An adaptive multi-objective algorithm based on decomposition and large neighborhood search for a green machine scheduling problem
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Patrick Siarry | Marcone Jamilson Freitas Souza | Frederico G. Guimarães | Luciano Perdigão Cota | Ivan Reinaldo Meneghini | Roberto G. Ribeiro | Fernando Bernardes de Oliveira | P. Siarry | F. Guimarães | M. Souza | F. B. Oliveira | L. P. Cota | I. R. Meneghini
[1] Xiaodong Wang,et al. Bi-objective identical parallel machine scheduling to minimize total energy consumption and makespan , 2018, Journal of Cleaner Production.
[2] Dipti Srinivasan,et al. A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition , 2017, IEEE Transactions on Evolutionary Computation.
[3] John Edwin Raja Dhas,et al. Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools , 2013, Appl. Soft Comput..
[4] H. Scheffé. Experiments with Mixtures , 1958 .
[5] Pierre Lopez,et al. The energy scheduling problem: Industrial case-study and constraint propagation techniques , 2013 .
[6] Sanja Petrovic,et al. A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance , 2016 .
[7] Ali Allahverdi,et al. The third comprehensive survey on scheduling problems with setup times/costs , 2015, Eur. J. Oper. Res..
[8] Marcone J. F. Souza,et al. AIV: A Heuristic Algorithm based on Iterated Local Search and Variable Neighborhood Descent for Solving the Unrelated Parallel Machine Scheduling Problem with Setup Times , 2014, ICEIS.
[9] Qingfu Zhang,et al. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.
[10] Joshua D. Knowles,et al. On metrics for comparing nondominated sets , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).
[11] Frederico G. Guimarães,et al. Bi-criteria formulation for green scheduling with unrelated parallel machines with sequence-dependent setup times , 2021, Int. Trans. Oper. Res..
[12] Patrick Siarry,et al. A survey on optimization metaheuristics , 2013, Inf. Sci..
[13] A. Sadegheih. Scheduling problem using genetic algorithm, simulated annealing and the effects of parameter values on GA performance , 2006 .
[14] Ghaith Rabadi,et al. Heuristics for the Unrelated Parallel Machine Scheduling Problem with Setup Times , 2006, J. Intell. Manuf..
[15] Lothar Thiele,et al. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..
[16] Kumpati S. Narendra,et al. Learning automata - an introduction , 1989 .
[17] Christian Blum,et al. Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.
[18] Seyed Taghi Akhavan Niaki,et al. Bi-objective green scheduling in uniform parallel machine environments , 2019, Journal of Cleaner Production.
[19] Michael Pinedo,et al. Scheduling: Theory, Algorithms, and Systems , 1994 .
[20] M. J. F. Souza,et al. A hybrid heuristic algorithm for the open-pit-mining operational planning problem , 2010, Eur. J. Oper. Res..
[21] Yassine Ouazene,et al. Production scheduling optimisation with machine state and time-dependent energy costs , 2018, Int. J. Prod. Res..
[22] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[23] Marcone J. F. Souza,et al. Solving the Unrelated Parallel Machine Scheduling Problem with Setup Times by Efficient Algorithms Based on Iterated Local Search , 2014, ICEIS.
[24] Alcione de Paiva Oliveira,et al. Multi-objective Variable Neighborhood Search Algorithms for a Single Machine Scheduling Problem with Distinct due Windows , 2011, CLEI Selected Papers.
[25] J. Christopher Beck,et al. Decomposition Methods for the Parallel Machine Scheduling Problem with Setups , 2016, INFORMS J. Comput..
[26] S. Shapiro,et al. An Analysis of Variance Test for Normality (Complete Samples) , 1965 .
[27] A. J. Clewett,et al. Introduction to sequencing and scheduling , 1974 .
[28] Marco Laumanns,et al. Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..
[29] Hao Zhang,et al. Energy-conscious flow shop scheduling under time-of-use electricity tariffs , 2014 .
[30] Siti Zawiah Md Dawal,et al. Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling , 2016, Appl. Soft Comput..
[31] Frederico G. Guimarães,et al. An Adaptive Large Neighborhood Search with Learning Automata for the Unrelated Parallel Machine Scheduling Problem , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).
[32] David Pisinger,et al. An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows , 2006, Transp. Sci..
[33] Alice Yalaoui,et al. Complexity analysis of energy-efficient single machine scheduling problems , 2019, Operations Research Perspectives.
[34] Martin Josef Geiger,et al. Decision support for multi-objective flow shop scheduling by the Pareto Iterated Local Search methodology , 2011, Comput. Ind. Eng..
[35] Tom Van Woensel,et al. An adaptive large neighborhood search heuristic for the Pickup and Delivery Problem with Time Windows and Scheduled Lines , 2016, Comput. Oper. Res..
[36] A quality metric for multi-objective optimization based on Hierarchical Clustering Techniques , 2009, 2009 IEEE Congress on Evolutionary Computation.
[37] Mohammad Reza Meybodi,et al. Multi swarm bare bones particle swarm optimization with distribution adaption , 2016, Appl. Soft Comput..
[38] Glaydston Mattos Ribeiro,et al. An adaptive large neighborhood search heuristic for the cumulative capacitated vehicle routing problem , 2012, Comput. Oper. Res..
[39] Kumpati S. Narendra,et al. Learning Automata - A Survey , 1974, IEEE Trans. Syst. Man Cybern..
[40] Ye Tian,et al. PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] , 2017, IEEE Computational Intelligence Magazine.
[41] Kenneth R. Baker,et al. Principles of Sequencing and Scheduling , 2018 .
[42] Paul Shaw,et al. Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems , 1998, CP.
[43] Marco Laumanns,et al. SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .
[44] Shijin Wang,et al. Bi-objective optimization of a single machine batch scheduling problem with energy cost consideration , 2016 .
[45] Mir Mohammad Alipour,et al. A Learning Automata based Algorithm for Solving Traveling Salesman Problem improved by Frequency-based Pruning , 2012 .
[46] Qingfu Zhang,et al. Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..
[47] Kalyanmoy Deb,et al. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.
[48] S. Afshin Mansouri,et al. Minimizing energy consumption and makespan in a two-machine flowshop scheduling problem , 2016, J. Oper. Res. Soc..
[49] Richard M. Karp,et al. Reducibility Among Combinatorial Problems , 1972, 50 Years of Integer Programming.
[50] Ada Che,et al. A memetic differential evolution algorithm for energy-efficient parallel machine scheduling , 2019, Omega.
[51] Gilbert Laporte,et al. Scheduling identical parallel machines with tooling constraints , 2015, Eur. J. Oper. Res..
[52] Rubén Ruiz,et al. A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times , 2011, Eur. J. Oper. Res..
[53] E.L. Lawler,et al. Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey , 1977 .
[54] Helena Ramalhinho Dias Lourenço,et al. Iterated Local Search , 2001, Handbook of Metaheuristics.
[55] S. Afshin Mansouri,et al. Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption , 2016, Eur. J. Oper. Res..
[56] Cheng Wu,et al. Carbon-efficient scheduling of flow shops by multi-objective optimization , 2016, Eur. J. Oper. Res..
[57] Jacques Teghem,et al. Two-phase Pareto local search for the biobjective traveling salesman problem , 2010, J. Heuristics.
[58] J. Paulo Davim,et al. Computational Methods for Application in Industry 4.0 , 2018, SpringerBriefs in Applied Sciences and Technology.