How to cite this paper
Pisa, M., Molina, J & Eguí, I. (2024). Ant colony algorithms for minimizing costs in multi-mode resource constrained project scheduling problems with spatial constraints.International Journal of Industrial Engineering Computations , 15(3), 667-684.
Refrences
Afshar, M. R., Shahhosseini, V., & Sebt, M. H. (2022). A genetic algorithm with a new local search method for solving the multimode resource-constrained project scheduling problem. International Journal of Construction Management, 22(3), 357-365.
Ahmadpour, S., & Ghezavati, V. (2019). Modeling and solving multi-skilled resource-constrained project scheduling problem with calendars in fuzzy condition. Journal of Industrial Engineering International, 15(1), 179-197.
Arkhipov, D., Battaïa, O., Cegarra, J., & Lazarev, A. (2018). Operator assignment problem in aircraft assembly lines: a new planning approach taking into account economic and ergonomic constraints. Procedia CIRP, 76, 63-66.
Baradaran, S., Ghomi, S. F., Ranjbar, M., & Hashemin, S. S. (2012). Multi-mode renewable resource-constrained allocation in PERT networks. Applied Soft Computing, 12(1), 82-90.
Blazewicz, J., Lenstra, J. K., & Kan, A. R. (1983). Scheduling subject to resource constraints: classification and complexity. Discrete applied mathematics, 5(1), 11-24.
Borreguero, T. (2019). Scheduling with limited resources along the aeronautical supply chain: from parts manufacturing plants to final assembly lines. PhD diss., E.T.S.I. Industriales (UPM).
Borreguero, T., García, A., & Ortega, M. (2015). Scheduling in the aeronautical industry using a mixed integer linear problem formulation. Procedia engineering,132, 982-989.
Cheng, H., & Chu, X. (2012). Task assignment with multiskilled employees and multiple modes for product development projects. The International Journal of Advanced Manufacturing Technology, 61(1), 391-403.
Chiang, C. W., & Huang, Y. Q. (2012). Multi-mode resource-constrained project scheduling by ant colony optimization with a dynamic tournament strategy. In 2012 Third international conference on innovations in bio-inspired computing and applications (pp. 110-115). IEEE.
Correia, I., Lourenço, L. L. & Saldanha-da-Gama, F. (2012). Project scheduling with flexible resources: formulation and inequalities. OR Spectrum, 34, 635–663.
Dolgui, A., Kovalev, S., Kovalyov, M. Y., Malyutin, S., & Soukhal, A. (2018). Optimal workforce assignment to operations of a paced assembly line. European Journal of Operational Research, 264(1), 200-211.
Dorigo, M., & Stützle, T. (2019). Ant colony optimization: overview and recent advances. Handbook of metaheuristics, 311-351.
Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE computational intelligence magazine, 1(4), 28-39.
Duarte, A., Mladenovic, N., Sánchez-Oro, J., & Todosijević, R. (2018). Variable neighborhood descent. In Handbook of Heuristics, Springer International Publishing, 341-367.
Heike, G., Ramulu, M., Sorenson, E., Shanahan, P., & Moinzadeh, K. (2001). Mixed model assembly alternatives for low-volume manufacturing: the case of the aerospace industry. International Journal of Production Economics, 72(2), 103-120.
Kadrou, Y., & Najid, N. M. (2006). A new heuristic to solve RCPSP with multiple execution modes and Multi-Skilled Labor. In The Proceedings of the Multiconference on Computational Engineering in Systems Applications (Vol. 2, pp. 1302-1309). IEEE.
Khalilzadeh, M. (2015). A honey bee swarm optimization algorithm for minimizing the total costs of resources in MRCPSP. Indian Journal of Science and Technology, 8(11).
Kolisch, R., & A. Sprecher. (1997). PSPLIB-a project scheduling problem library: OR software-ORSEP operations research software exchange program. European journal of operational research, 96(1), 205-216.
Li, C., Wang, F., & Chung, T. (2024). Multi-mode multi-skill resource-constrained project scheduling problem with differentiated professional capabilities. Journal of Project Management, 9(1), 27-44.
Li, H., & Zhang, H. (2013). Ant colony optimization-based multi-mode scheduling under renewable and non-renewable resource constraints. Automation in construction, 35, 431-438.
Miralles, C., Garcia-Sabater, J. P., Andrés, C., & Cardos, M. (2007). Advantages of Assembly Lines in Sheltered Work Centres for Disabled. A Case Study. International Journal of Production Research, 110(2), 187–197.
Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers and operations research, 24(11), 1097-1100.
Molina, J. C., Salmeron, J. L., & Eguia, I. (2020). An ACS-based memetic algorithm for the heterogeneous vehicle routing problem with time windows. Expert Systems with Applications, 157, 113379.
Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989.
Pellerin, R., Perrier, N., & Berthaut, F. (2020). A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 280(2), 395-416.
Povéda, G., Alvarez, N., & Artigues, C. (2023). Partially Preemptive Multi Skill/Mode Resource-Constrained Project Scheduling with Generalized Precedence Relations and Calendars. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 31:1-31:21
Reed, M., Yiannakou, A., & Evering, R. (2014). An ant colony algorithm for the multi-compartment vehicle routing problem. Applied Soft Computing, 15, 169-176.
Ritt, M., Costa, A. M., & Miralles, C. (2016). The assembly line worker assignment and balancing problem with stochastic worker availability. International Journal of Production Research, 54(3), 907-922.
Russell, A., & Taghipour, S. (2019). Multi-objective optimization of complex scheduling problems in low-volume low-variety production systems. International Journal of Production Economics, 208, 1-16.
Shahnazari-Shahrezaei, P., Zabihi, S., & Kia, R. (2017). Solving a multi-objective mathematical model for a multi-skilled project scheduling problem by particle swarm optimization and differential evolution algorithms. Industrial Engineering and Management Systems, 16(3), 288-306.
Shan, M., Hong, Q., & Juan, W. (2007). Multi-mode multi-project scheduling problem for mould production in MC enterprise. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing (pp. 5316-5320). IEEE.
Shen, H., & Li, X. (2013). Cooperative discrete particle swarms for multi-mode resource-constrained projects. In Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 31-36). IEEE.
Taguchi, G., Chowdhury, S., & Wu. Y. (2005). Taguchi’s Engineering Quality Handbook, John Willey and Sons. Inc, Hoboken, New Jersey.
Van Peteghem, V., & Vanhoucke, M. (2014). An experimental investigation of metaheuristics for the multi-mode resource-constrained project scheduling problem on new dataset instances. European Journal of Operational Research, 235(1), 62-72.
Vanhoucke, M., Coelho, J., Debels, D., Maenhout, B., & Tavares, L. V. (2008). An evaluation of the adequacy of project network generators with systematically sampled networks. European Journal of Operational Research, 187(2), 511-524.
Vinay, V. P., & Sridharan, R. (2013). Taguchi method for parameter design in ACO algorithm for distribution–allocation in a two-stage supply chain. The International Journal of Advanced Manufacturing Technology, 64(9), 1333-1343.
Wuliang, P., Min, H., & Yongping, H. (2014). An improved ant algorithm for Multi-mode Resource Constrained Project Scheduling Problem. RAIRO-Operations Research, 48(4), 595-614.
Xu, G., Lin, H., Cheng, Y., & Li, S. (2023). An Improved Ant Colony Optimization for Solving Task Scheduling Problem in Radar Signal Processing System. Journal of Signal Processing Systems, 1-18.
Zhang, H. (2012). Ant colony optimization for multimode resource-constrained project scheduling. Journal of Management in Engineering, 28(2), 150-159.
Ahmadpour, S., & Ghezavati, V. (2019). Modeling and solving multi-skilled resource-constrained project scheduling problem with calendars in fuzzy condition. Journal of Industrial Engineering International, 15(1), 179-197.
Arkhipov, D., Battaïa, O., Cegarra, J., & Lazarev, A. (2018). Operator assignment problem in aircraft assembly lines: a new planning approach taking into account economic and ergonomic constraints. Procedia CIRP, 76, 63-66.
Baradaran, S., Ghomi, S. F., Ranjbar, M., & Hashemin, S. S. (2012). Multi-mode renewable resource-constrained allocation in PERT networks. Applied Soft Computing, 12(1), 82-90.
Blazewicz, J., Lenstra, J. K., & Kan, A. R. (1983). Scheduling subject to resource constraints: classification and complexity. Discrete applied mathematics, 5(1), 11-24.
Borreguero, T. (2019). Scheduling with limited resources along the aeronautical supply chain: from parts manufacturing plants to final assembly lines. PhD diss., E.T.S.I. Industriales (UPM).
Borreguero, T., García, A., & Ortega, M. (2015). Scheduling in the aeronautical industry using a mixed integer linear problem formulation. Procedia engineering,132, 982-989.
Cheng, H., & Chu, X. (2012). Task assignment with multiskilled employees and multiple modes for product development projects. The International Journal of Advanced Manufacturing Technology, 61(1), 391-403.
Chiang, C. W., & Huang, Y. Q. (2012). Multi-mode resource-constrained project scheduling by ant colony optimization with a dynamic tournament strategy. In 2012 Third international conference on innovations in bio-inspired computing and applications (pp. 110-115). IEEE.
Correia, I., Lourenço, L. L. & Saldanha-da-Gama, F. (2012). Project scheduling with flexible resources: formulation and inequalities. OR Spectrum, 34, 635–663.
Dolgui, A., Kovalev, S., Kovalyov, M. Y., Malyutin, S., & Soukhal, A. (2018). Optimal workforce assignment to operations of a paced assembly line. European Journal of Operational Research, 264(1), 200-211.
Dorigo, M., & Stützle, T. (2019). Ant colony optimization: overview and recent advances. Handbook of metaheuristics, 311-351.
Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE computational intelligence magazine, 1(4), 28-39.
Duarte, A., Mladenovic, N., Sánchez-Oro, J., & Todosijević, R. (2018). Variable neighborhood descent. In Handbook of Heuristics, Springer International Publishing, 341-367.
Heike, G., Ramulu, M., Sorenson, E., Shanahan, P., & Moinzadeh, K. (2001). Mixed model assembly alternatives for low-volume manufacturing: the case of the aerospace industry. International Journal of Production Economics, 72(2), 103-120.
Kadrou, Y., & Najid, N. M. (2006). A new heuristic to solve RCPSP with multiple execution modes and Multi-Skilled Labor. In The Proceedings of the Multiconference on Computational Engineering in Systems Applications (Vol. 2, pp. 1302-1309). IEEE.
Khalilzadeh, M. (2015). A honey bee swarm optimization algorithm for minimizing the total costs of resources in MRCPSP. Indian Journal of Science and Technology, 8(11).
Kolisch, R., & A. Sprecher. (1997). PSPLIB-a project scheduling problem library: OR software-ORSEP operations research software exchange program. European journal of operational research, 96(1), 205-216.
Li, C., Wang, F., & Chung, T. (2024). Multi-mode multi-skill resource-constrained project scheduling problem with differentiated professional capabilities. Journal of Project Management, 9(1), 27-44.
Li, H., & Zhang, H. (2013). Ant colony optimization-based multi-mode scheduling under renewable and non-renewable resource constraints. Automation in construction, 35, 431-438.
Miralles, C., Garcia-Sabater, J. P., Andrés, C., & Cardos, M. (2007). Advantages of Assembly Lines in Sheltered Work Centres for Disabled. A Case Study. International Journal of Production Research, 110(2), 187–197.
Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers and operations research, 24(11), 1097-1100.
Molina, J. C., Salmeron, J. L., & Eguia, I. (2020). An ACS-based memetic algorithm for the heterogeneous vehicle routing problem with time windows. Expert Systems with Applications, 157, 113379.
Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989.
Pellerin, R., Perrier, N., & Berthaut, F. (2020). A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 280(2), 395-416.
Povéda, G., Alvarez, N., & Artigues, C. (2023). Partially Preemptive Multi Skill/Mode Resource-Constrained Project Scheduling with Generalized Precedence Relations and Calendars. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 31:1-31:21
Reed, M., Yiannakou, A., & Evering, R. (2014). An ant colony algorithm for the multi-compartment vehicle routing problem. Applied Soft Computing, 15, 169-176.
Ritt, M., Costa, A. M., & Miralles, C. (2016). The assembly line worker assignment and balancing problem with stochastic worker availability. International Journal of Production Research, 54(3), 907-922.
Russell, A., & Taghipour, S. (2019). Multi-objective optimization of complex scheduling problems in low-volume low-variety production systems. International Journal of Production Economics, 208, 1-16.
Shahnazari-Shahrezaei, P., Zabihi, S., & Kia, R. (2017). Solving a multi-objective mathematical model for a multi-skilled project scheduling problem by particle swarm optimization and differential evolution algorithms. Industrial Engineering and Management Systems, 16(3), 288-306.
Shan, M., Hong, Q., & Juan, W. (2007). Multi-mode multi-project scheduling problem for mould production in MC enterprise. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing (pp. 5316-5320). IEEE.
Shen, H., & Li, X. (2013). Cooperative discrete particle swarms for multi-mode resource-constrained projects. In Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 31-36). IEEE.
Taguchi, G., Chowdhury, S., & Wu. Y. (2005). Taguchi’s Engineering Quality Handbook, John Willey and Sons. Inc, Hoboken, New Jersey.
Van Peteghem, V., & Vanhoucke, M. (2014). An experimental investigation of metaheuristics for the multi-mode resource-constrained project scheduling problem on new dataset instances. European Journal of Operational Research, 235(1), 62-72.
Vanhoucke, M., Coelho, J., Debels, D., Maenhout, B., & Tavares, L. V. (2008). An evaluation of the adequacy of project network generators with systematically sampled networks. European Journal of Operational Research, 187(2), 511-524.
Vinay, V. P., & Sridharan, R. (2013). Taguchi method for parameter design in ACO algorithm for distribution–allocation in a two-stage supply chain. The International Journal of Advanced Manufacturing Technology, 64(9), 1333-1343.
Wuliang, P., Min, H., & Yongping, H. (2014). An improved ant algorithm for Multi-mode Resource Constrained Project Scheduling Problem. RAIRO-Operations Research, 48(4), 595-614.
Xu, G., Lin, H., Cheng, Y., & Li, S. (2023). An Improved Ant Colony Optimization for Solving Task Scheduling Problem in Radar Signal Processing System. Journal of Signal Processing Systems, 1-18.
Zhang, H. (2012). Ant colony optimization for multimode resource-constrained project scheduling. Journal of Management in Engineering, 28(2), 150-159.