Abstract
The complex problems in the real world, an increase in competition among producers, the advancements in equipment and manufacturing products, the high cost of factory equipment and, etc., have led to the production structure change from a centralized structure to a decentralized one. In recent years, distributed systems have become increasingly important. So in order to adapt to market competition and to respond quickly to changing market demand, there is a need to study this problem. An important aspect of planning in a distributed environment is decentralized production. In such cases, it becomes more important to consider the problem of distributed scheduling. In this regard, this paper provides a systematic literature review on the multi-factory scheduling problems in the past eleven years and report the research gaps. To this end, first, the related research was classified based on the shop environments. Then, after reviewing the existing papers and summarizing them, future researches and emerging research fields of the multi-factory scheduling problem are reported. This review indicates that future research should focus on open shop production environments. The results also show only 4% of the papers focus on the virtual alliance. Therefore, researchers need to consider the virtual alliance in the production network and investigate the participation and competition between the partners in such network. Studying the topic of Industry 4.0 in multi-factory scheduling and subsequently investigating the related topics such as information sharing and real-time data are also the new trends in this field. Considering the complex series–parallel structures in the multi-factory production and defining objective functions related to environmental issues such as reducing pollutants and noise are other suggestions for future studies.
Similar content being viewed by others
References
Ali A (2020) Optimization of the distributed permutation flowshop scheduling problem,” parallel flowshop, distributed permutation flowshop, tabu search, total completion time, makespan, no-wait heterogenous DPFSP
Ali A, Gajpal Y, Elmekkawy TY (2021) Distributed permutation flowshop scheduling problem with total completion time objective. Opsearch 58(2):425–447
Baker KR, Trietsch D (2013) Principles of sequencing and scheduling. John Wiley & Sons, New York
Bargaoui H, Driss OB, Ghédira K (2017) Towards a distributed implementation of chemical reaction optimization for the multi-factory permutation flowshop scheduling problem. Proc Comput Sci 112:1531–1541
Beheshtinia MA, Ghasemi A, Farokhnia M (2018) Supply chain scheduling and routing in multi-site manufacturing system (case study: a drug manufacturing company). J Model Manag 13(1):27–49
Behnamian J (2014) Decomposition based hybrid VNS–TS algorithm for distributed parallel factories scheduling with virtual corporation. Comput Oper Res 52:181–191
Behnamian J (2016) Graph colouring-based algorithm to parallel jobs scheduling on parallel factories. Int J Comput Integr Manuf 29(6):622–635
Behnamian J (2017a) Matheuristic for decentralized factories scheduling problem. Appl Math Modell 47:668–684
Behnamian J (2017b) Heterogeneous networked cooperative scheduling with anarchic particle swarm optimization. IEEE Trans Eng Manage 64(2):166–178
Behnamian J, Fatemi Ghomi SMT (2013) The heterogeneous multi-factory production network scheduling with adaptive communication policy and parallel machine. Inf Sci 219:181–196
Behnamian J, Fatemi Ghomi SMT (2016) A survey of multi-factory scheduling. J Intell Manuf 27(1):231–249
Behnamian J, Ghomi SF (2020) Multi-objective multi-factory scheduling. RAIRO Oper Res 55:S1447–S1467
Cai J, Lei D (2020) Fuzzy distributed two-stage hybrid flow shop scheduling problem with setup time: collaborative variable search. J Intell Fuzzy Syst 38:1–11
Cai S, Yang K, Liu K (2018) Multi-objective optimization of the distributed permutation flow shop scheduling problem with transportation and eligibility constraints. J Oper Res Soc China 6(3):391–416
Cai J, Zhou R, Lei D (2020a) Dynamic shuffled frog-leaping algorithm for distributed hybrid flow shop scheduling with multiprocessor tasks. Eng Appl Artif Intell 90:103540
Cai J, Lei D, Li M (2020b) A shuffled frog-leaping algorithm with memeplex quality for bi-objective distributed scheduling in hybrid flow shop. Int J Prod Res 1–18:2020
Cai J, Lei D (2021) A cooperated shuffled frog-leaping algorithm for distributed energy-efficient hybrid flow shop scheduling with fuzzy processing time. Complex Intell Syst
Chan FTS, Chung SH, Chan PLY (2005) An adaptive genetic algorithm with dominated genes for distributed scheduling problems. Expert Syst Appl 29(2):364–371
Chan FTS, Chung SH, Chan PLY (2006) Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems. Int J Prod Res 44(3):523–543
Chang HC, Liu T-K (2017) Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms. J Intell Manuf 28(8):1973–1986
Chaouch I, Driss OB, Ghedira K (2017) A modified ant colony optimization algorithm for the distributed job shop scheduling problem. Proc Comput Sci 112:296–305
Chen J, Wang L, Peng Z (2019b) A collaborative optimization algorithm for energy-efficient multi-objective distributed no-idle flow-shop scheduling. Swarm Evolut Comput 50:100557
Chen S, Pan Q-K, Gao L (2021) Production scheduling for blocking flowshop in distributed environment using effective heuristics and iterated greedy algorithm. Robot Comput Integr Manuf 71:102155
Chen J, Wang L, He X, Huang D (2019) A probability model-based memetic algorithm for distributed heterogeneous flow-shop scheduling. In: 2019 IEEE congress on evolutionary computation (CEC), pp 411–418
Chung SH, Lau HCW, Ho GTS, Ip WH (2009) Optimization of system reliability in multi-factory production networks by maintenance approach. Expert Syst Appl 36(6):10188–10196
Deng J, Wang L (2017) A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm Evol Comput 32:121–131
Deng J, Wang L, Shen J, Zheng X (2016) An improved harmony search algorithm for the distributed two machine flow-shop scheduling problem. Harmony search algorithm. Springer, Berlin, pp 97–108
Du Y, Li J, Luo C, Meng L (2021) A hybrid estimation of distribution algorithm for distributed flexible job shop scheduling with crane transportations. Swarm Evolut Comput 62:100861
Emin Baysal M, Sarucan A, Büyüközkan K, Engin O (2021) Distributed fuzzy permutation flow shop scheduling problem: a bee colony algorithm. Intelligent and fuzzy techniques: smart and innovative solutions. Springer, Cham, pp 1440–1446
Fernandez-Viagas V, Perez-Gonzalez P, Framinan JM (2018) The distributed permutation flow shop to minimise the total flowtime. Comput Ind Eng 118:464–477
Fu Y, Wang H, Huang M (2019a) Integrated scheduling for a distributed manufacturing system: a stochastic multi-objective model. Enterp Inf Syst 13(4):557–573
Fu Y, Tian G, Fathollahi-Fard AM, Ahmadi A, Zhang C (2019b) Stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint. J Clean Prod 226:515–525
Gao J (2011) A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem. Int J Comput Intell Syst 497–508:2012
Gao J, Chen R (2011) A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem. Int J Comput Intell Syst 4(4):497–508
Gharaei A, Jolai F (2019) A Pareto approach for the multi-factory supply chain scheduling and distribution problem. Oper Res
Gnoni MG, Iavagnilio R, Mossa G, Mummolo G, Di Leva A (2003) Production planning of a multi-site manufacturing system by hybrid modelling: a case study from the automotive industry. Int J Prod Econ 85(2):251–262
Gonzalez-Neira EM, Ferone D, Hatami S, Juan AA (2017) A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times. Simul Model Pract Theory 79:23–36
Hamzadayı A (2020) An effective benders decomposition algorithm for solving the distributed permutation flowshop scheduling problem. Comput Oper Res 123:105006
Hatami S, Ruiz R, Andrés-Romano C (2013) The distributed assembly permutation flowshop scheduling problem. Int J Prod Res 51(17):5292–5308
Hatami S, Ruiz R, Andrés Romano C (2014) Two Simple constructive algorithms for the distributed assembly permutation flowshop scheduling problem. Managing complexity. Springer, Cham, pp 139–145
Huang Z, Kim J, Sadri A, Dowey S, Dargusch MS (2019) Industry 4.0: development of a multi-agent system for dynamic value stream mapping in SMEs. J Manuf Syst 52:1–12
Huang Y-Y, Pan Q-K, Huang J-P, Suganthan P, Gao L (2021) An improved iterated greedy algorithm for the distributed assembly permutation flowshop scheduling problem. Comput Ind Eng 152:107021
İşgüder H, Hamzadayi A (2021) A hybrid benders decomposition algorithm and new models for the distributed permutation flowshop scheduling problem. Avrupa Bilim ve Teknoloji Dergisi, No. 23, Article No. 23
Jia HZ, Fuh JYH, Nee AYC, Zhang YF (2002) Web-based multi-functional scheduling system for a distributed manufacturing environment. Concurr Eng 10(1):27–39
Jia HZ, Nee AYC, Fuh JYH, Zhang YF (2003) A modified genetic algorithm for distributed scheduling problems. J Intell Manuf 14(3):351–362
Jia HZ, Fuh JYH, Nee AYC, Zhang YF (2007) Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems. Comput Ind Eng 53(2):313–320
Jiang E, Wang L, Peng Z (2020) Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition. Swarm Evolut Comput 58:100745
Jing X-L, Pan Q-K, Gao L, Wang Y-L (2020) An effective Iterated Greedy algorithm for the distributed permutation flowshop scheduling with due windows. Appl Soft Comput 96:106629
Jing X-L, Pan Q-K, Gao L (2021) Local search-based metaheuristics for the robust distributed permutation flowshop problem. Appl Soft Comput 105:107247
Karimi N, Davoudpour H (2017a) Integrated production and delivery scheduling for multi-factory supply chain with stage-dependent inventory holding cost. Comput Appl Math 36(4):1529–1544
Karimi N, Davoudpour H (2017b) A knowledge-based approach for multi-factory production systems. Comput Oper Res 77:72–85
Khare A, Agrawal S (2020) Effective heuristics and metaheuristics to minimise total tardiness for the distributed permutation flowshop scheduling problem. Int J Prod Res 1–17:2020
Lei D, Liu M (2020) An artificial bee colony with division for distributed unrelated parallel machine scheduling with preventive maintenance. Comput Ind Eng 141:106320
Lei D, Wang T (2020) Solving distributed two-stage hybrid flowshop scheduling using a shuffled frog-leaping algorithm with memeplex grouping. Eng Optim 52(9):1461–1474
Lei D, Yuan Y, Cai J, Bai D (2020a) An imperialist competitive algorithm with memory for distributed unrelated parallel machines scheduling. Int J Prod Res 58(2):597–614
Lei D, Yuan Y, Cai J (2020b) An improved artificial bee colony for multi-objective distributed unrelated parallel machine scheduling. Int J Prod Res 59:1–13
Lei D, Su B, Li M (2020c) Cooperated teaching-learning-based optimisation for distributed two-stage assembly flow shop scheduling. Int J Prod Res 1–14:2020
Leung SC, Wu Y, Lai KK (2003) Multi-site aggregate production planning with multiple objectives: a goal programming approach. Prod Plan Control 14(5):425–436
Li J-Q, Duan P, Cao J, Lin X-P, Han Y-Y (2018) A hybrid pareto-based Tabu search for the distributed flexible job shop scheduling problem with E/T criteria. IEEE Access 6:58883–58897
Li Y et al (2020) A discrete artificial bee colony algorithm for distributed hybrid flowshop scheduling problem with sequence-dependent setup times. Int J Prod Res 1–20:2020
Li M, Su B, Lei D (2021a) A novel imperialist competitive algorithm for fuzzy distributed assembly flow shop scheduling. J Intell Fuzzy Syst 40(3):4545–4561
Li Y-Z, Pan Q-K, Li J-Q, Gao L, Tasgetiren MF (2021b) An Adaptive Iterated Greedy algorithm for distributed mixed no-idle permutation flowshop scheduling problems. Swarm Evolut Comput 63:100874
Li H, Li X, Gao L (2021c) A discrete artificial bee colony algorithm for the distributed heterogeneous no-wait flowshop scheduling problem. Appl Soft Comput 100:106946
Liberati A et al (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 339:b2700
Lin S-W, Ying K-C (2016) Minimizing makespan for solving the distributed no-wait flowshop scheduling problem. Comput Ind Eng 99:202–209
Lin J, Zhang S (2016) An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem. Comput Ind Eng 97:128–136
Liu T-K, Chen Y-P, Chou J-H (2014) Solving distributed and flexible job-shop scheduling problems for a real-world fastener manufacturer. IEEE Access 2:1598–1606
Liu Y (2020) Effective heuristics to minimize total flowtime for distributed flowshop group scheduling problems. In: 2020 5th international conference on mechanical, control and computer engineering (ICMCCE), pp 708–711
Liu X, Hongguang B, Yue M, Qiunan M (2006) A new approach for planning and scheduling problems in hybrid distributed manufacturing execution system. In: 2006 6th world congress on intelligent control and automation. vol 2, pp 7357–7361
Lohmer J, Spengler D, Lasch R (2020) Multi-factory job shop scheduling with due date objective. In: 2020 IEEE international conference on industrial engineering and engineering management (IEEM), pp 79–84
Lu P-H, Wu M-C, Tan H, Peng Y-H, Chen C-F (2018) A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems. J Intell Manuf 29(1):19–34
Luo Q, Deng Q, Gong G, Zhang L, Han W, Li K (2020) An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers. Exp Syst Appl 160:113721
Macarthur K, Vinyals M, Farinelli A, Ramchurn S, Jennings N (2011) Decentralised parallel machine scheduling for multi-agent task allocation. In: Fourth international workshop on optimisation in multi-agent systems, Taipei, Taiwan
Mahmoodjanloo M, Tavakkoli-Moghaddam R, Baboli A, Bozorgi-Amiri A (2020) Dynamic distributed job-shop scheduling problem consisting of reconfigurable machine tools”, in advances in production management systems. Towards smart and digital manufacturing. Springer, Cham, pp 460–468
Mao J, Pan Q, Miao Z, Gao L (2021) An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance. Exp Syst Appl 169:114495
Marandi F, Fatemi Ghomi SMT (2019a) Integrated multi-factory production and distribution scheduling applying vehicle routing approach. Int J Prod Res 57(3):722–748
Marandi F, Fatemi Ghomi SMT (2019b) Network configuration multi-factory scheduling with batch delivery: a learning-oriented simulated annealing approach. Comput Ind Eng 132:293–310
Marzouki B, Driss OB, Ghédira K (2018) Solving Distributed and flexible job shop scheduling problem using a chemical reaction optimization metaheuristic. Proc Comput Sci 126:1424–1433
Meng T, Pan Q-K (2021) A distributed heterogeneous permutation flowshop scheduling problem with lot-streaming and carryover sequence-dependent setup time. Swarm Evolut Comput 60:100804
Meng T, Pan Q-K, Wang L (2019) A distributed permutation flowshop scheduling problem with the customer order constraint. Knowl Based Syst 184:104894
Meng L, Zhang C, Ren Y, Zhang B, Lv C (2020a) Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem. Comput Ind Eng 142:106347
Meng L, Ren Y, Zhang B, Li J-Q, Sang H, Zhang C (2020b) MILP modeling and optimization of energy- efficient distributed flexible job shop scheduling problem. IEEE Access 8:191191–191203
Moon C, Seo Y (2005) Evolutionary algorithm for advanced process planning and scheduling in a multi-plant. Comput Ind Eng 48(2):311–325
Muth JF, Thompson GL, Winters PR (1963) Industrial scheduling. Prentice-Hall, Englewood Cliffs
Naderi B, Azab A (2014) Modeling and heuristics for scheduling of distributed job shops. Expert Syst Appl 41(17):7754–7763
Naderi B, Azab A (2015) An improved model and novel simulated annealing for distributed job shop problems. Int J Adv Manuf Technol 81(1):693–703
Naderi B, Ruiz R (2014) A scatter search algorithm for the distributed permutation flowshop scheduling problem. Eur J Oper Res 239(2):323–334
Pan Q-K, Gao L, Wang L, Liang J, Li X-Y (2019a) Effective heuristics and metaheuristics to minimize total flowtime for the distributed permutation flowshop problem. Expert Syst Appl 124:309–324
Pan Q-K, Gao L, Xin-Yu L, Jose FM (2019b) Effective constructive heuristics and meta-heuristics for the distributed assembly permutation flowshop scheduling problem. Appl Soft Comput 81:105492
Pan Z, Lei D, Wang L (2020) A knowledge-based two-population optimization algorithm for distributed energy-efficient parallel machines scheduling. IEEE Trans Cybern 1–13:2020
Pinedo ML (2012) Scheduling: theory, algorithms and systems, 4th edn. Springer, New York
Rifai AP, Nguyen H-T, Dawal SZM (2016) Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling. Appl Soft Comput 40:42–57
Rifai AP, Mara STW, Sudiarso A (2021) Multi-objective distributed reentrant permutation flow shop scheduling with sequence-dependent setup time. Exp Syst Appl 183:115339
Ruifeng C, Subramaniam V (2011) Performance evaluation for tandem multi-factory supply chains: an approximate solution. Int J Prod Res 49(11):3285–3305
Ruiz R, Pan Q-K, Naderi B (2019) Iterated Greedy methods for the distributed permutation flowshop scheduling problem. Omega 83:213–222
Şahman MA (2021) A discrete spotted hyena optimizer for solving distributed job shop scheduling problems. Appl Soft Comput 106:107349
Sambasivan M, Yahya S (2005) A Lagrangian-based heuristic for multi-plant, multi-item, multi-period capacitated lot-sizing problems with inter-plant transfers. Comput Oper Res 32(3):537–555
Shao W, Pi D, Shao Z (2019) 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 16(3):1344–1360
Shao Z, Pi D, Shao W (2020a) Hybrid enhanced discrete fruit fly optimization algorithm for scheduling blocking flow-shop in distributed environment. Exp Syst Appl 145:113147
Shao Z, Shao W, Pi D (2020b) Effective heuristics and metaheuristics for the distributed fuzzy blocking flow-shop scheduling problem. Swarm Evolut Comput 59:100747
Shao W, Shao Z, Pi D (2020c) Modeling and multi-neighborhood iterated greedy algorithm for distributed hybrid flow shop scheduling problem. Knowl Based Syst 194:105527
Shao Z, Shao W, Pi D (2021a) Effective constructive heuristic and iterated greedy algorithm for distributed mixed blocking permutation flow-shop scheduling problem. Knowl Based Syst 221:106959
Shao W, Shao Z, Pi D (2021b) Multi-objective evolutionary algorithm based on multiple neighborhoods local search for multi-objective distributed hybrid flow shop scheduling problem. Exp Syst Appl 2021:115453
Shi Y, Gregory M (2003) From original equipment manufacturers to total solution providers: emergence of a global manufacturing virtual network in the electronics industry. Int J Serv Technol Manage 4:331–346
Snyder H (2019) Literature review as a research methodology: an overview and guidelines. J Bus Res 104:333–339
Song H-B, Lin J (2021) A genetic programming hyper-heuristic for the distributed assembly permutation flow-shop scheduling problem with sequence dependent setup times. Swarm Evolut Comput 60:100807
Sun XT, Chung SH, Chan FTS (2015) Integrated scheduling of a multi-product multi-factory manufacturing system with maritime transport limits. Transp Res Part E Logist Transp Rev 79:110–127
Terrazas-Moreno S, Grossmann IE (2011) A multiscale decomposition method for the optimal planning and scheduling of multi-site continuous multiproduct plants. Chem Eng Sci 66(19):4307–4318
Timpe CH, Kallrath J (2000) Optimal planning in large multi-site production networks. Eur J Oper Res 126(2):422–435
Wachtel G, Elalouf A (2020) Efficient approximation scheme for job assignment in a multi-factory environment. J Ind Prod Eng 37(7):313–320
Wang L, Li D (2020) Fuzzy distributed hybrid flow shop scheduling problem with heterogeneous factory and unrelated parallel machine: a shuffled frog leaping algorithm with collaboration of multiple search strategies. IEEE Access 8:214209–214223
Wang S-Y, Wang L (2016) An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem. IEEE Trans Systems Man Cybern Syst 46(1):139–149
Wang J, Wang L (2020) A bi-population cooperative memetic algorithm for distributed hybrid flow-shop scheduling. IEEE Trans Emerg Top Comput Intell 1–15:2020
Wang K, Huang Y, Qin H (2016) A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown. J Oper Res Soc 67(1):68–82
Wang G, Li X, Gao L, Li P (2019) A multi-objective whale swarm algorithm for energy-efficient distributed permutation flow shop scheduling problem with sequence dependent setup times. IFAC-PapersOnLine 52:235–240
Wang G, Gao L, Li X, Li P, Tasgetiren MF (2020) Energy-efficient distributed permutation flow shop scheduling problem using a multi-objective whale swarm algorithm. Swarm Evolut Comput 57:100716
Wang Y, Yan L, Zhu H, Yin C (2006) A genetic algorithm for solving dynamic scheduling problems in distributed manufacturing systems. In: 2006 6th world congress on intelligent control and automation. vol 2, pp 7343–7347
Webster J, Watson RT (2002) Analyzing the past to prepare for the future: writing a literature review. MIS Q 26(2):13–23
Westfield FM (1955) Marginal analysis, multi-plant firms and business practice: an example. Q J Econ 69(2):253–268
Williams J (1981) Heuristic techniques for simultaneous scheduling of production and distribution in multi-echelon structures: theory and empirical comparisons. Manage Sci 27:336–352
Wu X, Xie Z (2021) Heterogeneous distributed flow shop scheduling and reentrant hybrid flow shop scheduling in seamless steel tube manufacturing, in bio-inspired computing: theories and applications. Springer, Singapore, pp 78–89
Wu M-C, Lin C-S, Lin C-H, Chen C-F (2017) Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems. Comput Oper Res 80:101–112
Wu X, Liu X, Zhao N (2019) An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem. Memetic Comput 11(4):335–355
Wu X, Liu X (2018) An improved differential evolution algorithm for solving a distributed flexible job shop scheduling problem. In: 2018 IEEE 14th international conference on automation science and engineering (CASE), pp 968–973
Xie J, Gao L, Pan Q, Tasgetiren MF (2019) An effective multi-objective artificial bee colony algorithm for energy efficient distributed job shop scheduling. Proc Manuf 39:1194–1203
Xu W, Hu Y, Luo W, Wang L, Wu R (2021) A multi-objective scheduling method for distributed and flexible job shop based on hybrid genetic algorithm and tabu search considering operation outsourcing and carbon emission. Comput Ind Eng 157:107318
Yang X, Wang R, Zhang T (2020) Single-objective distributed permutation flowshop scheduling optimization via multi-objectivization by a helper objective. In: 2020 6th international conference on big data and information analytics (BigDIA), pp 23–27
Ying K-C, Lin S-W (2018) Minimizing makespan for the distributed hybrid flowshop scheduling problem with multiprocessor tasks. Exp Syst Appl Int J 92:132–141
Zhang G, Xing K (2018) Memetic social spider optimization algorithm for scheduling two-stage assembly flowshop in a distributed environment. Comput Ind Eng 125:423–433
Zhang X, Yin M (2020) An enhanced genetic algorithm for the distributed assembly permutation flowshop scheduling problem. Int J Bio Inspired Comput 15:113
Zhang G, Xing K, Cao F (2018a) Scheduling distributed flowshops with flexible assembly and set-up time to minimise makespan. Int J Prod Res 56(9):3226–3244
Zhang G, Xing K, Cao F (2018b) Discrete differential evolution algorithm for distributed blocking flowshop scheduling with makespan criterion. Eng Appl Artif Intell 76:96–107
Zhang G, Xing K, Zhang G, He Z (2020) Memetic algorithm with meta-lamarckian learning and simplex search for distributed flexible assembly permutation flowshop scheduling problem. IEEE Access 8:96115–96128
Zhang Z-Q, Qian B, Hu R, Jin H-P, Wang L (2021) A matrix-cube-based estimation of distribution algorithm for the distributed assembly permutation flow-shop scheduling problem. Swarm Evolut Comput 60:100785
Zhao F, Zhao L, Wang L, Song H (2020) An ensemble discrete differential evolution for the distributed blocking flowshop scheduling with minimizing makespan criterion. Exp Syst Appl 160:100557
Ziaee M (2014) A heuristic algorithm for the distributed and flexible job-shop scheduling problem. J Supercomput 67(1):69–83
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bagheri Rad, N., Behnamian, J. Recent trends in distributed production network scheduling problem. Artif Intell Rev 55, 2945–2995 (2022). https://doi.org/10.1007/s10462-021-10081-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-021-10081-5