Mostrar el registro sencillo del ítem

Addressing robustness and multiple objectives in stochastic flow shop environments

dc.contributor.advisorMontoya Torres, Jairo Rafael
dc.contributor.authorGonzález Neira, Eliana María
dc.date.accessioned2018-11-09T19:56:08Z
dc.date.available2018-11-09T19:56:08Z
dc.date.issued2018-10-01
dc.identifier.citationAdressi, a., Tasouji Hassanpour, S., & Azizi, V. (2016). Solving group scheduling problem in no-wait flexible flowshop with random machine breakdown. Decision Science Letters, 5(JANUARY), 157–168. https://doi.org/10.5267/j.dsl.2015.7.001
dc.identifier.citationAkturk, M. S., & Yildirim, M. B. (1998). A new lower bounding scheme for the total weighted tardiness problem. Computers & Operations Research, 25(4), 265–278. https://doi.org/10.1016/S0305-0548(97)00073-7
dc.identifier.citationAlbaum, G. (1997). The Likert scale revisited: an alternative version. Journal of the Market Research Society, 39(2), 331–348. Retrieved from https://www.mrs.org.uk/ijmr_article/article/13740
dc.identifier.citationAlcaide, D., Rodriguez-Gonzalez, A., & Sicilia, J. (2002). An approach to solve the minimum expected makespan flow-shop problem subject to breakdowns. European Journal of Operational Research, 140(2), 384–398. https://doi.org/10.1016/S0377- 2217(02)00077-2
dc.identifier.citationAlfieri, A. (2007). Due date quoting and scheduling interaction in production lines. International Journal of Computer Integrated Manufacturing, 20(6), 579–587. https://doi.org/10.1080/09511920601079363
dc.identifier.citationAlfieri, A. (2009, September 15). Workload simulation and optimisation in multi-criteria hybrid flowshop scheduling: a case study. International Journal of Production Research. Taylor & Francis Group. https://doi.org/10.1080/00207540802010823
dc.identifier.citationAllahverdi, A. (2004). Stochastically minimizing makespan on a three-machine flowshop. International Journal of Industrial Engineering : Theory Applications and Practice, 11(2), 124–131. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0- 3943102113&partnerID=tZOtx3y1
dc.identifier.citationAllahverdi, A. (2006). Two-machine flowshop scheduling problem to minimize total completion time with bounded setup and processing times. International Journal of Production Economics, 103(1), 386–400. https://doi.org/10.1016/j.ijpe.2005.10.002
dc.identifier.citationAllahverdi, A., & Al-Anzi, F. S. (2006). Evolutionary heuristics and an algorithm for the two-stage assembly scheduling problem to minimize makespan with setup times. International Journal of Production Research, 44(22), 4713–4735. https://doi.org/10.1080/00207540600621029
dc.identifier.citationAllahverdi, A., Aldowaisan, T., & Sotskov, Y. N. (2003). Two-machine flowshop scheduling problem to minimize makespan or total completion time with random and bounded setup times. International Journal of Mathematics and Mathematical Sciences, 2003(39), 2475–2486. https://doi.org/10.1155/S016117120321019X
dc.identifier.citationAllahverdi, A., & Aydilek, H. (2010a). Heuristics for the two-machine flowshop scheduling problem to minimise makespan with bounded processing times. International Journal of Production Research, 48(21), 6367–6385. https://doi.org/10.1080/00207540903321657
dc.identifier.citationAllahverdi, A., & Aydilek, H. (2010b). Heuristics for the two-machine flowshop scheduling problem to minimize maximum lateness with bounded processing times. Computers & Mathematics with Applications, 60(5), 1374–1384. https://doi.org/10.1016/j.camwa.2010.06.019
dc.identifier.citationAllahverdi, A., & Aydilek, H. (2015). The two stage assembly flowshop scheduling problem to minimize total tardiness. Journal of Intelligent Manufacturing, 26(2), 225– 237. https://doi.org/10.1007/s10845-013-0775-5
dc.identifier.citationAllahverdi, A., & Savsar, M. (2001). Stochastic proportionate flowshop scheduling with setups. Computers & Industrial Engineering, 39(3–4), 357–369. https://doi.org/10.1016/S0360-8352(01)00011-0
dc.identifier.citationAllahverdi, A., & Tatari, M. F. F. (1996). Simulation of different rules in stochastic flowshops. Computers and Industrial Engineering, 31(1–2), 209–212. https://doi.org/10.1016/0360-8352(96)00113-1
dc.identifier.citationAlmeder, C., & Hartl, R. F. (2013). A metaheuristic optimization approach for a real-world stochastic flexible flow shop problem with limited buffer. International Journal of Production Economics, 145(1), 88–95. https://doi.org/10.1016/j.ijpe.2012.09.014
dc.identifier.citationArmentano, V. A., & Arroyo, J. E. C. (2004). An application of a multi-objective tabu search algorithm to a bicriteria flowshop problem. Journal of Heuristics, 10(5), 463– 481. https://doi.org/10.1023/B:HEUR.0000045320.79875.e3
dc.identifier.citationArroyo, J. E. C., & de Souza Pereira, A. A. (2011). A GRASP heuristic for the multiobjective permutation flowshop scheduling problem. The International Journal of Advanced Manufacturing Technology, 55(5–8), 741–753. https://doi.org/10.1007/s00170-010-3100-x
dc.identifier.citationAverbakh, I. (2006). The minmax regret permutation flow-shop problem with two jobs. European Journal of Operational Research, 169(3), 761–766. https://doi.org/10.1016/j.ejor.2004.07.073
dc.identifier.citationAydilek, A., Aydilek, H., & Allahverdi, A. (2013). Increasing the profitability and competitiveness in a production environment with random and bounded setup times. International Journal of Production Research, 51(1), 106–117. https://doi.org/10.1080/00207543.2011.652263
dc.identifier.citationAydilek, A., Aydilek, H., & Allahverdi, A. (2015). Production in a two-machine flowshop scheduling environment with uncertain processing and setup times to minimize makespan. International Journal of Production Research, 53(9), 2803–2819. https://doi.org/10.1080/00207543.2014.997403
dc.identifier.citationAydilek, H., & Allahverdi, A. (2010). Two-machine flowshop scheduling problem with bounded processing times to minimize total completion time. Computers & Mathematics with Applications, 59(2), 684–693. https://doi.org/10.1016/j.camwa.2009.10.025
dc.identifier.citationAydilek, H., & Allahverdi, A. (2013). A polynomial time heuristic for the two-machine flowshop scheduling problem with setup times and random processing times. Applied Mathematical Modelling, 37(12–13), 7164–7173. https://doi.org/10.1016/j.apm.2013.02.003
dc.identifier.citationAzadeh, A., Jeihoonian, M., Shoja, B. M., & Seyedmahmoudi, S. H. (2012). An integrated neural network–simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow-shop scheduling problem with stochastic activities. International Journal of Production Research, 50(24), 7271–7284. https://doi.org/10.1080/00207543.2011.645511
dc.identifier.citationAzadeh, A., Moghaddam, M., Geranmayeh, P., & Naghavi, A. (2010). A flexible artificial neural network–fuzzy simulation algorithm for scheduling a flow shop with multiple processors. The International Journal of Advanced Manufacturing Technology, 50(5– 8), 699–715. https://doi.org/10.1007/s00170-010-2533-6
dc.identifier.citationAzadeh, A., Negahban, A., & Moghaddam, M. (2012). A hybrid computer simulationartificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems. International Journal of Production 132 Research, 50(2), 551–566. https://doi.org/10.1080/00207543.2010.539281
dc.identifier.citationAzaron, A., Katagiri, H., Kato, K., & Sakawa, M. (2006). Longest path analysis in networks of queues: Dynamic scheduling problems. European Journal of Operational Research, 174(1), 132–149. https://doi.org/10.1016/j.ejor.2005.02.018
dc.identifier.citationAzizoǧlu, M., Kondakci, S., & Kirca, Ö. (1991). Bicriteria scheduling problem involving total tardiness and total earliness penalties. International Journal of Production Economics, 23(1–3), 17–24. https://doi.org/10.1016/0925-5273(91)90044-T
dc.identifier.citationBadger, D., Nursten, J., Williams, P., & Woodward, M. (2000). Should All Literature Reviews be Systematic? Evaluation & Research in Education, 14(3–4), 220–230. https://doi.org/10.1080/09500790008666974
dc.identifier.citationBaker, A. D. (1998). A survey of factory control algorithms that can be implemented in a multi-agent heterarchy: Dispatching, scheduling, and pull. Journal of Manufacturing Systems, 17(4), 297–320. https://doi.org/10.1016/s0278-6125(98)80077-0
dc.identifier.citationBaker, K. R. (1974). Introduction to sequencing and scheduling (1st ed.). John Wiley & Sons
dc.identifier.citationBaker, K. R., & Altheimer, D. (2012). Heuristic solution methods for the stochastic flow shop problem. European Journal of Operational Research, 216(1), 172–177. https://doi.org/10.1016/j.ejor.2011.07.021
dc.identifier.citationBaker, K. R., & Trietsch, D. (2011). Three heuristic procedures for the stochastic, twomachine flow shop problem. Journal of Scheduling, 14(5), 445–454. https://doi.org/10.1007/s10951-010-0219-4
dc.identifier.citationBalasubramanian, J., & Grossmann, I. E. (2002). A novel branch and bound algorithm for scheduling flowshop plants with uncertain processing times. Computers & Chemical Engineering, 26(1), 41–57. https://doi.org/10.1016/S0098-1354(01)00735-9
dc.identifier.citationBalasubramanian, J., & Grossmann, I. E. (2003). Scheduling optimization under uncertainty—an alternative approach. Computers & Chemical Engineering, 27(4), 469–490. https://doi.org/10.1016/S0098-1354(02)00221-1
dc.identifier.citationBang, J.-Y., & Kim, Y.-D. (2011). Scheduling algorithms for a semiconductor probing facility. Computers & Operations Research, 38(3), 666–673. https://doi.org/10.1016/j.cor.2010.08.010
dc.identifier.citationBayat, M., Heydari, M., & Mahdavi Mazdeh, M. (2013). Heuristic for stochastic online flowshop problem with preemption penalties. Discrete Dynamics in Nature and Society, 2013, 1–10. https://doi.org/10.1155/2013/916978
dc.identifier.citationBehnamian, J. (2016). Survey on fuzzy shop scheduling. Fuzzy Optimization and Decision Making, 15(3), 331–366. https://doi.org/10.1007/s10700-015-9225-5
dc.identifier.citationBehnamian, J., & Fatemi Ghomi, S. M. T. (2014). Multi-objective fuzzy multiprocessor flowshop scheduling. Applied Soft Computing, 21, 139–148. https://doi.org/10.1016/j.asoc.2014.03.031
dc.identifier.citationBertsimas, D., & Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1–3), 49–71. https://doi.org/10.1007/s10107-003- 0396-4
dc.identifier.citationBeyer, H. G., & Sendhoff, B. (2007). Robust optimization - A comprehensive survey. Computer Methods in Applied Mechanics and Engineering, 196(33–34), 3190–3218. https://doi.org/10.1016/j.cma.2007.03.003
dc.identifier.citationCalvet, L., Juan, A. A., Fernandez-Viagas, V., & Framinan, J. M. (2016). Combining simulation with metaheuristics in distributed scheduling problems with stochastic processing times. In 2016 Winter Simulation Conference (WSC) (pp. 2347–2357). 133 IEEE. https://doi.org/10.1109/WSC.2016.7822275
dc.identifier.citationCelano, G., Costa, A., & Fichera, S. (2003). An evolutionary algorithm for pure fuzzy flowshop scheduling problems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(06), 655–669. https://doi.org/10.1142/S0218488503002466
dc.identifier.citationChaari, T., Chaabane, S., Loukil, T., & Trentesaux, D. (2011). A genetic algorithm for robust hybrid flow shop scheduling. International Journal of Computer Integrated Manufacturing, 24(9), 821–833. https://doi.org/10.1080/0951192X.2011.575181
dc.identifier.citationChang, P.-T., Lin, K.-P., Pai, P.-F., Zhong, C.-Z., Lin, C.-H., & Hung, L.-T. (2008). Ant colony optimization system for a multi-quantitative and qualitative objective job-shop parallel-machine-scheduling problem. International Journal of Production Research, 46(20), 5719–5759. https://doi.org/10.1080/00207540600693523
dc.identifier.citationChang, P.-T., & Lo, Y.-T. (2001). Modelling of job-shop scheduling with multiple quantitative and qualitative objectives and a GA/TS mixture approach. International Journal of Computer Integrated Manufacturing, 14(4), 367–384. https://doi.org/10.1080/0951120010020749
dc.identifier.citationChang, Y.-C., & Huang, W.-T. (2014). An enhanced model for SDBR in a random reentrant flow shop environment. International Journal of Production Research, 52(6), 1808–1826. https://doi.org/10.1080/00207543.2013.848491
dc.identifier.citationChen, C.-L., & Chen, C.-L. (2009a). A bottleneck-based heuristic for minimizing makespan in a flexible flow line with unrelated parallel machines. Computers & Operations Research, 36(11), 3073–3081. https://doi.org/10.1016/j.cor.2009.02.004
dc.identifier.citationChen, C.-L., & Chen, C.-L. (2009b). Bottleneck-based heuristics to minimize total tardiness for the flexible flow line with unrelated parallel machines. Computers & Industrial Engineering, 56(4), 1393–1401. https://doi.org/10.1016/j.cie.2008.08.016
dc.identifier.citationChen, G., & Shen, Z.-J. M. (2007). Probabilistic asymptotic analysis of stochastic online scheduling problems. IIE Transactions, 39(5), 525–538. https://doi.org/10.1080/07408170600941623
dc.identifier.citationChen, X., Wen Lin, H., & Murata, T. (2012). Composite dispatching rule design for dynamic scheduling with customer-oriented production priority control. IEEJ Transactions on Electrical and Electronic Engineering, 7(1), 53–61. https://doi.org/10.1002/tee.21695
dc.identifier.citationChoi, S. H., & Wang, K. (2012). Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach. Computers & Industrial Engineering, 63(2), 362–373. https://doi.org/10.1016/j.cie.2012.04.001
dc.identifier.citationChutima, P., & Yiangkamolsing, C. (2003). Application of fuzzy genetic algorithm for sequencing in mixed-model assembly line with processing time. International Journal of Industrial Engineering : Theory Applications and Practice, 10(4), 325–331. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0- 1942422550&partnerID=tZOtx3y1
dc.identifier.citationCiavotta, M., Minella, G., & Ruiz, R. (2013). Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study. European Journal of Operational Research, 227(2), 301–313. https://doi.org/10.1016/j.ejor.2012.12.031
dc.identifier.citationCroes, G. A. (1958). A method for solving traveling-salesman Problems. Operations Research, 6(6), 791–812. https://doi.org/10.1287/opre.6.6.791
dc.identifier.citationCui, W., Lu, Z., Li, C., & Han, X. (2018). A proactive approach to solve integrated 134 production scheduling and maintenance planning problem in flow shops. Computers & Industrial Engineering, 115, 342–353. https://doi.org/10.1016/j.cie.2017.11.020
dc.identifier.citationCui, Z., & Gu, X. (2014). A Discrete Group Search Optimizer for Hybrid Flowshop Scheduling Problem with Random Breakdown. Mathematical Problems in Engineering, 2014, 1–11. https://doi.org/10.1155/2014/621393
dc.identifier.citationĆwik, M., & Józefczyk, J. (2015). Evolutionary Algorithm for Minmax Regret Flow-Shop Problem. Management and Production Engineering Review, 6(3), 3–9. https://doi.org/10.1515/mper-2015-0021
dc.identifier.citationĆwik, M., & Józefczyk, J. (2018). Heuristic algorithms for the minmax regret flow-shop problem with interval processing times. Central European Journal of Operations Research, 26(1), 215–238. https://doi.org/10.1007/s10100-017-0485-8
dc.identifier.citationDas, I. (2000). Robustness optimization for constrained nonlinear programming problems. Engineering Optimization, 32(5), 585–618. https://doi.org/10.1080/03052150008941314
dc.identifier.citationde Armas, J., Juan, A. A., Marquès, J. M., & Pedroso, J. P. (2016). Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic. Journal of the Operational Research Society. https://doi.org/10.1057/s41274-016-0155-6
dc.identifier.citationDelbufalo, E. (2012). Outcomes of inter‐organizational trust in supply chain relationships: a systematic literature review and a meta‐analysis of the empirical evidence. Supply Chain Management: An International Journal, 17(4), 377–402. https://doi.org/10.1108/13598541211246549
dc.identifier.citationDiaz, J. E., Handl, J., & Xu, D.-L. (2017). Evolutionary robust optimization in production planning – interactions between number of objectives, sample size and choice of robustness measure. Computers & Operations Research, 79, 266–278. https://doi.org/10.1016/j.cor.2016.06.020
dc.identifier.citationDiep, T., Kenné, J.-P., & Dao, T.-M. (2010). Feedback optimal control of dynamic stochastic two-machine flowshop with a finite buffer. International Journal of Industrial Engineering Computations, 1(2), 95–120. https://doi.org/10.5267/j.ijiec.2010.02.001
dc.identifier.citationDominguez, O., Juan, A. A., & Faulin, J. (2014). A biased-randomized algorithm for the two-dimensional vehicle routing problem with and without item rotations. International Transactions in Operational Research, 21(3), 375–398. https://doi.org/10.1111/itor.12070
dc.identifier.citationDu, J., & Leung, J. Y.-T. (1990). Minimizing Total Tardiness on One Machine Is NP-Hard. Mathematics of Operations Research, 15(3), 483–495. https://doi.org/10.2307/3689992
dc.identifier.citationDu, X., Ji, M., Li, Z., & Liu, B. (2016). Scheduling of Stochastic Distributed Assembly Flowshop under Complex Constraints *. In 2016 IEEE Symposium Series on Computational Intelligence. Athens, Greece
dc.identifier.citationEbrahimi, M., Fatemi Ghomi, S. M. T. M. T., & Karimi, B. (2014). Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Applied Mathematical Modelling, 38(9–10), 2490–2504. https://doi.org/10.1016/j.apm.2013.10.061
dc.identifier.citationEl-Bouri, A. (2012). A cooperative dispatching approach for minimizing mean tardiness in a dynamic flowshop. Computers & Operations Research, 39(7), 1305–1314. https://doi.org/10.1016/j.cor.2011.07.004
dc.identifier.citationElyasi, A., & Salmasi, N. (2013a). Due date assignment in single machine with stochastic processing times. International Journal of Production Research, 51(8), 2352–2362. https://doi.org/10.1080/00207543.2012.737945
dc.identifier.citationElyasi, A., & Salmasi, N. (2013b). Stochastic flow-shop scheduling with minimizing the expected number of tardy jobs. International Journal of Advanced Manufacturing Technology, 66(1–4), 337–346. https://doi.org/10.1007/s00170-012-4328-4
dc.identifier.citationElyasi, A., & Salmasi, N. (2013c). Stochastic scheduling with minimizing the number of tardy jobs using chance constrained programming. Mathematical and Computer Modelling, 57(5–6), 1154–1164. https://doi.org/10.1016/j.mcm.2012.10.017
dc.identifier.citationEnns, S. T. (1995). An economic approach to job shop performance analysis. International Journal of Production Economics, 38(2–3), 117–131. https://doi.org/10.1016/0925- 5273(94)00082-L
dc.identifier.citationFazayeli, M., Aleagha, M.-R., Bashirzadeh, R., & Shafaei, R. (2016). A hybrid metaheuristic algorithm for flowshop robust scheduling under machine breakdown uncertainty. International Journal of Computer Integrated Manufacturing, 29(7), 709– 719. https://doi.org/10.1080/0951192X.2015.1067907
dc.identifier.citationFeng, X., Zheng, F., & Xu, Y. (2016). Robust scheduling of a two-stage hybrid flow shop with uncertain interval processing times. International Journal of Production Research, 54(12), 3706–3717. https://doi.org/10.1080/00207543.2016.1162341
dc.identifier.citationFeo, T. A., & Resende, M. G. C. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 6(2), 109–133. https://doi.org/10.1007/BF01096763
dc.identifier.citationFernandez-Viagas, V., & Framinan, J. M. (2015). NEH-based heuristics for the permutation flowshop scheduling problem to minimise total tardiness. Computers & Operations Research, 60, 27–36. https://doi.org/10.1016/j.cor.2015.02.002
dc.identifier.citationFerone, D., Gruler, A., Festa, P., & Juan, A. A. (2016). Combining simulation with a GRASP metaheuristic for solving the permutation flow-shop problem with stochastic processing times. In T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, & S. E. Chick (Eds.), Proceedings of the 2016 Winter Simulation Conference (pp. 2205–2215). Arlington, Virginia. Retrieved from http://www.informs-sim.org/wsc16papers/192.pdf
dc.identifier.citationFesta, P., & Resende, M. G. C. (2009). An annotated bibliography of GRASP-Part II: Applications. International Transactions in Operational Research, 16(2), 131–172. https://doi.org/10.1111/j.1475-3995.2009.00664.x
dc.identifier.citationFigueira, G., & Almada-Lobo, B. (2014). Hybrid simulation–optimization methods: A taxonomy and discussion. Simulation Modelling Practice and Theory, 46, 118–134. https://doi.org/10.1016/j.simpat.2014.03.007
dc.identifier.citationFink, A. (1998). Conducting Research Literature Reviews: From Paper to the Internet. (A. Fink, Ed.), BMS: Bulletin of Sociological Methodology / Bulletin de Méthodologie Sociologique (Vol. 60). Thousand Oaks, CA, USA: Sage Publications, Ltd. https://doi.org/10.2307/24359723
dc.identifier.citationForst, F. G. (1983). Minimizing total expected costs in the two-machine, stochastic flow shop. Operations Research Letters, 2(2), 58–61. https://doi.org/10.1016/0167- 6377(83)90037-8
dc.identifier.citationFraminan, J. M., & Perez-Gonzalez, P. (2015). On heuristic solutions for the stochastic flowshop scheduling problem. European Journal of Operational Research, 246(2), 413–420. https://doi.org/10.1016/j.ejor.2015.05.006
dc.identifier.citationFu, Y., Ding, J., Wang, H., & Wang, J. (2017). Two-objective stochastic flow-shop 136 scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2017.12.009
dc.identifier.citationFu, Y., Wang, H., Tian, G., Li, Z., & Hu, H. (2018). Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-017-1385-4
dc.identifier.citationFunke, B., Grünert, T., & Irnich, S. (2005). Local search for vehicle routing and scheduling problems: Review and conceptual integration. Journal of Heuristics, 11(4), 267–306. https://doi.org/10.1007/s10732-005-1997-2
dc.identifier.citationGabrel, V., Murat, C., & Thiele, A. (2018). Portfolio optimization with pw-robustness. EURO Journal on Computational Optimization. https://doi.org/10.1007/s13675-018- 0096-8
dc.identifier.citationGao, J., Chen, R., & Deng, W. (2013). An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem. International Journal of Production Research, 51(3), 641–651. https://doi.org/10.1080/00207543.2011.644819
dc.identifier.citationGarcía-Cáceres, R. G. (2007). Integral optimization and some supply chain developments. Universidad de los Andes, Bogotá, Colombia
dc.identifier.citationGarcía-Cáceres, R. G., Aráoz-Durand, J. A., & Gómez, F. P. (2009). Integral analysis method – IAM. European Journal of Operational Research, 192(3), 891–903. https://doi.org/10.1016/j.ejor.2007.10.001
dc.identifier.citationGarcía-Cáceres, R., Vega-Mejía, C., & Caballero-Villalobos, J. (2011). Integral optimization of the container loading problem. In D. I. Dritsas (Ed.), Stochastic Optimization – Seeing the Optimal for the Uncertain (pp. 225–254). Rijeka: In Tech. https://doi.org/10.5772/15744
dc.identifier.citationGarey, M. R., Johnson, D. S., & Sethi, R. (1976). The Complexity of Flowshop and Jobshop Scheduling. Mathematics of Operations Research, 1(2), 117–129. https://doi.org/10.1287/moor.1.2.117
dc.identifier.citationGeismar, H. N., & Pinedo, M. (2010). Robotic cells with stochastic processing times. IIE Transactions, 42(12), 897–914. https://doi.org/10.1080/0740817X.2010.491505
dc.identifier.citationGeng, J.-C., Cui, Z., & Gu, X.-S. (2016). Scatter search based particle swarm optimization algorithm for earliness/tardiness flowshop scheduling with uncertainty. International Journal of Automation and Computing, 13(3), 285–295. https://doi.org/10.1007/s11633-016-0964-8
dc.identifier.citationGholami-Zanjani, S. M., Hakimifar, M., Nazemi, N., & Jolai, F. (2017). Robust and Fuzzy Optimisation Models for a Flow shop Scheduling Problem with Sequence Dependent Setup Times: A real case study on a PCB assembly company. International Journal of Computer Integrated Manufacturing, 30(6), 552–563. https://doi.org/10.1080/0951192X.2016.1187293
dc.identifier.citationGholami, M., Zandieh, M., & Alem-Tabriz, A. (2009). Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. The International Journal of Advanced Manufacturing Technology, 42(1–2), 189–201. https://doi.org/10.1007/s00170-008-1577-3
dc.identifier.citationGonzalez-Martin, S., Juan, A. A., Riera, D., Elizondo, M. G., & Ramos, J. J. (2016). A simheuristic algorithm for solving the arc-routing problem with stochastic demands. Journal of Simulation. https://doi.org/10.1057/jos.2016.11
dc.identifier.citationGonzalez-Neira, E. M., Ferone, D., Hatami, S., & Juan, A. A. A. (2017). A biasedrandomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times. Simulation Modelling Practice and Theory, 79, 23– 137 36. https://doi.org/10.1016/j.simpat.2017.09.001
dc.identifier.citationGonzález-Neira, E. M., García-Cáceres, R. G., Caballero-Villalobos, J. P., Molina-Sánchez, L. P., & Montoya-Torres, J. R. (2016). Stochastic flexible flow shop scheduling problem under quantitative and qualitative decision criteria. Computers & Industrial Engineering, 101, 128–144. https://doi.org/10.1016/j.cie.2016.08.026
dc.identifier.citationGonzález-Neira, E. M., Montoya-Torres, J. R., & Barrera, D. (2017). Flow-shop scheduling problem under uncertainties: Review and trends. International Journal of Industrial Engineering Computations, 8(4), 399–426. https://doi.org/10.5267/j.ijiec.2017.2.001
dc.identifier.citationGören, S., & Pierreval, H. (2013). Taking advantage of a diverse set of efficient production schedules: A two-step approach for scheduling with side concerns. Computers & Operations Research, 40(8), 1979–1990. https://doi.org/10.1016/j.cor.2013.02.016
dc.identifier.citationGotoh, J., Kim, M. J., & Lim, A. E. B. (2018). Robust empirical optimization is almost the same as mean–variance optimization. Operations Research Letters, 46(4), 448–452. https://doi.org/10.1016/j.orl.2018.05.005
dc.identifier.citationGourgand, M., Grangeon, N., & Norre, S. (2000). A review of the static stochastic flowshop scheduling problem. Journal of Decision Systems, 9(2), 1–31. https://doi.org/10.1080/12460125.2000.9736710
dc.identifier.citationGourgand, M., Grangeon, N., & Norre, S. (2003). A contribution to the stochastic flow shop scheduling problem. European Journal of Operational Research, 151(2), 415– 433. https://doi.org/10.1016/S0377-2217(02)00835-4
dc.identifier.citationGourgand, M., Grangeon, N., & Norre, S. (2005). Markovian analysis for performance evaluation and scheduling in m machine stochastic flow-shop with buffers of any capacity. European Journal of Operational Research, 161(1), 126–147. https://doi.org/10.1016/j.ejor.2003.08.032
dc.identifier.citationGraham, R. L., Lawler, E. L., Lenstra, J. K., & Kan, A. H. G. R. (1979). Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey. In Annals of Discrete Mathematics (Vol. 5, pp. 287–326). Elsevier. https://doi.org/10.1016/S0167- 5060(08)70356-X
dc.identifier.citationGrasas, A., Juan, A. A., Faulin, J., de Armas, J., & Ramalhinho, H. (2017). Biased randomization of heuristics using skewed probability distributions: A survey and some applications. Computers & Industrial Engineering, 110, 216–228. https://doi.org/10.1016/j.cie.2017.06.019
dc.identifier.citationGrasas, A., Juan, A. A., & Lourenço, H. R. (2016). SimILS : a simulation-based extension of the iterated local search metaheuristic for stochastic combinatorial optimization. Journal of Simulation, 10(1), 69–77. https://doi.org/10.1057/jos.2014.25
dc.identifier.citationGupta, J. N. D. (1988). Two-Stage, Hybrid Flowshop Scheduling Problem. Journal of the Operational Research Society, 39(4), 359. https://doi.org/10.1057/jors.1988.63
dc.identifier.citationHan, Y., Gong, D., Jin, Y., & Pan, Q. (2016). Evolutionary multi-objective blocking lotstreaming flow shop scheduling with interval processing time. Applied Soft Computing, 42, 229–245. https://doi.org/10.1016/j.asoc.2016.01.033
dc.identifier.citationHan, Y., Gong, D., Jin, Y., & Pan, Q. (2017). Evolutionary Multiobjective Blocking LotStreaming Flow Shop Scheduling With Machine Breakdowns. IEEE Transactions on Cybernetics, 1–14. https://doi.org/10.1109/TCYB.2017.2771213
dc.identifier.citationHatami, S., Ruiz, R., & Andrés-Romano, C. (2013). The Distributed Assembly Permutation Flowshop Scheduling Problem. International Journal of Production Research, 51(17), 5292–5308. https://doi.org/10.1080/00207543.2013.807955
dc.identifier.citationHeydari, M., Mahdavi Mazdeh, M., & Bayat, M. (2013). Scheduling stochastic two- 138 machine flow shop problems to minimize expected makespan. Decision Science Letters, 2(3), 163–174. https://doi.org/10.5267/j.dsl.2013.04.005
dc.identifier.citationHolthaus, O. (1999). Scheduling in job shops with machine breakdowns: An experimental study. Computers and Industrial Engineering, 36(1), 137–162. https://doi.org/10.1016/S0360-8352(99)00006-6
dc.identifier.citationHong, T. P., & Chuang, T. N. (2005). Fuzzy Gupta scheduling for flow shops with more than two machines. International Journal of Computers and Applications. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0- 23444452878&partnerID=tZOtx3y1
dc.identifier.citationHuang, C.-S., Huang, Y.-C., & Lai, P.-J. (2012). Modified genetic algorithms for solving fuzzy flow shop scheduling problems and their implementation with CUDA. Expert Systems with Applications, 39(5), 4999–5005. https://doi.org/10.1016/j.eswa.2011.10.013
dc.identifier.citationJames, R. J. W., & Buchanan, J. T. (1998). Robustness of single machine scheduling problems to earliness and tardiness penalty errors. Annals of Operations Research, 76(0), 219–232. https://doi.org/10.1023/A:1018996505037
dc.identifier.citationJavadi, B., Saidi-Mehrabad, M., Haji, A., Mahdavi, I., Jolai, F., & Mahdavi-Amiri, N. (2008). No-wait flow shop scheduling using fuzzy multi-objective linear programming. Journal of the Franklin Institute, 345(5), 452–467. https://doi.org/10.1016/j.jfranklin.2007.12.003
dc.identifier.citationJi, M., Yang, Y., Duan, W., Wang, S., & Liu, B. (2016). Scheduling of no-wait stochastic distributed assembly flowshop by hybrid PSO. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 2649–2654). Vancouver, Canada: IEEE. https://doi.org/10.1109/CEC.2016.7744120
dc.identifier.citationJia, Z., & Ierapetritou, M. G. (2007). Generate Pareto optimal solutions of scheduling problems using normal boundary intersection technique. Computers & Chemical Engineering, 31(4), 268–280. https://doi.org/10.1016/j.compchemeng.2006.07.001
dc.identifier.citationJiao, B., Chen, Q., & Yan, S. (2011). A Cooperative Co-evolution PSO for Flow Shop Scheduling Problem with Uncertainty. Journal of Computers, 6(9), 1955–1961. https://doi.org/10.4304/jcp.6.9.1955-1961
dc.identifier.citationJiao, B., & Yan, S. (2014). A cooperative co-evolutionary particle swarm optimiser based on a niche sharing scheme for the flow shop scheduling problem under uncertainty. Mathematical Structures in Computer Science, 24(05), e240502. https://doi.org/10.1017/S0960129512000461
dc.identifier.citationJones, D. ., Mirrazavi, S. ., & Tamiz, M. (2002). Multi-objective meta-heuristics: An overview of the current state-of-the-art. European Journal of Operational Research, 137(1), 1–9. https://doi.org/10.1016/S0377-2217(01)00123-0
dc.identifier.citationJuan, A. A., Barrios, B. B., Vallada, E., Riera, D., & Jorba, J. (2014). A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times. Simulation Modelling Practice and Theory, 46, 101–117. https://doi.org/10.1016/j.simpat.2014.02.005
dc.identifier.citationJuan, A. A., Faulin, J., Ferrer, A., Lourenço, H. R., & Barrios, B. (2013). MIRHA: multistart biased randomization of heuristics with adaptive local search for solving nonsmooth routing problems. TOP, 21(1), 109–132. https://doi.org/10.1007/s11750-011- 0245-1
dc.identifier.citationJuan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial 139 optimization problems. Operations Research Perspectives, 2, 62–72. https://doi.org/10.1016/j.orp.2015.03.001
dc.identifier.citationJuan, A. A., Lourenço, H. R., Mateo, M., Luo, R., & Castella, Q. (2014). Using iterated local search for solving the flow-shop problem: Parallelization, parametrization, and randomization issues. International Transactions in Operational Research, 21(1), 103–126. https://doi.org/10.1111/itor.12028
dc.identifier.citationJung, H. (2009). An available-to-promise model considering customer priority and variance of penalty costs. The International Journal of Advanced Manufacturing Technology, 49(1–4), 369–377. https://doi.org/10.1007/s00170-009-2389-9
dc.identifier.citationJungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2009). A comparison of scheduling algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria. Computers & Operations Research, 36(2), 358–378. https://doi.org/10.1016/j.cor.2007.10.004
dc.identifier.citationKalaı¨, R., Lamboray, C., & Vanderpooten, D. (2012). Lexicographic α-robustness: An alternative to min–max criteria. European Journal of Operational Research, 220(3), 722–728. https://doi.org/10.1016/j.ejor.2012.01.056
dc.identifier.citationKalczynski, P. J., & Kamburowski, J. (2004). Generalization of Johnson’s and Talwar’s scheduling rules in two-machine stochastic flow shops. Journal of the Operational Research Society, 55(12), 1358–1362. https://doi.org/10.1057/palgrave.jors.2601802
dc.identifier.citationKalczynski, P. J., & Kamburowski, J. (2005). Two-Machine Stochastic Flow Shops With Blocking and the Traveling Salesman Problem. Journal of Scheduling, 8(6), 529–536. https://doi.org/10.1007/s10951-005-4782-z
dc.identifier.citationKalczynski, P. J., & Kamburowski, J. (2006). A heuristic for minimizing the expected makespan in two-machine flow shops with consistent coefficients of variation. European Journal of Operational Research, 169(3), 742–750. https://doi.org/10.1016/j.ejor.2004.08.045
dc.identifier.citationKanet, J. J., & Li, X. (2004). A weighted modified due date rule for sequencing to minimize weighted tardiness. Journal of Scheduling, 7(4), 261–276. https://doi.org/10.1023/B:JOSH.0000031421.64487.95
dc.identifier.citationKarimi, N., Zandieh, M., & Karamooz, H. R. (2010). Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach. Expert Systems with Applications, 37(6), 4024–4032. https://doi.org/10.1016/j.eswa.2009.09.005
dc.identifier.citationKasperski, A., Kurpisz, A., & Zieliński, P. (2012). Approximating a two-machine flow shop scheduling under discrete scenario uncertainty. European Journal of Operational Research, 217(1), 36–43. https://doi.org/10.1016/j.ejor.2011.08.029
dc.identifier.citationKatragjini, K., Vallada, E., & Ruiz, R. (2013). Flow shop rescheduling under different types of disruption. International Journal of Production Research, 51(3), 780–797. https://doi.org/10.1080/00207543.2012.666856
dc.identifier.citationKhoshjahan, Y., Najafi, A. A., & Afshar-Nadjafi, B. (2013). Resource constrained project scheduling problem with discounted earliness–tardiness penalties: Mathematical modeling and solving procedure. Computers & Industrial Engineering, 66(2), 293– 300. https://doi.org/10.1016/j.cie.2013.06.017
dc.identifier.citationKim, Y.-D. (1993). Heuristics for Flowshop Scheduling Problems Minimizing Mean Tardiness. The Journal of the Operational Research Society, 44(1), 19–28. https://doi.org/10.2307/2584431
dc.identifier.citationKim, Y.-D., Lim, H.-G., & Park, M.-W. (1996). Search heuristics for a flowshop scheduling problem in a printed circuit board assembly process. European Journal of 140 Operational Research, 91(1), 124–143. https://doi.org/10.1016/0377-2217(95)00119-0
dc.identifier.citationKnowles, J. D., & Corne, D. W. (2000). Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2), 149–172. https://doi.org/10.1162/106365600568167
dc.identifier.citationKoulamas, C., & J. Kyparisis, G. (2001). The three-stage assembly flowshop scheduling problem. Computers & Operations Research, 28(7), 689–704. https://doi.org/10.1016/S0305-0548(00)00004-6
dc.identifier.citationKouvelis, P., & Yu, G. (1997). Robust Discrete Optimization and Its Applications. (P. Pardalos & R. Horst, Eds.) (Vol. 14). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4757-2620-6
dc.identifier.citationKucharska, E., Grobler-Dębska, K., & Rączka, K. (2017). Algebraic-logical meta-model based approach for scheduling manufacturing problem with defects removal. Advances in Mechanical Engineering, 9(4), 168781401769229. https://doi.org/10.1177/1687814017692291
dc.identifier.citationLahdelma, R., Miettinen, K., & Salminen, P. (2003). Ordinal criteria in stochastic multicriteria acceptability analysis (SMAA). European Journal of Operational Research, 147(1), 117–127. https://doi.org/10.1016/S0377-2217(02)00267-9
dc.identifier.citationLahdelma, R., & Salminen, P. (2001). SMAA-2: Stochastic multicriteria acceptability analysis for group decision making. Operations Research, 49(3), 444–454. https://doi.org/10.2307/3088639
dc.identifier.citationLaslo, Z., Golenko-Ginzburg, D., & Keren, B. (2008). Optimal booking of machines in a virtual job-shop with stochastic processing times to minimize total machine rental and job tardiness costs. International Journal of Production Economics, 111(2), 812–821. https://doi.org/10.1016/j.ijpe.2007.03.018
dc.identifier.citationLau, H.-S. (1986). The production rate of a two-stage system with stochastic processing times. International Journal of Production Research, 24(2), 401–412. https://doi.org/10.1080/00207548608919737
dc.identifier.citationLee, C.-Y., Cheng, T. C. E., & Lin, B. M. T. (1993). Minimizing the Makespan in the 3- Machine Assembly-Type Flowshop Scheduling Problem. Management Science, 39(5), 616–625. https://doi.org/10.1287/mnsc.39.5.616
dc.identifier.citationLee, G.-C., Kim, Y.-D., & Choi, S.-W. (2004). Bottleneck-focused scheduling for a hybrid flowshop. International Journal of Production Research, 42(1), 165–181. https://doi.org/10.1080/00207540310001602892
dc.identifier.citationLei, D. (2012). Interval job shop scheduling problems. The International Journal of Advanced Manufacturing Technology, 60(1–4), 291–301. https://doi.org/10.1007/s00170-011-3600-3
dc.identifier.citationLi, Z., & Ierapetritou, M. (2008). Process scheduling under uncertainty: Review and challenges. Computers & Chemical Engineering, 32(4–5), 715–727. https://doi.org/10.1016/j.compchemeng.2007.03.001
dc.identifier.citationLiefooghe, A., Basseur, M., Humeau, J., Jourdan, L., & Talbi, E.-G. (2012). On optimizing a bi-objective flowshop scheduling problem in an uncertain environment. Computers & Mathematics with Applications, 64(12), 3747–3762. https://doi.org/10.1016/j.camwa.2012.02.051
dc.identifier.citationLin, J. T., & Chen, C.-M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simulation Modelling Practice and Theory, 51, 100–114. https://doi.org/10.1016/j.simpat.2014.10.008
dc.identifier.citationLin, J., & Zhang, S. (2016). An effective hybrid biogeography-based optimization 141 algorithm for the distributed assembly permutation flow-shop scheduling problem. Computers & Industrial Engineering, 97, 128–136. https://doi.org/10.1016/j.cie.2016.05.005
dc.identifier.citationLiu, B., Wang, L., & Jin, Y. (2005). Hybrid Particle Swarm Optimization for Flow Shop Scheduling with Stochastic Processing Time. In Y. Hao, J. Liu, Y. Wang, Y. Cheung, H. Yin, L. Jiao, … Y.-C. Jiao (Eds.), 2005 Computational Intelligence and Security International Conference (CIS) (pp. 630–637). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/11596448_93
dc.identifier.citationLiu, F., Wang, S., Hong, Y., & Yue, X. (2017). On the Robust and Stable Flowshop Scheduling Under Stochastic and Dynamic Disruptions. IEEE Transactions on Engineering Management, 64(4), 539–553. https://doi.org/10.1109/TEM.2017.2712611
dc.identifier.citationLiu, G.-S., Zhou, Y., & Yang, H.-D. (2017). Minimizing energy consumption and tardiness penalty for fuzzy flow shop scheduling with state-dependent setup time. Journal of Cleaner Production, 147, 470–484. https://doi.org/10.1016/j.jclepro.2016.12.044
dc.identifier.citationLiu, Q., Ullah, S., & Zhang, C. (2011). An improved genetic algorithm for robust permutation flowshop scheduling. The International Journal of Advanced Manufacturing Technology, 56(1–4), 345–354. https://doi.org/10.1007/s00170-010- 3149-6
dc.identifier.citationManzini, R., & Bindi, F. (2009). Strategic design and operational management optimization of a multi stage physical distribution system. Transportation Research Part E: Logistics and Transportation Review, 45(6), 915–936. https://doi.org/10.1016/j.tre.2009.04.011
dc.identifier.citationMartí, R., Campos, V., Resende, M. G. C., & Duarte, A. (2015). Multiobjective GRASP with Path Relinking. European Journal of Operational Research, 240(1), 54–71. https://doi.org/10.1016/j.ejor.2014.06.042
dc.identifier.citationMatsveichuk, N. M., Sotskov, Y. N., Egorova, N. G., & Lai, T.-C. (2009). Schedule execution for two-machine flow-shop with interval processing times. Mathematical and Computer Modelling, 49(5–6), 991–1011. https://doi.org/10.1016/j.mcm.2008.02.004
dc.identifier.citationMatsveichuk, N. M., Sotskov, Y. N., & Werner, F. (2011). The dominance digraph as a solution to the two-machine flow-shop problem with interval processing times. Optimization, 60(12), 1493–1517. https://doi.org/10.1080/02331931003657691
dc.identifier.citationMinella, G., Ruiz, R., & Ciavotta, M. (2008). A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem. INFORMS Journal on Computing, 20(3), 451–471. https://doi.org/10.1287/ijoc.1070.0258
dc.identifier.citationMolina-Sánchez, L. P., & González-Neira, E. M. (2016). GRASP to minimize total weighted tardiness in a permutation flow shop environment. International Journal of Industrial Engineering Computations, 7(1), 161–176. https://doi.org/10.5267/j.ijiec.2015.6.004
dc.identifier.citationMoore, R. E., & Bierbaum, F. (1979). Methods and applications of interval analysis (Vol. 2). Philadelphia: Siam
dc.identifier.citationMou, J., Gao, L., Guo, Q., & Mu, J. (2017). A hybrid heuristic algorithm for flowshop inverse scheduling problem under a dynamic environment. Cluster Computing, 20(1), 439–453. https://doi.org/10.1007/s10586-017-0734-6
dc.identifier.citationMou, J., Li, X., Gao, L., & Yi, W. (2015). An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems. Journal of Intelligent 142 Manufacturing. https://doi.org/10.1007/s10845-015-1129-2
dc.identifier.citationNaderi, B., & Ruiz, R. (2010). The distributed permutation flowshop scheduling problem. Computers & Operations Research, 37(4), 754–768. https://doi.org/10.1016/j.cor.2009.06.019
dc.identifier.citationNagasawa, K., Ikeda, Y., & Irohara, T. (2015). Robust flow shop scheduling with random processing times for reduction of peak power consumption. Simulation Modelling Practice and Theory. https://doi.org/10.1016/j.simpat.2015.08.001
dc.identifier.citationNakhaeinejad, M., & Nahavandi, N. (2013). An interactive algorithm for multi-objective flow shop scheduling with fuzzy processing time through resolution method and TOPSIS. The International Journal of Advanced Manufacturing Technology, 66(5–8), 1047–1064. https://doi.org/10.1007/s00170-012-4388-5
dc.identifier.citationNawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, njob flow-shop sequencing problem. Omega, 11(1), 91–95. https://doi.org/10.1016/0305-0483(83)90088-9
dc.identifier.citationNejlaoui, M., Houidi, A., Affi, Z., & Romdhane, L. (2017). A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle. Comptes Rendus Mécanique, 345(10), 712–723. https://doi.org/10.1016/j.crme.2017.05.014
dc.identifier.citationNezhad, S. S., & Assadi, R. G. (2008). Preference ratio-based maximum operator approximation and its application in fuzzy flow shop scheduling. Applied Soft Computing, 8(1), 759–766. https://doi.org/10.1016/j.asoc.2007.06.004
dc.identifier.citationNg, C. T., Matsveichuk, N. M., Sotskov, Y. N., & Cheng, T. C. E. (2009). TWOMACHINE FLOW-SHOP MINIMUM-LENGTH SCHEDULING WITH INTERVAL PROCESSING TIMES. Asia-Pacific Journal of Operational Research, 26(06), 715– 734. https://doi.org/10.1142/S0217595909002432
dc.identifier.citationNiu, Q., & Gu, X. S. (2008). A novel particle swarm optimization for flow shop scheduling with fuzzy processing time. Journal of Donghua University (English Edition), 25(2), 115–122. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0- 54849420518&partnerID=tZOtx3y1
dc.identifier.citationNoroozi, A., & Mokhtari, H. (2015). Scheduling of printed circuit board (PCB) assembly systems with heterogeneous processors using simulation-based intelligent optimization methods. Neural Computing and Applications, 26(4), 857–873. https://doi.org/10.1007/s00521-014-1765-z
dc.identifier.citationOltean, M., Grosan, C., Abraham, A., & Koppen, M. (2005). Multiobjective optimization using adaptive Pareto archived evolution strategy. In 5th International Conference on Intelligent Systems Design and Applications (ISDA’05) (pp. 558–563). IEEE. https://doi.org/10.1109/ISDA.2005.69
dc.identifier.citationPan, Q.-K., & Ruiz, R. (2012). Local search methods for the flowshop scheduling problem with flowtime minimization. European Journal of Operational Research, 222(1), 31– 43. https://doi.org/10.1016/j.ejor.2012.04.034
dc.identifier.citationPaternina-Arboleda, C. D., Montoya-Torres, J. R., Acero-Dominguez, M. J., & HerreraHernandez, M. C. (2008). Scheduling jobs on a k-stage flexible flow-shop. Annals of Operations Research, 164(1), 29–40. https://doi.org/10.1007/s10479-007-0257-2
dc.identifier.citationPaul, S. K., & Azeem, A. (2010). Minimization of work-in-process inventory in hybrid flow shop scheduling using fuzzy logic. International Journal of Industrial Engineering : Theory Applications and Practice, 17(2), 115–127. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0- 143 77955062869&partnerID=tZOtx3y1
dc.identifier.citationPetrovic, S., & Song, X. (2006). A new approach to two-machine flow shop problem with uncertain processing times. Optimization and Engineering, 7(3), 329–342. https://doi.org/10.1007/s11081-006-9975-6
dc.identifier.citationPinedo, M. L. (2012). Scheduling: Theory, algorithms and systems. Springer (4th ed., Vol. 4). New York: Springer Science & Business Media. https://doi.org/10.1007/978-1- 4614-2361-4
dc.identifier.citationPotts, C. N., Sevast’janov, S. V., Strusevich, V. A., Van Wassenhove, L. N., & Zwaneveld, C. M. (1995). The Two-stage Assembly Scheduling Problem: Complexity and Approximation. Operations Research, 43(2), 346–355. https://doi.org/10.1287/opre.43.2.346
dc.identifier.citationQin, W., Zhang, · J, Song, · D, Zhang, J., & Song, D. (2015). An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-015-1144-3
dc.identifier.citationRahmani, D. (2017). A new proactive-reactive approach to hedge against uncertain processing times and unexpected machine failures in the two-machine flow shop scheduling problems. Scientia Iranica, 24(3), 1571–1584. https://doi.org/10.24200/sci.2017.4136
dc.identifier.citationRahmani, D., & Heydari, M. (2014). Robust and stable flow shop scheduling with unexpected arrivals of new jobs and uncertain processing times. Journal of Manufacturing Systems, 33(1), 84–92. https://doi.org/10.1016/j.jmsy.2013.03.004
dc.identifier.citationRahmani, D., Ramezanian, R., & Mehrabad, M. S. (2014). Multi-objective flow shop scheduling problem with stochastic parameters: fuzzy goal programming approach. International Journal of Operational Research, 21(3), 322–340. https://doi.org/10.1504/IJOR.2014.065411
dc.identifier.citationRajendran, C., & Holthaus, O. (1999). A comparative study of dispatching rules in dynamic flowshops and jobshops. European Journal of Operational Research, 116(1), 156– 170. https://doi.org/10.1016/S0377-2217(98)00023-X
dc.identifier.citationRamezanian, R., & Saidi-Mehrabad, M. (2013). Hybrid simulated annealing and MIP-based heuristics for stochastic lot-sizing and scheduling problem in capacitated multi-stage production system. Applied Mathematical Modelling, 37(7), 5134–5147. https://doi.org/10.1016/j.apm.2012.10.024
dc.identifier.citationResende, M. C., & Ribeiro, C. (2003). Greedy Randomized Adaptive Search Procedures. In F. Glover & G. Kochenberger (Eds.), Handbook of Metaheuristics SE - 8 (Vol. 57, pp. 219–249). Springer US. https://doi.org/10.1007/0-306-48056-5_8
dc.identifier.citationResende, M. C., & Ribeiro, C. (2010). Greedy randomized adaptive search procedures: Advances, hybridizations, and applications. In M. Gendreau & J.-Y. Potvin (Eds.), Handbook of Metaheuristics SE - 10 (Vol. 146, pp. 283–319). Springer US. https://doi.org/10.1007/978-1-4419-1665-5_10
dc.identifier.citationRonconi, D. P., & Henriques, L. R. S. (2009). Some heuristic algorithms for total tardiness minimization in a flowshop with blocking. Omega, 37(2), 272–281. https://doi.org/10.1016/j.omega.2007.01.003
dc.identifier.citationRoy, B. (2010). Robustness in operational research and decision aiding: A multi-faceted issue. European Journal of Operational Research, 200(3), 629–638. https://doi.org/10.1016/j.ejor.2008.12.036
dc.identifier.citationRuiz, R., & Maroto, C. (2005). A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research, 165(2), 479–494. 144 https://doi.org/10.1016/j.ejor.2004.04.017
dc.identifier.citationRuiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1–18. https://doi.org/10.1016/j.ejor.2009.09.024
dc.identifier.citationSaaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. https://doi.org/10.1016/0377- 2217(90)90057-I
dc.identifier.citationSaaty, T. L. (2013). Analytic network process. In Multi-Criteria Decision Analysis (pp. 59– 80). Chichester, UK: John Wiley & Sons Ltd. https://doi.org/10.1002/9781118644898.ch3
dc.identifier.citationSafari, E., Sadjadi, S. J., & Shahanaghi, K. (2009). Minimizing expected makespan in flowshop with on condition based maintenance constraint by integrating heuristics and simulation. World Journal of Modelling and Simulation, 5(4), 261–271. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0- 78649455491&partnerID=tZOtx3y1
dc.identifier.citationSancar Edis, R., & Ornek, M. A. (2009). A tabu search-based heuristic for single-product lot streaming problems in flow shops. The International Journal of Advanced Manufacturing Technology, 43(11–12), 1202–1213. https://doi.org/10.1007/s00170- 008-1798-5
dc.identifier.citationSchultmann, F., Fröhling, M., & Rentz, O. (2006). Fuzzy approach for production planning and detailed scheduling in paints manufacturing. International Journal of Production Research, 44(8), 1589–1612. https://doi.org/10.1080/00207540500353939
dc.identifier.citationSeidgar, H., Rad, S. T., & Shafaei, R. (2017). Scheduling of assembly flow shop problem and machines with random breakdowns. International Journal of Operational Research, 29(2), 273. https://doi.org/10.1504/IJOR.2017.083959
dc.identifier.citationSeidmann, A., & Smith, M. L. (1981). Due date assignment for production systems. Management Science, 27(5), 571–581. https://doi.org/10.1287/mnsc.27.5.571
dc.identifier.citationShahmoradi-Moghaddam, H., Akbari, K., Sadjadi, S. J., & Heydari, M. (2016). A scenariobased robust optimization approach for batch processing scheduling. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 230(12), 2286–2295. https://doi.org/10.1177/0954405415584977
dc.identifier.citationShahnaghi, K., Shahmoradi-Moghadam, H., Noroozi, A., & Mokhtari, H. (2016). A robust modelling and optimisation framework for a batch processing flow shop production system in the presence of uncertainties. International Journal of Computer Integrated Manufacturing, 29(1), 92–106. https://doi.org/10.1080/0951192X.2014.1002814
dc.identifier.citationSoroush, H. M., & Allahverdi, A. (2005). Stochastic two-machine flowshop scheduling problem with total completion time criterion. International Journal of Industrial Engineering : Theory Applications and Practice, 12(2), 159–171. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0- 33746598230&partnerID=tZOtx3y1
dc.identifier.citationSotskov, Y. N., Allahverdi, A., & Lai, T.-C. (2004). Flowshop scheduling problem to minimize total completion time with random and bounded processing times. Journal of the Operational Research Society, 55(3), 277–286. https://doi.org/10.1057/palgrave.jors.2601682
dc.identifier.citationSturrock, D. (2012). New solutions for production dilemmas. Industrial Engineer, 44(12), 47–52
dc.identifier.citationSwaminathan, R., Pfund, M. E., Fowler, J. W., Mason, S. J., & Keha, A. (2007). Impact of 145 permutation enforcement when minimizing total weighted tardiness in dynamic flowshops with uncertain processing times. Computers & Operations Research, 34(10), 3055–3068. https://doi.org/10.1016/j.cor.2005.11.014
dc.identifier.citationTaillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278–285. https://doi.org/10.1016/0377-2217(93)90182- M
dc.identifier.citationTemİz, İ., & Erol, S. (2004). Fuzzy branch-and-bound algorithm for flow shop scheduling. Journal of Intelligent Manufacturing, 15(4), 449–454. https://doi.org/10.1023/B:JIMS.0000034107.72423.b6
dc.identifier.citationThomé, A. M. T., Scavarda, L. F., & Scavarda, A. J. (2016). Conducting systematic literature review in operations management. Production Planning & Control, 27(5), 408–420. https://doi.org/10.1080/09537287.2015.1129464
dc.identifier.citationVallada, E., Ruiz, R., & Minella, G. (2008). Minimising total tardiness in the m-machine flowshop problem: A review and evaluation of heuristics and metaheuristics. Computers & Operations Research, 35(4), 1350–1373. https://doi.org/10.1016/j.cor.2006.08.016
dc.identifier.citationvan Ooijen, H. P. G., & Bertrand, J. W. M. (2001). Economic due-date setting in job-shops based on routing and workload dependent flow time distribution functions. International Journal of Production Economics, 74(1–3), 261–268. https://doi.org/10.1016/S0925-5273(01)00131-1
dc.identifier.citationVepsalainen, A., & Morton, T. E. (1987). Priority rules and lead time estimation for job shop scheduling with weighted tardiness costs. Management Science, 33(8), 1036– 1047
dc.identifier.citationVin, J. P., & Ierapetritou, M. G. (2001). Robust Short-Term Scheduling of Multiproduct Batch Plants under Demand Uncertainty. Industrial & Engineering Chemistry Research, 40(21), 4543–4554. https://doi.org/10.1021/ie0007724
dc.identifier.citationWang, B. (Ed.). (1997). Integrated Product, Process and Enterprise Design. Boston, MA: Springer US. https://doi.org/10.1007/978-1-4615-6383-9
dc.identifier.citationWang, K., & Choi, S. H. (2010). Decomposition-based scheduling for makespan minimisation of flexible flow shop with stochastic processing times. Engineering Letters, 18(1). Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0- 76549084080&partnerID=tZOtx3y1
dc.identifier.citationWang, K., & Choi, S. H. (2011). A decomposition-based approach to flexible flow shop scheduling under machine breakdown. International Journal of Production Research, 50(1), 215–234. https://doi.org/10.1080/00207543.2011.571456
dc.identifier.citationWang, K., & Choi, S. H. (2014). A holonic approach to flexible flow shop scheduling under stochastic processing times. Computers & Operations Research, 43(1), 157– 168. https://doi.org/10.1016/j.cor.2013.09.013
dc.identifier.citationWang, K., Choi, S. H., & Qin, H. (2014). An estimation of distribution algorithm for hybrid flow shop scheduling under stochastic processing times. International Journal of Production Research, 52(24), 7360–7376. https://doi.org/10.1080/00207543.2014.930535
dc.identifier.citationWang, K., Choi, S. H., Qin, H., & Huang, Y. (2013). A cluster-based scheduling model using SPT and SA for dynamic hybrid flow shop problems. The International Journal of Advanced Manufacturing Technology, 67(9–12), 2243–2258. https://doi.org/10.1007/s00170-012-4645-7
dc.identifier.citationWang, K., Huang, Y., & Qin, H. (2016). A fuzzy logic-based hybrid estimation of 146 distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown. Journal of the Operational Research Society, 67(1), 68– 82. https://doi.org/10.1057/jors.2015.50
dc.identifier.citationWang, L., Zhang, L., & Zheng, D.-Z. Z. (2005a). A class of hypothesis-test-based genetic algorithms for flow shop scheduling with stochastic processing time. The International Journal of Advanced Manufacturing Technology, 25(11–12), 1157–1163. https://doi.org/10.1007/s00170-003-1961-y
dc.identifier.citationWang, L., Zhang, L., & Zheng, D.-Z. Z. (2005b). Genetic ordinal optimisation for stochastic flow shop scheduling. The International Journal of Advanced Manufacturing Technology, 27(1–2), 166–173. https://doi.org/10.1007/s00170-004- 2154-z
dc.identifier.citationWang, S.-Y., & Wang, L. (2016). An Estimation of Distribution Algorithm-Based Memetic Algorithm for the Distributed Assembly Permutation Flow-Shop Scheduling Problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(1), 139–149. https://doi.org/10.1109/TSMC.2015.2416127
dc.identifier.citationWang, S., Wang, L., Liu, M., & Xu, Y. (2015). An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem. International Journal of Computer Integrated Manufacturing, 28(3), 307–320. https://doi.org/10.1080/0951192X.2014.880803
dc.identifier.citationYang *, T., Kuo, Y., & Chang, I. (2004). Tabu-search simulation optimization approach for flow-shop scheduling with multiple processors — a case study. International Journal of Production Research, 42(19), 4015–4030. https://doi.org/10.1080/00207540410001699381
dc.identifier.citationYenisey, M. M., & Yagmahan, B. (2014). Multi-objective permutation flow shop scheduling problem: Literature review, classification and current trends. Omega, 45(C), 119–135. https://doi.org/10.1016/j.omega.2013.07.004
dc.identifier.citationYimer, A. D., & Demirli, K. (2009). Fuzzy scheduling of job orders in a two-stage flowshop with batch-processing machines. International Journal of Approximate Reasoning, 50(1), 117–137. https://doi.org/10.1016/j.ijar.2007.08.013
dc.identifier.citationYing, K. (2015). Scheduling the two-machine flowshop to hedge against processing time uncertainty. Journal of the Operational Research Society, 66(9), 1413–1425. https://doi.org/10.1057/jors.2014.100
dc.identifier.citationZandieh, M., & Gholami, M. (2009). An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. International Journal of Production Research, 47(24), 6999–7027. https://doi.org/10.1080/00207540802400636
dc.identifier.citationZandieh, M., & Hashemi, A. (2015). Group scheduling in hybrid flexible flowshop with sequence-dependent setup times and random breakdowns via integrating genetic algorithm and simulation. International Journal of Industrial and Systems Engineering, 21(3), 377. https://doi.org/10.1504/IJISE.2015.072273
dc.identifier.urihttp://hdl.handle.net/10818/34412
dc.description146 Páginases_CO
dc.description.abstractLogistics and supply chain concepts have evolved over the years, initially involving only transport activities and then expanding to include product, information and financial flows until finally reverse flows, integrated chains, and networks were incorporated. Although there is diversity in definitions, there is a common understanding that logistics involves three principal stages called supply, production, and distribution (Pinedo, 2012). Supply stage is often composed by two or more tier suppliers, a manufacturer that is the focal business and two or more tier customers. Inside focal business exists three types of decisional levels, the strategic, tactical and operative ones. Figure 1 presents the complete supply chain, focusing in Manufacturer supply chain. This focus shows the different processes and activities carried out at each decision level. As it can be seen, production scheduling receives information from Material Requirements Plan, the Production Master schedule and gives information to the Distribution Resource Planning and routing of transportation.es_CO
dc.formatapplication/pdfes_CO
dc.language.isoenges_CO
dc.publisherUniversidad de La Sabanaes_CO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceUniversidad de La Sabana
dc.sourceIntellectum Repositorio Universidad de La Sabana
dc.subjectLogísticaes_CO
dc.subjectCadena de suministroes_CO
dc.subjectLogística en los negocioses_CO
dc.subjectCanales de comercializaciónes_CO
dc.subjectAdministración de la producciónes_CO
dc.titleAddressing robustness and multiple objectives in stochastic flow shop environmentses_CO
dc.typedoctoralThesises_CO
dc.publisher.programDoctorado en Logística y Gestión de Cadenas de Suministroses_CO
dc.publisher.departmentFacultad de Ingenieríaes_CO
dc.identifier.local270000
dc.identifier.localTE09861
dc.type.hasVersionpublishedVersiones_CO
dc.rights.accessRightsrestrictedAccesses_CO
dc.creator.degreeDoctor en Logística y Gestión de Cadenas de Suministroses_CO


Ficheros en el ítem

Thumbnail
Nombre:
Tesis ElianaMariaGonzalezNeira.pdf
Tamaño:
4.444Mb
Formato:
PDF
Descripción:
Ver documento en PDF
Ver/

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 InternationalExcepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International