How to cite this paper
Velandia, R., Martinez, D & Escobar, J. (2024). A metaheuristic algorithm based on Ant Colony Based approach for the assigning tasks problem to a workforce with different skills.Decision Science Letters , 13(3), 729-740.
Refrences
Abreu, L. R., & Prata, B. A. (2018). A Hybrid Genetic Algorithm for Solving the Unrelated Parallel Machine Scheduling Problem with Sequence Dependent Setup Times. IEEE Latin America Transactions, 16(6), 1715-1722.
Abreu, L. R., (2019). Set of instances for the Unrelated Parallel Machine Scheduling problem with Sequence Dependent Setup Times. Available in:
https://www.researchgate.net/publication/315771587_Instances_Tested. Consulted 10 of January of 2019.
Afzalirad, M., & Rezaeian, J. (2016). Resource-constrained unrelated parallel machine scheduling problem with sequence dependent setup times, precedence constraints and machine eligibility restrictions. Computers & Industrial Engineering, 98, 40-52.
Algethami, H., Pinheiro, R. L., & Landa-Silva, D. (2016). A genetic algorithm for a workforce scheduling and routing problem. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 927-934). IEEE.
Algethami, H., Martínez-Gavara, A., & Landa-Silva, D. (2019). Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem. Journal of Heuristics, 25(4), 753-792.
Allahverdi, A., Gupta, J. N., & Aldowaisan, T. (1999). A review of scheduling research involving setup considerations. Omega, 27(2), 219-239.
Anagnostopoulos, G. C., & Rabadi, G. (2002). A simulated annealing algorithm for the unrelated parallel machine scheduling problem. In Proceedings of the 5th Biannual world automation congress (Vol. 14, pp. 115-120). IEEE.
Arnaout, J. P., Rabadi, G., & Musa, R. (2010). A two-stage ant colony optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times. Journal of Intelligent Manufacturing, 21(6), 693-701.
Avalos-Rosales, O., Angel-Bello, F., & Alvarez, A. (2015). Efficient metaheuristic algorithm and re-formulations for the unrelated parallel machine scheduling problem with sequence and machine-dependent setup times. The International Journal of Advanced Manufacturing Technology, 76(9-12), 1705-1718.
Baker, K. R., & Magazine, M. J. (1977). Workforce scheduling with cyclic demands and day-off constraints. Management Science, 24(2), 161-167.
Balaprakash, P., Birattari, M., Stützle, T., Yuan, Z., & Dorigo, M. (2009). Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem. Swarm Intelligence, 3(3), 223-242.
Barrera, D., Velasco, N., & Amaya, C. A. (2012). A network-based approach to the multi-activity combined timetabling and crew scheduling problem: Workforce scheduling for public health policy implementation. Computers & Industrial Engineering, 63(4), 802-812.
Bechtold, S. E., & Brusco, M. J. (1994). Working set generation methods for labor tour scheduling. European Journal of Operational Research, 74(3), 540-551.
Becker, T. (2020). A decomposition heuristic for rotational workforce scheduling. Journal of Scheduling, 23(5), 539-554.
Billionnet, A. (1999). Integer programming to schedule a hierarchical workforce with variable demands. European Journal of Operational Research, 114(1), 105-114.
Blum, C. (2005). Beam-ACO—Hybridizing ant colony optimization with beam search: An application to open shop scheduling. Computers & Operations Research, 32(6), 1565-1591.
Blum, C. (2008). Beam-ACO for simple assembly line balancing. INFORMS Journal on Computing, 20(4), 618-627.
Blum, C., Vallès, M. Y., & Blesa, M. J. (2008). An ant colony optimization algorithm for DNA sequencing by hybridization. Computers & Operations Research, 35(11), 3620-3635.
Çakırgil, S., Yücel, E., & Kuyzu, G. (2020). An integrated solution approach for multi-objective, multi-skill workforce scheduling and routing problems. Computers & Operations Research, 118, 104908.
Castillo, I., Joro, T., & Li, Y. Y. (2009). Workforce scheduling with multiple objectives. European Journal of Operational Research, 196(1), 162-170.
Chen, J. F., & Wu, T. H. (2006). Total tardiness minimization on unrelated parallel machine scheduling with auxiliary equipment constraints. Omega, 34(1), 81-89.
Chen, J. F. (2015). Unrelated parallel-machine scheduling to minimize total weighted completion time. Journal of Intelligent Manufacturing, 26(6), 1099-1112.
Cheng, T. C. E., & Sin, C. C. S. (1990). A state-of-the-art review of parallel-machine scheduling research. European Journal of Operational Research, 47(3), 271-292.
Cowling, P., Colledge, N., Dahal, K., & Remde, S. (2006). The trade off between diversity and quality for multi-objective workforce scheduling. In European Conference on Evolutionary Computation in Combinatorial Optimization (pp. 13-24). Springer, Berlin, Heidelberg.
De Bruecker, P., Van den Bergh, J., Beliën, J., & Demeulemeester, E. (2015). Workforce planning incorporating skills: State of the art. European Journal of Operational Research, 243(1), 1-16.
De Paula, M. R., Ravetti, M. G., Mateus, G. R., & Pardalos, P. M. (2007). Solving parallel machines scheduling problems with sequence-dependent setup times using variable neighbourhood search. IMA Journal of Management Mathematics, 18(2), 101-115.
Di Caro, G., & Dorigo, M. (1998). AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research, 9, 317-365.
Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano.
Dorigo, M., & Stützle, T. (2003). The ant colony optimization metaheuristic: Algorithms, applications, and advances. In Handbook of metaheuristics (pp. 250-285). Springer, Boston, MA.
Ernst, A. T., Jiang, H., Krishnamoorthy, M., & Sier, D. (2004). Staff scheduling and rostering: A review of applications, methods and models. European journal of operational research, 153(1), 3-27.
Fırat, M., & Hurkens, C. A. (2012). An improved MIP-based approach for a multi-skill workforce scheduling problem. Journal of Scheduling, 15(3), 363-380.
Florez, L., Castro-Lacouture, D., & Medaglia, A. L. (2013). Sustainable workforce scheduling in construction program management. Journal of the Operational Research Society, 64(8), 1169-1181.
Fozveh, K. I., Salehi, H., & Mogharehabed, K. (2016). Presentation of Multi-Skill Workforce Scheduling Model and Solving the Model Using Meta-Heuristic Algorithms. Modern Applied Science, 2(10), 194-205.
Gambardella, L. M., & Dorigo, M. (2000). An ant colony system hybridized with a new local search for the sequential ordering problem. INFORMS Journal on Computing, 12(3), 237-255.
Gendreau, M., & Potvin, J. Y. (Eds.). (2010). Handbook of metaheuristics (Vol. 2, p. 9). New York: Springer.
Gérard, M., Clautiaux, F., & Sadykov, R. (2016). Column generation based approaches for a tour scheduling problem with a multi-skill heterogeneous workforce. European Journal of Operational Research, 252(3), 1019-1030.
Joo, C. M., & Kim, B. S. (2015). Hybrid genetic algorithms with dispatching rules for unrelated parallel machine scheduling with setup time and production availability. Computers & Industrial Engineering, 85, 102-109.
Kim, D. W., Kim, K. H., Jang, W., & Chen, F. F. (2002). Unrelated parallel machine scheduling with setup times using simulated annealing. Robotics and Computer-Integrated Manufacturing, 18(3-4), 223-231.
Korb, O., Stützle, T., & Exner, T. E. (2007). An ant colony optimization approach to flexible protein–ligand docking. Swarm Intelligence, 1(2), 115-134.
Kuo, W. H., Hsu, C. J., & Yang, D. L. (2011). Some unrelated parallel machine scheduling problems with past-sequence-dependent setup time and learning effects. Computers & Industrial Engineering, 61(1), 179-183.
Laesanklang, W., Silva, D. L., & Castillo-Salazar, J. A. (2015). Mixed Integer Programming with Decomposition to Solve a Workforce Scheduling and Routing Problem. In ICORES (pp. 283-293).
Lee, J. H., Yu, J. M., & Lee, D. H. (2013). A tabu search algorithm for unrelated parallel machine scheduling with sequence-and machine-dependent setups: minimizing total tardiness. The International Journal of Advanced Manufacturing Technology, 69(9-12), 2081-2089.
Li, J., Burke, E. K., Curtois, T., Petrovic, S., & Qu, R. (2012). The falling tide algorithm: a new multi-objective approach for complex workforce scheduling. Omega, 40(3), 283-293.
Lin, S. W., & Ying, K. C. (2015). A multi-point simulated annealing heuristic for solving multiple objective unrelated parallel machine scheduling problems. International Journal of Production Research, 53(4), 1065-1076.
Liu, M., & Liu, X. (2019). Satisfaction-driven bi-objective multi-skill workforce scheduling problem. IFAC-PapersOnLine, 52(13), 229-234.
Moodie, C. L., & Roberts, S. D. (1967). Experiments with priority dispatching rules in a parallel processor shop. International Journal of Production Research, 6(4), 303-312.
Muntz, R. R., & Coffman, E. G. (1969). Optimal preemptive scheduling on two-processor systems. IEEE Transactions on Computers, 100(11), 1014-1020.
Musliu, N. (2006). Heuristic methods for automatic rotating workforce scheduling. International Journal of Computational Intelligence Research, 2(4), 309-326.
Othman, M., Gouw, G. J., & Bhuiyan, N. (2012). Workforce scheduling: A new model incorporating human factors. Journal of Industrial Engineering and Management (JIEM), 5(2), 259-284.
Passmark, (2019). Sitio web especializado para benchmark de computadores. Disponible en línea: http://www.passmark.com. Consultado 20 February of 2019.
Pereira, D. L., Alves, J. C., & de Oliveira Moreira, M. C. (2020). A multiperiod workforce scheduling and routing problem with dependent tasks. Computers & Operations Research, 118, 104930.
Pinedo, M. (2016). Scheduling Theory, Algorithms, and Systems. ed. 5, Editorial Springer, Cap 5.
Reinelt, G. (2003). The traveling salesman: computational solutions for TSP applications (Vol. 840). Springer.
Remde, S., Cowling, P., Dahal, K., & Colledge, N. (2007). Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling. In European Conference on Evolutionary Computation in Combinatorial Optimization (pp. 188-197). Springer, Berlin, Heidelberg.
Seçkiner, S. U., Gökçen, H., & Kurt, M. (2007). An integer programming model for hierarchical workforce scheduling problem. European Journal of Operational Research, 183(2), 694-699.
Shmygelska, A., & Hoos, H. H. (2005). An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC bioinformatics, 6(1), 1-22.
Simeunović, N., Kamenko, I., Bugarski, V., Jovanović, M., & Lalić, B. (2017). Improving workforce scheduling using artificial neural networks model. Advances in Production Engineering & Management, 12(4), 337-352.
Thompson, G. M., & Goodale, J. C. (2006). Variable employee productivity in workforce scheduling. European Journal of Operational Research, 170(2), 376-390.
Tsang, E., & Voudouris, C. (1997). Fast local search and guided local search and their application to British Telecom's workforce scheduling problem. Operations Research Letters, 20(3), 119-127.
Vallada, E., & Ruiz, R. (2011). A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. European Journal of Operational Research, 211(3), 612-622.
Valls, V., Pérez, Á., & Quintanilla, S. (2009). Skilled workforce scheduling in service centres. European Journal of Operational Research, 193(3), 791-804.
Xie, F., Potts, C. N., & Bektaş, T. (2017). Iterated local search for workforce scheduling and routing problems. Journal of Heuristics, 23(6), 471-500.
Yamashita, D. S. (2000). Tabu search for scheduling on identical parallel machines to minimize mean tardiness. Journal of Intelligent Manufacturing, 11(5), 453-460.
Yaoyuenyong, K., & Nanthavanij, S. (2005). Energy-based workforce scheduling problem: mathematical model and solution algorithms. ScienceAsia, 31, 383-93.
Yurtkuran, A., Yagmahan, B., & Emel, E. (2018). A novel artificial bee colony algorithm for the workforce scheduling and balancing problem in sub-assembly lines with limited buffers. Applied Soft Computing, 73, 767-782.
Abreu, L. R., (2019). Set of instances for the Unrelated Parallel Machine Scheduling problem with Sequence Dependent Setup Times. Available in:
https://www.researchgate.net/publication/315771587_Instances_Tested. Consulted 10 of January of 2019.
Afzalirad, M., & Rezaeian, J. (2016). Resource-constrained unrelated parallel machine scheduling problem with sequence dependent setup times, precedence constraints and machine eligibility restrictions. Computers & Industrial Engineering, 98, 40-52.
Algethami, H., Pinheiro, R. L., & Landa-Silva, D. (2016). A genetic algorithm for a workforce scheduling and routing problem. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 927-934). IEEE.
Algethami, H., Martínez-Gavara, A., & Landa-Silva, D. (2019). Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem. Journal of Heuristics, 25(4), 753-792.
Allahverdi, A., Gupta, J. N., & Aldowaisan, T. (1999). A review of scheduling research involving setup considerations. Omega, 27(2), 219-239.
Anagnostopoulos, G. C., & Rabadi, G. (2002). A simulated annealing algorithm for the unrelated parallel machine scheduling problem. In Proceedings of the 5th Biannual world automation congress (Vol. 14, pp. 115-120). IEEE.
Arnaout, J. P., Rabadi, G., & Musa, R. (2010). A two-stage ant colony optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times. Journal of Intelligent Manufacturing, 21(6), 693-701.
Avalos-Rosales, O., Angel-Bello, F., & Alvarez, A. (2015). Efficient metaheuristic algorithm and re-formulations for the unrelated parallel machine scheduling problem with sequence and machine-dependent setup times. The International Journal of Advanced Manufacturing Technology, 76(9-12), 1705-1718.
Baker, K. R., & Magazine, M. J. (1977). Workforce scheduling with cyclic demands and day-off constraints. Management Science, 24(2), 161-167.
Balaprakash, P., Birattari, M., Stützle, T., Yuan, Z., & Dorigo, M. (2009). Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem. Swarm Intelligence, 3(3), 223-242.
Barrera, D., Velasco, N., & Amaya, C. A. (2012). A network-based approach to the multi-activity combined timetabling and crew scheduling problem: Workforce scheduling for public health policy implementation. Computers & Industrial Engineering, 63(4), 802-812.
Bechtold, S. E., & Brusco, M. J. (1994). Working set generation methods for labor tour scheduling. European Journal of Operational Research, 74(3), 540-551.
Becker, T. (2020). A decomposition heuristic for rotational workforce scheduling. Journal of Scheduling, 23(5), 539-554.
Billionnet, A. (1999). Integer programming to schedule a hierarchical workforce with variable demands. European Journal of Operational Research, 114(1), 105-114.
Blum, C. (2005). Beam-ACO—Hybridizing ant colony optimization with beam search: An application to open shop scheduling. Computers & Operations Research, 32(6), 1565-1591.
Blum, C. (2008). Beam-ACO for simple assembly line balancing. INFORMS Journal on Computing, 20(4), 618-627.
Blum, C., Vallès, M. Y., & Blesa, M. J. (2008). An ant colony optimization algorithm for DNA sequencing by hybridization. Computers & Operations Research, 35(11), 3620-3635.
Çakırgil, S., Yücel, E., & Kuyzu, G. (2020). An integrated solution approach for multi-objective, multi-skill workforce scheduling and routing problems. Computers & Operations Research, 118, 104908.
Castillo, I., Joro, T., & Li, Y. Y. (2009). Workforce scheduling with multiple objectives. European Journal of Operational Research, 196(1), 162-170.
Chen, J. F., & Wu, T. H. (2006). Total tardiness minimization on unrelated parallel machine scheduling with auxiliary equipment constraints. Omega, 34(1), 81-89.
Chen, J. F. (2015). Unrelated parallel-machine scheduling to minimize total weighted completion time. Journal of Intelligent Manufacturing, 26(6), 1099-1112.
Cheng, T. C. E., & Sin, C. C. S. (1990). A state-of-the-art review of parallel-machine scheduling research. European Journal of Operational Research, 47(3), 271-292.
Cowling, P., Colledge, N., Dahal, K., & Remde, S. (2006). The trade off between diversity and quality for multi-objective workforce scheduling. In European Conference on Evolutionary Computation in Combinatorial Optimization (pp. 13-24). Springer, Berlin, Heidelberg.
De Bruecker, P., Van den Bergh, J., Beliën, J., & Demeulemeester, E. (2015). Workforce planning incorporating skills: State of the art. European Journal of Operational Research, 243(1), 1-16.
De Paula, M. R., Ravetti, M. G., Mateus, G. R., & Pardalos, P. M. (2007). Solving parallel machines scheduling problems with sequence-dependent setup times using variable neighbourhood search. IMA Journal of Management Mathematics, 18(2), 101-115.
Di Caro, G., & Dorigo, M. (1998). AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research, 9, 317-365.
Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano.
Dorigo, M., & Stützle, T. (2003). The ant colony optimization metaheuristic: Algorithms, applications, and advances. In Handbook of metaheuristics (pp. 250-285). Springer, Boston, MA.
Ernst, A. T., Jiang, H., Krishnamoorthy, M., & Sier, D. (2004). Staff scheduling and rostering: A review of applications, methods and models. European journal of operational research, 153(1), 3-27.
Fırat, M., & Hurkens, C. A. (2012). An improved MIP-based approach for a multi-skill workforce scheduling problem. Journal of Scheduling, 15(3), 363-380.
Florez, L., Castro-Lacouture, D., & Medaglia, A. L. (2013). Sustainable workforce scheduling in construction program management. Journal of the Operational Research Society, 64(8), 1169-1181.
Fozveh, K. I., Salehi, H., & Mogharehabed, K. (2016). Presentation of Multi-Skill Workforce Scheduling Model and Solving the Model Using Meta-Heuristic Algorithms. Modern Applied Science, 2(10), 194-205.
Gambardella, L. M., & Dorigo, M. (2000). An ant colony system hybridized with a new local search for the sequential ordering problem. INFORMS Journal on Computing, 12(3), 237-255.
Gendreau, M., & Potvin, J. Y. (Eds.). (2010). Handbook of metaheuristics (Vol. 2, p. 9). New York: Springer.
Gérard, M., Clautiaux, F., & Sadykov, R. (2016). Column generation based approaches for a tour scheduling problem with a multi-skill heterogeneous workforce. European Journal of Operational Research, 252(3), 1019-1030.
Joo, C. M., & Kim, B. S. (2015). Hybrid genetic algorithms with dispatching rules for unrelated parallel machine scheduling with setup time and production availability. Computers & Industrial Engineering, 85, 102-109.
Kim, D. W., Kim, K. H., Jang, W., & Chen, F. F. (2002). Unrelated parallel machine scheduling with setup times using simulated annealing. Robotics and Computer-Integrated Manufacturing, 18(3-4), 223-231.
Korb, O., Stützle, T., & Exner, T. E. (2007). An ant colony optimization approach to flexible protein–ligand docking. Swarm Intelligence, 1(2), 115-134.
Kuo, W. H., Hsu, C. J., & Yang, D. L. (2011). Some unrelated parallel machine scheduling problems with past-sequence-dependent setup time and learning effects. Computers & Industrial Engineering, 61(1), 179-183.
Laesanklang, W., Silva, D. L., & Castillo-Salazar, J. A. (2015). Mixed Integer Programming with Decomposition to Solve a Workforce Scheduling and Routing Problem. In ICORES (pp. 283-293).
Lee, J. H., Yu, J. M., & Lee, D. H. (2013). A tabu search algorithm for unrelated parallel machine scheduling with sequence-and machine-dependent setups: minimizing total tardiness. The International Journal of Advanced Manufacturing Technology, 69(9-12), 2081-2089.
Li, J., Burke, E. K., Curtois, T., Petrovic, S., & Qu, R. (2012). The falling tide algorithm: a new multi-objective approach for complex workforce scheduling. Omega, 40(3), 283-293.
Lin, S. W., & Ying, K. C. (2015). A multi-point simulated annealing heuristic for solving multiple objective unrelated parallel machine scheduling problems. International Journal of Production Research, 53(4), 1065-1076.
Liu, M., & Liu, X. (2019). Satisfaction-driven bi-objective multi-skill workforce scheduling problem. IFAC-PapersOnLine, 52(13), 229-234.
Moodie, C. L., & Roberts, S. D. (1967). Experiments with priority dispatching rules in a parallel processor shop. International Journal of Production Research, 6(4), 303-312.
Muntz, R. R., & Coffman, E. G. (1969). Optimal preemptive scheduling on two-processor systems. IEEE Transactions on Computers, 100(11), 1014-1020.
Musliu, N. (2006). Heuristic methods for automatic rotating workforce scheduling. International Journal of Computational Intelligence Research, 2(4), 309-326.
Othman, M., Gouw, G. J., & Bhuiyan, N. (2012). Workforce scheduling: A new model incorporating human factors. Journal of Industrial Engineering and Management (JIEM), 5(2), 259-284.
Passmark, (2019). Sitio web especializado para benchmark de computadores. Disponible en línea: http://www.passmark.com. Consultado 20 February of 2019.
Pereira, D. L., Alves, J. C., & de Oliveira Moreira, M. C. (2020). A multiperiod workforce scheduling and routing problem with dependent tasks. Computers & Operations Research, 118, 104930.
Pinedo, M. (2016). Scheduling Theory, Algorithms, and Systems. ed. 5, Editorial Springer, Cap 5.
Reinelt, G. (2003). The traveling salesman: computational solutions for TSP applications (Vol. 840). Springer.
Remde, S., Cowling, P., Dahal, K., & Colledge, N. (2007). Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling. In European Conference on Evolutionary Computation in Combinatorial Optimization (pp. 188-197). Springer, Berlin, Heidelberg.
Seçkiner, S. U., Gökçen, H., & Kurt, M. (2007). An integer programming model for hierarchical workforce scheduling problem. European Journal of Operational Research, 183(2), 694-699.
Shmygelska, A., & Hoos, H. H. (2005). An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC bioinformatics, 6(1), 1-22.
Simeunović, N., Kamenko, I., Bugarski, V., Jovanović, M., & Lalić, B. (2017). Improving workforce scheduling using artificial neural networks model. Advances in Production Engineering & Management, 12(4), 337-352.
Thompson, G. M., & Goodale, J. C. (2006). Variable employee productivity in workforce scheduling. European Journal of Operational Research, 170(2), 376-390.
Tsang, E., & Voudouris, C. (1997). Fast local search and guided local search and their application to British Telecom's workforce scheduling problem. Operations Research Letters, 20(3), 119-127.
Vallada, E., & Ruiz, R. (2011). A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. European Journal of Operational Research, 211(3), 612-622.
Valls, V., Pérez, Á., & Quintanilla, S. (2009). Skilled workforce scheduling in service centres. European Journal of Operational Research, 193(3), 791-804.
Xie, F., Potts, C. N., & Bektaş, T. (2017). Iterated local search for workforce scheduling and routing problems. Journal of Heuristics, 23(6), 471-500.
Yamashita, D. S. (2000). Tabu search for scheduling on identical parallel machines to minimize mean tardiness. Journal of Intelligent Manufacturing, 11(5), 453-460.
Yaoyuenyong, K., & Nanthavanij, S. (2005). Energy-based workforce scheduling problem: mathematical model and solution algorithms. ScienceAsia, 31, 383-93.
Yurtkuran, A., Yagmahan, B., & Emel, E. (2018). A novel artificial bee colony algorithm for the workforce scheduling and balancing problem in sub-assembly lines with limited buffers. Applied Soft Computing, 73, 767-782.