How to cite this paper
Filcek, G., Józefczyk, J & Ławrynowicz, M. (2021). An evolutionary algorithm for joint bi-criteria location-scheduling problem.International Journal of Industrial Engineering Computations , 12(2), 159-176.
Refrences
Abounacer R., Rekik M., & Renaud J. (2014). An exact solution approach for multiobjective location–transportation problem for disaster response. Computers & Operations Research, 41, 83–93
Deb K. (2001). Multi-objective optimization using evolutionary algorithms (Vol. 16).
John Wiley & Sons
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Drezner, Z., & Suzuki, A. (2004). The big triangle small triangle method for the solution of nonconvex facility location problems. Operations Research, 52(1), 128-135
Elvikis D., Hamacher H., & Kalsch M. (2007). Scheduling and Location (ScheLoc): Makespan Problem with Variable Release Dates. Report in Wirtschaftsmathematik, Technische Universität Kaiserslautern, Fachbereich Mathematik
Elvikis, D., Hamacher, H. W., & Kalsch, M. T. (2009). Simultaneous scheduling and location (ScheLoc): the planar ScheLoc makespan problem. Journal of Scheduling, 12(4), 361-374.
Amideo, A. E., & Scaparra, M. P. (2017, September). A Scenario Planning Approach for Shelter Location and Evacuation Routing. In International Conference on Optimization and Decision Science (pp. 567-576). Springer, Cham.
Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on systems, man, and cybernetics, 16(1), 122-128.
Hennes H., Hamacher H. (2002). Integrated Scheduling and Location Models: Single Machine Makespan Problems. Report in Wirtschaftsmathematik, Univ., Fachbereich Mathematik
Hesser, J., & Männer, R. (1990, October). Towards an optimal mutation probability for genetic algorithms. In International Conference on Parallel Problem Solving from Nature (pp. 23-32). Springer, Berlin, Heidelberg.
Heßler, C., & Deghdak, K. (2017). Discrete parallel machine makespan ScheLoc problem. Journal of Combinatorial Optimization, 34(4), 1159-1186.
Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
Hou, Y. C., & Chang, Y. H. (2004). Short Paper. Journal of Information Science and Engineering, 20, 1019-1034.
Kalsch, M. T. (2009). Scheduling-location (ScheLoc) models, theory and algorithms. Verlag Dr. Hut.
Kalsch, M. T., & Drezner, Z. (2010). Solving scheduling and location problems in the plane simultaneously. Computers & operations research, 37(2), 256-264.
Jakob, K. R. A. R., & Pruzan, P. M. (1983). The simple plant location problem: survey and synthesis. European journal of operational research, 12, 36-81.
Laszczyk, M., & Myszkowski, P. B. (2019). Survey of quality measures for multi-objective optimization: Construction of complementary set of multi-objective quality measures. Swarm and Evolutionary Computation, 48, 109-133.
Lenstra, J. K., Kan, A. R., & Brucker, P. (1977). Complexity of machine scheduling problems. In Annals of discrete mathematics (Vol. 1, pp. 343-362). Elsevier.
Liu, M., Liu, X., Zhang, E., Chu, F., & Chu, C. (2019). Scenario-based heuristic to two-stage stochastic program for the parallel machine ScheLoc problem. International Journal of Production Research, 57(6), 1706-1723.
Ławrynowicz, M., & Józefczyk, J. (2019, August). A memetic algorithm for the discrete scheduling-location problem with unrelated machines. In 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR) (pp. 158-163). IEEE.
Oliver, I. M., Smith, D., & Holland, J. R. (1987). Study of permutation crossover operators on the traveling salesman problem. In Genetic algorithms and their applications: proceedings of the second International Conference on Genetic Algorithms: July 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, MA. Hillsdale, NJ: L. Erlhaum Associates, 1987.
Osman, M. S., & Ram, B. (2017). Routing and scheduling on evacuation path networks using centralized hybrid approach. Computers & Operations Research, 88, 332-339.
Panneton, F., L'ecuyer, P., & Matsumoto, M. (2006). Improved long-period generators based on linear recurrences modulo 2. ACM Transactions on Mathematical Software (TOMS), 32(1), 1-16.
Paraskevopoulos, D. C., Laporte, G., Repoussis, P. P., & Tarantilis, C. D. (2017). Resource constrained routing and scheduling: Review and research prospects. European Journal of Operational Research, 263(3), 737-754.
Pasandideh, S. H. R., Niaki, S. T. A., & Abdollahi, R. (2018). Modeling and solving a bi-objective joint replenishment-location problem under incremental discount: MOHSA and NSGA-II. Operational Research, 1-32.
Piasecki, B. (2018). Application of AI-based algorithms for joint problem of task scheduling and deployment of executors (in Polish). Master’s thesis, Wroclaw University of Science and Technology. Poland
Piasecki, B., & Józefczyk, J. (2018). Evolutionary algorithm for joint task scheduling and deployment of executors. Automation of Discrete Processes. Theory and Applications, Silesian University of Technology, 1, 169-178.
Pinedo, M. (2012). Scheduling: theory, algorithms and systems development (Vol. 29).
Springer-Verlag NY
Prodhon, C., & Prins, C. (2014). A survey of recent research on location-routing problems. European Journal of Operational Research, 238(1), 1-17.
Rajabzadeh, M., Ziaee, M., & Bozorgi-Amiri, A. (2016). Integrated approach in solving parallel machine scheduling and location (ScheLoc) problem. International Journal of Industrial Engineering Computations, 7(4), 573-584.
Rostami, M., & Bagherpour, M. (2020). A lagrangian relaxation algorithm for facility location of resource-constrained decentralized multi-project scheduling problems. Operational Research, 1-41.
Saldana, J., & Suznjevic, M. (2015). Qoe and latency issues in networked games.
Sbayti, H., & Mahmassani, H. S. (2006). Optimal scheduling of evacuation operations. Transportation Research Record, 1964(1), 238-246.
Scholz, D. (2011). Deterministic global optimization: geometric branch-and-bound methods and their applications (Vol. 63). Springer Science & Business Media.
Shahabi, M., Tafreshian, A., Unnikrishnan, A., & Boyles, S. D. (2018). Joint production–inventory–location problem with multi-variate normal demand. Transportation Research Part B: Methodological, 110, 60-78.
Syswerda, G. (1991). Scheduling optimization using genetic algorithms. Handbook of genetic algorithms.
Wesolkowski, S., Francetić, N., & Grant, S. C. (2014, July). TraDE: Training device selection via multi-objective optimization. In 2014 IEEE Congress on Evolutionary Computation (CEC) (pp. 2617-2624). IEEE.
Zitzler, E., Brockhoff, D., & Thiele, L. (2007, March). The hypervolume indicator revisited: On the design of Pareto-compliant indicators via weighted integration. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 862-876). Springer, Berlin, Heidelberg.
Deb K. (2001). Multi-objective optimization using evolutionary algorithms (Vol. 16).
John Wiley & Sons
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Drezner, Z., & Suzuki, A. (2004). The big triangle small triangle method for the solution of nonconvex facility location problems. Operations Research, 52(1), 128-135
Elvikis D., Hamacher H., & Kalsch M. (2007). Scheduling and Location (ScheLoc): Makespan Problem with Variable Release Dates. Report in Wirtschaftsmathematik, Technische Universität Kaiserslautern, Fachbereich Mathematik
Elvikis, D., Hamacher, H. W., & Kalsch, M. T. (2009). Simultaneous scheduling and location (ScheLoc): the planar ScheLoc makespan problem. Journal of Scheduling, 12(4), 361-374.
Amideo, A. E., & Scaparra, M. P. (2017, September). A Scenario Planning Approach for Shelter Location and Evacuation Routing. In International Conference on Optimization and Decision Science (pp. 567-576). Springer, Cham.
Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on systems, man, and cybernetics, 16(1), 122-128.
Hennes H., Hamacher H. (2002). Integrated Scheduling and Location Models: Single Machine Makespan Problems. Report in Wirtschaftsmathematik, Univ., Fachbereich Mathematik
Hesser, J., & Männer, R. (1990, October). Towards an optimal mutation probability for genetic algorithms. In International Conference on Parallel Problem Solving from Nature (pp. 23-32). Springer, Berlin, Heidelberg.
Heßler, C., & Deghdak, K. (2017). Discrete parallel machine makespan ScheLoc problem. Journal of Combinatorial Optimization, 34(4), 1159-1186.
Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
Hou, Y. C., & Chang, Y. H. (2004). Short Paper. Journal of Information Science and Engineering, 20, 1019-1034.
Kalsch, M. T. (2009). Scheduling-location (ScheLoc) models, theory and algorithms. Verlag Dr. Hut.
Kalsch, M. T., & Drezner, Z. (2010). Solving scheduling and location problems in the plane simultaneously. Computers & operations research, 37(2), 256-264.
Jakob, K. R. A. R., & Pruzan, P. M. (1983). The simple plant location problem: survey and synthesis. European journal of operational research, 12, 36-81.
Laszczyk, M., & Myszkowski, P. B. (2019). Survey of quality measures for multi-objective optimization: Construction of complementary set of multi-objective quality measures. Swarm and Evolutionary Computation, 48, 109-133.
Lenstra, J. K., Kan, A. R., & Brucker, P. (1977). Complexity of machine scheduling problems. In Annals of discrete mathematics (Vol. 1, pp. 343-362). Elsevier.
Liu, M., Liu, X., Zhang, E., Chu, F., & Chu, C. (2019). Scenario-based heuristic to two-stage stochastic program for the parallel machine ScheLoc problem. International Journal of Production Research, 57(6), 1706-1723.
Ławrynowicz, M., & Józefczyk, J. (2019, August). A memetic algorithm for the discrete scheduling-location problem with unrelated machines. In 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR) (pp. 158-163). IEEE.
Oliver, I. M., Smith, D., & Holland, J. R. (1987). Study of permutation crossover operators on the traveling salesman problem. In Genetic algorithms and their applications: proceedings of the second International Conference on Genetic Algorithms: July 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, MA. Hillsdale, NJ: L. Erlhaum Associates, 1987.
Osman, M. S., & Ram, B. (2017). Routing and scheduling on evacuation path networks using centralized hybrid approach. Computers & Operations Research, 88, 332-339.
Panneton, F., L'ecuyer, P., & Matsumoto, M. (2006). Improved long-period generators based on linear recurrences modulo 2. ACM Transactions on Mathematical Software (TOMS), 32(1), 1-16.
Paraskevopoulos, D. C., Laporte, G., Repoussis, P. P., & Tarantilis, C. D. (2017). Resource constrained routing and scheduling: Review and research prospects. European Journal of Operational Research, 263(3), 737-754.
Pasandideh, S. H. R., Niaki, S. T. A., & Abdollahi, R. (2018). Modeling and solving a bi-objective joint replenishment-location problem under incremental discount: MOHSA and NSGA-II. Operational Research, 1-32.
Piasecki, B. (2018). Application of AI-based algorithms for joint problem of task scheduling and deployment of executors (in Polish). Master’s thesis, Wroclaw University of Science and Technology. Poland
Piasecki, B., & Józefczyk, J. (2018). Evolutionary algorithm for joint task scheduling and deployment of executors. Automation of Discrete Processes. Theory and Applications, Silesian University of Technology, 1, 169-178.
Pinedo, M. (2012). Scheduling: theory, algorithms and systems development (Vol. 29).
Springer-Verlag NY
Prodhon, C., & Prins, C. (2014). A survey of recent research on location-routing problems. European Journal of Operational Research, 238(1), 1-17.
Rajabzadeh, M., Ziaee, M., & Bozorgi-Amiri, A. (2016). Integrated approach in solving parallel machine scheduling and location (ScheLoc) problem. International Journal of Industrial Engineering Computations, 7(4), 573-584.
Rostami, M., & Bagherpour, M. (2020). A lagrangian relaxation algorithm for facility location of resource-constrained decentralized multi-project scheduling problems. Operational Research, 1-41.
Saldana, J., & Suznjevic, M. (2015). Qoe and latency issues in networked games.
Sbayti, H., & Mahmassani, H. S. (2006). Optimal scheduling of evacuation operations. Transportation Research Record, 1964(1), 238-246.
Scholz, D. (2011). Deterministic global optimization: geometric branch-and-bound methods and their applications (Vol. 63). Springer Science & Business Media.
Shahabi, M., Tafreshian, A., Unnikrishnan, A., & Boyles, S. D. (2018). Joint production–inventory–location problem with multi-variate normal demand. Transportation Research Part B: Methodological, 110, 60-78.
Syswerda, G. (1991). Scheduling optimization using genetic algorithms. Handbook of genetic algorithms.
Wesolkowski, S., Francetić, N., & Grant, S. C. (2014, July). TraDE: Training device selection via multi-objective optimization. In 2014 IEEE Congress on Evolutionary Computation (CEC) (pp. 2617-2624). IEEE.
Zitzler, E., Brockhoff, D., & Thiele, L. (2007, March). The hypervolume indicator revisited: On the design of Pareto-compliant indicators via weighted integration. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 862-876). Springer, Berlin, Heidelberg.