Free Access
RAIRO-Oper. Res.
Volume 55, 2021
Regular articles published in advance of the transition of the journal to Subscribe to Open (S2O). Free supplement sponsored by the Fonds National pour la Science Ouverte
Page(s) S1447 - S1467
Published online 02 March 2021
  • A. Alarcon-Rodriguez, G. Ault and S. Galloway, Multi-objective planning of distributed energy resources: a review of the state-of-the-art. Renewable Sustainable Energy Rev. 14 (2010) 1353–1366. [Google Scholar]
  • J.C. Beck and P. Refalo, A hybrid approach to scheduling with earliness and tardiness costs. Ann. Oper. Res. 118 (2003) 49–71. [Google Scholar]
  • J. Behnamian and S.M.T. Fatemi Ghomi, The heterogeneous multi-factory production network scheduling with adaptive communication policy and parallel machines. Inf. Sci. 219 (2013) 181–196. [Google Scholar]
  • J. Behnamian, S.M.T. Fatemi Ghomi and M. Zandieh, A multi-phase covering Pareto-optimal front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid metaheuristic. Expert Syst. App. 36 (2009) 11057–11069. [Google Scholar]
  • T.E. Carroll and D. Grosu, Selfish multi-user task scheduling. In: The Fifth International Symposium on Parallel and Distributed Computing (ISPDC ‘06), Timisoara (2006) 99–106. [Google Scholar]
  • H.C. Chang and T.K. Liu, Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms. J. Intell. Manuf. 28 (2017) 1973–1986. [Google Scholar]
  • V.A. Cicirello and S.F. Smith, Wasp-like agents for distributed factory coordination. Auton. Agents Multi-Agent Syst. 8 (2004) 237–266. [Google Scholar]
  • K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley and Sons, Delhi (2010). [Google Scholar]
  • M.M. Dessouky, Scheduling identical jobs with unequal ready times on uniform parallel machines to minimize the maximum lateness. Comput. Ind. Eng. 34 (1998) 793–806. [Google Scholar]
  • G. Feng and H.C. Lau, Efficient algorithms for machine scheduling problems with earliness and tardiness penalties. Ann. Oper. Res. 159 (2008) 83–95. [Google Scholar]
  • M. Gendreau and J.-Y. Potvin, editors. Handbook of Metaheuristics, 2nd edition. Vol. 272 of International Series in Operations Research & Management Science. Springer, New York, NY (2010). [Google Scholar]
  • M.G. Gnonia, R. Iavagnilioa, G. Mossaa, G. Mummoloa and A. Di Leva, Production planning of a multi-site manufacturing system by hybrid modelling: a case study from the automotive industry. Int. J. Prod. Econ. 85 (2003) 251–262. [Google Scholar]
  • J. Grobler, A.P. Engelbrecht, S. Kok and S. Yadavalli, Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time. Ann. Oper. Res. 180 (2010) 165–196. [Google Scholar]
  • Ö. Hazır and S. Kedad-Sidhoum, Batch sizing and just-in-time scheduling with common due date. Ann. Oper. Res. 213 (2014) 187–202. [Google Scholar]
  • T. İnkaya and M. Akansel, Coordinated scheduling of the transfer lots in an assembly-type supply chain: a genetic algorithm approach. J. Intell. Manuf. 28 (2017) 1005–1015. [Google Scholar]
  • S.C.H Leung, Y. Wu and K.K. Lai, Multi-site aggregate production planning with multiple objectives: a goal programming approach. Prod. Planning Control 14 (2003) 425–436. [Google Scholar]
  • J. Lin, Z.J. Wang and X. Li, A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem. Swarm Evol. Comput. 36 (2017) 124–135. [Google Scholar]
  • T. Meng, Q.-K. Pan and L. Wang, A distributed permutation flowshop scheduling problem with the customer order constraint. Knowl. Based Syst. 184 (2019) 104894. [Google Scholar]
  • M. Ramanauskas, D. Šešok, R. Belevičius, E. Kurilovas and S. Valentinavičius, Genetic algorithm with modified crossover for grillage optimization. Int. J. Comput. Commun. Control 12 (2017) 393–403. [Google Scholar]
  • R. Ruiz and T. Stuetzle, An iterated greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. Eur. J. Oper. Res. 187 (2008) 1143–1159. [Google Scholar]
  • M. Sambasivan and S. Yahya, A Lagrangean-based heuristic for multi-plant, multi-item, multi-period capacitated lot-sizing problems with inter-plant transfers. Comput. Oper. Res. 32 (2005) 537–555. [Google Scholar]
  • W. Shao, D. Pi and Z. Shao, Optimization of makespan for the distributed no-wait flow shop scheduling problem with iterated greedy algorithms. Knowl. Based Syst. 137 (2017) 163–181. [Google Scholar]
  • S. Terrazas-Moreno and I.E. Grossmann, A multiscale decomposition method for the optimal planning and scheduling of multi-site continuous multiproduct plants. Chem. Eng. Sci. 66 (2011) 4307–4318. [Google Scholar]
  • C.H. Timpe and J. Kallrath, Optimal planning in large multi-site production networks. Eur. J. Oper. Res. 126 (2000) 422–435. [Google Scholar]
  • F.M. Westfield, Marginal analysis, multi-plant firms, and business practice: an example. Q. J. Econ. 69 (1955) 253–268. [Google Scholar]
  • J.F.H Williams, Heuristic techniques for simultaneous scheduling of production and distribution in multi-echelon structures: theory and empirical comparisons. Manage. Sci. 27 (1981) 336–352. [Google Scholar]
  • X. Wu, X. Liu and N. Zhao, An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem. Memet. Comput. 11 (2019) 335–355. [Google Scholar]
  • G. Zhang and K. Xing, Differential evolution metaheuristics for distributed limited-buffer flowshop scheduling with makespan criterion. Comput. Oper. Res. 108 (2019) 33–43. [Google Scholar]

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