How to cite this paper
Farmand, N., Zarei, H & Rasti-Barzoki, M. (2021). Two meta-heuristic algorithms for optimizing a multi-objective supply chain scheduling problem in an identical parallel machines environment.International Journal of Industrial Engineering Computations , 12(3), 249-272.
Refrences
Aminzadegan, S., Tamannaei, M., & Rasti-Barzoki, M. (2019). Multi-agent supply chain scheduling problem by considering resource allocation and transportation. Computers & Industrial Engineering, 137, 106003.
Assarzadegan, P., & Rasti-Barzoki, M. (2016). Minimizing sum of the due date assignment costs, maximum tardiness and distribution costs in a supply chain scheduling problem. Applied Soft Computing, 47, 343-356.
Attar, S., Mohammadi, M., Tavakkoli-Moghaddam, R., & Yaghoubi, S. (2014). Solving a new multi-objective hybrid flexible flowshop problem with limited waiting times and machine-sequence-dependent set-up time constraints. International Journal of Computer Integrated Manufacturing, 27(5), 450-469.
Bose, A., Biswas, T., & Kuila, P. (2019). A Novel Genetic Algorithm Based Scheduling for Multi-core Systems Smart Innovations in Communication and Computational Sciences (pp. 45-54): Springer.
Cakici, E., Mason, S. J., Geismar, H. N., & Fowler, J. W. (2014). Scheduling parallel machines with single vehicle delivery. Journal of Heuristics, 20(5), 511-537.
Cakici, E., Mason, S. J., & Kurz, M. E. (2012). Multi-objective analysis of an integrated supply chain scheduling problem. International Journal of Production Research, 50(10), 2624-2638.
Chang, Y.-C., Li, V. C., & Chiang, C.-J. (2014). An ant colony optimization heuristic for an integrated production and distribution scheduling problem. Engineering Optimization, 46(4), 503-520.
Chen, Y., Lu, L., & Yuan, J. (2015). Preemptive scheduling on identical machines with delivery coordination to minimize the maximum delivery completion time. Theoretical Computer Science, 583, 67-77.
Chen, Y., Lu, L., & Yuan, J. (2016). Two-stage scheduling on identical machines with assignable delivery times to minimize the maximum delivery completion time. Theoretical Computer Science, 622, 45-65.
Chen, Z.-L. (2010). Integrated production and outbound distribution scheduling: review and extensions. Operations research, 58(1), 130-148.
Chen, Z.-L., & Vairaktarakis, G. L. (2005). Integrated scheduling of production and distribution operations. Management Science, 51(4), 614-628.
Cheng, T., & Sin, C. (1990). A state-of-the-art review of parallel-machine scheduling research. European Journal of Operational Research, 47(3), 271-292.
Cheng, T. C. E., & Kahlbacher, H. G. (1993). Single-machine scheduling to minimize earliness and number of tardy jobs. Journal of optimization theory and applications, 77(3), 563-573.
Coello, C. A. C. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer methods in applied mechanics and engineering, 191(11-12), 1245-1287.
Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (Vol. 5): Springer, 79-104.
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 8(3), 256-279.
Coello, C. C. (2006). Evolutionary multi-objective optimization: a historical view of the field. IEEE computational intelligence magazine, 1(1), 28-36.
Deb, K. (2014). Multi-objective optimization Search methodologies (pp. 403-449): Springer.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on.
Ekici, A., Elyasi, M., Özener, O. Ö., & Sarıkaya, M. B. (2019). An application of unrelated parallel machine scheduling with sequence-dependent setups at Vestel Electronics. Computers & Operations Research, 111, 130-140.
Ganji, M., Kazemipoor, H., Molana, S. M. H., & Sajadi, S. M. (2020). A green multi-objective integrated scheduling of production and distribution with heterogeneous fleet vehicle routing and time windows. Journal of Cleaner Production, 259, 120824.
Gao, S., Qi, L., & Lei, L. (2015). Integrated batch production and distribution scheduling with limited vehicle capacity. International Journal of Production Economics, 160, 13-25.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
Guo, Z., Zhang, D., Leung, S. Y.-S., & Shi, L. (2016). A bi-level evolutionary optimization approach for integrated production and transportation scheduling. Applied Soft Computing, 42, 215-228.
Hamidinia, A., Khakabimamaghani, S., Mazdeh, M. M., & Jafari, M. (2012). A genetic algorithm for minimizing total tardiness/earliness of weighted jobs in a batched delivery system. Computers & Industrial Engineering, 62(1), 29-38.
Hassanzadeh, A., Rasti-Barzoki, M., & Khosroshahi, H. (2016). Two new meta-heuristics for a bi-objective supply chain scheduling problem in flow-shop environment. Applied Soft Computing, 49, 335-351.
Ho, J. C., & Chang, Y.-L. (1995). Minimizing the number of tardy jobs for m parallel machines. European Journal of Operational Research, 84(2), 343-355.
Jiang, L., Pei, J., Liu, X., Pardalos, P. M., Yang, Y., & Qian, X. (2017). Uniform parallel batch machines scheduling considering transportation using a hybrid DPSO-GA algorithm. The International Journal of Advanced Manufacturing Technology, 89(5-8), 1887-1900.
Joo, C. M., & Kim, B. S. (2017). Rule-based meta-heuristics for integrated scheduling of unrelated parallel machines, batches, and heterogeneous delivery trucks. Applied Soft Computing, 53, 457-476.
Lenstra, J. K., Kan, A. R., & Brucker, P. (1977). Complexity of machine scheduling problems Annals of discrete mathematics (Vol. 1, pp. 343-362): Elsevier.
Lin, B., & Jeng, A. (2004). Parallel-machine batch scheduling to minimize the maximum lateness and the number of tardy jobs. International Journal of Production Economics, 91(2), 121-134.
Liu, L., Li, W., Li, K., & Zou, X. (2020). A coordinated production and transportation scheduling problem with minimum sum of order delivery times. Journal of Heuristics, 26(1), 33-58.
Liu, P., & Lu, X. (2016). Integrated production and job delivery scheduling with an availability constraint. International Journal of Production Economics, 176, 1-6.
Munoz-Villamizar, A., Santos, J., Montoya-Torres, J., & Alvaréz, M. (2019). Improving effectiveness of parallel machine scheduling with earliness and tardiness costs: A case study. International Journal of Industrial Engineering Computations, 10(3), 375-392.
Nikabadi, M., & Naderi, R. (2016). A hybrid algorithm for unrelated parallel machines scheduling. International Journal of Industrial Engineering Computations, 7(4), 681-702.
Ojstersek, R., Brezocnik, M., & Buchmeister, B. (2020). Multi-objective optimization of production scheduling with evolutionary computation: A review. International Journal of Industrial Engineering Computations, 11(3), 359-376.
Piroozfard, H., Wong, K. Y., & Wong, W. P. (2018). Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling, 128, 267-283.
Potts, C. N. (1980). Analysis of a heuristic for one machine sequencing with release dates and delivery times. Operations Research, 28(6), 1436-1441.
Raghavan, V. A., Yoon, S. W., & Srihari, K. (2018). A Modified Genetic Algorithm Approach to Minimize Total Weighted Tardiness with Stochastic Rework and Reprocessing Times. Computers & Industrial Engineering, 123, 42-53.
Rajkanth, R., Rajendran, C., & Ziegler, H. (2017). Heuristics to minimize the completion time variance of jobs on a single machine and on identical parallel machines. The International Journal of Advanced Manufacturing Technology, 88(5-8), 1923-1936.
Saeidi, S. (2016). A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II. International Journal of Information Technology and Computer Science (IJITCS), 8(8), 43-49.
Schaller, J. E. (2014). Minimizing total tardiness for scheduling identical parallel machines with family setups. Computers & Industrial Engineering, 72, 274-281.
Shahidi-Zadeh, B., Tavakkoli-Moghaddam, R., Taheri-Moghadam, A., & Rastgar, I. (2017). Solving a bi-objective unrelated parallel batch processing machines scheduling problem: a comparison study. Computers & Operations Research, 88, 71-90.
Sheikh, S., Komaki, G., & Kayvanfar, V. (2018). Multi objective two-stage assembly flow shop with release time. Computers & Industrial Engineering, 124, 276-292.
Shen, J. (2019). An uncertain parallel machine problem with deterioration and learning effect. Computational and Applied Mathematics, 38(1), 3.
Shim, S.-O., & Kim, Y.-D. (2008). A branch and bound algorithm for an identical parallel machine scheduling problem with a job splitting property. Computers & Operations Research, 35(3), 863-875.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2004). Managing the Supply Chain: Definitive Guide: Tata McGraw-Hill Education.
Sivrikaya-Şerifoǧlu, F., & Ulusoy, G. (1999). Parallel machine scheduling with earliness and tardiness penalties. Computers & Operations Research, 26(8), 773-787.
Thomas, D. J., & Griffin, P. M. (1996). Coordinated supply chain management. European journal of operational research, 94(1), 1-15.
Tyagi, R., & Gupta, S. K. (2018). A Survey on Scheduling Algorithms for Parallel and Distributed Systems Silicon Photonics & High Performance Computing (pp. 51-64): Springer.
Ullrich, C. A. (2013). Integrated machine scheduling and vehicle routing with time windows. European Journal of Operational Research, 227(1), 152-165.
Wang, D., Zhu, J., Wei, X., Cheng, T., Yin, Y., & Wang, Y. (2019). Integrated production and multiple trips vehicle routing with time windows and uncertain travel times. Computers & Operations Research, 103, 1-12.
Wang, D. Y., Grunder, O., & Moudni, A. E. (2014). Integrated scheduling of production and distribution operations: a review. International Journal of Industrial and Systems Engineering, 19(1), 94-122.
Wang, G., & Cheng, T. E. (2000). Parallel machine scheduling with batch delivery costs. International Journal of Production Economics, 68(2), 177-183.
Wang, S., & Liu, M. (2015). Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning. Journal of Manufacturing Systems, 37, 182-192.
Wang, S., Wu, R., Chu, F., & Yu, J. (2029). Variable neighborhood search-based methods for integrated hybrid flow shop scheduling with distribution. Soft Computing, 24, 8917–8936.
Wu, X., & Che, A. (2019). A memetic differential evolution algorithm for energy-efficient parallel machine scheduling. Omega, 82, 155-165.
Zarei, H., & Rasti-Barzoki, M. (2019). Mathematical programming and three metaheuristic algorithms for a bi-objective supply chain scheduling problem. Neural Computing and Applications, 31, 9073–9093.
Zhou, S., Li, X., Du, N., Pang, Y., & Chen, H. (2018). A multi-objective differential evolution algorithm for parallel batch processing machine scheduling considering electricity consumption cost. Computers & Operations Research, 96, 55-68.
Assarzadegan, P., & Rasti-Barzoki, M. (2016). Minimizing sum of the due date assignment costs, maximum tardiness and distribution costs in a supply chain scheduling problem. Applied Soft Computing, 47, 343-356.
Attar, S., Mohammadi, M., Tavakkoli-Moghaddam, R., & Yaghoubi, S. (2014). Solving a new multi-objective hybrid flexible flowshop problem with limited waiting times and machine-sequence-dependent set-up time constraints. International Journal of Computer Integrated Manufacturing, 27(5), 450-469.
Bose, A., Biswas, T., & Kuila, P. (2019). A Novel Genetic Algorithm Based Scheduling for Multi-core Systems Smart Innovations in Communication and Computational Sciences (pp. 45-54): Springer.
Cakici, E., Mason, S. J., Geismar, H. N., & Fowler, J. W. (2014). Scheduling parallel machines with single vehicle delivery. Journal of Heuristics, 20(5), 511-537.
Cakici, E., Mason, S. J., & Kurz, M. E. (2012). Multi-objective analysis of an integrated supply chain scheduling problem. International Journal of Production Research, 50(10), 2624-2638.
Chang, Y.-C., Li, V. C., & Chiang, C.-J. (2014). An ant colony optimization heuristic for an integrated production and distribution scheduling problem. Engineering Optimization, 46(4), 503-520.
Chen, Y., Lu, L., & Yuan, J. (2015). Preemptive scheduling on identical machines with delivery coordination to minimize the maximum delivery completion time. Theoretical Computer Science, 583, 67-77.
Chen, Y., Lu, L., & Yuan, J. (2016). Two-stage scheduling on identical machines with assignable delivery times to minimize the maximum delivery completion time. Theoretical Computer Science, 622, 45-65.
Chen, Z.-L. (2010). Integrated production and outbound distribution scheduling: review and extensions. Operations research, 58(1), 130-148.
Chen, Z.-L., & Vairaktarakis, G. L. (2005). Integrated scheduling of production and distribution operations. Management Science, 51(4), 614-628.
Cheng, T., & Sin, C. (1990). A state-of-the-art review of parallel-machine scheduling research. European Journal of Operational Research, 47(3), 271-292.
Cheng, T. C. E., & Kahlbacher, H. G. (1993). Single-machine scheduling to minimize earliness and number of tardy jobs. Journal of optimization theory and applications, 77(3), 563-573.
Coello, C. A. C. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer methods in applied mechanics and engineering, 191(11-12), 1245-1287.
Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (Vol. 5): Springer, 79-104.
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 8(3), 256-279.
Coello, C. C. (2006). Evolutionary multi-objective optimization: a historical view of the field. IEEE computational intelligence magazine, 1(1), 28-36.
Deb, K. (2014). Multi-objective optimization Search methodologies (pp. 403-449): Springer.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on.
Ekici, A., Elyasi, M., Özener, O. Ö., & Sarıkaya, M. B. (2019). An application of unrelated parallel machine scheduling with sequence-dependent setups at Vestel Electronics. Computers & Operations Research, 111, 130-140.
Ganji, M., Kazemipoor, H., Molana, S. M. H., & Sajadi, S. M. (2020). A green multi-objective integrated scheduling of production and distribution with heterogeneous fleet vehicle routing and time windows. Journal of Cleaner Production, 259, 120824.
Gao, S., Qi, L., & Lei, L. (2015). Integrated batch production and distribution scheduling with limited vehicle capacity. International Journal of Production Economics, 160, 13-25.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
Guo, Z., Zhang, D., Leung, S. Y.-S., & Shi, L. (2016). A bi-level evolutionary optimization approach for integrated production and transportation scheduling. Applied Soft Computing, 42, 215-228.
Hamidinia, A., Khakabimamaghani, S., Mazdeh, M. M., & Jafari, M. (2012). A genetic algorithm for minimizing total tardiness/earliness of weighted jobs in a batched delivery system. Computers & Industrial Engineering, 62(1), 29-38.
Hassanzadeh, A., Rasti-Barzoki, M., & Khosroshahi, H. (2016). Two new meta-heuristics for a bi-objective supply chain scheduling problem in flow-shop environment. Applied Soft Computing, 49, 335-351.
Ho, J. C., & Chang, Y.-L. (1995). Minimizing the number of tardy jobs for m parallel machines. European Journal of Operational Research, 84(2), 343-355.
Jiang, L., Pei, J., Liu, X., Pardalos, P. M., Yang, Y., & Qian, X. (2017). Uniform parallel batch machines scheduling considering transportation using a hybrid DPSO-GA algorithm. The International Journal of Advanced Manufacturing Technology, 89(5-8), 1887-1900.
Joo, C. M., & Kim, B. S. (2017). Rule-based meta-heuristics for integrated scheduling of unrelated parallel machines, batches, and heterogeneous delivery trucks. Applied Soft Computing, 53, 457-476.
Lenstra, J. K., Kan, A. R., & Brucker, P. (1977). Complexity of machine scheduling problems Annals of discrete mathematics (Vol. 1, pp. 343-362): Elsevier.
Lin, B., & Jeng, A. (2004). Parallel-machine batch scheduling to minimize the maximum lateness and the number of tardy jobs. International Journal of Production Economics, 91(2), 121-134.
Liu, L., Li, W., Li, K., & Zou, X. (2020). A coordinated production and transportation scheduling problem with minimum sum of order delivery times. Journal of Heuristics, 26(1), 33-58.
Liu, P., & Lu, X. (2016). Integrated production and job delivery scheduling with an availability constraint. International Journal of Production Economics, 176, 1-6.
Munoz-Villamizar, A., Santos, J., Montoya-Torres, J., & Alvaréz, M. (2019). Improving effectiveness of parallel machine scheduling with earliness and tardiness costs: A case study. International Journal of Industrial Engineering Computations, 10(3), 375-392.
Nikabadi, M., & Naderi, R. (2016). A hybrid algorithm for unrelated parallel machines scheduling. International Journal of Industrial Engineering Computations, 7(4), 681-702.
Ojstersek, R., Brezocnik, M., & Buchmeister, B. (2020). Multi-objective optimization of production scheduling with evolutionary computation: A review. International Journal of Industrial Engineering Computations, 11(3), 359-376.
Piroozfard, H., Wong, K. Y., & Wong, W. P. (2018). Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling, 128, 267-283.
Potts, C. N. (1980). Analysis of a heuristic for one machine sequencing with release dates and delivery times. Operations Research, 28(6), 1436-1441.
Raghavan, V. A., Yoon, S. W., & Srihari, K. (2018). A Modified Genetic Algorithm Approach to Minimize Total Weighted Tardiness with Stochastic Rework and Reprocessing Times. Computers & Industrial Engineering, 123, 42-53.
Rajkanth, R., Rajendran, C., & Ziegler, H. (2017). Heuristics to minimize the completion time variance of jobs on a single machine and on identical parallel machines. The International Journal of Advanced Manufacturing Technology, 88(5-8), 1923-1936.
Saeidi, S. (2016). A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II. International Journal of Information Technology and Computer Science (IJITCS), 8(8), 43-49.
Schaller, J. E. (2014). Minimizing total tardiness for scheduling identical parallel machines with family setups. Computers & Industrial Engineering, 72, 274-281.
Shahidi-Zadeh, B., Tavakkoli-Moghaddam, R., Taheri-Moghadam, A., & Rastgar, I. (2017). Solving a bi-objective unrelated parallel batch processing machines scheduling problem: a comparison study. Computers & Operations Research, 88, 71-90.
Sheikh, S., Komaki, G., & Kayvanfar, V. (2018). Multi objective two-stage assembly flow shop with release time. Computers & Industrial Engineering, 124, 276-292.
Shen, J. (2019). An uncertain parallel machine problem with deterioration and learning effect. Computational and Applied Mathematics, 38(1), 3.
Shim, S.-O., & Kim, Y.-D. (2008). A branch and bound algorithm for an identical parallel machine scheduling problem with a job splitting property. Computers & Operations Research, 35(3), 863-875.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2004). Managing the Supply Chain: Definitive Guide: Tata McGraw-Hill Education.
Sivrikaya-Şerifoǧlu, F., & Ulusoy, G. (1999). Parallel machine scheduling with earliness and tardiness penalties. Computers & Operations Research, 26(8), 773-787.
Thomas, D. J., & Griffin, P. M. (1996). Coordinated supply chain management. European journal of operational research, 94(1), 1-15.
Tyagi, R., & Gupta, S. K. (2018). A Survey on Scheduling Algorithms for Parallel and Distributed Systems Silicon Photonics & High Performance Computing (pp. 51-64): Springer.
Ullrich, C. A. (2013). Integrated machine scheduling and vehicle routing with time windows. European Journal of Operational Research, 227(1), 152-165.
Wang, D., Zhu, J., Wei, X., Cheng, T., Yin, Y., & Wang, Y. (2019). Integrated production and multiple trips vehicle routing with time windows and uncertain travel times. Computers & Operations Research, 103, 1-12.
Wang, D. Y., Grunder, O., & Moudni, A. E. (2014). Integrated scheduling of production and distribution operations: a review. International Journal of Industrial and Systems Engineering, 19(1), 94-122.
Wang, G., & Cheng, T. E. (2000). Parallel machine scheduling with batch delivery costs. International Journal of Production Economics, 68(2), 177-183.
Wang, S., & Liu, M. (2015). Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning. Journal of Manufacturing Systems, 37, 182-192.
Wang, S., Wu, R., Chu, F., & Yu, J. (2029). Variable neighborhood search-based methods for integrated hybrid flow shop scheduling with distribution. Soft Computing, 24, 8917–8936.
Wu, X., & Che, A. (2019). A memetic differential evolution algorithm for energy-efficient parallel machine scheduling. Omega, 82, 155-165.
Zarei, H., & Rasti-Barzoki, M. (2019). Mathematical programming and three metaheuristic algorithms for a bi-objective supply chain scheduling problem. Neural Computing and Applications, 31, 9073–9093.
Zhou, S., Li, X., Du, N., Pang, Y., & Chen, H. (2018). A multi-objective differential evolution algorithm for parallel batch processing machine scheduling considering electricity consumption cost. Computers & Operations Research, 96, 55-68.