Publikációk

2016.11.01 - 2018.09.12

A projekt kezdete (2016.11.01) óta megjelent vagy elfogadott nemzetközi referált IF-al rendelkező folyóiratcikkek:

  • [1]Csáji B Cs, Kemény Zs, Pedone G, Kuti A, Váncza J: Wireless multi-sensor networks for smart cities: A prototype system with statistical data analysis. IEEE Sensors Journal, 17(23):7667-7676, (2017), DOI: 10.1109/JSEN.2017.2736785, IF: 2.51.
  • [2]Györgyi P, Kis T: Approximation schemes for parallel machine scheduling with non-renewable resources. European Journal of Operational Research, 258(1):113-123, (2017), DOI: 10.1016/j.ejor.2016.09.007, IF: 3.297.
  • [3]Györgyi P: A PTAS for a resource scheduling problem with arbitrary number of parallel machines. Operations Research Letters, 45(6):604-609, (2017), DOI: 10.1016/j.orl.2017.09.007, IF: 0.657.
  • [4]Györgyi P, Kis T: Minimizing the maximum lateness on a single machine with raw material constraints by branch-and-cut. Computers & Industrial Engineering, 115:220-225, (2018), DOI: 10.1016/j.cie.2017.11.016, IF: 2.623.
  • [5]Gyulai D, Monostori L: Capacity management of modular assembly systems. Journal of Manufacturing Systems, 43(1):88-99, (2017), DOI: 10.1016/j.jmsy.2017.02.008, IF: 3.699
  • [6]Horváth G, Erdős G: Point cloud based robot cell calibration. CIRP Annals – Manufacturing Technology, 66(1):145-148, (2017), DOI: 10.1016/j.cirp.2017.04.0, IF: 2.893.
  • [7]Horváth M, Kis T: Computing strong lower and upper bounds for the integrated multiple-depot vehicle and crew scheduling problem with branch-and-price. Central European Journal of Operations Research, (2017), published online, DOI: 10.1007/s10100-017-0489-4, IF: 0.659.
  • [8]Horváth M, Kis T: Multi-criteria approximation schemes for the resource constrained shortest path problem. Optimization Letters, 12: 475-483 (2018), DOI: 10.1007/s11590-017-1212-z, IF: 1.31
  • [9]Kádár B, Egri P, Pedone G, Chida T: Smart, simulation-based resource sharing in federated production networks. CIRP Annals – Manufacturing Technology, 67(1):503-506, (2018), DOI: 10.1016/j.cirp.2018.04.046, IF: 3.333.
  • [10]Kaihara T, Katsumura Y, Suginishi Y, Kádár B: Simulation model study for manufacturing effectiveness evaluation in crowdsourced manufacturing. CIRP Annals – Manufacturing Technology, 66(1):445-448, (2017) DOI: 10.1016/j.cirp.2017.04.094, IF: 2.893.
  • [11]Kaihara T, Nishino N, Ueda K, Tseng M, Váncza J, Schönsleben P, Teti R, Takenaka T: Value creation in production: Reconsideration from interdisciplinary approaches. CIRP Annals – Manufacturing Technology, 67(2):791-813, (2018), DOI: 10.1016/j.cirp.2018.05.002, IF: 3.333.
  • [12]Kardos Cs, Kovács A, Váncza J: Decomposition approach to optimal feature-based assembly planning. CIRP Annals – Manufacturing Technology, 66(1):417-420 (2017), DOI: 10.1016/j.cirp.2017.04.002, IF: 2.893
  • [13]Kardos Cs, Váncza J: Mixed-initiative assembly planning combining geometric reasoning and constrained optimization. CIRP Annals – Manufacturing Technology, 67(1):463-466, (2018), DOI: 10.1016/j.cirp.2018.04.034, IF: 3.333
  • [14]Kovács A: On the computational complexity of tariff optimization for demand response management. IEEE Transactions on Power Systems, 33(3):3204-3206, (2018), DOI: 10.1109/TPWRS.2018.2802198, IF: 5.255.
  • [15]Pedone G, Mezgár I: Model similarity evidence and interoperability affinity in cloud-ready Industry 4.0 technologies. Computers in Industry, 100:278-286, (2018), DOI: 10.1016/j.compind.2018.05.003. IF: 2.850
  • [16]Urgo M, Váncza J: A branch-and-bound approach for the single machine maximum lateness stochastic scheduling problem to minimize the value-at-risk. Flexible Services and Manufacturing Journal, (2018), DOI: 10.1007/s10696-018-9316-z, IF: 1.980.

A projekt kezdete óta megjelent vagy elfogadott nemzetközi referált folyóiratcikkek:

  • [17]Carè A, Csáji B Cs, Campi M C, Weyer E: Finite-sample system identification: An overview and a new correlation method. IEEE Control Systems Letters, 2(1):61-66 (2018), DOI: 10.1109/LCSYS.2017.2720969.
  • [18]Horváth G, Kardos Cs, Kemény Zs, Kovács A, Pataki B, Váncza J: Multi-modal interfaces for human-robot communication in collaborative assembly. ERCIM News, 114:15-16 (2018), https://ercim-news.ercim.eu/en114/special/multi-modal-interfaces-for-hum....

A projekt kezdete óta megjelent vagy elfogadott nemzetközi konferencia cikkek:

  • [19]Beregi R, Szaller Á, Kádár B: Synergy of multi-modelling for process control. Preprints of the 16th IFAC Symposium - INCOM 2018, Bergamo, Italy, June 11-13, 2018, p. 6.
  • [20]Bokor J, Szabó Z: State and loop equivalence for linear parameter varying systems. 22nd IEEE International Conference on Intelligent Engineering Systems 2018 (INES 2018), Las Palmas de Gran Canaria, Spain, 21-23 June, 2018.
  • [21]Carè A, Csáji B Cs, Campi M C, Weyer E: Finite-sample system identification: An overview and a new correlation method. 56th IEEE Conference on Decision and Control (CDC), Melbourne, Australia, 12-15 December, 2017.
  • [22]Carè A, Campi M C, Csáji B Cs, Weyer E: Undermodelling detection with sign-perturbed sums. IFAC-PapersOnLine 50(1):2799-2804, IFAC World Congress, Toulouse, France, (2017), DOI: 10.1016/j.ifacol.2017.08.581.
  • [23]Cserteg T, Erdős G, Horváth G: Assisted assembly process by gesture controlled robots. Procedia CIRP, 72:51-56, 51st CIRP Conference on Manufacturing Systems, Stockholm, Sweden, 16-18 May, 2018DOI: 10.1016/j.procir.2018.03.028.
  • [24]Fényes D, Németh B, Asszonyi M, Gáspár P: Side-slip angle estimation of autonomous road vehicles based on big data analysis. 26th Mediterranean Conference on Control and Automation (MED), Zadar, Croatia, 19-22 June,2018, pp. 849-854.
  • [25]Fényes D, Németh B, Gáspár P: A novel big-data-based estimation method of side-slip angles for autonomous road vehicles. In: Madani K, Gusikhin O (Eds.) Proceedings of the 15th International Conference on Informatics in Control. Automation and Robotics (ICINCO 2018). Vol. 1, pp. 420-426, Porto, Portugal, 29-31 July, 2018.
  • [26]Gödri I, Kardos Cs, Pfeiffer A, Váncza J: Scenario-based analysis of a high-mix low-volume production environment. The International Conference on Modelling & Applied Simulation (MAS 2018), Budapest, Hungary, 17-19 September, 2018.
  • [27]Gyulai D, Pfeiffer A, Nick G, Gallina V, Sihn W, Monostori L: Lead time prediction in a flow-shop environment with analytical and machine learning approaches. Preprints of the 16th IFAC Symposium - INCOM 2018, Bergamo, Italy, June 11-13, 2018, p. 6, https://d1keuthy5s86c8.cloudfront.net/static/ems/upload/files/evtyh_0189_FI.pdf
  • [28]Gyulai D, Pfeiffer A, Bergmann J, Gallina V: Online lead time prediction supporting situation-aware production control. Procedia CIRP, 6th CIRP Global Web Conference – CIRPe 2018, p. 6, accepted.
  • [29]Horváth G, Erdős G: Gesture control of cyber physical systems. Procedia CIRP, 63:184-188, 50th CIRP Conference on Manufacturing Systems, Taichung, Taiwan, (2017), DOI: 10.1016/j.procir.2017.03.312
  • [30]Kardos Cs, Váncza J: Application of generic CAD models for supporting feature based assembly process planning. Procedia CIRP, 67:446-451, (2018), 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Ischia, Italy, 19-21 July, 2017, DOI: 10.1016/j.procir.2017.12.240.
  • [31]Kardos Cs, Kemény Zs, Kovács A, Pataki B, Váncza J: Context-dependent multimodal communication in human-robot collaboration. Procedia CIRP, 72:15-20, 51st CIRP Conference on Manufacturing Systems, Stockholm, Sweden,16-18 May, 2018,
  • [32]Kardos Cs, Kovács A, Pataki B, Váncza J: Generating human work instructions from assembly plans. The 28th International Conference on Automated Planning and Scheduling, Workshop on User Interfaces and Scheduling and Planning, 24 – 29 June 2018, Delft, The Netherlands, http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop10/proceedings.pdf
  • [33]Kemény Zs, Beregi R, Nacsa J, Kardos Cs, Horváth D: Human–robot collaboration in the MTA SZTAKI learning factory facility at Győr. Procedia Manufacturing, 23:105-110, (2018), 8th Conference on Learning Factories 2018 - Advanced Engineering
    Education & Training for Manufacturing Innovation, DOI: 10.1016/j.promfg.2018.04.001.
  • [34]Kemény Zs, Beregi R, Nacsa J, Glawar R, Sihn W: Expanding production perspectives by collaborating learning factories—perceived needs and possibilities. Procedia Manufacturing, 23:111-116, (2018), 8th Conference on Learning Factories 2018 - Advanced Engineering Education & Training for Manufacturing Innovation, DOI: 10.1016/j.promfg.2018.04.002.
  • [35]Kolumbán S, Csáji B Cs: Towards D-optimal input design for finite-sample system identification. 18th IFAC Symposium on System Identification (SYSID 2018), 9-11 July 2018, Stockholm, Sweden, p. 6.
  • [36]Luspay T, Péni T, Seiler P, Vanek B: A model decomposition framework for LPV systems. 57th IEEE Conference on Decision and Control, Miami Beach, FL, USA, December 17-19, 2018, accepted.
  • [37]Mihály A, Gáspár P: Reconfiguration control of in-wheel electric vehicle based on battery state of charge. European Control Conference, Limassol, Cyprus, 12-15 June, 2018, pp. 243-248.
  • [38]Monostori J: Supply chains’ robustness: Challenges and opportunities. Procedia CIRP, 67:110-115, (2018), 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Ischia, Italy, 19-21 July, 2017, DOI: 10.1016/j.procir.2017.12.185.
  • [39]Németh B, Gáspár P, Szőcs D, Mihály A: Design of the optimal motions of autonomous vehicles in intersections through neural networks. IFAC-PapersOnLine 51(9):19-24, (2018), 15th IFAC Symposium on Control in Transportation Systems, CTS 2018, Savona, Italy, 6-8 June, 2018, DOI: 10.1016/j.ifacol.2018.07.004.
  • [40]Németh B, Fazekas M, Gáspár P: Anti-lock braking control design for electric vehicles using LPV methods. 26th Mediterranean Conference on Control and Automation (MED), Zadar, Croatia, 19-22 June,2018. pp. 511-516.
  • [41]Pfeiffer A, Gyulai D, Monostori L: Improving the accuracy of cycle time estimation for simulation in volatile manufacturing execution environments. In: Wenzel S, Peter T (Eds.), Simulation in Produktion und Logistik 2017, pp. 413-422, Kassel University Press, ASIM 2017 Conference, Kassel, Germany, September 20-22, 2017, http://www.asim-fachtagung-spl.de/asim2017/papers/Proof_160_Pfeiffer.pdf
  • [42]Pfeiffer A, Gyulai D, Szaller Á, Monostori L: Production log data analysis for reject rate prediction and workload estimation. Winter Simulation Conference 2018, Gothenburg, Sweden, accepted.
  • [43]Sherwan M. Najm, Paniti I: Experimental investigation on the single point incremental forming of AlMn1Mg1 foils using flat end tools. IOP Conference Series – Materials Science and Engineering - Manufacturing 2018, Kecskemét, Hungary, 7-8 June, 2018.
  • [44]Szabó Z, Bokor J, Hara S: Realization of homogeneous multi-agent networks. European Control Conference, Limassol, Cyprus, 12-15 June, 2018.
  • [45]Szabó Z, Péni T, Bokor J: Annihilator design for linear parameter varying systems. 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2018, Warsaw, Poland, 29 August, 2018.
  • [46]Szabó Z, Bokor J, Hara S: Transformations for linear parameter varying systems. 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2018, Warsaw, Poland, 29 August, 2018.
  • [47]Szabó Z, Bokor J: Transformations for linear parameter varying systems. The Joint 9th IFAC Symposium on Robust Control Design (ROCOND'18) and 2nd IFAC Workshop on Linear Parameter Varying Systems (LPVS'18), Florianopolis, Brazil, 3-5 September, 2018.
  • [48]Szaller Á, Béres F, Piller É, Gyulai D, Pfeiffer A, Benczúr A: Real-time prediction of manufacturing lead times in complex production environments. Proceedings of the 25th Annual EurOMA Conference – EurOMA 2018, Budapest, Hungary, 24-26 June, 2018.
  • [49]Váncza J, Monostori L: Cyber-physical manufacturing in the light of Professor Kanji Ueda's legacy. Procedia CIRP, 63:631-638, 50th CIRP Conference on Manufacturing Systems, Taichung, Taiwan (2017), DOI: 10.1016/j.procir.2017.04.059.

A projekt kezdete óta megjelent vagy közlésre elfogadott könyv és könyvfejezet:

  • [50]Kis T, Drótos M: Hard planning and scheduling problems in the digital factory. In: Ghezzi L, Hömberg D, Landry C. (Eds.) Math for the Digital Factory. Mathematics in Industry, Vol 27, pp. 3-19, (2017) Springer, Cham, DOI: 10.1007/978-3-319-63957-4_1.
  • [51]Mezgár I, Pedone G: Cloud-based manufacturing (CBM) interoperability in Industry 4.0. In: Ferreira L et al. (Eds.), Technological Developments in Industry 4.0 for Business Applications, IGI Global, (2019), p. 28, DOI: 10.4018/978-1-5225-4936-9.ch008.
  • [52]Paniti I: New Solutions in Incremental Sheet Forming. GlobeEdit, managed by OmniScriptum AraPers GmbH, (2017), ISBN: 978-620-2-48728-3, in print.

A projekt kezdete óta megjelent előadáskivonatok:

  • [53]Egri P. Kis T: Pareto optimal material allocation mechanism. International Conference on Algorithmic Game Theory, London, UK, (2017).
  • [54]Egri P, Kis T: Pareto-optimális anyagelosztó mechanizmus. XXXII. Magyar Operációkutatás Konferencia, p. 17, Cegléd, (2017).
  • [55]Kovács A: Kétszintű programozási megközelítés áramtarifa optimalizálására a keresletoldali szabályozáshoz. XXXII. Magyar Operációkutatás Konferencia, p. 28 Cegléd, (2017).
  • [56]Horváth M, Kis T: Poliéderes eredmények projekt ütemezési feladatokra. XXXII. Magyar Operációkutatás Konferencia, p. 24, Cegléd, (2017)

A projekt kezdete óta védésre benyújtott MTA doktori értekezés:

  • [57]Kis T: Complex scheduling problems. (2017). Nyilvános védés időpontja: 2018. október 5.

A projekt kezdete óta elfogadott PhD értekezés:

  • [58]Györgyi P: Approximation and exact methods for machine scheduling problems with non-renewable resources. ELTE, (2018).
  • [59]Karnok D: High resolution and transparent production informatics. BME, (2018).

A projekt kezdete óta védésre benyújtott PhD értekezés:

  • [60]Gyulai D: Production and capacity planning methods for flexible and reconfigurable assembly systems. BME, (2018). Nyilvános védés időpontja: 2018. szeptember 28.

A projekt kezdete óta készített jelentős tanulmány: