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Tsinghua Science and Technology 2021, 26(5): 625-645 https://doi.org/10.26599/TST.2021.9010009
Open Access | Issue | Published: 20 April 2021

Distributed Scheduling Problems in Intelligent Manufacturing Systems

Show Author's Information Yaping Fu,  Yushuang Hou,  Zifan Wang,  Xinwei Wu,  Kaizhou Gao( ),  Ling Wang( )
College of Business, Qingdao University, Qingdao 266071, China
Institute of Systems Engineering, Macau University of Science and Technology, Macao 999078, China
Department of Automation, Tsinghua University, Beijing 100084, China
Keywords:
distributed manufacturing systems, distributed scheduling problems, modeling and optimization, intelligent optimization methods
Cite this article:
Fu Y, Hou Y, Wang Z, et al. Distributed Scheduling Problems in Intelligent Manufacturing Systems. Tsinghua Science and Technology, 2021, 26(5): 625-645. https://doi.org/10.26599/TST.2021.9010009
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Abstract Full text About this article

Currently, manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization. Hence, they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations. Nowadays, distributed manufacturing systems have been widely adopted in industrial production processes. In recent years, many studies have been done on the modeling and optimization of distributed scheduling problems. This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems. By summarizing and evaluating existing studies on distributed scheduling problems, we analyze the achievements and current research status in this field and discuss ongoing studies. Insights regarding prior works are discussed to uncover future research directions, particularly swarm intelligence and evolutionary algorithms, which are used for managing distributed scheduling problems in manufacturing systems. This work focuses on journal papers discovered using Google Scholar. After reviewing the papers, in this work, we discuss the research trends of distributed scheduling problems and point out some directions for future studies.


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About this article

Distributed Scheduling Problems in Intelligent Manufacturing Systems

Show Author's information Yaping Fu, Yushuang Hou, Zifan Wang, Xinwei Wu, Kaizhou Gao( ), Ling Wang( )
College of Business, Qingdao University, Qingdao 266071, China
Institute of Systems Engineering, Macau University of Science and Technology, Macao 999078, China
Department of Automation, Tsinghua University, Beijing 100084, China

Abstract

Currently, manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization. Hence, they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations. Nowadays, distributed manufacturing systems have been widely adopted in industrial production processes. In recent years, many studies have been done on the modeling and optimization of distributed scheduling problems. This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems. By summarizing and evaluating existing studies on distributed scheduling problems, we analyze the achievements and current research status in this field and discuss ongoing studies. Insights regarding prior works are discussed to uncover future research directions, particularly swarm intelligence and evolutionary algorithms, which are used for managing distributed scheduling problems in manufacturing systems. This work focuses on journal papers discovered using Google Scholar. After reviewing the papers, in this work, we discuss the research trends of distributed scheduling problems and point out some directions for future studies.

Keywords: distributed manufacturing systems, distributed scheduling problems, modeling and optimization, intelligent optimization methods

References(152)

[1]
M. G. Gnonia, R. Iavagnilioa, G. Mossaa, G. Mummoloa, and A. Di Leva, Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry, Int. J. Prod. Econ., vol. 85, no. 5, pp. 251–262, 2003.
Google Scholar
[2]
Sambasivan M. and Yahya S., A lagrangian-based heuristic for multi-plant, multi-item, multi-period lot-sizing problems with inter-plant transfers, Comput. Oper. Res., vol. 32, no. 3, pp. 537–555, 2000.
Google Scholar
[3]
C. H. Timpe and J. Kallrath, Optimal planning in large multi-site production networks, Eur. J. Oper. Res., vol. 126, no. 20, pp. 422–435, 2000.
Google Scholar
[4]
Parente M., Figueira G., Amorim P., and Marques A., Production scheduling in the context of Industry 4.0: Review and trends, Int. J. Prod. Res., vol. 58, no. 17, pp. 5401–5431, 2020.
Google Scholar
[5]
M. Sambasivan and S. Yahya, A lagrangian-based heuristic for multi-plant, multi-item, multi-period lot-sizing problems with inter-plant transfers, IEEE Trans. Syst. Man Cyber. Part C, vol. 36, no. 4, pp. 563–577, 2006.
Google Scholar
[6]
Y. K. Liu, L. Wang, X. V. Wang, X. Xu, and L. Zhang, Scheduling in cloud manufacturing: State-of-the-art and research challenges, Int. J. Prod. Res., vol. 57, no. 15, pp. 4854–4879, 2019.
Google Scholar
[7]
B. Çaliş and S. Bulkan, A research survey: Review of AI solution strategies of job shop scheduling problem, J. Intell. Manuf., vol. 26, no. 5, pp. 961–973, 2015.
Google Scholar
[8]
M. Akbar and T. Irohara, Scheduling for sustainable manufacturing: A review, J. Clean. Prod., vol. 205, no. 20, pp. 866–883, 2018.
Google Scholar
[9]
A. Whitbrook, Q. Meng, and P. W. H. Chung, Reliable, distributed scheduling and rescheduling for time-critical, multiagent systems, IEEE Trans. Autom. Sci. Eng., vol. 15, no. 2, pp. 732–747, 2017.
Google Scholar
[10]
K. Li, T. Zhou, B. H. Liu, and H. Li, A multi-agent system for sharing distributed manufacturing resources, Expert Syst. Appl., vol. 99, no. 3, pp. 32–43, 2018.
Google Scholar
[11]
Cao Z. C., Lin C. R., Zhou M. C., and Huang R., Scheduling semiconductor testing facility by using cuckoo search algorithm with reinforcement learning and surrogate modeling, IEEE Trans. Autom. Sci. Eng., vol. 16, no. 2, pp. 825–837, 2019.
Google Scholar
[12]
Y. P. Fu, M. C. Zhou, X. W. Guo, and L. Qi, Scheduling dual-objective stochastic hybrid flow shop with deteriorating jobs via bi-population evolutionary algorithm, IEEE Trans. Syst. Man Cyber. Syst., vol. 50, no. 12, pp. 5037–5048, 2020.
Google Scholar
[13]
Y. Wang, J. Q. Wang, and Y. Q. Yin, Due date assignment and multitasking scheduling with deterioration effect and efficiency promotion, Comput. Ind. Eng., .
DOI Google Scholar
[14]
Wang D. J., Yu Y. G., Qiu H. X., Yin Y. Q., and Cheng T. C. E., Two-agent scheduling with linear resource-dependent processing times, Nav. Res. Log., vol. 67, no. 7, pp. 573–591, 2020.
Google Scholar
[15]
Z. X. Pan, D. M. Lei, and L. Wang, A knowledge-based two-population optimization algorithm for distributed energy-efficient parallel machines scheduling, IEEE Trans. Cyber., .
DOI Google Scholar
[16]
X. Q. Wu and A. D. Che, A memetic differential evolution algorithm for energy-efficient parallel machine scheduling, Omega, vol. 82, no. 1, pp. 155–165, 2019.
Google Scholar
[17]
E. D. Jiang and L. Wang, An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time, Int. J. Prod. Res., vol. 57, no. 6, pp. 1756–1771, 2019.
Google Scholar
[18]
E. D. Jiang and L. Wang, Multi-objective optimization based on decomposition for flexible job shop scheduling under time-of-use electricity prices, Know. Based Syst., .
DOI Google Scholar
[19]
Fu Y. P., Ding J. L., Wang H. F., and Wang J. W., Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system, Appl. Soft Comput., vol. 68, pp. 847–855, 2018.
Google Scholar
[20]
K. Z. Gao, F. J. Yang, M. C. Zhou, Q. K. Pan, and P. N. Suganthan, Flexible job-shop rescheduling for new job insertion by using discrete Jaya algorithm, IEEE Trans. Cyber., vol. 49, no. 5, pp. 1944–1955, 2018.
Google Scholar
[21]
Gao K. Z., Cao C. G., Zhang L., Han Y. Y., and Pan Q. K., A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems, IEEE/CAA J. of Automatic Sinica, vol. 6, no. 4, pp. 904–916, 2019.
Google Scholar
[22]
Y. P. Fu, M. C. Zhou, X. W. Guo, and L. Qi, Scheduling dual-objective stochastic hybrid flow shop with deteriorating jobs via bi-population evolutionary algorithm, IEEE Trans. Syst. Man. Cyber. Syst., vol. 50, no. 12, pp. 5037–5048, 2020.
Google Scholar
[23]
Meng L. L., Zhang C. Y., Shao X. Y., Ren Y. P., and Ren C. L., Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines, Int. J. Prod. Res., vol. 57, no. 4, pp. 1119–1145, 2019.
Google Scholar
[24]
L. Wang, J. Deng, and S. Y. Wang, Survey on optimization algorithms for distributed shop scheduling, Contr. Decis., vol. 31, no. 1, pp. 1–11, 2016.
Google Scholar
[25]
Wang S., Roshanaei V., Aleman D., and Urbach D., A discrete event simulation evaluation of distributed operating room scheduling, IIE Trans. Health. Syst. Eng., vol. 6, no. 4, pp. 236–245, 2016.
Google Scholar
[26]
V. Roshanaei, C. Luong, D. M. Aleman, and D. R. Urbach, Reformulation, linearization, and decomposition techniques for balanced distributed operating room scheduling, Omega, .
DOI Google Scholar
[27]
V. Roshanaei, C. Luong, D. M. Aleman, and D. Urbach, Propagating logic-based Benders’ decomposition approaches for distributed operating room scheduling, Eur. J. Oper. Res., vol. 257, no. 2, pp. 439–455, 2017.
Google Scholar
[28]
M. H. Shirvani, A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems, Eng. Appl. Artif. Intel., .
DOI Google Scholar
[29]
F. Camci, Maintenance scheduling of geographically distributed assets with prognostics information, Eur. J. Oper. Res., vol. 245, no. 2, pp. 506–516, 2015.
Google Scholar
[30]
F. T. S. Chan, A. Prakash, H. L. Ma, and C. S. Wong, A hybrid tabu sample-sort simulated annealing approach for solving distributed scheduling problem, Int. J. Prod. Res., vol. 51, no. 9, pp. 2602–2619, 2013.
Google Scholar
[31]
A. Toptal and I. Sabuncuoglu, Distributed scheduling: A review of concepts and applications, Int. J. Prod. Res., vol. 48, no. 18, pp. 5235–5262, 2010.
Google Scholar
[32]
J. Behnamian and S. M. T. F. Ghomi, A survey of multi-factory scheduling, J. Intel. Manuf., vol. 27, no. 1, pp. 231–249, 2016.
Google Scholar
[33]
I. Chaouch, O. B. Driss, and K. Ghedira, A survey of optimization techniques for distributed job shop scheduling problems in multi-factories, Cyber. Math. Appl. Intel. Syst., .
DOI Google Scholar
[34]
J. Lohmer and R. Lasch, Production planning and scheduling in multi-factory production networks: A systematic literature review, Int. J. Prod. Res., .
DOI Google Scholar
[35]
Y. C. Chang, V. C. Li, and C. J. Chiang, An ant colony optimization heuristic for an integrated production and distribution scheduling problem, Eng. Optimiz., vol. 46, no. 4, pp. 503–520, 2014.
Google Scholar
[36]
A. Gharaei and F. Jolai, A multi-agent approach to the integrated production scheduling and distribution problem in multi-factory supply chain, Appl. Soft Comput., vol. 65, no. 2, pp. 577–589, 2018.
Google Scholar
[37]
F. Marandi and G. S. M. T. Fatemi, Integrated multi-factory production and distribution scheduling applying vehicle routing approach, Int. J. Prod. Res., vol. 57, no. 3, pp. 722–748, 2019.
Google Scholar
[38]
N. Mishra, V. Kumar, F. T. S. Chan, and M. K. Tiwari, A CBFSA approach to resolve the distributed manufacturing process planning problem in a supply chain environment, Int. J. Prod. Res., vol. 50, no. 2, pp. 535–550, 2012.
Google Scholar
[39]
S. Zhang, Y. B. Xu, Z. N. Yu, W. Y. Zhang, and D. J. Yu, Combining extended imperialist competitive algorithm with a genetic algorithm to solve the distributed integration of process planning and scheduling problem, Math. Probl. Eng., .
DOI Google Scholar
[40]
I. Ribas, R. Companys, and X. Tort-Martorell, Efficient heuristics for the parallel blocking flow shop scheduling problem, Expert Syst. Appl., vol. 74, no. 3, pp. 41–54, 2017.
Google Scholar
[41]
J. Dong and C. M. Ye, Research on collaborative optimization of green manufacturing in semiconductor wafer distributed heterogeneous factory, Appl. Sci., vol. 9, no. 14, pp. 28–79, 2019.
Google Scholar
[42]
F. L. Xiong, K. Y. Xing, F. Wang, H. Lei, and L. B. Han, Minimizing the total completion time in a distributed two stage assembly system with setup times, Comput. Oper. Res., vol. 47, no. 7, pp. 92–105, 2014.
Google Scholar
[43]
J. Behnamian, Decomposition based hybrid VNS–TS algorithm for distributed parallel factories scheduling with virtual corporation, Comput. Oper. Res., vol. 52, no. 12, pp. 181–191, 2014.
Google Scholar
[44]
S. Zhang, Z. N. Yu, W. Y. Zhang, D. J. Yu, and Y. B. Xu, An extended genetic algorithm for distributed integration of fuzzy process planning and scheduling, Math. Probl. Eng., .
DOI Google Scholar
[45]
E. M. G. Neira, D. Ferone, S. Hatami, and A. A. Juan, A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times, Simul. Model. Pract. Th., vol. 79, no. 6, pp. 23–36, 2017.
Google Scholar
[46]
Y. P. Fu, H. F. Wang, and M. Huang, Integrated scheduling for a distributed manufacturing system: A stochastic multi-objective model, Enterp. Inf. Syst., vol. 13, no. 4, pp. 557–573, 2019.
Google Scholar
[47]
Z. S. Shao, D. C. Pi, and W. S. Shao, Hybrid enhanced discrete fruit fly optimization algorithm for scheduling blocking flow-shop in distributed environment, Expert Syst. Appl., vol. 145, no. 3, pp.113–147, 2019.
Google Scholar
[48]
Y. Li, X. Li, L. Gao, and L. L. Meng, An improved artificial bee colony algorithm for distributed heterogeneous hybrid flowshop scheduling problem with sequence-dependent setup times, Comput. Ind. Eng., .
DOI Google Scholar
[49]
K. C. Ying, P. Pourhejazy, C. Y. Cheng, and R. S. Syu, Supply chain-oriented permutation flowshop scheduling considering flexible assembly and setup times, Int. J. Prod. Res., vol. 58, no. 20, pp. 1–24, 2020.
Google Scholar
[50]
J. Zheng, L. Wang, and J. J. Wang, A cooperative coevolution algorithm for multi-objective fuzzy distributed hybrid flow shop, Know. Based Syst., .
DOI Google Scholar
[51]
H. B. Song and J. Lin, A genetic programming hyper-heuristic for the distributed assembly permutation flow-shop scheduling problem with sequence dependent setup times, Swarm Evol. Comput., .
DOI Google Scholar
[52]
H. R. Li, X. Y. Li, and L. Gao, A discrete artificial bee colony algorithm for the distributed heterogeneous no-wait flowshop scheduling problem, Appl. Soft Comput., .
DOI Google Scholar
[53]
M. Komaki and B. Malakooti, General variable neighborhood search algorithm to minimize makespan of the distributed no-wait flow shop scheduling problem, Prod. Eng., vol. 11, no. 3, pp. 315–329, 2017.
Google Scholar
[54]
K. C. Ying, S. W. Lin, C. Y. Cheng, and C. D. He, Iterated reference greedy algorithm for solving distributed no-idle permutation flowshop scheduling problems, Comput. Ind. Eng., vol. 110, no. 4, pp. 413–423, 2017.
Google Scholar
[55]
K. C. Ying and S. W. Lin, Minimizing makespan in distributed blocking flowshops using hybrid iterated greedy algorithms, IEEE Access, vol. 5, no. 99, pp. 15694–15705, 2017.
Google Scholar
[56]
Shao W., Pi D., and Shao Z., Optimization of makespan for the distributed no-wait flow shop scheduling problem with iterated greedy algorithms, Know. Based Syst., vol. 137, no. 6, pp. 163–181, 2017.
Google Scholar
[57]
C. Y. Cheng, K. C. Ying, H. H. Chen, and H. S. Lu, Minimising makespan in distributed mixed no-idle flowshops, Int. J. Prod. Res., vol. 57, no. 7, pp. 48–60, 2019.
Google Scholar
[58]
G. Zhang, K. Xing, and F. Cao, Discrete differential evolution algorithm for distributed blocking flowshop scheduling with makespan criterion, Eng. Appl. Artif. Intel., vol. 76, no. 5, pp. 96–107, 2018.
Google Scholar
[59]
I. Ribas, R. Companys, and X. Tort-Martorell, An iterated greedy algorithm for solving the total tardiness parallel blocking flow shop scheduling problem, Expert Syst. Appl., vol. 121, no. 3, pp. 347–361, 2019.
Google Scholar
[60]
J. Chen, L. Wang, and Z. Peng, A collaborative optimization algorithm for energy-efficient multi-objective distributed no-idle flow-shop scheduling, Swarm Evol. Comput., .
DOI Google Scholar
[61]
F. Q. Zhao, L. X. Zhao, L. Wang, and H. B. Song, An ensemble discrete differential evolution for the distributed blocking flowshop scheduling with minimizing makespan criterion, Expert Syst. Appl., .
DOI Google Scholar
[62]
F. Q. Zhao, L. X. Zhang, J. Cao, and J. X. Tang. A cooperative water wave optimization algorithm with reinforcement learning for the distributed assembly no-idle flowshop scheduling problem, Comput. Ind. Eng.,
DOI Google Scholar
[63]
Z. S. Shao, W. S. Shao, and D. C. Pi, Effective heuristics and metaheuristics for the distributed fuzzy blocking flow-shop scheduling problem, Swarm Evol. Comput., .
DOI Google Scholar
[64]
S. Hatami, R. Ruiz, and C. A. Romano, The distributed assembly permutation flowshop scheduling problem, Int. J. Prod. Res., vol. 51, no. 17, pp. 5292–5308, 2013.
Google Scholar
[65]
S. Y. Wang and L. Wang, An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem, IEEE Trans. Syst. Man. Cyber. Syst., vol. 46, no. 1, pp. 139–149, 2015.
Google Scholar
[66]
J. Deng, L. Wang, S. Y. Wang, and X. L. Zheng, A competitive memetic algorithm for the distributed two-stage assembly flow-shop scheduling problem, Int. J. Prod. Res., vol. 54, no. 12, pp. 3561–3577, 2016.
Google Scholar
[67]
J. Lin and S. Zhang, An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem, Comput. Ind. Eng., vol. 97, no. 4, pp. 128–136, 2016.
Google Scholar
[68]
Lin J., Wang Z. J., and Li X. D., A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem, Swarm Evol. Comput., vol. 36, no. 5, pp. 124–135, 2017.
Google Scholar
[69]
G. Zhang and K. Xing, Memetic social spider optimization algorithm for scheduling two-stage assembly flowshop in a distributed environment, Comput. Ind. Eng., vol. 125, no. 6, pp. 423–433, 2018.
Google Scholar
[70]
Wu X. L., Liu X. J., and Zhao N., An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem, Memet. Comput., vol. 11, no. 4, pp. 335–355, 2019.
Google Scholar
[71]
G. Zhang, K. Xing, G. Zhang, and Z. He, Memetic algorithm with meta-lamarckian learning and simplex search for distributed flexible assembly permutation flowshop scheduling problem, IEEE Access, .
DOI Google Scholar
[72]
D. M. Lei, B. Su, and M. Li, Cooperated teaching-learning-based optimisation for distributed two-stage assembly flow shop scheduling, Int. J. Prod. Res., .
DOI Google Scholar
[73]
A. P. Rifai, H. T. Nguyen, and S. Z. M. Dawal, Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling, Appl. Soft Comput., vol. 40, no. 1, pp. 42–57, 2016.
Google Scholar
[74]
D. M. Lei, Y. Yuan, J. C. Cai, and D. Y. Bai, An imperialist competitive algorithm with memory for distributed unrelated parallel machines scheduling, Int. J. Prod. Res., vol. 58, no. 2, pp. 597–614, 2020.
Google Scholar
[75]
T. Meng and Q. K. Pan, A distributed heterogeneous permutation flowshop scheduling problem with lot-streaming and carryover sequence-dependent setup time, Swarm Evol. Comput., .
DOI Google Scholar
[76]
B. Naderi and R. Ruiz, The distributed permutation flowshop scheduling problem, Comput. Oper. Res., vol. 37, no. 4, pp. 754–768, 2010.
Google Scholar
[77]
A. Azab and B. Naderi, Greedy heuristics for distributed job shop problems, Procedia CIRP, .
DOI Google Scholar
[78]
B. Naderi and A. Azab, An improved model and novel simulated annealing for distributed job shop problems, Int. J. Adv. Manuf. Tech., vol. 81, no. 5, pp. 693–703, 2015.
Google Scholar
[79]
J. Behnamian and S. M. T. F. Ghomi, Minimizing cost-related objective in synchronous scheduling of parallel factories in the virtual production network, Appl. Soft Comput., vol. 29, no. 2, pp. 221–232, 2015.
Google Scholar
[80]
Ying K. C. and Lin S. W., Minimizing makespan for the distributed hybrid flowshop scheduling problem with multiprocessor tasks, Expert Syst. Appl., vol. 92, no. 1, pp. 132–141, 2018.
Google Scholar
[81]
W. S. Shao, D. C. Pi, and Z. S. Shao, A Pareto-based estimation of distribution algorithm for solving multiobjective distributed no-wait flow-shop scheduling problem with sequence-dependent setup time, IEEE Trans. Autom. Sci. Eng., vol. 16, no. 3, pp. 1344–1360, 2019.
Google Scholar
[82]
Q. K. Pan, L. Gao, X. Y. Li, and F. M. Jose, Effective constructive heuristics and meta-heuristics for the distributed assembly permutation flowshop scheduling problem, Appl. Soft Comput., .
DOI Google Scholar
[83]
J. P. Huang, Q. K. Pan, and L. Gao, An effective iterated greedy method for the distributed permutation flowshop scheduling problem with sequence-dependent setup times, Swarm Evol. Comput., .
DOI Google Scholar
[84]
L. L. Meng, C. Y. Zhang, Y. P. Ren, B. Zhang, and C. Lv, Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem, Comput. Ind. Eng., .
DOI Google Scholar
[85]
G. Gong, R. Chiong, Q. Deng, and Q. Luo, A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption, J. Intel. Manuf., vol. 31, no. 1, pp. 1443–1466, 2020.
Google Scholar
[86]
C. Lu, L. Gao, J. Yi, and X. Li, Energy-efficient scheduling of distributed flow shop with heterogeneous factories: A real-world case from automobile industry in China, IEEE Trans. Ind. Inform., .
DOI Google Scholar
[87]
G. C. Wang, L. Gao, X. Y. Li, P. G. Li, and M. F. Tasgetiren, Energy-efficient distributed permutation flow shop scheduling problem using a multi-objective whale swarm algorithm, Swarm Evol. Comput., .
DOI Google Scholar
[88]
Q. K. Pan, L. Gao, and L. Wang, An effective cooperative co-evolutionary algorithm for distributed flowshop group scheduling problems, IEEE Trans. Cyber., .
DOI Google Scholar
[89]
F. L. Xiong, M. L. Chu, Z. Li, Y. Du, and L. Wang, Just-in-time scheduling for a distributed concrete precast flow shop system, Comput. Oper. Res., .
DOI Google Scholar
[90]
Wang J. and Wang L., A knowledge-based cooperative algorithm for energy-efficient scheduling of distributed flow-shop, IEEE Ttans. Syst. Man. Cyber. Syst., vol. 50, no. 1, pp. 1805–1819, 2018.
Google Scholar
[91]
Y. P. Fu, G. D. Tian, A. M. F. Fard, A. Ahmadi, and C. Y. Zhang, Stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint, J. Clean. Prod., vol. 226, no. 14, pp. 515–525, 2019.
Google Scholar
[92]
Q. Luo, Q. W. Deng, G. L. Gong, L. K. Zhang, and W. W. Han, An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers, Expert Syst. Appl., .
DOI Google Scholar
[93]
E. N. Jiang, L. Wang, and Z. B. Peng, Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition, Swarm Evol. Comput., .
DOI Google Scholar
[94]
Z. X. Guo, E. W. T. Ngai, C. Yang, and X. D. Liang, An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment, Int. J. Prod. Econ., vol. 159, no. 2, pp. 16–28, 2015.
Google Scholar
[95]
X. X. Zou, L. Liu, K. P. Li, and W. L. Li, A coordinated algorithm for integrated production scheduling and vehicle routing problem, Int. J. Prod. Res., vol. 56, no. 15, pp. 5005–5024, 2018.
Google Scholar
[96]
Zhang W. and Gen M., Process planning and scheduling in distributed manufacturing system using multiobjective genetic algorithm, IEEE Trans. Electron. Eng., vol. 5, no. 1, pp. 62–72, 2010.
Google Scholar
[97]
L. D. Giovanni and F. Pezzella, An improved genetic algorithm for the distributed and flexible job-shop scheduling problem, Eur. J. Oper. Res., vol. 200, no. 2, pp. 395–408, 2010.
Google Scholar
[98]
J. Gao and R. Chen, A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem, Int. J. Comput. Intel. Syst., vol. 4, no. 4, pp. 497–508, 2011.
Google Scholar
[99]
T. K. Liu, Y. P. Chen, and J. H. Chou, Solving distributed and flexible job-shop scheduling problems for a real-world fastener manufacturer, IEEE Access, .
DOI Google Scholar
[100]
H. C. Chang and T. K. Liu, Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms, J. Intel. Manuf., vol. 28, no. 8, pp. 1973–1986, 2017.
Google Scholar
[101]
M. C. Wu, C. S. Lin, C. H. Lin, and C. F. Chen, Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems, Comput. Oper. Res., vol. 80, no. 4, pp. 101–112, 2017.
Google Scholar
[102]
V. F. Viagas, P. P. Gonzalez, and J. M. Framinan, The distributed permutation flow shop to minimise the total flowtime, Comput. Ind. Eng., vol. 118, no. 2, pp. 464–477, 2018.
Google Scholar
[103]
P. H. Lu, M. C. Wu, H. Tan, Y. H. Peng, and C. F. Chen, A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems, J. Intel. Manuf., vol. 29, no. 1, pp. 19–34, 2018.
Google Scholar
[104]
S. Cai, K. Yang, and K. Liu, Multi-objective optimization of the distributed permutation flow shop scheduling problem with transportation and eligibility constraints, J. Oper. Res. Soci. China, vol. 6, no. 3, pp. 391–416, 2018.
Google Scholar
[105]
S. Y. Wang, L. Wang, M. Liu, and Y. Xu, An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem, Int. J. Prod. Econ., vol. 145, no. 1, pp. 387–396, 2013.
Google Scholar
[106]
Y. Xu, L. Wang, S. Y. Wang, and M. Liu, An effective hybrid immune algorithm for solving the distributed permutation flow-shop scheduling problem, Eng. Optimiz., vol. 46, no. 9, pp. 1269–1283, 2014.
Google Scholar
[107]
Zhang G., Xing K., and Cao F., Scheduling distributed flowshops with flexible assembly and set-up time to minimise makespan, Int. J. Prod. Res., vol. 56, no. 9, pp. 3226–3244, 2018.
Google Scholar
[108]
T. Meng, Q. K. Pan, and L. Wang, A distributed permutation flowshop scheduling problem with the customer order constraint, Know.Based Syst., .
DOI Google Scholar
[109]
S. Yang and Z. Xu, The distributed assembly permutation flowshop scheduling problem with flexible assembly and batch delivery, Int. J. Prod. Res., .
DOI Google Scholar
[110]
J. Gao, R. Chen, and W. Deng, An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem, Int. J. Prod. Res., vol. 51, no. 3, pp. 641–651, 2013.
Google Scholar
[111]
J. Q. Li, P. Y. Duan, J. D. Cao, X. P. Lin, and Y. Y. Han, A hybrid Pareto-based tabu search for the distributed flexible job shop scheduling problem with E/T criteria, IEEE Access, .
DOI Google Scholar
[112]
I. Chaouch, O. B. Driss, and K. Ghedira, A modified ant colony optimization algorithm for the distributed job shop scheduling problem, Proc. Comput. Sci., .
DOI Google Scholar
[113]
G. H. Zhang and K. Y. Xing, Differential evolution metaheuristics for distributed limited-buffer flowshop scheduling with makespan criterion, Comput. Oper. Res., no. 8, vol. 108, pp. 33–43, 2019.
Google Scholar
[114]
B. Naderi and R. Ruiz, A scatter search algorithm for the distributed permutation flowshop scheduling problem, Eur. J. Oper. Res., vol. 239, no. 2, pp. 323–334, 2014.
Google Scholar
[115]
Q. K. Pan, L. Gao, L. Wang, J. Liang, and X. Y. Li, Effective heuristics and metaheuristics to minimize total flowtime for the distributed permutation flowshop problem, Expert Syst. Appl., vol. 124, no. 3, pp. 309–324, 2019.
Google Scholar
[116]
S. W. Lin, K. C. Ying, and C. Y. Huang, Minimising makespan in distributed permutation flowshops using a modified iterated greedy algorithm, Int. J. Prod. Res., vol. 51, no. 15, pp. 5029–5038, 2013.
Google Scholar
[117]
V. F. Viagas and J. M. Framinan, A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem, Int. J. Prod. Res., vol. 53, no. 4, pp. 1111–1123, 2015.
Google Scholar
[118]
R. Ruiz, Q. K. Pan, and B. Naderi, Iterated greedy methods for the distributed permutation flowshop scheduling problem, Omega, vol. 83, no. 2, pp. 213–222, 2019.
Google Scholar
[119]
W. S. Shao, Z. Shao, and D. Pi, Modeling and multi-neighborhood iterated greedy algorithm for distributed hybrid flow shop scheduling problem, Know. Based Syst., .
DOI Google Scholar
[120]
J. Y. Mao, Q. K. Pan, Z. H. Miao, and L. Gao. An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance, Expert Syst. Appl., .
DOI Google Scholar
[121]
J. Deng and L. Wang, A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem, Swarm Evol. Comput., vol. 32, no. 1, pp. 121–131, 2017.
Google Scholar
[122]
J. Wang and L. Wang, A bi-population cooperative memetic algorithm for distributed hybrid flow-shop scheduling, IEEE Trans. Emerg. Topics. Comput. Intel., .
DOI Google Scholar
[123]
H. Bargaoui, O. B. Driss, and K. Ghédira, A novel chemical reaction optimization for the distributed permutation flowshop scheduling problem with makespan criterion, Comput. Ind. Eng., vol. 111, no. 5, pp. 239–250, 2017.
Google Scholar
[124]
W. Y. Zhang, S. Zhang, S. S. Guo, Y. S. Yang, and Y. Chen, Concurrent optimal allocation of distributed manufacturing resources using extended teaching-learning-based optimization, Int. J. Prod. Res., vol. 55, no. 3, pp. 718–735, 2017.
Google Scholar
[125]
J. H. Hao, J. Q. Li, Y. Du, M. X. Song, and Y. Y. Zhang, Solving distributed hybrid flowshop scheduling problems by a hybrid brain storm optimization algorithm, IEEE Access, .
DOI Google Scholar
[126]
J. Q. Li, M. X. Song, L. Wang, P. Y. Duan, Y. Y. Han, H. Y. Sang, and Q. K. Pan, Hybrid artificial bee colony algorithm for a parallel batching distributed flow-shop problem with deteriorating jobs, IEEE Trans. Cyber., vol. 50, no. 6, pp. 2425–2439, 2019.
Google Scholar
[127]
J. Q. Li, S. C. Bai, P. Y. Duan, H. Y. Sang, Y. Y. Han, and Z. X. Zheng, An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system, Int. J. Prod. Res., vol. 57, no. 22, pp. 6922–6942, 2019.
Google Scholar
[128]
J. P. Huang, Q. K. Pan, Z. H. Miao, and L. Gao, Effective constructive heuristics and discrete bee colony optimization for distributed flowshop with setup times, Eng. Appl. Artif. Intel., .
DOI Google Scholar
[129]
Lei D. M. and Wang T., Solving distributed two-stage hybrid flowshop scheduling using a shuffled frog-leaping algorithm with memeplex grouping, Eng. Optimiz., vol. 52, no. 5, pp. 1461–1474, 2019.
Google Scholar
[130]
J. C. Cai, D. M. Lei, and M. Li, A shuffled frog-leaping algorithm with memeplex quality for bi-objective distributed scheduling in hybrid flow shop, Int. J. Prod. Res., .
DOI Google Scholar
[131]
H. Y. Sang, Q. K. Pan, J. Q. Li, P. Wang, X. Y. Han, K. Z. Gao, and P. Duan, Effective invasive weed optimization algorithms for distributed assembly permutation flowshop problem with total flowtime criterion, Swarm Evol. Comput., vol. 44, no. 1, pp. 64–73, 2019.
Google Scholar
[132]
Y. Ming, J. Yang, J. Cao, Z. Zhou, and C. Xing, Distributed energy sharing in energy internet through distributed averaging, Tsinghua Sci. Technol., vol. 23, no. 3, pp. 233–242, 2018.
Google Scholar
[133]
K. Z. Gao, Y. Huang, A. Sadollah, and L. Wang, A review of energy-efficient scheduling in intelligent production systems, Complex. Intel. Syst., vol. 6, no. 2, pp. 237–249, 2020.
Google Scholar
[134]
Y. Huang, K. Wang, K. Z. Gao, T. Qu, and H. Liu, Jointly optimizing microgrid configuration and energy consumption scheduling of smart homes, Swarm Evol. Comput., vol. 48, no. 4, pp. 251–261, 2019.
Google Scholar
[135]
Y. Dong, J. Chen, Y. Tang, J. Wu, H. Wang, and E. Zhou, Lazy scheduling based disk energy optimization method, Tsinghua Sci. Technol., vol. 25, no. 2, pp. 203–216, 2020.
Google Scholar
[136]
Y. P. Fu, H. F. Wang, J. W. Wang, and X. J. Pu, Multi-objective modelling and optimization for scheduling a stochastic hybrid flow with maximizing processing quality and minimizing total tardiness, IEEE Syst. J., .
DOI Google Scholar
[137]
Fu Y. P., Wang H. F., Tian G. D., Li Z. W., and Hu H. S., Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm, J. Intel. Manuf., vol. 30, no. 5, pp. 2257–2272, 2019.
Google Scholar
[138]
Y. Xu, L. Wang, S. Y. Wang, and M. Liu, An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time, Neurocomputing, vol. 148, no. 2, pp. 260–268, 2015.
Google Scholar
[139]
S. Y. Wang, L. Wang, M. Liu, and Y. Xu, An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem, Int. J. Comput. Integ. Manuf., vol. 28, no. 3, pp. 307–320, 2015.
Google Scholar
[140]
Y. P. Fu, Y. Wang, D. Wu, and H. F. Wang, Facility location and capacity planning considering policy preference and uncertain demand under the One Belt One Road initiative, Trans. Res. A-Pol., vol. 138, no. 8, pp. 172–186, 2020.
Google Scholar
[141]
Y. P. Fu, M. C. Zhou, X. W. Guo, and L. Qi, Multi-verse optimization algorithm for stochastic bi-objective disassembly sequence planning subject to operation failures, IEEE Trans. Syst. Man. Cyber. Syst., .
DOI Google Scholar
[142]
K. Z. Gao, F. J. Yang, M. C. Zhou, Q. K. Pan, and P. N. Sugnathan, Flexible job shop rescheduling for new job insertion by using discrete Jaya algorithm, IEEE Trans. Cyber., vol. 49, no. 5, pp. 1944–1955, 2019.
Google Scholar
[143]
K. Z. Gao, Z. M. He, Y. Huang, P. Y. Duan, and P. N. Suganthan, A survey on meta-heuristics for solving disassembly line balancing, planning and scheduling problems in remanufacturing, Swarm Evol. Comput., .
DOI Google Scholar
[144]
F. Dong, X. Guo, P. Zhou, and D. Shen, Task-aware flow scheduling with heterogeneous utility characteristics for data center networks, Tsinghua. Sci. Technol., vol. 24, no. 4, pp. 400–411, 2019.
Google Scholar
[145]
D. Y. Bai, Z. H. Zhang, and Q. Zhang, Flexible open shop scheduling problem to minimize makespan, Comput. Oper. Res., vol. 67, no. 3, pp. 207–215, 2016.
Google Scholar
[146]
Pan Q. K., Wang L., and Qian B., A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems, Comput. Oper. Res., vol. 36, no. 8, pp. 2498–2511, 2009.
Google Scholar
[147]
H. B. Li, Z. Q. Sun, B. D. Chen, and H. P. Liu, Intelligent scheduling controller design for networked control systems based on estimation of distribution algorithm, Tsinghua Sci. Technol., vol. 13, no. 1, pp. 71–77, 2008.
Google Scholar
[148]
H. Zhang, J. Xie, J. Ge, J. Shi, and Z. Zhang, Hybrid particle swarm optimization algorithm based on entropy theory for solving DAR scheduling problem, Tsinghua Sci. Technol., vol. 24, no. 3, pp. 282–290, 2019.
Google Scholar
[149]
Zhu K., Li J., and Baoyin H., Trajectory optimization of the exploration of asteroids using swarm intelligent algorithms, Tsinghua Sci. Technol., vol. 14, no. S2, pp. 7–11, 2009.
Google Scholar
[150]
Y. W. Zhang, G. M. Cui, Y. Wang, X. Guo, and S. Zhao, An optimization algorithm for service composition based on an improved FOA, Tsinghua Sci. Technol., vol. 20, no. 1, pp. 90–99, 2015.
Google Scholar
[151]
L. Zhang, N. R. Alharbe, G. Luo, Z. Yao, and Y. Li, A hybrid forecasting framework based on support vector regression with a modified genetic algorithm and a random forest for traffic flow prediction, Tsinghua Sci. Technol., vol. 23, no. 4, pp. 479–492, 2018.
Google Scholar
[152]
Y. Zhang, Z. Jing, and Y. Zhang, MR-IDPSO: A novel algorithm for large-scale dynamic service composition, Tsinghua Sci. Technol., vol. 20, no. 6, pp. 602–612, 2015.
Google Scholar
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Received: 05 January 2021
Accepted: 27 January 2021
Published: 20 April 2021
Issue date: October 2021

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© The author(s) 2021

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Nos. 61603169, 61703220, and 61873328), China Postdoctoral Science Foundation Funded Project (No. 2019T120569), Shandong Province Outstanding Youth Innovation Team Project of Colleges and Universities of China (No. 2020RWG011), Shandong Province Colleges and Universities Youth Innovation Talent Introduction and Education Program, the Faculty Research Grants (FRG) from Macau University of Science and Technology, and Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology.

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