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Received: 2023-11-22

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 ORCID:

Bin Xin

https://orcid.org/0000-0001-9989-0418

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Frontiers of Information Technology & Electronic Engineering  2025 Vol.26 No.3 P.332-353

http://doi.org/10.1631/FITEE.2300795


A review of flexible job shop scheduling problems considering transportation vehicles


Author(s):  Bin XIN, Sai LU, Qing WANG, Fang DENG

Affiliation(s):  School of Automation, Beijing Institute of Technology, Beijing 100081, China; more

Corresponding email(s):   brucebin@bit.edu.cn, bit_lusai@163.com, wangqing1020@bit.edu.cn, dengfang@bit.edu.cn

Key Words:  Flexible manufacturing system, Transportation vehicle, Processing machine, Integrated scheduling


Bin XIN, Sai LU, Qing WANG, Fang DENG. A review of flexible job shop scheduling problems considering transportation vehicles[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(3): 332-353.

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doi="10.1631/FITEE.2300795"
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Abstract: 
The flexible job shop scheduling problem for processing machines and transportation vehicles (FJSP_PT) has garnered significant attention from academia and industry. Due to the inclusion of transportation vehicle scheduling in the scheduling problem of flexible manufacturing systems, solving FJSP_PT becomes more challenging and significantly more practically relevant compared to the flexible job shop scheduling problem. We summarize the assumptions, constraints, objective functions, and benchmarks of FJSP_PT. Then, statistical analysis is conducted on the literature up to 2023, including journals, number of articles published each year, and solution algorithms. We analyze recent literature on FJSP_PT, categorizing it based on algorithms into exact algorithms, heuristic algorithms, meta-heuristic algorithms, and swarm intelligence based algorithms. Finally, the research trends and challenges faced by FJSP_PT are summarized.

考虑转运车辆的柔性车间调度问题综述

辛斌1,鲁赛1,王晴1,邓方1,2
1北京理工大学自动化学院,中国北京市,100081
2北京理工大学重庆创新中心,中国重庆市,401120
摘要:柔性制造系统中加工机器与转运车辆的联合调度问题已引起学术界和工业界的广泛关注。与传统柔性车间调度问题相比,将转运车辆调度纳入柔性制造系统的调度,使得联合调度问题的求解更加具有挑战性和实践意义。本文对联合调度问题的常见假设、约束、目标函数和基准算例作了归纳总结。然后,从出版期刊、历年发表文章数量以及求解方法等角度,对截至2023年的相关文献进行了统计和讨论。随后,根据解决方法的类型,将其分为精确算法、启发式算法、元启发式算法和群体智能算法,并梳理已有文献。最后,总结了联合调度问题的研究趋势和未来挑战。

关键词:柔性制造系统;转运车辆;加工机器;集成调度

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Abdelmaguid TF, Nassef AO, 2010. A constructive heuristic for the integrated scheduling of machines and multiple-load material handling equipment in job shops. Int J Adv Manuf Technol, 46(9-12):1239-1251.

[2]Abdelmaguid TF, Nassef AO, Kamal BA, et al., 2004. A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. Int J Prod Res, 42(2):267-281.

[3]Aldaihani MM, 2015. Scheduling methodologies for a flexible manufacturing cell with non-identical parallel machines and a robot. Int J Ind Syst Eng, 21(4):499-514.

[4]Amirteimoori A, Kia R, 2023. Concurrent scheduling of jobs and AGVs in a flexible job shop system: a parallel hybrid PSO-GA meta-heuristic. Flex Serv Manuf J, 35(3):727-753.

[5]Anandaraman C, Vikram A, Sankar M, et al., 2012. Evolutionary approaches for scheduling a flexible manufacturing system with automated guided vehicles and robots. Int J Ind Eng Comput, 3(4):627-648.

[6]Babu KP, Babu VV, Medikondu NR, 2018. Implementation of heuristic algorithms to synchronized planning of machines and AGVs in FMS. Manag Sci Lett, 8(6):543-554.

[7]Badakhshian M, Sulaiman SB, Ariffin MKABM, 2012. Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm. Int J Phys Sci, 7(9):1461-1471.

[8]Balogun OO, Popplewell K, 1999. Towards the integration of flexible manufacturing system scheduling. Int J Prod Res, 37(15):3399-3428.

[9]Baruwa OT, Piera MA, 2016. A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles. Int J Prod Res, 54(16):4773-4792.

[10]Bilge Ü, Ulusoy G, 1995. A time window approach to simultaneous scheduling of machines and material handling system in an FMS. Oper Res, 43(6):1058-1070.

[11]Brandimarte P, 1993. Routing and scheduling in a flexible job shop by tabu search. Annu Oper Res, 41(3):157-183.

[12]Brucker P, Burke EK, Groenemeyer S, 2012. A mixed integer programming model for the cyclic job-shop problem with transportation. Discr Appl Math, 160(13-14):1924-1935.

[13]Chambers JB, Barnes JW, 1996. New Tabu Search Results for the Job Shop Scheduling Problem. Technical Report Series ORP96-06, The University of Texas, Austin, USA.

[14]Chan FTS, Chan HK, 2004. A comprehensive survey and future trend of simulation study on FMS scheduling. J Intell Manuf, 15(1):87-102.

[15]Chaudhry IA, Mahmood S, Shami M, 2011. Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms. J Centr South Univ, 18(5):1473-1486.

[16]Chaudhry IA, Rafique AF, Elbadawi IAQ, et al., 2022. Integrated scheduling of machines and automated guided vehicles (AGVs) in flexible job shop environment using genetic algorithms. Int J Ind Eng Comput, 13(3):343-362.

[17]Chen J, Zhang SY, Gao Z, et al., 2010. Feature-based initial population generation for the optimization of job shop problems. J Zhejiang Univ-Sci C (Comput & Electron), 11(10):767-777.

[18]Chen K, Bi L, Wang WY, 2022. Research on integrated scheduling of AGV and machine in flexible job shop. J Syst Simul, 34(3):4.

[19]Chen L, Bostel N, Dejax P, et al., 2007. A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal. Eur J Oper Res, 181(1):40-58.

[20]Chetto H, Castagna P, Plot C, 1995. Performance evaluation of dynamic scheduling strategies for manufacturing systems. IFAC Proc Vol, 28(10):347-352.

[21]Chikhi N, Abbas M, Benmansour R, et al., 2015. A two-stage flow shop scheduling problem with transportation considerations. 4OR, 13(4):381-402.

[22]Dai M, Tang DB, Adriana G, et al., 2019. Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Rob Comput Integr Manuf, 59:143-157.

[23]Dang QV, Nguyen L, 2016. A heuristic approach to schedule mobile robots in flexible manufacturing environments. Proc CIRP, 40:390-395.

[24]Dang QV, Rudová H, Nguyen CT, 2019a. Adaptive large neighborhood search for scheduling of mobile robots. Proc Genetic and Evolutionary Computation Conf, p.224-232.

[25]Dang QV, Nguyen CT, Rudová H, 2019b. Scheduling of mobile robots for transportation and manufacturing tasks. J Heurist, 25(2):175-213.

[26]Deroussi L, 2014. A hybrid PSO applied to the flexible job shop with transport. Proc 1st Int Conf on Swarm Intelligence Based Optimization, p.115-122.

[27]Deroussi L, Gourgand M, Tchernev N, 2006. Combining optimization methods and discrete event simulation: a case study in flexible manufacturing systems. Proc Int Conf on Service Systems and Service Management, p.495-500.

[28]Deroussi L, Gourgand M, Tchernev N, 2008. A simple metaheuristic approach to the simultaneous scheduling of machines and automated guided vehicles. Int J Prod Res, 46(8):2143-2164.

[29]Elmi A, Topaloglu S, 2013. A scheduling problem in blocking hybrid flow shop robotic cells with multiple robots. Comput Oper Res, 40(10):2543-2555.

[30]Erol R, Sahin C, Baykasoglu A, et al., 2012. A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems. Appl Soft Comput, 12(6):1720-1732.

[31]Fattahi P, Saidi Mehrabad M, Jolai F, 2007. Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. J Intell Manuf, 18(3):331-342.

[32]Gao KZ, Cao ZG, Zhang L, et al., 2019. A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems. IEEE/CAA J Autom Sin, 6(4):904-916.

[33]Gnanavel Babu A, Jerald J, Noorul Haq A, et al., 2010. Scheduling of machines and automated guided vehicles in FMS using differential evolution. Int J Prod Res, 48(16):4683-4699.

[34]Godinho Filho M, Barco CF, Tavares Neto RF, 2014. Using genetic algorithms to solve scheduling problems on flexible manufacturing systems (FMS): a literature survey, classification and analysis. Flex Serv Manuf J, 26(3):408-431.

[35]Gu WB, Li YX, Zheng KH, et al., 2020. A bio-inspired scheduling approach for machines and automated guided vehicles in flexible manufacturing system using hormone secretion principle. Adv Mech Eng, 12(2):1-17.

[36]Gu WN, Li YX, Li Z, et al., 2019. An intelligent approach for dynamic AGV scheduling problem in the discrete manufacturing system. Proc IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conf, p.1736-1739.

[37]Gupta YP, Gupta MC, Bector CR, 1989. A review of scheduling rules in flexible manufacturing systems. Int J Comput Integr Manuf, 2(6):356-377.

[38]Ham A, 2020. Transfer-robot task scheduling in flexible job shop. J Intell Manuf, 31(7):1783-1793.

[39]He LJ, Chiong R, Li WF, et al., 2022. A multiobjective evolutionary algorithm for achieving energy efficiency in production environments integrated with multiple automated guided vehicles. Knowl-Based Syst, 243:108315.

[40]He YA, Xia MH, 2021. Research on mass personalization production model based on the “Industry 4.0.” Manuf Autom, 43(1):25-29 (in Chinese).

[41]He YM, Xin B, Lu S, et al., 2022. Memetic algorithm for dynamic joint flexible job shop scheduling with machines and transportation robots. J Adv Comput Intell Intell Inform, 26(6):974-982.

[42]Heger J, Voss T, 2017. Optimal scheduling for automated guided vehicles (AGV) in blocking job-shops. Proc IFIP Int Conf on Advances in Production Management Systems, p.151-158.

[43]Hemmati FM, Haleh H, Saghaei A, 2018. A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy. Sci Iran, 25(1):339-358.

[44]Homayouni SM, Fontes DBMM, 2021. Production and transport scheduling in flexible job shop manufacturing systems. J Glob Optim, 79(2):463-502.

[45]Homayouni SM, Fontes DBMM, Gonçalves JF, 2023. A multistart biased random key genetic algorithm for the flexible job shop scheduling problem with transportation. Int Trans Oper Res, 30(2):688-716.

[46]Hoshino S, Seki H, Naka Y, 2008. Development of a flexible and agile multi-robot manufacturing system. IFAC Proc Vol, 41(2):15786-15791.

[47]Jerald J, Asokan P, Saravanan R, et al., 2006. Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm. Int J Adv Manuf Technol, 29(5):584-589.

[48]Kumar MVS, Janardhana R, Rao CSP, 2011. Simultaneous scheduling of machines and vehicles in an FMS environment with alternative routing. Int J Adv Manuf Technol, 53(1-4):339-351.

[49]Kumar N, Chandna P, Joshi D, 2019. Integrated scheduling of part, tool and automated guided vehicles in a flexible manufacturing system using modified genetic algorithm. Int J Ind Syst Eng, 32(4):443-468.

[50]Lacomme P, Moukrim A, Tchernev N, 2002. A new lower bound for scheduling of FMS based on AGV material handling. IFAC Proc Vol, 35(1):217-222.

[51]Lacomme P, Moukrim A, Tchernev N, 2005. Simultaneous job input sequencing and vehicle dispatching in a single-vehicle automated guided vehicle system: a heuristic branch-and-bound approach coupled with a discrete events simulation model. Int J Prod Res, 43(9):1911-1942.

[52]Lacomme P, Larabi M, Tchernev N, 2013. Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles. Int J Prod Econom, 143(1):24-34.

[53]Lee DY, DiCesare F, 1993. Integrated models for scheduling flexible manufacturing systems. Proc IEEE Int Conf on Robotics and Automation, p.827-832.

[54]Lee DY, DiCesare F, 1994. Integrated scheduling of flexible manufacturing systems employing automated guided vehicles. IEEE Trans Ind Electron, 41(6):602-610.

[55]Lin JT, Chiu CC, Chang YH, et al., 2015. A hybrid genetic algorithm for simultaneous scheduling of machines and AGVs in FMS. In: Gen M, Kim KJ, Huang XX, et al. (Eds.), Industrial Engineering, Management Science and Applications 2015. Springer, Berlin, p.277-286.

[56]Liu ZC, Ma S, Shi YJ, et al., 2013. Solving multi-objective flexible job shop scheduling with transportation constraints using a micro artificial bee colony algorithm. Proc IEEE 17th Int Conf on Computer Supported Cooperative Work in Design, p.427-432.

[57]Liu ZC, Guo SS, Wang L, 2019. Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. J Clean Prod, 211:765-786.

[58]Lv YL, Zhang G, Zhang J, et al., 2011. Integrated scheduling of the job and AGV for flexible manufacturing system. Appl Mech Mater, 80-81:1335-1339.

[59]Mishra A, Dash A, Bishoyee N, et al., 2009. Simultaneous scheduling of machines and AGVs in FMS environment using swarm optimization and comparison with genetic algorithm. Proc POMS 20th Annual Conf, p.172-181.

[60]Nageswara RM, Narayana RK, Ranga JG, 2017. Integrated scheduling of machines and AGVs in FMS by using dispatching rules. J Prod Eng, 20(1):75-84.

[61]Nageswararao M, Narayanarao K, Ranagajanardhana G, 2014. Simultaneous scheduling of machines and AGVs in flexible manufacturing system with minimization of tardiness criterion. Proc Mater Sci, 5:1492-1501.

[62]Nageswararao M, Narayanarao K, Ranagajanardhana G, 2015. Hybrid meta heuristic algorithm for simultaneous scheduling of machines and AGVs in flexible manufacturing environment. Can J Basic Appl Sci, 3(2):29-44.

[63]Nageswararao M, Narayanarao K, Rangajanardhana G, 2017. Scheduling of machines and automated guided vehicles in FMS using gravitational search algorithm. Appl Mech Mater, 867:307-313.

[64]Nouri HE, Driss OB, Ghédira K, 2016a. A classification schema for the job shop scheduling problem with transportation resources: state-of-the-art review. In: Silhavy R, Senkerik R, Oplatkova ZK, et al. (Eds.), Artificial Intelligence Perspectives in Intelligent Systems. Springer, Cham, p.1-11.

[65]Nouri HE, Driss OB, Ghédira K, 2016b. Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment. Appl Intell, 45(3):808-828.

[66]Nouri HE, Driss OB, Ghédira K, 2016c. Simultaneous scheduling of machines and a single moving robot in a job shop environment by metaheuristics based clustered holonic multiagent model. Proc 8th Int Conf on Agents and Artificial Intelligence, p.51-62.

[67]Nouri HE, Driss OB, Ghédira K, 2016d. Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multiagent model. Comput Ind Eng, 102:488-501.

[68]Nouri HE, Driss OB, Ghédira K, 2018. Controlling a single transport robot in a flexible job shop environment by hybrid metaheuristics. In: Nguyen NT, Kowalczyk R, van den Herik J, et al. (Eds.), Transactions on Computational Collective Intelligence XXVIII. Springer, Cham, p.93-115.

[69]Poppenborg J, Knust S, Hertzberg J, 2012. Online scheduling of flexible job-shops with blocking and transportation. Eur J Ind Eng, 6(4):497-518.

[70]Priore P, de la Fuente D, Gomez A, et al., 2001. A review of machine learning in dynamic scheduling of flexible manufacturing systems. AI EDAM, 15(3):251-263.

[71]Rathore K, Chauhan NR, 2015. FMS scheduling using neural networks: a review. Proc Int Conf on Soft Computing Techniques and Implementations, p.39-44.

[72]Reddy BSP, Rao CSP, 2006. A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS. Int J Adv Manuf Technol, 31(5):602-613.

[73]Reddy KS, Reddy NS, 2017. Simultaneous scheduling of machines and AGVs in FMS by using symbiotic organisms search (SOS) algorithm. Int J Res Appl Sci Eng Technol, 5(11):1780-1790.

[74]Saidi-Mehrabad M, Dehnavi-Arani S, Evazabadian F, et al., 2015. An ant colony algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Comput Ind Eng, 86:2-13.

[75]Sanches DS, da Silva Rocha J, Castoldi MF, et al., 2015. An adaptive genetic algorithm for production scheduling on manufacturing systems with simultaneous use of machines and AGVs. J Contr Autom Electr Syst, 26(3):225-234.

[76]Sawik T, 1996. A multilevel machine and vehicle scheduling in a flexible manufacturing system. Math Comput Model, 23(7):45-57.

[77]Soukhal A, Oulamara A, Martineau P, 2005. Complexity of flow shop scheduling problems with transportation constraints. Eur J Oper Res, 161(1):32-41.

[78]Subbaiah KV, Nageswara Rao M, Narayana Rao K, 2009. Scheduling of AGVs and machines in FMS with makespan criteria using sheep flock heredity algorithm. Int J Phys Sci, 4(2):139-148.

[79]Tabatabaei A, Torabi F, Paitoon T, 2018. Simultaneous scheduling of machines and automated guided vehicles utilizing heuristic search algorithm. Proc IEEE 8th Annual Computing and Communication Workshop and Conf, p.54-59.

[80]Tang DB, Zheng K, Gu WB, 2020. Hormone regulation based approach for distributed and on-line scheduling of machines and AGVs. In: Tang DB, Zheng K, Gu WB (Eds.), Adaptive Control of Bio-inspired Manufacturing Systems. Springer, Singapore, p.47-72.

[81]Tuma CCM, Morandin O, Caridá VF, 2013. Minimizing the makespan for the problem of reactive production scheduling in a FMS with AGVs using a new structure of chromosome in a hybrid GA with TS. Proc IEEE 18th Conf on Emerging Technologies & Factory Automation, p.1-6.

[82]Türkyılmaz A, Şenvar Ö, Ünal I, et al., 2020. A research survey: heuristic approaches for solving multi objective flexible job shop problems. J Intell Manuf, 31(8):1949-1983.

[83]Ulusoy G, Bilge Ü, 1993. Simultaneous scheduling of machines and automated guided vehicles. Int J Prod Res, 31(12):2857-2873.

[84]Ulusoy G, Sivrikaya-Şerifoǧlu F, Bilge Ü, 1997. A genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles. Comput Oper Res, 24(4):335-351.

[85]Umar UA, Ariffin MKA, Ismail N, et al., 2015. Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment. Int J Adv Manuf Technol, 81(9):2123-2141.

[86]Wang H, Sheng BY, Lu QB, et al., 2021. A novel multi-objective optimization algorithm for the integrated scheduling of flexible job shops considering preventive maintenance activities and transportation processes. Soft Comput, 25(4):2863-2889.

[87]Wang HY, Zhao F, Gao HM, et al., 2019. A three-stage method with efficient calculation for lot streaming flow-shop scheduling. Front Inform Technol Electron Eng, 20(7):1002-1020.

[88]Wang M, Xin B, 2019. A genetic algorithm for solving flexible flow shop scheduling problem with autonomous guided vehicles. Proc IEEE 15th Int Conf on Control and Automation, p.922-927.

[89]Wang XK, Wu WM, Xing ZC, et al., 2022. A neural network based multi-state scheduling algorithm for multi-AGV system in FMS. J Manuf Syst, 64:344-355.

[90]Xie J, Gao L, Peng KK, et al., 2019. Review on flexible job shop scheduling. IET Coll Intell Manuf, 1(3):67-77.

[91]Xin B, Lu S, Wang Q, et al., 2023. Simultaneous scheduling of processing machines and automated guided vehicles via a multi-view modeling-based hybrid algorithm. IEEE Trans Autom Sci Eng, 21(3):4753-4767.

[92]Xin B, Lu S, He YM, et al., 2025. Automatic design of dynamic collaboration strategies for machines and automated guided vehicles via multiobjective genetic programming. Unman Syst, 13(1):233-246.

[93]Xu GJ, Bao Q, Zhang HL, 2023. Multi-objective green scheduling of integrated flexible job shop and automated guided vehicles. Eng Appl Artif Intell, 126:106864.

[94]Yung TW, Ponnambalam SG, Yogeswaran M, 2009. Multi-objective ACO for integrated scheduling of machines and material handling equipment in flexible manufacturing systems. Proc IEEE Int Conf on Automation Science and Engineering, p.304-309.

[95]Zeng CK, Tang JF, 2014. Blocking job shop cell scheduling with automated guided vehicles. Proc 11th World Congress on Intelligent Control and Automation, p.438-442.

[96]Zeng CK, Tang JF, Yan CJ, 2014. Scheduling of no buffer job shop cells with blocking constraints and automated guided vehicles. Appl Soft Comput, 24:1033-1046.

[97]Zeng CK, Tang JF, Yan CJ, 2015. Job-shop cell-scheduling problem with inter-cell moves and automated guided vehicles. J Intell Manuf, 26(5):845-859.

[98]Zhang GH, Sun JH, Liu X, et al., 2019. Solving flexible job shop scheduling problems with transportation time based on improved genetic algorithm. Math Biosci Eng, 16(3):1334-1347.

[99]Zhang L, 2021. Path selection of manufacturing transformation and upgrading under the background of Made in China 2025. China Collect Econ, (4):9-10 (in Chinese).

[100]Zhang RW, Dou LH, Xin B, et al., 2024. A review on the truck and drone cooperative delivery problem. Unman Syst, 12(5):823-847.

[101]Zheng K, Tang DB, Giret A, et al., 2018. A hormone regulation-based approach for distributed and on-line scheduling of machines and automated guided vehicles. Proc Inst Mech Eng Part B J Eng Manuf, 232(1):99-113.

[102]Zheng Y, Xiao YJ, Seo Y, 2014. A tabu search algorithm for simultaneous machine/AGV scheduling problem. Int J Prod Res, 52(19):5748-5763.

[103]Zhou BH, Liao XM, 2020. Particle filter and Levy flight-based decomposed multi-objective evolution hybridized particle swarm for flexible job shop greening scheduling with crane transportation. Appl Soft Comput, 91:106217.

[104]Zhu ZQ, He YY, 2019. An improved genetic algorithm for production scheduling on FMS with simultaneous use of machines and AGVs. Proc 11th Int Conf on Intelligent Human-Machine Systems and Cybernetics, p.245-249.

[105]Zou WQ, Pan QK, Wang L, 2021. An effective multi-objective evolutionary algorithm for solving the AGV scheduling problem with pickup and delivery. Knowl-Based Syst, 218:106881.

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