A Review on Metaheuristic Approaches for Job-Shop Scheduling Problems
DOI:
https://doi.org/10.32734/jocai.v8.i1-17138Keywords:
Heuristics, Job-shop, Metaheuristics, Scheduling, ReviewAbstract
Over the past several decades, interest in metaheuristic approaches to address job-shop scheduling problems (JSSPs) has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides a significant attention on reviewing state-of-the-art metaheuristic approaches that have been developed to solve JSSPs. These approaches are analysed with respect to three steps: (i) preprocessing, (ii) initialization procedures and (iii) improvement algorithms. Through this review, the paper highlights the gaps in the literature and potential avenues for further research.
Downloads
References
A.Zai, A, Benmedjdoub, B, Boudhar, M. A branch and bound and parallel genetic algorithm for the job shop scheduling problem with blocking. International Journal of Operational Research, 2012;14(3): 343-365.
Brucker, P, Burke, EK, Groenemeyer, S. A branch and bound algorithm for the cyclic job-shop problem with transportation. Computers & Operations Research. 2012b; 39(12): 3200-3214.
Chen, B, Matis, TI. A flexible dispatching rule for minimizing tardiness in job shop scheduling. International Journal of Production Economics. 2012; 141(1): 360-365.
Chiang, TC, & Fu, LC. Using dispatching rules for job shop scheduling with due date-based objectives. International Journal of Production Research. 2007; 45(14): 3245-3262.
Lu, H, Huang, GQ, Yang, H. Integrating order review/release and dispatching rules for assembly job shop scheduling using a simulation approach. International Journal of Production Research. 2011; 49(3): 647-669.
Moghaddam, RT, Daneshmand-Mehr, M. A Computer Simulation Model for Job Shop Scheduling Problems Minimizing Makespan. Computer & Industrial Engineering. 2005; 48: 811-823.
Shahzad, A, Mebarki, N, IRCCyN, I. Discovering dispatching rules for job shop scheduling problem through data mining. Paper presented at the 8th International Conference of Modeling and Simulation, Tunisi; 2010.
Subramaniam, V, Ramesh, T, Lee, G, Wong, Y, Hong, G. Job shop scheduling with dynamic fuzzy selection of dispatching rules. The International Journal of Advanced Manufacturing Technology. 2000; 16(10),:759-764.
Liu, SQ, Kozan, E. A hybrid shifting bottleneck procedure algorithm for the parallel-machine job-shop scheduling problem. Journal of the Operational Research Society. 2011; 63(2): 168-182.
Bülbül, K. A hybrid shifting bottleneck-tabu search heuristic for the job shop total weighted tardiness problem. Computers & Operations Research. 2011; 38(6): 967-983.
Cheng, HC, Chiang, TC, Fu, LC. Multiobjective job shop scheduling using memetic algorithm and shifting bottleneck procedure. Paper presented at the IEEE Symposium on Computational Intelligence in Scheduling. CI-Sched'09. 2009.
Amirthagadeswaran, KS, Arunachalam, VP. Improved Solutions for Job Shop Scheduling Problmes through Genetic Algorithm with a Different Method of Schedule Deduction. The International Journal of Advanced Manufacturing Technology. 2006; 28:532-540.
Chandrasekaran, M, Asokan, P, Kumanan, S, Balamurugan, T, Nickolas, S. Solving job shop scheduling problems using artificial immune system. The International Journal of Advanced Manufacturing Technology. 2006; 31(5): 580-593.
Coello, C, Rivera, D, Cortés, N. Use of an artificial immune system for job shop scheduling. Artificial Immune Systems. 2003: 1-10.
Ge, H. W., Sun, L., Liang, Y. C., & Qian, F. An effective PSO and AIS-based hybrid intelligent algorithm for job-shop scheduling. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans. 2008; 38(2): 358-368.
Kuczapski, AM, Micea, MV, Maniu, LA, Cretu, VI. Efficient Generation of Near Optimal Initial Populations to Enhance Genetic Algorithms for Job-Shop Scheduling. Information Technology and Control. 2010; 39: 32-37.
Li, J, Pan, Y. A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem. The International Journal of Advanced Manufacturing Technology. 2013; 66: 583-596.
Ponsich, A, Coello, CA. A Hybrid Differential Evolution-Tabu Search Algorithm for the Solution of Job-Shop Scheduling Problems. Applied Soft Computing. 2013; 13(1): 462-474.
Tamilarasi, A. An enhanced genetic algorithm with simulated annealing for job-shop scheduling. International Journal of Engineering, Science and Technology. 2010; 2(1): 144-151.
Vela, CR, Varela, R, González, MA. Local search and genetic algorithm for the job shop scheduling problem with sequence dependent setup times. Journal of Heuristics. 2010; 16(2): 139-165.
Wang, L, Tang, D. An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem. Expert Systems with Applications. 2011; 38(6): 7243-7250.
Weckman, GR, Ganduri, CV, Koonce, DA. A neural network job-shop scheduler. Journal of intelligent Manufacturing. 2008; 19(2): 191-201.
Yusof, R, Khalid, M, Hui, GT, Yusof, SM. Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm. Applied Soft Computing. 2011; 11(8): 5782-5792.
Zhang, R, Wu, C. A simulated annealing algorithm based on block properties for the job shop scheduling problem with total weighted tardiness objective. Computers & Operations Research. 2011; 38(5): 854-867.
Zhang, R, Wu, C. A hybrid approach to large-scale job shop scheduling. Applied intelligence. 2010; 32(1): 47-59.
Zribi, N, El Kamel, A, Borne, P. Minimizing the makespan for the MPM job-shop with availability constraints. International Journal of Production Economics. 2008; 112(1): 151-160.
Li, J, Pan, Q, Xie, S. An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Applied Mathematics and Computation. 2012; 218(18): 9353-9371.
Zhang, R, Wu, C. A hybrid immune simulated annealing algorithm for the job shop scheduling problem. Applied Soft Computing. 2010; 10(1): 79-89.
Cavory, G, Dupas, R, Goncalves, G. A genetic approach to solving the problem of cyclic job shop scheduling with linear constraints. European Journal of Operational Research. 2005; 161(1): 73-85.
Cheung, W, Zhou, H. Using genetic algorithms and heuristics for job shop scheduling with sequence-dependent setup times. Annals of Operations Research. 2001; 107(1): 65-81.
Essafi, I, Mati, Y, Dauzère-Pérès, S. A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem. Computers & Operations Research. 2008; 35(8): 2599-2616.
Baptiste, P, Flamini, M, Sourd, F. Lagrangian bounds for just-in-time job-shop scheduling. Computers & Operations Research, 2008; 35(3): 906-915.
Nasiri, MM, Kianfar, F. A GES/TS algorithm for the job shop scheduling. Computers & industrial engineering. 2012; 62(4): 946-952.
Schuster, CJ, Framinan, JM. Approximative procedures for no-wait job shop scheduling. Operations Research Letters. 2003; 31(4): 308-318.
Yazdani, M, Amiri, M, Zandieh, M. Flexible job-shop scheduling with parallel variable neighborhood search algorithm. Expert Systems with Applications. 2010; 37(1): 678-687.
Zhang, G, Gao, L, Shi, Y. An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications. 2011; 38(4): 3563-3573.
Zhang, G, Shao, X, Li, P, Gao, L. An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers & industrial engineering. 2009; 56(4): 1309-1318.
Zhou, H, Feng, Y, Han, L. The hybrid heuristic genetic algorithm for job shop scheduling. Computers & industrial engineering. 2001; 40(3): 191-200.
Nakano, R, Yamada, T. Conventional genetic algorithm for job shop problems. Paper presented at the Proceedings of the Fourth International Conference on Genetic Algorithms; 1991.
Ombuki, BM, Ventresca, M. Local search genetic algorithms for the job shop scheduling problem. Applied Intelligence. 2004; 21(1): 99-109.
Vinod, V, Sridharan, R. Scheduling a dynamic job shop production system with sequence-dependent setups: An experimental study. Robotics and Computer-Integrated Manufacturing. 2008; 24(3): 435-449.
Azadeh, A, Negahban, A, Moghaddam, M. A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems. International Journal of Production Research. 2012; 50(2):551-566.
El-Bouri, A, Shah, P. A neural network for dispatching rule selection in a job shop. The International Journal of Advanced Manufacturing Technology. 2006; 31(3): 342-349.
Brucker, P, Kampmeyer, T. A general model for cyclic machine scheduling problems. Discrete applied mathematics. 2008; 156(13): 2561-2572.
Eswaramurthy, V, Tamilarasi, A. Hybridizing tabu search with ant colony optimization for solving job shop scheduling problems. The International Journal of Advanced Manufacturing Technology. 2009; 40(9): 1004-1015.
Heinonen, J, Pettersson, F. Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem. Applied Mathematics and Computation. 2007; 187(2): 989-998.
Huang, KL, Liao, CJ. Ant colony optimization combined with taboo search for the job shop scheduling problem. Computers & Operations Research. 2008; 35(4): 1030-1046.
Zhang, J, Hu, X, Tan, X, Zhong, J, Huang, Q. Implementation of an ant colony optimization technique for job shop scheduling problem. Transactions of the Institute of Measurement and Control. 2006; 28(1): 93-108.
Zhixiang, Y, Jianzhong, C, Yan, Y, Ying, M. Job shop scheduling problem based on DNA computing. Journal of Systems Engineering and Electronics. 2006; 17(3): 654-659.
Wu, C-S, Li, D-C, Tsai, T-I. Applying the fuzzy ranking method to the shifting bottleneck procedure to solve scheduling problems of uncertainty. The International Journal of Advanced Manufacturing Technology. 2006; 31(1-2): 98-106.
Gonzalez-Rodriguez, I, Vela, CR, Puente, J. A memetic approach to fuzzy job shop based on expectation model. Paper presented at the IEEE International Fuzzy Systems Conference, FUZZ-IEEE 2007. 2007.
Yahyaoui, A, Fnaiech, N, Fnaiech, F. A Suitable Initialization Procedure for Speeding a Neural Network Job-Shop Scheduling. IEEE Transactions on Industrial Electronics. 2011; 58(3): 1052 - 1060.
Yang, S, Wang, D. A new adaptive neural network and heuristics hybrid approach for job-shop scheduling. Computers & Operations Research. 2001; 28(10): 955-971.
Yang, S, Wang, D, Chai, T, Kendall, G. An improved constraint satisfaction adaptive neural network for job-shop scheduling. Journal of Scheduling. 2010; 13(1): 17-38.
Zhang, CY, Li, PG, Rao, YQ, Guan, ZL. A very fast TS/SA algorithm for the job shop scheduling problem. Computers & Operations Research. 2008; 35(1): 282-294.
Fattahi, P, Saidi, M.M. A New Approach in Job Shop Scheduling: Overlapping Operation. Journal of Industrial Engineering. 2009a.
Wang, L, Zhou, G, Xu, Y, Wang, S, Liu, M. An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. The International Journal of Advanced Manufacturing Technology. 2012; 60(1): 303-315.
Wong, T, Ngan, S. A comparison of hybrid genetic algorithm and hybrid particle swarm optimization to minimize makespan for assembly job shop. Applied Soft Computing. 2013; 13(3): 1391-1399.
Jamili, A, Shafia, M, Tavakkoli-Moghaddam, R. A hybridization of simulated annealing and electromagnetism-like mechanism for a periodic job shop scheduling problem. Expert Systems with Applications. 2011a; 38(5): 5895-5901.
Moslehi, G, Mahnam, M. A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. International Journal of Production Economics. 2011; 129(1): 14-22.
Naderi, B, Ghomi, S, Aminnayeri, M. A high performing metaheuristic for job shop scheduling with sequence-dependent setup times. Applied Soft Computing. 2010; 10(3): 703-710.
Roshanaei, V, Balagh, AKG, Esfahani, MMS, Vahdani, B. A mixed-integer linear programming model along with an electromagnetism-like algorithm for scheduling job shop production system with sequence-dependent set-up times. The International Journal of Advanced Manufacturing Technology. 2010; 47(5): 783-793.
Roshanaei, V, Naderi, B, Jolai, F, Khalili, M. A variable neighborhood search for job shop scheduling with set-up times to minimize makespan. Future Generation Computer Systems. 2009; 25(6): 654-661.
Tavakkoli-Moghaddam, R, Taheri, F, Bazzazi, M, Izadi, M, Sassani, F. Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints. Computers & Operations Research. 2009; 36(12): 3224-3230.
Zhang, R, Song, S, Wu, C. A hybrid artificial bee colony algorithm for the job shop scheduling problem. International Journal of Production Economics. 2013; 141(1): 167-178.
Lin, TL, Horng, SJ., Kao, TW, Chen, YH, Run, RS, Chen, RJ, Kuo, I. An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Systems with Applications. 2010; 37(3): 2629-2636.
Yin, M, Li, X, Zhou, J. An efficient job shop scheduling algorithm based on artificial bee colony. Scientific Research and Essays. 2011; 5(24): 2578-2596.
Luh, GC, Chueh, CH. A multi-modal immune algorithm for the job-shop scheduling problem. Information Sciences, 2009; 179(10): 1516-1532.
Qing, R, Wang, Y. A new hybrid genetic algorithm for job shop scheduling problem. Computers and Operations Research. 2012; 39(10): 2291-2299.
Jamili, A, Shafia, MA, Tavakkoli-Moghaddam, R. A hybrid algorithm based on particle swarm optimization and simulated annealing for a periodic job shop scheduling problem. The International Journal of Advanced Manufacturing Technology. 2011b; 54(1): 309-322.
Nie, L, Gao, L, Li, P, Li, X. A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates. Journal of intelligent Manufacturing. 2012: 1-12.
Abdolrazzagh-Nezhad, M, Abdullah, S. Robust start for population-based algorithms solving job-shop scheduling problems. Paper presented at the 3rd Conference on Data Mining and Optimization (DMO). 2011. Malaysia
Abdullah, S, Abdolrazzagh-Nezhad, M. Fuzzy job-shop scheduling problems: A review. Information Sciences. 2014; 278(0): 380-407.
Bagheri, A, Zandieh, M, Mahdavi, I, Yazdani, M. An artificial immune algorithm for the flexible job-shop scheduling problem. Future Generation Computer Systems, 2010; 26(4): 533-541.
Tavakkoli-Moghaddam, R, Azarkish, M, Sadeghnejad, A. A new hybrid multi-objective pareto archive PSO algorithm for a classic job shop scheduling problem with ready time. Paper presented at the Advanced Intelligent Computing. 6th International Conference on Intelligent Computing Theories and Applications; 2010.
Vilcot, G, Billaut, JC. A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem. European Journal of Operational Research. 2008;190(2): 398-411.
Chiang, T-C, & Lin, H-J. A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. International Journal of Production Economics. 2013; 141(1): 87-98.
Gu, J, Gu, M, Cao, C, Gu, X. A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem. Computers & Operations Research. 2010; 37(5): 927-937.
Hu, Y, Yin, M, Li, X. A novel objective function for job-shop scheduling problem with fuzzy processing time and fuzzy due date using differential evolution algorithm. The International Journal of Advanced Manufacturing Technology. 2011; 56(9-12): 1125-1138.
Lei, D, Wu, Z. Crowding-measure-based multiobjective evolutionary algorithm for job shop scheduling. The International Journal of Advanced Manufacturing Technology. 2006; 30(1-2): 112-117.
Horng, S-C, Lin, S-S, Yang, F-Y. Evolutionary algorithm for stochastic job shop scheduling with random processing time. Expert Systems with Applications. 2012; 39(3): 3603-3610.
Wang, X, Gao, L, Zhang, C, Shao, X. A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. The International Journal of Advanced Manufacturing Technology. 2010; 51(5-8), 757-767.
Qing, R, Wang, Y. A new hybrid genetic algorithm for job shop scheduling problem. Computers and Operations Research. 2012; 39(10): 2291-2299.
Burke, E, Silva, JL. The design of memetic algorithms for scheduling and timetabling problems. Recent Advances in Memetic Algorithms: Springer; 2005.
Caumond, A, Lacomme, P, Tchernev, N. A memetic algorithm for the job-shop with time-lags. Computers & Operations Research. 2008; 35(7): 2331-2356.
Cheng, HC, Chiang, TC, & Fu, LC. A two-stage hybrid memetic algorithm for multiobjective job shop scheduling. Expert Systems with Applications. 2011; 38(9): 10983-10998.
Chiang, T-C, Fu, L-C. A rule-centric memetic algorithm to minimize the number of tardy jobs in the job shop. International Journal of Production Research.2008; 46(24): 6913-6931.
Hasan, SK, Sarker, R, Essam, D, Cornforth, D. Memetic algorithms for solving job-shop scheduling problems. Memetic Computing. 2009; 1(1): 69-83.
Qian, B, Wang, L, Huang, D-X., Wang, X. Scheduling multi-objective job shops using a memetic algorithm based on differential evolution. The International Journal of Advanced Manufacturing Technology. 2008; 35(9-10), 1014-1027.
Yang, J-H, Sun, L, Lee, HP, Qian, Y, Liang, Y-C.. Clonal selection based memetic algorithm for job shop scheduling problems. Journal of Bionic Engineering. 2008; 5(2): 111-119.
Nowicki, E, Smutnicki, C. An advanced tabu search algorithm for the job shop problem. Journal of Scheduling. 2005; 8(2): 145-159.
Frutos, M, Olivera, AC, Tohmé, F. A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem. Annals of Operations Research. 2010; 181(1): 745-765.
Gao, L, Zhang, G, Zhang, Li, X. An efficient memetic algorithm for solving the job shop scheduling problem. Computers & Industrial Engineering. 2011; 60(4): 699-705.
Ho, NB, Tay, JC. GENACE: An efficient cultural algorithm for solving the flexible job-shop problem. Paper presented at the Congress on Evolutionary Computation. 2004.
Becerra, RL, Coello, CAC. A Cultural Algorithm for Solving the Job Shop Scheduling Problem. Knowledge Incorporation in Evolutionary Computation. Springer; 2005.
Cortés Rivera, D, Landa Becerra, R, Coello Coello, CA. Cultural algorithms, an alternative heuristic to solve the job shop scheduling problem. Engineering Optimization, 2007; 39(1): 69-85.
Lianghui, Z. Cultural Algorithm of Job Shop Scheduling Problem. Compute Engineering. 2009; 35(l3): 196-198.
Xia, Z. Cultural particle swarm optimization algorithm for Job-Shop scheduling problem. Application Research of Computers. 2012; 4: 011.
Raeesi, MR, Kobti, Z. A multiagent system to solve JSSP using a multi-population cultural algorithm Advances in Artificial Intelligence: Springer; 2012: pp. 362-367
Feng, Y JSSZ. Job-Shop Scheduling Study by Dynamic Evaluation based Immune Algorithm. Chinese Journal of Mechanical Engineering. 2005; 3: 004.
Ge, HW, Sun, L, Liang, Y-C. Solving job-shop scheduling problems by a novel artificial immune system Advances in Artificial Intelligence. Springer; 2005. pp: 839-842.
Yu, J-J, Sun, S-D, Hao, J.-H. Multi objective flexible job-shop scheduling based on immune algorithm. Computer Integrated Manufacturing Systems-Beijing. 2006; 12(10): 1643.
Tsai, JT, Ho, WH, Liu, TK, Chou, JH. Improved immune algorithm for global numerical optimization and job-shop scheduling problems. Applied Mathematics and Computation. 2007; 194(2): 406-424.
Hong, L. A novel artificial immune algorithm for job shop scheduling. Paper presented at the International Conference on Computational Intelligence and Natural Computin. CINC'09. 2009.
Luh, GC, Chueh, CH. A multi-modal immune algorithm for the job-shop scheduling problem. Information Sciences, 2009; 179(10): 1516-1532.
Song, XJ, Lu, JY, Sui, M-l. Job-shop scheduling problems based on immune ant colony optimization. Journal of Computer Applications. 2007; 27(5): 1183-1186.
Naderi, B., Khalili, M., & Tavakkoli-Moghaddam, R. A hybrid artificial immune algorithm for a realistic variant of job shops to minimize the total completion time. Computers & Industrial Engineering. 2009; 56(4): 1494-1501.
Wu, C, Zhang, N, Jiang, J, Yang, J, Liang, Y. Improved bacterial foraging algorithms and their applications to job shop scheduling problems. Adaptive and Natural Computing Algorithms. Springer. 2007; pp. 562-569
Jing-jinga, C, Yan-minga, S, Lan-xiub, C. Improved bacteria foraging optimization algorithm for Job-Shop scheduling problems [J]. Application Research of Computers. 2011; 9: 034.
Zhao, F, Jiang, X, Zhang, C, Wang, J. A chemotaxis-enhanced bacterial foraging algorithm and its application in job shop scheduling problem. International Journal of Computer Integrated Manufacturing(ahead-of-print). 2014: 1-16.
Narendhar, S, Amudha, T. A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems. arXiv preprint arXiv:1211.4971; 2012.
Ge, H., & Tan, G. A Cooperative Intelligent Approach for Job-shop Scheduling Based on Bacterial Foraging Strategy and Particle Swarm Optimization. Computational Intelligence Systems in Industrial Engineering. Springer; 2012. pp: 363-383.
Zhang, J, Zhang, P, Yang, JX, Huang, Y. Solving the job shop scheduling problem using the imperialist competitive algorithm. Paper presented at the Advanced Materials Research; (2012).
Karimi, N, Zandieh, M. Najafi, A. Group scheduling in flexible flow shops: a hybridised approach of imperialist competitive algorithm and electromagnetic-like mechanism. International Journal of Production Research. 2011; 49(16): 4965-4977.
Moradinasab, N, Shafaei, R, Rabiee, M, Ramezani, P. No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms. Journal of Experimental & Theoretical Artificial Intelligence. 2013; 25(2): 207-225.
Seidgar, H, Kiani, M, Abedi, M, Fazlollahtabar, H. An efficient imperialist competitive algorithm for scheduling in the two-stage assembly flow shop problem. International Journal of Production Research. 2014; 52(4): 1240-1256.
Shokrollahpour, E, Zandieh, M, Dorri, B. A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem. International Journal of Production Research. 2011; 49(11): 3087-3103.
Goldansaz, SM, Jolai, F, Anaraki, AHZ.. A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop. Applied Mathematical Modelling. 2013; 37(23): 9603-9616.
Banisadr, A, Zandieh, M, Mahdavi, I. A hybrid imperialist competitive algorithm for single-machine scheduling problem with linear earliness and quadratic tardiness penalties. The International Journal of Advanced Manufacturing Technology, 2013; 65(5-8): 981-989.
Zobolas, G, Tarantilis, C, Ioannou, G. Exact, Heuristic and Meta-heuristic Algorithms for Solving Shop Scheduling Problems. Metaheuristics for Scheduling in Industrial and Manufacturing Applications. Springer; 2008. pp. 1-40
Ahandani, M, Shirjoposht, N, Banimahd, R. Job-shop scheduling using hybrid shuffled frog leaping. Paper presented at the 14th International CSI Computer Conference CSICC. 2009.
Teekeng, W, Thammano, A. A combination of shuffled frog leaping and fuzzy logic for flexible job-shop scheduling problems. Procedia Computer Science. 2011; 6: 69-75.
Junqing-Li, Quan-Ke Pan, Yun-Chia Liang. An effective hybrid tabu search algortihm for multi-objective flexible job shop scheduling problem. Computers &Industrial Engineering. 2010; 59(4):647-662.
Chong, C.S, Low, MYH, Sivakumar, AI, Gay, KL. A bee colony optimization algorithm to job shop scheduling. Paper presented at the Simulation Conference. WSC 06. Proceedings of the Winter. 2006.
Sezgin Kiliç, Cengiz Kahraman. Solution of a Fuzzy Flowshop Scheduling Problem Using a Necessity Measure. Journal of Multiple-valued Logic and Soft computing. 2016; 15(1):51-64
El-Bouri, A, Azizi, N, Zolfaghari, S. A comparative study of a new heuristic based on adaptive memory programming and simulated annealing: The case of job shop scheduling. European Journal of Operational Research, 2007; 177(3): 1894-1910.
Fayad, C, Petrovic. S. A Fuzzy Genetic Algorithm for Real-World Job Shop Scheduling. Dlm. M. Ali & F. Esposito (pnyt.). Ed. Innovations in Applied Artificial Intelligence 3533. pp. 524-533. Springer Berlin Heidelberg. 2005.
Chen, C-M, C.-Y. Liu, M.-H. Chang. Personalized curriculum sequencing utilizing modified item response theory for web-based instruction. Expert Systems with Applications. 2006; 30(2): 378-396.
Mehrabad, MS, A. Pahlavani. A fuzzy multi-objective programming for scheduling of weighted jobs on a single machine. The International Journal of Advanced Manufacturing Technology. 2009; 45(1-2): 122-139.
Li, J, Q-K. Pan, PN. Suganthan, MF. Tasgetiren. Solving fuzzy job-shop scheduling problem by a hybrid PSO algorithm. Dlm. (pnyt.). Ed. Swarm and Evolutionary Computation, Springer; 2012. pp. 275-282.
Petrovic, S, Fayad, C, Petrovic, D, Burke, E, Kendall, G. Fuzzy job shop scheduling with lot-sizing. Annals of Operations Research. 2008; 159(1): 275-292.
Palacios, JJ, MA González, CR. Vela, I. González-RodrÃguez, J. Puente. Genetic tabu search for the fuzzy flexible job shop problem. Computers & Operations Research. 2015; 54(0): 74-89.
Palacios, JJ, J Puente, CR Vela, I. González-RodrÃguez. Benchmarks for fuzzy job shop problems. Information Sciences. 2016; 329: 736-752.
Tavakkoli-Moghaddam, R, Khalili, M, Naderi, B. A hybridization of simulated annealing and electromagnetic-like mechanism for job shop problems with machine availability and sequence-dependent setup times to minimize total weighted tardiness. Soft Computing. 2009. 13(10): 995-1006.
Chao, CW, Liao, CJ. A discrete electromagnetism-like mechanism for single machine total weighted tardiness problem with sequence-dependent setup times. Applied Soft Computing. 2012; 12(9): 3079-3087.
Published
How to Cite
Issue
Section
Copyright (c) 2024 Data Science: Journal of Computing and Applied Informatics
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Data Science: Journal of Informatics Technology and Computer Science (JoCAI) and Faculty of Computer Science and Information Technology as well as TALENTA Publisher Universitas Sumatera Utara as publisher of the journal.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, will be allowed only with a written permission fromData Science: Journal of Informatics Technology and Computer Science (JoCAI).
The Copyright Transfer Form can be downloaded here.
The copyright form should be signed originally and sent to the Editorial Office in the form of original mail or scanned document.