Open Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 3, May-June 2021
Page(s) 1501 - 1522
DOI https://doi.org/10.1051/ro/2021062
Published online 08 June 2021
  • R. Baker, Principles of Sequencing and Scheduling. Wiley, New Jersey (1943). [Google Scholar]
  • J.C. Bean, Genetic algorithms and random keys for sequencing and optimization. ORSA J. Comput. 6 (1994) 154–160. [Google Scholar]
  • P.Y. Chang, P. Damodaran and S. Melouk, Minimizing makespan on parallel batch processing machines. Int. J. Prod. Res. 42 (2004) 4211–4220. [Google Scholar]
  • P. Damodaran and P.Y. Chang, Heuristics to minimize makespan of parallel batch processing machines. Int. J. Adv. Manuf. Technol. 37 (2008) 1005–1013. [Google Scholar]
  • P. Damodaran, P.K. Manjeshwar and K. Srihari, Minimizing makespan on a batch-processing machine with non-identical job sizes using genetic algorithms. Int. J. Prod. Econ. 103 (2006) 882–891. [Google Scholar]
  • P. Damodaran, N.S. Hirani and M.C. Velez-Gallego, Scheduling identical parallel batch processing machines to minimize makespan using genetic algorithms. Eur. J. Ind. Eng. 3 (2009) 187–206. [Google Scholar]
  • S. Dauzère-Pérès and L. Mönch, Scheduling jobs on a single batch processing machine with incompatible job families and weighted number of tardy jobs objective. Comput. Oper. Res. 40 (2013) 1224–1233. [Google Scholar]
  • J. Holland, Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975). [Google Scholar]
  • C.A. José Elias and Y.-T.L. Joseph, Scheduling unrelated parallel batch processing machines with non-identical job sizes and unequal ready times. Comput. Oper. Res. 78 (2017) 117–128. [Google Scholar]
  • C.A. José Elias, Y.-T.L. Joseph and T. Ricardo Gonçalves, An iterated greedy algorithm for total flow time minimization in unrelated parallel batch machines with unequal job release times. Eng. App. Artif. Intell. 77 (2019) 239–254. [Google Scholar]
  • A.H. Kashan, B. Karimi and M. Jenabi, A hybrid genetic heuristic for scheduling parallel batch processing machines with arbitrary job sizes. Comput. Oper. Res. 35 (2008) 1084–1098. [Google Scholar]
  • S.-G. Koh, P.-H. Koo, J.-W. Ha and W.S. Lee, Scheduling parallel batch processing machines with arbitrary job sizes and incompatible job families. Int. J. Prod. Res. 42 (2004) 4091–4107. [Google Scholar]
  • S. Koh, P.H. Koo, D.C. Kim and W.S. Hur, Scheduling a single batch processing machine with arbitrary job sizes and incompatible job families. Int. J. Prod. Econ. 98 (2005) 81–96. [Google Scholar]
  • M. Lars and S. Roob, A Mata heuristic framework for batch machine scheduling problems with incompatible job families and regular sum objective. Appl. Soft Comput. J. 68 (2018) 835–846. [Google Scholar]
  • C.-Y. Lee, R. Uzsoy and L.-A. Martin-Vega, Efficient algorithms for scheduling semi-conductor burn-in operations. Oper. Res. 40 (1992) 764–775. [Google Scholar]
  • S. Malvea and R. Uzsoy, A genetic algorithm for minimizing maximum lateness on parallel identical batch processing machines with dynamic job arrivals and incompatible job families. Computers & Operations Research 34 (2007) 3016–3028. [Google Scholar]
  • M. Mathirajan and A.I. Sivakumar, A literature review, classification and simple meta-analysis on scheduling of batch processors in semiconductor. Int. J. Adv. Manuf. Technol. 29 (2006) 990–1001. [Google Scholar]
  • S. Melouk, P. Damodaran and P.Y. Chang, Minimizing makespan for single machine batch processing with non-identical job sizes using simulated annealing. Int. J. Prod. Econ. 87 (2004) 141–147. [Google Scholar]
  • L. Monch, J.W. Flower, S. Dauzere-Peres, S.J. Mason and O. Rose, A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations. J. Scheduling 14 (2011) 583–599. [Google Scholar]
  • I. Muter, Exact algorithms to minimize makespan on single and parallel batch processing machines. Eur. J. Oper. Res. 285 (2020) 470–483. [Google Scholar]
  • M.L. Pinedo, Scheduling Theory, Algorithms, and Systems, 3rd edition. Springer, New York (2008). [Google Scholar]
  • X. Rui, C. Huaping and L. Xueping, Makespan minimization on single batch-processing machine via ant colony optimization. Comput. Oper. Res. 39 (2012) 582–593. [Google Scholar]
  • Z. Shengchao, C. Huaping and L. Xueping, Distance matrix based heuristics to minimize makespan of parallel-batch processing machines with arbitrary job sizes and release times. Appl. Soft Comput. 52 (2017) 630–641. [Google Scholar]
  • G. Taguchi, Introduction to Quality Engineering. Asian Productivity Organization/UNIPUB White Plains (1986). [Google Scholar]
  • R. Uzsoy, Scheduling a single batch processing machine with non-identical job sizes. Int. J. Prod. Res. 32 (1994) 1615–1635. [Google Scholar]
  • S. Xu and J.C. Bean, A genetic algorithm for scheduling parallel non-identical batch processing machines. In: . IEEE Symposium on Computational Intelligence in Scheduling (2007) 143–150. [Google Scholar]
  • R. Xu, H.P. Chen and X.P. Li, A bi-objective scheduling problem on batch machines via a pareto-based ant colony system. Int. J. Prod. Econ. 145 (2013) 371–386. [Google Scholar]
  • Y. Zarook, J. Rezaeian, R. Tavakkoli-Moghaddam, I. Mahdavi and N. Javadian, Minimization of makespan for the single batch-processing machine scheduling problem with considering aging effect and multi-maintenance activities. Int. J. Adv. Manuf. Technol. 76 (2015) 1879–1892. [Google Scholar]
  • J. Zhao-Hong, W. Ting-Ting, Y.-T. Joseph and K.L. Leung, Effective heuristics for makespan minimization in parallel batch machines with non-identical capacities and job release times. J. Ind. Manage. Optim. 13 (2017) 977–993. [Google Scholar]
  • S. Zhou, M. Jin and N. Du, Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times. Energy 209 (2020) 118420. [Google Scholar]

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