Outranking-based multi-objective PSO for scheduling unrelated parallel machines with a freight industry-oriented application

Engineering Applications of Artificial Intelligence - Tập 108 - Trang 104556 - 2022
Gilberto Rivera1, Raúl Porras1, J. Patricia Sánchez-Solís1, Rogelio Florencia1, Vicente García1
1Universidad Autónoma de Ciudad Juárez, División Multidisciplinaria de Ciudad Universitaria, Av. José de Jesús Macías Delgado #18100, Cd. Juárez, Chihuahua, 32000, Mexico

Tóm tắt

Từ khóa


Tài liệu tham khảo

Åblad, 2021, Exact makespan minimization of unrelated parallel machines, Open J. Math. Optim., 2, 1, 10.5802/ojmo.4

Afzalirad, 2017, A realistic variant of bi-objective unrelated parallel machine scheduling problem: NSGA-II and MOACO approaches, Appl. Soft Comput., 50, 109, 10.1016/j.asoc.2016.10.039

Akbar, 2018, Scheduling for sustainable manufacturing: A review, J. Cleaner Prod., 205, 866, 10.1016/j.jclepro.2018.09.100

Alvarez, 2018, A new disaggregation preference method for new products design

Alvarez, 2018, Disaggregating preferences for a supplier development problem in the mexican aerospace industry, 1

Bai, 2010, Analysis of particle swarm optimization algorithm, Comput. Inf. Sci., 3, 180

Bhardwaj, 2020, Heart: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta-heuristic algorithms, Softw. - Pract. Exp., 50, 2231, 10.1002/spe.2890

Bitar, 2021, Unrelated parallel machine scheduling with new criteria: Complexity and models, Comput. Oper. Res., 132, 10.1016/j.cor.2021.105291

Chang, 2020, A framework for scheduling dependent programs on GPU architectures, J. Syst. Archit., 106, 10.1016/j.sysarc.2020.101712

Cheng, 2020, Learning-based metaheuristic for scheduling unrelated parallel machines with uncertain setup times, IEEE Access, 8, 74065, 10.1109/ACCESS.2020.2988274

Coello Coello, 2007

Cruz-Reyes, 2020, Hybrid evolutionary multi-objective optimisation using outranking-based ordinal classification methods, Swarm Evol. Comput., 54, 10.1016/j.swevo.2020.100652

Cruz-Reyes, 2018, Performance analysis of an a priori strategy to elicitate and incorporate preferences in multi-objective optimization evolutionary algorithms, 401

Doumpos, 2019, Preference disaggregation for multicriteria decision aiding: An overview and perspectives, 115, 10.1007/978-3-030-11482-4_4

Ezugwu, 2019, Enhanced symbiotic organisms search algorithm for unrelated parallel machines manufacturing scheduling with setup times, Knowl.-Based Syst., 172, 15, 10.1016/j.knosys.2019.02.005

Fanjul-Peyro, 2020, Models and an exact method for the unrelated parallel machine scheduling problem with setups and resources, Expert Syst. Appl.: X, 5

Fernandez, 2015, Hybrid metaheuristic approach for handling many objectives and decisions on partial support in project portfolio optimisation, Inform. Sci., 315, 102, 10.1016/j.ins.2015.03.064

Fernandez, 2019, An interval-based evolutionary approach to portfolio optimization of new product development projects, Math. Probl. Eng., 2019, 10.1155/2019/4065424

Fernandez, 2020, Using evolutionary computation to infer the decision maker’s preference model in presence of imperfect knowledge: A case study in portfolio optimization, Swarm Evol. Comput., 54, 10.1016/j.swevo.2020.100648

Fernandez, 2019, Inferring parameters of a relational system of preferences from assignment examples using an evolutionary algorithm, Technol. Econ. Dev. Econ., 2019, 693

Frausto-Solis, 2021, Chaotic multi-objective simulated annealing and threshold accepting for job shop scheduling problem, Math. Comput. Appl., 26, 1

Fuchigami, 2018, A survey of case studies in production scheduling: Analysis and perspectives, J. Comput. Sci., 25, 425, 10.1016/j.jocs.2017.06.004

Garavito-Hernández, 2019, A meta-heuristic based on the imperialist competitive algorithm (ICA) for solving hybrid flow shop (HFS) scheduling problem with unrelated parallel machines, J. Ind. Prod. Eng., 36, 362

Gilvaei, 2020, A novel hybrid optimization approach for reactive power dispatch problem considering voltage stability index, Eng. Appl. Artif. Intell., 96

Harbaoui, 2020, Tabu-search optimization approach for no-wait hybrid flow-shop scheduling with dedicated machines, Procedia Comput. Sci., 176, 706, 10.1016/j.procs.2020.09.043

Kayvanfar, 2017, An intelligent water drop algorithm to identical parallel machine scheduling with controllable processing times: a just-in-time approach, Comput. Appl. Math., 36, 159, 10.1007/s40314-015-0218-3

Kennedy, 1995, Particle swarm optimization, 1942

Kianpour, 2021, Optimising unrelated parallel machine scheduling in job shops with maximum allowable tardiness limit, Int. J. Ind. Syst. Eng., 37, 359

Kim, 2020, Insertion of new idle time for unrelated parallel machine scheduling with job splitting and machine breakdowns, Comput. Ind. Eng., 147, 10.1016/j.cie.2020.106630

Kurniawan, 2020, Mathematical models of energy-conscious bi-objective unrelated parallel machine scheduling, J. Tek. Ind., 21, 115

Lei, 2020, An imperialist competitive algorithm with memory for distributed unrelated parallel machines scheduling, Int. J. Prod. Res., 58, 597, 10.1080/00207543.2019.1598596

Lin, 2016, Multi-objective unrelated parallel machine scheduling: a tabu-enhanced iterated Pareto greedy algorithm, Int. J. Prod. Res., 54, 1110, 10.1080/00207543.2015.1047981

Lu, 2018, A hybrid ABC-TS algorithm for the unrelated parallel-batching machines scheduling problem with deteriorating jobs and maintenance activity, Appl. Soft Comput., 66, 168, 10.1016/j.asoc.2018.02.018

Manupati, 2017, A hybrid multi-objective evolutionary algorithm approach for handling sequence-and machine-dependent set-up times in unrelated parallel machine scheduling problem, Sādhanā, 42, 391, 10.1007/s12046-017-0611-2

Meng, 2019, Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines, Int. J. Prod. Res., 57, 1119, 10.1080/00207543.2018.1501166

Murakami, 2010, A method for generating robust schedule under uncertainty in processing time, Int. J. Biomed. Soft Comput. Hum. Sci.: Off. J. Biomed. Fuzzy Syst. Assoc., 15, 45

Naderi, 2019, An efficient particle swarm optimization algorithm to solve optimal power flow problem integrated with FACTS devices, Appl. Soft Comput., 80, 243, 10.1016/j.asoc.2019.04.012

Naderi, 2021, A novel hybrid self-adaptive heuristic algorithm to handle single- and multi-objective optimal power flow problems, Int. J. Electr. Power Energy Syst., 125, 10.1016/j.ijepes.2020.106492

Naderi, 2020, Transmission expansion planning integrated with wind farms: A review, comparative study, and a novel profound search approach, Int. J. Electr. Power Energy Syst., 115, 10.1016/j.ijepes.2019.105460

Ojstersek, 2020, Multi-objective optimization of production scheduling with evolutionary computation: a review, Int. J. Ind. Eng. Comput., 11, 359

Pouria, 2021, A bi-objective home health care routing and scheduling model with considering nurse downgrading costs, Int. J. Environ. Res. Public Health, 18

Ramos-Figueroa, 2020, Parallel-machine scheduling problem: An experimental study of instances difficulty and algorithms performance, 13

Rangel-Valdez, 2018, Robustness analysis of an outranking model parameters’ elicitation method in the presence of noisy examples, Math. Probl. Eng., 2018, 10.1155/2018/2157937

Rangel-Valdez, 2015, Multiobjective optimization approach for preference-disaggregation analysis under effects of intensity, 451

Rivera, 2020, Genetic algorithm for scheduling optimization considering heterogeneous containers: A real-world case study, Axioms, 9, 27, 10.3390/axioms9010027

Roy, 1996, The European school of MCDA: Emergence, basic features and current works, J. Multi-Criteria Decis. Anal., 5, 22, 10.1002/(SICI)1099-1360(199603)5:1<22::AID-MCDA93>3.0.CO;2-F

Shabtay, 2018, Single machine scheduling with controllable processing times and an unavailability period to minimize the makespan, Int. J. Prod. Econ., 198, 191, 10.1016/j.ijpe.2017.12.025

Shahvari, 2017, An enhanced tabu search algorithm to minimize a bi-criteria objective in batching and scheduling problems on unrelated-parallel machines with desired lower bounds on batch sizes, Comput. Oper. Res., 77, 154, 10.1016/j.cor.2016.07.021

Tirkolaee, 2020, A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect, Comput. Ind. Eng., 149, 10.1016/j.cie.2020.106790

Wang, 2019, Effective heuristic for large-scale unrelated parallel machines scheduling problems, Omega, 83, 261, 10.1016/j.omega.2018.07.005

Wang, 2018, Bi-objective optimal scheduling with raw material’s shelf-life constraints in unrelated parallel machines production, IEEE Trans. Syst. Man Cybern.: Syst., 50, 4598, 10.1109/TSMC.2018.2855700

Wojakowski, 2014, The classification of scheduling problems under production uncertainty, Res. Logist. Prod., 4, 245

Yan, 2020, A novel k-MPSO clustering algorithm for the construction of typical driving cycles, IEEE Access, 8, 64028, 10.1109/ACCESS.2020.2985207

Yepes-Borrero, 2021, Bi-objective parallel machine scheduling with additional resources during setups, European J. Oper. Res., 292, 443, 10.1016/j.ejor.2020.10.052

Yin, 2020, Delay, throughput and emission tradeoffs in airport runway scheduling with uncertainty considerations, Netw. Spat. Econ., 21, 85, 10.1007/s11067-020-09508-3

Zhang, 2021, Probability-optimal leader comprehensive learning particle swarm optimization with Bayesian iteration, Appl. Soft Comput., 103, 10.1016/j.asoc.2021.107132

Zhao, 2018, Two-generation Pareto ant colony algorithm for multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines, J. Intell. Manuf., 29, 93, 10.1007/s10845-015-1091-z

Zhou, 2021, Energy-awareness scheduling of unrelated parallel machine scheduling problems with multiple resource constraints, Int. J. Oper. Res., 41, 196, 10.1504/IJOR.2021.115623

Zhu, 2019, A novel multi-objective scheduling method for energy based unrelated parallel machines with auxiliary resource constraints, IEEE Access, 7, 168688, 10.1109/ACCESS.2019.2954601