Open Access
RAIRO-Oper. Res.
Volume 57, Number 3, May-June 2023
Page(s) 1179 - 1193
Published online 18 May 2023
  • B. Basciftci, S. Ahmed and S. Shen, Distributionally robust facility location problem under decisiondependent stochastic demand. Eur. J. Oper. Res. 292 (2021) 548–561. [CrossRef] [Google Scholar]
  • V. Belvedere, A. Grando and P. Bielli, A quantitative investigation of the role of information and communication technologies in the implementation of a product-service system. Int. J. Prod. Res. 51 (2013) 410–426. [CrossRef] [Google Scholar]
  • M.L. Bentaha, A. Dolgui, O. Battaïa, R.J. Riggs and J. Hu, Profit-oriented partial disassembly line design: dealing with hazardous parts and task processing times uncertainty. Int. J. Prod. Res. 56 (2018) 7220–7242. [CrossRef] [Google Scholar]
  • G.C. Calafiore and L. El Ghaoui, On distributionally robust chance-constrained linear programs. J. Optim. Theory Appl. 130 (2006) 1–22. [CrossRef] [MathSciNet] [Google Scholar]
  • E. Delage and Y. Ye, Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58 (2010) 595–612. [CrossRef] [MathSciNet] [Google Scholar]
  • B. Do Chung, T. Yao and B. Zhang, Dynamic traffic assignment under uncertainty: a distributional robust chance-constrained approach. Netw. Spat. Econ. 12 (2012) 167–181. [CrossRef] [MathSciNet] [Google Scholar]
  • L. Han, P. Wu and C. Chu, Service-oriented distributionally robust lane reservation. J. Ind. Inf. Integr. 25 (2022) 100302. [Google Scholar]
  • C. Heßler and K. Deghdak, Discrete parallel machine makespan ScheLoc problem. J. Comb. Optimiz. 34 (2017) 1159–1186. [CrossRef] [Google Scholar]
  • H. Hennes and H.W. Hamacher, Integrated scheduling and location models: single machine makespan problems. Technical Report, University of Kaiserslautern, Shaker Verlag, Aachen (2002) 82. [Google Scholar]
  • M.T. Kalsch, Scheduling-Location (ScheLoc) Models, Theory and Algorithms, Verlag Dr. Hut (2009). [Google Scholar]
  • M.T. Kalsch and Z. Drezner, Solving scheduling and location problems in the plane simultaneously. Comput. Oper. Res. 37 (2010) 256–264. [CrossRef] [MathSciNet] [Google Scholar]
  • R. Kramer and A. Kramer, An exact framework for the discrete parallel machine scheduling location problem. Comput. Oper. Res. 132 (2021) 105318. [CrossRef] [Google Scholar]
  • Y. Li, J.F. Côté, L. Callegari-Coelho and P. Wu, Novel formulations and logic-based benders decomposition for the integrated parallel machine scheduling and location problem. INFORMS J. Comput. 34 (2021) 1048–1069. [Google Scholar]
  • Y. Li, X. Li, J. Shu, M. Song and K. Zhang, A general model and efficient algorithms for reliable facility location problem under uncertain disruptions. INFORMS J. Comput. 34 (2022) 407–426. [CrossRef] [MathSciNet] [Google Scholar]
  • Y. Liao, F. Deschamps, E.d.F.R. Loures and L.F.P. Ramos, Past, present and future of industry 4.0 – a systematic literature review and research agenda proposal. Int. J. Prod. Res. 55 (2017) 3609–3629. [CrossRef] [Google Scholar]
  • M. Liu and X. Liu, Distributionally robust parallel machine ScheLoc problem under service level constraints. IFAC-PapersOnLine 52 (2019) 875–880. [CrossRef] [Google Scholar]
  • M. Liu, S. Wang, C. Chu and F. Chu, An improved exact algorithm for single-machine scheduling to minimise the number of tardy jobs with periodic maintenance. Int. J. Prod. Res. 54 (2016) 3591–3602. [CrossRef] [Google Scholar]
  • M. Liu, X. Liu, F. Chu, F. Zheng and C. Chu, Service-oriented robust parallel machine scheduling. Int. J. Prod. Res. 57 (2019) 3814–3830. [CrossRef] [Google Scholar]
  • M. Liu, X. Liu, E. Zhang, F. Chu and C. Chu, Scenario-based heuristic to two-stage stochastic program for the parallel machine ScheLoc problem. Int. J. Prod. Res. 57 (2019) 1706–1723. [CrossRef] [Google Scholar]
  • M. Liu, X. Liu, F. Chu, F. Zheng and C. Chu, Profit-oriented distributionally robust chance constrained flowshop scheduling considering credit risk. Int. J. Prod. Res. 58 (2020) 2527–2549. [CrossRef] [Google Scholar]
  • H. Scarf, A min-max solution of an inventory problem, in Stud. Mathematical Theory of Inventory and Production, Stanford University Press, Redwood City, CA (1958) 201–209. [Google Scholar]
  • C. Tsosie and S. Nicastro. (2017). [Google Scholar]
  • M.R. Wagner, Stochastic 0–1 linear programming under limited distributional information. Oper. Res. Lett. 36 (2008) 150–156. [CrossRef] [MathSciNet] [Google Scholar]
  • S. Wang, J. Wan, D. Li and C. Zhang, Implementing smart factory of industrie 4.0: an outlook. Int. J. Distrib. Sens. Netw. 12 (2016) 3159805. [CrossRef] [Google Scholar]
  • S. Wang, R. Wu, F. Chu, J. Yu and X. Liu, An improved formulation and efficient heuristics for the discrete parallel-machine makespan ScheLoc problem. Comput. Ind. Eng. 140 (2020) 106238. [CrossRef] [Google Scholar]
  • J.C. Yepes-Borrero, F. Perea, R. Ruiz and F. Villa, Bi-objective parallel machine scheduling with additional resources during setups. Eur. J. Oper. Res. 292 (2021) 443–455. [CrossRef] [Google Scholar]
  • P. Yunusoglu and S. Topaloglu Yildiz, Constraint programming approach for multi-resourceconstrained unrelated parallel machine scheduling problem with sequence-dependent setup times. Int. J. Prod. Res. 60 (2022) 2212–2229. [CrossRef] [Google Scholar]
  • C. Zhang, Y. Li, J. Cao, Z. Yang and L.C. Coelho, Exact and matheuristic methods for the parallel machine scheduling and location problem with delivery time and due date. Comput. Oper. Res. 147 (2022) 105936. [CrossRef] [Google Scholar]

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