A multi-objective pigeon inspired optimization algorithm for fuzzy production scheduling problem considering mould maintenance
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摘要
The fuzzy production scheduling problem considering mould maintenance(FPSP-MM) is studied. The processing time and the maintenance time are represented by triangular fuzzy numbers. When tasks are executed based on the sequence provided by the fuzzy schedule, the real duration of each task needs to be known so the posteriori solution with deterministic processing times can be obtained. Therefore, the concept of the schedule robustness needs to be considered for the fuzzy problem. The robustness is considered as the optimization objective except for the fuzzy makespan in this research. To optimize these two objective functions, a multi-objective pigeon inspired optimization(MOPIO) algorithm is developed. To extend the pigeon inspired optimization(PIO) algorithm from the single-objective case to the multi-objective case, nondominated solutions are used as candidates for the leader pigeon designation and a special crowding distance is used to ensure a good distribution of solutions in both the objective space and the corresponding decision space. Furthermore, an index-based ring topology is used to manage the convergence speed. Numerical experiments on a variety of simulated scenarios show the excellent efficiency and effectiveness of the proposed MOPIO algorithm by comparing it with other algorithms.
The fuzzy production scheduling problem considering mould maintenance(FPSP-MM) is studied. The processing time and the maintenance time are represented by triangular fuzzy numbers. When tasks are executed based on the sequence provided by the fuzzy schedule, the real duration of each task needs to be known so the posteriori solution with deterministic processing times can be obtained. Therefore, the concept of the schedule robustness needs to be considered for the fuzzy problem. The robustness is considered as the optimization objective except for the fuzzy makespan in this research. To optimize these two objective functions, a multi-objective pigeon inspired optimization(MOPIO) algorithm is developed. To extend the pigeon inspired optimization(PIO) algorithm from the single-objective case to the multi-objective case, nondominated solutions are used as candidates for the leader pigeon designation and a special crowding distance is used to ensure a good distribution of solutions in both the objective space and the corresponding decision space. Furthermore, an index-based ring topology is used to manage the convergence speed. Numerical experiments on a variety of simulated scenarios show the excellent efficiency and effectiveness of the proposed MOPIO algorithm by comparing it with other algorithms.
The fuzzy production scheduling problem considering mould maintenance(FPSP-MM) is studied. The processing time and the maintenance time are represented by triangular fuzzy numbers. When tasks are executed based on the sequence provided by the fuzzy schedule, the real duration of each task needs to be known so the posteriori solution with deterministic processing times can be obtained. Therefore, the concept of the schedule robustness needs to be considered for the fuzzy problem. The robustness is considered as the optimization objective except for the fuzzy makespan in this research. To optimize these two objective functions, a multi-objective pigeon inspired optimization(MOPIO) algorithm is developed. To extend the pigeon inspired optimization(PIO) algorithm from the single-objective case to the multi-objective case, nondominated solutions are used as candidates for the leader pigeon designation and a special crowding distance is used to ensure a good distribution of solutions in both the objective space and the corresponding decision space. Furthermore, an index-based ring topology is used to manage the convergence speed. Numerical experiments on a variety of simulated scenarios show the excellent efficiency and effectiveness of the proposed MOPIO algorithm by comparing it with other algorithms.
引文
1 Rajkumar M,Asokan P,Vamsikrishna V.A GRASP algorithm for flexible job-shop scheduling with maintenance constraints.Int J Prod Res,2010,48:6821-6836
2 Berrichi A,Yalaoui F,Amodeo L,et al.Bi-objective ant colony optimization approach to optimize production and maintenance scheduling.Comput Oper Res,2010,37:1584-1596
3 Wong C S,Chan F T S,Chung S H.A genetic algorithm approach for production scheduling with mould maintenance consideration.Int J Prod Res,2012,50:5683-5697
4 Wong C S,Chan F T S,Chung S H.A joint production scheduling approach considering multiple resources and preventive maintenance tasks.Int J Prod Res,2013,51:883-896
5 Wong C S,Chan F T S,Chung S H.Decision-making on multi-mould maintenance in production scheduling.Int JProd Res,2014,52:5640-5655
6 Wang S J,Liu M.Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning.J Manuf Syst,2015,37:182-192
7 Shen L,Yang H B,Gao S,et al.Production scheduling with mould maintenance in flow shop.In:Proceedings of the4th International Conference on Sensors,Mechatronics and Automation,Zhuhai,2016.730-733
8 Sakawa M,Mori T.An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate.Comput Ind Eng,1999,36:325-341
9 Arik O A,Toksari M D.Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects.Int J Prod Res,2018,56:2488-2505
10 Jamrus T,Chien C F,Gen M,et al.Hybrid particle swarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing time for semiconductor manufacturing.IEEE Trans Semicond Manuf,2018,31:32-41
11 Palacios J J,Gonzalez-Rodríguez I,Vela C R,et al.Robust multiobjective optimisation for fuzzy job shop problems.Appl Soft Comput,2017,56:604-616
12 Xiong J,Xing L N,Chen Y W.Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns.Int J Prod Econ,2013,141:112-126
13 Duan H B,Qiao P X.Pigeon-inspired optimization:a new swarm intelligence optimizer for air robot path planning.Int J Intel Comput Cyber,2014,7:24-37
14 Li C,Duan H B.Target detection approach for UAVs via improved pigeon-inspired optimization and edge potential function.Aerosp Sci Tech,2014,39:352-360
15 Zhang B,Duan H B.Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment.IEEE/ACM Trans Comput Biol Bioinf,2017,14:97-107
16 Duan H B,Wang X H.Echo state networks with orthogonal pigeon-inspired optimization for image restoration.IEEETrans Neural Netw Learn Syst,2016,27:2413-2425
17 Deng Y M,Duan H B.Control parameter design for automatic carrier landing system via pigeon-inspired optimization.Nonlinear Dyn,2016,85:97-106
18 Zhang S J,Duan H B.Gaussian pigeon-inspired optimization approach to orbital spacecraft formation reconfiguration.Chinese J Aeronaut,2015,28:200-205
19 Qiu H X,Duan H B.Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design.Sci China Technol Sci,2015,58:1915-1923
20 Qiu H X,Duan H B.A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles.Inf Sci,2018,doi:10.1016/j.ins.2018.06.061
21 Fortemps P.Jobshop scheduling with imprecise durations:a fuzzy approach.IEEE Trans Fuzzy Syst,1997,5:557-569
22 Bean J C.Genetic algorithms and random keys for sequencing and optimization.ORSA J Comput,1994,6:154-160
23 Tasgetiren M F,Liang Y C,Sevkli M,et al.A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem.Eur J Oper Res,2007,177:1930-1947
24 Yue C T,Qu B Y,Liang J.A multi-objective particle swarm optimizer using ring topology for solving multimodal multi-objective problems.IEEE Trans Evol Comput,2018,22:805-817
25 Deb K,Pratap A,Agarwal S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-II.IEEE Trans Evol Comput,2002,6:182-197
26 Lei D M.Scheduling fuzzy job shop with preventive maintenance through swarm-based neighborhood search.Int JAdv Manuf Technol,2011,54:1121-1128
27 Coello C A C,Pulido G T,Lechuga M S.Handling multiple objectives with particle swarm optimization.IEEE Trans Evol Comput,2004,8:256-279
28 Zitzler E,Thiele L,Laumanns M,et al.Performance assessment of multiobjective optimizers:an analysis and review.IEEE Trans Evol Comput,2003,7:117-132
29 Li J Q,Pan Q K,Mao K,et al.Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm.Knowledge-Based Syst,2014,72:28-36
2 Berrichi A,Yalaoui F,Amodeo L,et al.Bi-objective ant colony optimization approach to optimize production and maintenance scheduling.Comput Oper Res,2010,37:1584-1596
3 Wong C S,Chan F T S,Chung S H.A genetic algorithm approach for production scheduling with mould maintenance consideration.Int J Prod Res,2012,50:5683-5697
4 Wong C S,Chan F T S,Chung S H.A joint production scheduling approach considering multiple resources and preventive maintenance tasks.Int J Prod Res,2013,51:883-896
5 Wong C S,Chan F T S,Chung S H.Decision-making on multi-mould maintenance in production scheduling.Int JProd Res,2014,52:5640-5655
6 Wang S J,Liu M.Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning.J Manuf Syst,2015,37:182-192
7 Shen L,Yang H B,Gao S,et al.Production scheduling with mould maintenance in flow shop.In:Proceedings of the4th International Conference on Sensors,Mechatronics and Automation,Zhuhai,2016.730-733
8 Sakawa M,Mori T.An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate.Comput Ind Eng,1999,36:325-341
9 Arik O A,Toksari M D.Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects.Int J Prod Res,2018,56:2488-2505
10 Jamrus T,Chien C F,Gen M,et al.Hybrid particle swarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing time for semiconductor manufacturing.IEEE Trans Semicond Manuf,2018,31:32-41
11 Palacios J J,Gonzalez-Rodríguez I,Vela C R,et al.Robust multiobjective optimisation for fuzzy job shop problems.Appl Soft Comput,2017,56:604-616
12 Xiong J,Xing L N,Chen Y W.Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns.Int J Prod Econ,2013,141:112-126
13 Duan H B,Qiao P X.Pigeon-inspired optimization:a new swarm intelligence optimizer for air robot path planning.Int J Intel Comput Cyber,2014,7:24-37
14 Li C,Duan H B.Target detection approach for UAVs via improved pigeon-inspired optimization and edge potential function.Aerosp Sci Tech,2014,39:352-360
15 Zhang B,Duan H B.Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment.IEEE/ACM Trans Comput Biol Bioinf,2017,14:97-107
16 Duan H B,Wang X H.Echo state networks with orthogonal pigeon-inspired optimization for image restoration.IEEETrans Neural Netw Learn Syst,2016,27:2413-2425
17 Deng Y M,Duan H B.Control parameter design for automatic carrier landing system via pigeon-inspired optimization.Nonlinear Dyn,2016,85:97-106
18 Zhang S J,Duan H B.Gaussian pigeon-inspired optimization approach to orbital spacecraft formation reconfiguration.Chinese J Aeronaut,2015,28:200-205
19 Qiu H X,Duan H B.Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design.Sci China Technol Sci,2015,58:1915-1923
20 Qiu H X,Duan H B.A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles.Inf Sci,2018,doi:10.1016/j.ins.2018.06.061
21 Fortemps P.Jobshop scheduling with imprecise durations:a fuzzy approach.IEEE Trans Fuzzy Syst,1997,5:557-569
22 Bean J C.Genetic algorithms and random keys for sequencing and optimization.ORSA J Comput,1994,6:154-160
23 Tasgetiren M F,Liang Y C,Sevkli M,et al.A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem.Eur J Oper Res,2007,177:1930-1947
24 Yue C T,Qu B Y,Liang J.A multi-objective particle swarm optimizer using ring topology for solving multimodal multi-objective problems.IEEE Trans Evol Comput,2018,22:805-817
25 Deb K,Pratap A,Agarwal S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-II.IEEE Trans Evol Comput,2002,6:182-197
26 Lei D M.Scheduling fuzzy job shop with preventive maintenance through swarm-based neighborhood search.Int JAdv Manuf Technol,2011,54:1121-1128
27 Coello C A C,Pulido G T,Lechuga M S.Handling multiple objectives with particle swarm optimization.IEEE Trans Evol Comput,2004,8:256-279
28 Zitzler E,Thiele L,Laumanns M,et al.Performance assessment of multiobjective optimizers:an analysis and review.IEEE Trans Evol Comput,2003,7:117-132
29 Li J Q,Pan Q K,Mao K,et al.Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm.Knowledge-Based Syst,2014,72:28-36
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