Robust schedules for tardiness optimization in job shop with interval uncertainty

  1. Díaz, Hernán 1
  2. Palacios, Juan José 1
  3. Díaz, Irene 1
  4. Vela, Camino R 1
  5. González-Rodríguez, Inés 2
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 Universidad de Cantabria
    info

    Universidad de Cantabria

    Santander, España

    ROR https://ror.org/046ffzj20

Revista:
Logic Journal of the IGPL

ISSN: 1367-0751,  1368-9894

Año de publicación: 2022

Tipo: Artículo

DOI: 10.1093/JIGPAL/JZAC016 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Logic Journal of the IGPL

Resumen

This paper addresses a variant of the job shop scheduling problem with total tardiness minimization where task durations and due dates are uncertain. This uncertainty is modelled with intervals. Different ranking methods for intervals are considered and embedded into a genetic algorithm. A new robustness measure is proposed to compare the different ranking methods and assess their capacity to predict ‘expected delays’ of jobs. Experimental results show that dealing with uncertainty during the optimization process yields more robust solutions. A sensitivity analysis also shows that the robustness of the solutions given by the solving method increases when the uncertainty grows.

€ Ver financiación

Información de financiación

This research has been supported by the Spanish Government under research grants PID2019-106263RB-I00 and TIN2017-87600-P

Financiadores

  • Spanish Government Spain
    • PID2019-106263RB-I00
    • TIN2017-87600-P

Referencias bibliográficas

  • Aissi, (2009), European Journal of Operational Research, 197, pp. 427, 10.1016/j.ejor.2008.09.012
  • Allahverdi, (2014), Computers & Operations Research, 51, pp. 200, 10.1016/j.cor.2014.06.003
  • Amjad, (2018), Mathematical Problems in Engineering, 2018, pp. 9270802, 10.1155/2018/9270802
  • Aytung, (2005), European Journal of Operational Research, 161, pp. 86, 10.1016/j.ejor.2003.08.027
  • Behnamian, (2016), Fuzzy Optimization and Decision Making, 15, pp. 331, 10.1007/s10700-015-9225-5
  • Bierwirth, (1995), OR Spectrum, 17, pp. 87, 10.1007/BF01719250
  • Borda, (1784), Histoire de l’Academie Royale des Sciences (Jg. 1781), pp. 657
  • Bustince, (2013), Fuzzy Sets and Systems, 220, pp. 69, 10.1016/j.fss.2012.07.015
  • Çaliş, (2015), Journal of Intelligent Manufacturing, 26, pp. 961, 10.1007/s10845-013-0837-8
  • Chanas, (2003), European Journal of Operational Research, 147, pp. 281, 10.1016/S0377-2217(02)00561-1
  • Díaz, (2020), Hybrid Artificial Intelligent Systems, pp. 209, 10.1007/978-3-030-61705-9_18
  • Dubois, (1996), IEEE Transactions on Systems, Man and Cybernetics, Part A, 26, pp. 361, 10.1109/3468.487961
  • Dubois, (2003), European Journal of Operational Research, 147, pp. 231, 10.1016/S0377-2217(02)00558-1
  • Essafi, (2008), Computers & Operations Research, 35, pp. 2599, 10.1016/j.cor.2006.12.019
  • Fortin, (2010), Journal of Scheduling, 13, pp. 609, 10.1007/s10951-010-0163-3
  • Garey, (1976), Mathematics of Operations Research, 1, pp. 117, 10.1287/moor.1.2.117
  • González, (2012), Natural Computing, 11, pp. 151, 10.1007/s11047-011-9300-y
  • González Rodríguez, (2008), IEEE Transactions on Systems, Man and Cybernetics, Part A, 38, pp. 655, 10.1109/TSMCA.2008.918603
  • Harrabi, (2020), Logic Journal of IGPL, 29, pp. 951, 10.1093/jigpal/jzaa037
  • Kalaï, (2012), European Journal of Operational Research, 220, pp. 722, 10.1016/j.ejor.2012.01.056
  • Karmakar, (2012), Reliable Computing, 16, pp. 38
  • Katoch, (2021), Multimedia Tools and Applications, 80, pp. 8091, 10.1007/s11042-020-10139-6
  • Lei, (2011), Computers & Industrial Engineering, 61, pp. 1200, 10.1016/j.cie.2011.07.010
  • Lei, (2012), International Journal of Advanced Manufacturing Technology, 60, pp. 291, 10.1007/s00170-011-3600-3
  • Lei, (2013), International Journal of Advanced Manufacturing Technology, 66, pp. 1835, 10.1007/s00170-012-4463-y
  • Li, (2019), Computers & Industrial Engineering, 235, pp. 1036, 10.1016/j.cie.2019.04.028
  • Liqat, (2017), Logic Journal of the IGPL, 25, pp. 1006, 10.1093/jigpal/jzx048
  • Lohmer, (2020), Proceedings of the 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 79, 10.1109/IEEM45057.2020.9309905
  • Moore, (2009), Introduction to Interval Analysis, 10.1137/1.9780898717716
  • Mou, (2017), Cluster Computing, 20, pp. 371, 10.1007/s10586-016-0717-z
  • Palacios, (2015), Computers & Operations Research, 54, pp. 74, 10.1016/j.cor.2014.08.023
  • Palacios, (2014), Natural Computing, 13, pp. 145, 10.1007/s11047-014-9413-1
  • Palacios, (2015), Fuzzy Sets and Systems, 278, pp. 81, 10.1016/j.fss.2014.12.003
  • Pinedo, (2016), Scheduling: Theory, Algorithms, and Systems, 10.1007/978-3-319-26580-3
  • Rahmani Hosseinabadi, (2019), Soft Computing, 23, pp. 5099, 10.1007/s00500-018-3177-y
  • Roy, (2010), European Journal of Operational Research, 200, pp. 629, 10.1016/j.ejor.2008.12.036
  • Vela, (2020), Computers & Operations Research, 119, pp. 104931, 10.1016/j.cor.2020.104931
  • Xu, (2006), International Journal of General Systems, 35, pp. 417, 10.1080/03081070600574353
  • Zhang, (2008), Computers & Operations Research, 35, pp. 282, 10.1016/j.cor.2006.02.024
  • Zhang, (2019), Journal of Intelligent Manufacturing, 30, pp. 1809, 10.1007/s10845-017-1350-2