[1]GLOCK C H, GROSSE E H, JABER M Y, et al. Applications of Learning Curves in Production and Operations Management:a Systematic Literature Review[J]. Computers and Industrial Engineering, 2019, 131:422-441.
[2]CASTELLANO D, GALLO M, GRASSI A, et al. Batching Decisions in Multi-item Production Systems with Learning Effect[J]. Computers and Industrial Engineering, 2019, 131:578-591.
[3]XU J, WU C C, YIN Y, et al. An Order Scheduling Problem with Position-based Learning Effect[J]. Computers & Operations Research, 2016, 74:175-186.
[4]JI M, HU S, ZHANG Y, et al. Parallel-machine Scheduling with Identical Machine Resource Capacity Limits and DeJongs Learning Effect[J]. International Journal of Production Research, 2022, 60(9):2753-2765.
[5]LI Haitao. Stochastic Single-machine Scheduling with Learning Effect[J]. IEEE Transactions on Engineering Management, 2017, 64(1):94-102.
[6]LIN S S. A Note on Parallel-machine Scheduling with Controllable Processing Times and Job-dependent Learning Effects[J]. RAIRO-Operations Research, 2021, 55(2):561-569.
[7]LIANG X X, ZHANG B, WANG J B, et al. Study on Flow Shop Scheduling with Sum-of-logarithm-processing Times-based Learning Effects[J]. Journal of Applied Mathematics and Computing, 2019, 61(1/2):373-388.
[8]WANG J B, LIU F, WANG J J. Research on m-machine Flow Shop Scheduling with Truncated Learning Effects[J]. International Transactions in Operational Research, 2019, 26(3):1135-1151.
[9]董君, 叶春明. 具有学习效应的半导体晶圆制造绿色车间调度问题研究[J]. 运筹与管理, 2021, 30(4):217-223.
DONG Jun, YE Chunming. Research on Green Shop Scheduling Problem of Semiconductor Wafer Manufacturing with Learning Effect[J]. Operations Research & Management, 2021, 30(4):217-223.
[10]ZOU Y, WANG D, LIN W C, et al. Two-stage Three-machine Assembly Scheduling Problem with Sum-of-processing-times-based Learning Effect[J]. Soft Computing, 2020, 24(7):5445-5462.
[11]胡金昌, 刘紫薇, 马文凯, 等.考虑学习效应的单人作业车间多目标调度算法[J].计算机集成制造系统, 2021, 27(5):1361-1370.
HU Jinchang, LIU Ziwei, MA Wenkai, et al. Multiobjective Scheduling Algorithm for Single Job Shop Considering Learning Effect[J]. Computer Integrated Manufacturing Systems,2021,27(5):1361-1370.
[12]PARGAR F, ZANDIEH M, KAUPPILA O, et al. The Effect of Worker Learning on Scheduling Jobs in a Hybrid Flow Shop:a Bi-objective Approach[J]. Journal of Systems Science and Systems Engineering, 2018, 27(3):265-291.
[13]CHENG T C E, KUO W H, YANG D L. Scheduling with a Position-weighted Learning Effect[J]. Optimization Letters, 2014, 8(1):293-306.
[14]CAI X, SUN H, ZHANG Q, et al. A Grid Weighted Sum Pareto Local Search for Combinatorial Multi and Many-Objective Optimization[J]. IEEE Transactions on Cybernetics, 2019, 49(9):3586-3598.
[15]KHALILPOURAZARI S, NADERI B, KHALILPOURAZARY S. Multi-objective Stochastic Fractal Search:a Powerful Algorithm for Solving Complex Multi-objective Optimization Problems[J]. Soft Computing, 2020, 24(4):3037-3066.
[16]YU X, CHEN W N, GU T, et al. Set-based Discrete Particle Swarm Optimization Based on Decomposition for Permutation-based Multiobjective Combinatorial Optimization Problems[J]. IEEE Transactions on Cybernetics, 2018, 48(7):2139-2153.
[17]MANSON J A, CHAMBERLAIN T W, BOURNE R A. MVMOO:Mixed Variable Multi-objective Optimization[J]. Journal of Global Optimization, 2021, 80(4):865-886.
[18]WANG L, HU X, WANG Y, et al. Dynamic Job-shop Scheduling in Smart Manufacturing Using Deep Reinforcement Learning[J]. Computer Networks, 2021, 190:107969.
[19]DEB K, AGRAWAL S, PRATAP A, et al. A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization:NSGA-Ⅱ[C]∥Parallel Problem Solving from Nature PPSN Ⅵ:6th International Conference. Paris, 2000:849-858.
[20]ZHANG Q, LI H. MOEA/D:a Multiobjective Evolutionary Algorithm Based on Decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6):712-731.
[21]ABDEL-BASSET M, MANOGARAN G, EL-SHAHAT D, et al. A Hybrid Whale Optimization Algorithm Based on Local Search Strategy for the Permutation Flow Shop Scheduling Problem[J]. Future Generation Computer Systems, 2018, 85(1):129-145.
[22]SHANG K, ISHIBUCHI H, HE L, et al. A Survey on the Hypervolume Indicator in Evolutionary Multiobjective Optimization[J]. IEEE Transactions on Evolutionary Computation, 2020, 25(1):1-20.
|