Scheduling in flexible manufacturing systems (FMS) is acknowledged as a computationally hard problem, as it involves complex production schedules, flexible job parts, and automated guided vehicle (AGV) routings. Consequently, several efficient metaheuristics are proposed to obtain a near-optimal solution. However, each heuristic requires specific parameter calibration, further complicating the problem. Furthermore, efficient scheduling in FMS must address multiple conflicting objectives simultaneously, leading to another set of operational challenges. In this paper, two recently developed, simple yet efficient parameter-less algorithms, namely Jaya and teaching-learning-based optimization (TLBO), are proposed to solve the scheduling problem in FMS. The multi-objective function is used to minimize the machine’s idle time and penalties for jobs that do not meet their due dates. The jobs are categorized in flexible manufacturing cells (FMCs) and processed in a loop layout FMS. First, the external FMC sequence is optimized, followed by the internal job sequence. The rank-priority rule is applied in algorithms with encoding and decoding mechanisms to obtain the optimal production sequences. The effectiveness of the proposed approach is compared with the public benchmarks. Computational results show that TLBO and Jaya outperform most of the popular and efficient heuristics in the literature.
LiuJMaccarthBL. The classification of FMS scheduling problems. Int J Prod Res1996; 34(3): 647–656.
2.
TedfordJDLoweC. Scheduling for just-in-time flexible manufacturing using adaptive fuzzy logic. Proc Inst Mech Eng Part B: J Eng Manuf1999; 213(7): 741–745.
3.
Ahamdi-JavidARamsheN. Designing flexible loop-based material handling AGV paths with cell-adjacency priorities: an efficient cutting-plane algorithm. 4OR2019; 17(4): 373–400.
4.
LiSHanKPeiZ, et al. Green flexible job shop integrated scheduling optimization for machines and AGVs based on the INSGA-II algorithm. Proc Inst Mech Eng Part B: J Eng Manuf2024; 240(4): 439.
5.
ToaderFA. Production scheduling in flexible manufacturing systems: a state of the art survey. J Electr Eng Electron Control Comput Sci2017; 3(7): 1–6.
6.
DoulgeriZHibberdRDHusbandTM, et al. The scheduling of flexible manufacturing systems. CIRP Ann1987; 36(1): 343–346.
7.
HuangJDownDGLewisME, et al. Dynamically scheduling and maintaining a flexible server. Nav Res Logist2022; 69(2): 223–240.
8.
MárquezCRHRibeiroCC. Shop scheduling in manufacturing environments: a review. Int Trans Oper Res2022; 29(6): 3237–3293.
9.
HashimotoSMizunoS. A 3-approximation list scheduling algorithm for a single-machine scheduling problem with a non-renewable resource and total weighted completion time criterion. J Oper Res Soc Jpn2022; 65(4): 157–171.
10.
GuoSQiuTGuoJ, et al. A novel MOEA/D with Q-learning initialization for dual-resource constrained flexible job shop scheduling problem with limited multi-skilled workers. Proc Inst Mech Eng Part B: J Eng Manuf2025: 09544054251360043.
11.
Sanjeev KumarRPadmanabanKPRajkumarM. Minimizing makespan and total flow time in permutation flow shop scheduling problems using modified gravitational emulation local search algorithm. Proc Inst Mech Eng Part B: J Eng Manuf2018; 232(3): 534–545.
12.
Wrytin. Types of flexible manufacturing systems. Wrytin, 2023.
13.
BrowneJDuboisDRathmillK, et al. Classification of flexible manufacturing systems. FMS Mag1984; 2(2): 114–117.
14.
KumarRTiwariMKShankarR. Scheduling of flexible manufacturing systems: an ant colony optimization approach. Proc Inst Mech Eng Part B: J Eng Manuf2003; 217(10): 1443–1453.
15.
Nageswara RaoMGaya PrasadKPraneethI, et al. Flexible manufacturing system scheduling through branch and bound algorithm. In: ChaurasiyaPKSinghAVermaTN, et al. (eds.) Technology innovation in mechanical engineering. Lecture notes in mechanical engineering. Springer, 2022, pp.825–836.
16.
HussainMSAliM. A multi-agent based dynamic scheduling of flexible manufacturing systems. Glob J Flex Syst Manag2019; 20(3): 267–290.
17.
HanLXingKChenX, et al. Deadlock-free genetic scheduling for flexible manufacturing systems using Petri nets and deadlock controllers. Int J Prod Res2014; 52(5): 1557–1572.
18.
de AlmeidaFSNaganoMS. An efficient iterated greedy algorithm for a multi-objective no-wait flow shop problem with sequence dependent setup times. 4OR2023; 2023; 1–15.
19.
PriorePPonteBPuenteJ, et al. Learning-based scheduling of flexible manufacturing systems using ensemble methods. Comput Ind Eng2018; 126: 282–291.
20.
JeraldJAsokanPPrabaharanG, et al. Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm. Int J Adv Manuf Technol2005; 25(9–10): 964–971.
21.
BurnwalSDebS. Scheduling optimization of flexible manufacturing system using cuckoo search-based approach. Int J Adv Manuf Technol2013; 64(5–8): 951–959.
22.
YouPSHsiehYC. Heuristic algorithms for carton production scheduling problems with technicians and different machines. Proc Inst Mech Eng Part B: J Eng Manuf2025; 239(12): 1695–1707.
23.
SankarSSPonnanbalamSGRajendranC. A multiobjective genetic algorithm for scheduling a flexible manufacturing system. Int J Adv Manuf Technol2003; 22(3–4): 229–236.
24.
RaoRVSavsaniVJVakhariaDP. Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Des2011; 43(3): 303–315.
25.
Venkata RaoR. Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput2016; 7: 19–34.
26.
MishraAKShrivastavaD. Integration of production scheduling and group maintenance planning in multi-unit system employing TLBO algorithm. Procedia CIRP2020; 93: 949–954.
27.
HuangCWangLYeungRS-C, et al. A prediction model guided Jaya algorithm for the PV system maximum power point tracking. IEEE Trans Sustain Energy2017; 9(1): 45–55.
28.
RadhikaSSrinivasa RaoChKrishnaN, et al. Multi-objective optimization of master production scheduling problems using Jaya algorithm, In Proceedings of the 6th International Symposium on all India Manufacturing Technology, Design and Research Conference, 2016.
29.
ZhangYYangXCattaniC, et al. Tea category identification using a novel fractional fourier entropy and Jaya algorithm. Entropy2016; 18(3): 1–17.
30.
ZouFChenDXuQ. A survey of teaching–learning-based optimization. Neurocomputing2019; 335: 366–383.
31.
LinWDengQHanW, et al. An effective algorithm for flexible assembly job–shop scheduling with tight job constraints. Int Trans Oper Res2022; 29(1): 496–525.
32.
ShawMJWhinstonAB. An artificial intelligence approach to the scheduling of flexible manufacturing systems. IIE Trans1989; 21(2): 170–183.
33.
DerO. Multi-output prediction and optimization of CO2 laser cutting quality in FFF-printed ASA thermoplastics using machine learning approaches. Polymers2025; 17(14): 1910.
34.
BasarGKahramanFDerO. Multi-response optimization of drilling parameters in direct hot-pressed Al/B4C/SiC hybrid composites using Taguchi-based entropy–CoCoSo method. Materials2025; 18(18): 1–33.
35.
ŞirinTBDerOKuşH, et al. Optimization of end mill geometry for machining 1.2379 cold-work tool steel through hybrid RSM–ANN–GA coupled FEA approach. Machines2025; 14(1): 15.
36.
RennaP. A review of game theory models to support production planning, scheduling, cloud manufacturing and sustainable production systems. Designs2024; 8(2): 26.
37.
FernandesJMRCHomayouniSMFontesDBMM. Energy-efficient scheduling in job shop manufacturing systems: a literature review. Sustain2022; 14(10): 6264.
38.
ShaDYLinHH. A multi-objective PSO for job-shop scheduling problems. Expert Syst Appl2010; 37(2): 1065–1070.
39.
NicoarESFilipFGParaschivN. Simulation-based optimization using genetic algorithms for multi-objective flexible JSSP. Stud Inform Control2011; 20(4): 333–344.
40.
XingLNChenYWWangP, et al. A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl Soft Comput J2010; 10(3): 888–896.
41.
LiuLLHuRSHuXP, et al. A hybrid PSO-GA algorithm for job shop scheduling in machine tool production. Int J Prod Res2015; 53(19): 5755–5781.
42.
QiuXLauHYK. An AIS-based hybrid algorithm for static job shop scheduling problem. J Intell Manuf2014; 25(3): 489–503.
43.
Qing-Dao-Er-JiRWangY. A new hybrid genetic algorithm for job shop scheduling problem. Comput Oper Res2012; 39(10): 2291–2299.
44.
BurmeisterSCGuerickeDSchryenG. A memetic NSGA-II for the multi-objective flexible job shop scheduling problem with real-time energy tariffs. Flex Serv Manuf J2024; 36(4): 1530–1570.
45.
CarlucciDRennaPMateriS. A job–shop scheduling decision-making model for sustainable production planning with power constraint. IEEE Trans Eng Manag2023; 70(5): 1923–1932.
