In order to solve the problem of discrete manufacturing customization and personalized production scheduling, considering the influence of manual labor on processing time, we propose a multi-objective Hybrid Job-shop Scheduling with Multiprocessor Task(HJSMT) problem with cooperative effect model. Based on the actual production, two optimization objectives are set, i. e. minimizing the maximum completion time and the total tardiness. Firstly, considering the situation where workers’ cooperation reduces job processing time, the cooperative effect of workers co-processing is considered by referring to the learning effect curve in the model. Subsequently, we develop an Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) to solve the multi-objective HJSMT problem by improving Precedence Operation Crossover (POX) and Multiple Mutations (MM) operations. Finally, the scheduling results and the C values are compared with other algorithms to verify the effectiveness of the algorithm. Simultaneously, the multi-objective HJSMT problem with the cooperative effect is solved by the INSGA-II algorithm, and the experimental results also demonstrate the superior performance of the algorithm.
AzzouzA., EnnigrouM., SaidL.B., Scheduling problems under learning effects: classification and cartography, International Journal of Production Research56 (2018), 1642–1661.
3.
Aghajani, FouladiP., A multi-objective mathematical model and genetic algorithm for reliability analysis in flexible job-shop scheduling problem, International Journal of Management Concepts & Philosophy11 (2018), 1478–1484.
4.
CoelloC.A., LechugaM.S. MOPSO: A proposal for multiple objective particle swarm optimization, 2002 IEEE Proceedings of the 2002 Congress on Evolutionary Computation (2002).
5.
ZhangC.Y., DongX., WangX.J., LiX.Y., LiuQ., Improved NSGA-II for the Multi-objective Flexible Job-shop Scheduling Problem, Journal of Mechanical Engineering46 (2010), 156–164.
6.
ZhangC.Z., LiY.Z., YinS.G., PeiS., Optimization of multi-objective fuzzy flexible job shop scheduling problem, Science, Technology and Engineering20 (2020), 1099–1106.
7.
BiskupD., Single-machine scheduling with learning considerations, European Journal of Operational Research115 (1999), 173–178.
8.
Erfani EbrahimnejadS., MoosaviA., An integrated dynamic facility layout and job shop scheduling problem: A hybrid NSGA-II and local search algorithm, Journal of Industrial and Management Optimization16 (2020), 1801–1834.
9.
AmiriF., ShiraziB., TajdinA., Multi-objective simulation optimization for uncertain resource assignment and job sequence in automated flexible job shop, Applied Soft Computing Journal75 (2019), 1568–4946.
10.
GaoF. Research on production scheduling problem with learning effect and resource allocation, Dalian University of Technology (2018).
11.
MoslehiG., MahnamM., A Pareto approach to multi-objectiveflexible job-shop scheduling problem using particle swarmoptimization and local search, International Journal ofProduction Economics129 (2011), 14–22.
HuJ.C., WuY.Y., WangY.Y., WuY.H., Worker job shop scheduling algorithm considering learning effect, Control and Decision37 (2022), 37–46.
14.
DebK., PratapA., AgarwalS., MeyarivanT., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation6 (2002), 182–197.
15.
FanK., WangM., ZhaiY.F., LiX.N., Scatter search algorithm for the multiprocessor task job-shop scheduling problem, Computers & Industrial Engineering127 (2019), 677–686.
16.
FanK., ZhaiY., LiX., WangM., Review and classification of hybrid shop scheduling, Production Engineering12 (2018), 597–609.
17.
FanK., ZhaiY.F., LiX.N., ZhouL., Simulation research on Hybrid Job-shop Scheduling with Multiprocessor Task problems, Manufacturing Automation40 (2018), 94–99.
WangL., CaiJ., ShiX., Multi-objective flexible job shop energy-saving scheduling problem based on improved genetic algorithm, Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology41 (2017), 494–502.
20.
LeiD., A Pareto archive particle swarm optimization for multi-objective job shop scheduling, Computers & Industrial Engineering54 (2008), 960–971.
21.
WangM., FanK., ZhaiY.F., LiX.N., Study of multiprocessor task scheduling problem in network parallel computing system, Computer Engineering and Applications53 (2017), 264–270.
22.
BruckerP., KrämerA., Shop scheduling problems with multiprocessor tasks on dedicated processors, Annals of Operations Research57 (1999), 13–27.
23.
WangQ.L., DuanX.H., Scheduling of flexible job shop based on high-dimension and multi-objective migrating bird optimization algorithm, China Mechanical Engineering33 (2022), 2601–2612.
24.
WangQ.W., XuZ.H., GuX.S., Multi-processor combined production batch scheduling problem based on brain storm optimization algorithm, Journal of East China University of Science and Technology48 (2022), 685–695.
25.
PonnambalamS.G., RamkumarV., JawaharN., A multi-objective genetic algorithm for job shop scheduling, Production Planning & Control12 (2001), 764–774.
26.
GawiejnowiczS., A note on scheduling on a single processor with speed dependent on a number of executed job, Information Processing Letters57 (1996), 297–300.
27.
XuW.H., WangY., YanD.H., JiC.C., Flower pollination algorithm for multi-objective fuzzy flexible job shop scheduling, Journal of System Simulation30 (2018), 4403–4412.
28.
BaoX.X., YeC.M., JiL., HuangX., Improved chaotic firework algorithm for multi-objective scheduling optimization, Application Research of Computers33 (2016), 2601–2605.
29.
SunX.Y., Improved grey wolf optimization for solving multi-objective flexible job-shop scheduling problems, Software Engineering25 (2022), 15–18+14.
30.
AnY.J., Chen*X.H., Gao**K.Z., ZhangL., LiY.H., ZhaoZ.Y., Integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints, Swarm and Evolutionary Computation77 (2023), 101243.
31.
AnY.J., Chen*X.H., HuJ.W., ZhangL., LiY.h., JiangJ.W., Joint optimization of preventive maintenance and production rescheduling with new machine insertion and processing speed selection, Reliability Engineering & Systems Safety220 (2022), 108269.
32.
LvY.Y., FanK., QuH., ZhouL., Research on multi-objective particle swarm algorithm for solving hybrid job-shop scheduling with multiprocessor task problem, Journal of Chinese Computer Systems43 (2022), 218–224.
33.
LvY.Y. Research on multi-objective problem of hybrid jobshop scheduling with multiprocessor task problem, Beijing Forestry University (2021).
