle routing problem of perishable products with intermediate depots and time windows. Numerical Algebra, Control & Optimization, 7(4), 417-433.
Tirkolaee, E. B., Hosseinabadi, A. A. R., Soltani, M., Sangaiah, A. K., & Wang, J. (2018b). A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services. Sustainability, 10(5), 1-21
Tirkolaee, E. B., Mahdavi, I., & Esfahani, M. M. S (2018c). A robust periodic capacitated arc routing problem for urban waste collection considering drivers and crew’s working time. Waste Management, Available online 26 March 2018.
Vahedi Nouri, B., Fattahi, P., & Ramezanian, R. (2013). Hybrid firefly-simulated annealing algorithm for the flow shop problem with learning effects and flexible maintenance activities. International Journal of Production Research, 51(12), 3501-3515.
Vallada, E., Ruiz, R., & Framinan, J. M. (2015). New hard benchmark for flowshop scheduling problems minimising makespan. European Journal of Operational Research, 240(3), 666-677.
Wang, X., & Tang, L. (2017). A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem. Computers & Operations Research, 79, 60-77.
Wu, X., Shen, X., & Cui, Q. (2018). Multi-Objective Flexible Flow Shop Scheduling Problem Considering Variable Processing Time due to Renewable Energy. Sustainability, 10(3), 841.
Zokaee, S., Jabbarzadeh, A., Fahimnia, B., & Sadjadi, S. J. (2017). Robust supply chain network design: an optimization model with real world application. Annals of Operations Research, 257(1-2), 15-44.