Neighbourhood generation mechanism applied in simulated annealing to job shop scheduling problems

References

• Adams, J., Balas, E., & Zawack, D. (1988). The shifting bottleneck procedure for job shop scheduling. Management Science, 34, 391–401.
   Web of Science ®Google Scholar
• Applegate, D., & Cook, W. (1991). A computational study of the job-shop scheduling instance. ORSA Journal on Computing, 3, 149–156.
   Google Scholar
• Beasley, J.E. (1990). OR-library: Distributing test problems by electronic mail. Journal of the Operational Research Society, 41(11), 1069–1072.
   Web of Science ®Google Scholar
• Cheng, B., Yang, S., & Ma, Y. (2012). Minimising makespan for two batch-processing machines with non-identical job sizes in job shop. International Journal of Systems Science, 43(12), 2185–2192.
   Web of Science ®Google Scholar
• Cruz-Chávez, M.A., Díaz-Parra, O., Juárez-Romero, D., & Martínez-Rangel, M.G. (2008). Memetic algorithm based on a constraint satisfaction technique for VRPTW, Lecture notes in artificial intelligence (Vol. 5097, pp. 376–387). Berlin: Springer-Verlag. ISSN: 0302-9743.
   Google Scholar
• Cruz-Chávez, M.A., & Frausto-Solís, J. (2004). Simulated annealing with restart to job shop scheduling problem using upper bounds. In Lecture notes in computer science (Vol. 3070, pp. 860–865). Berlin: Springer-Verlag. ISSN: 0302-9743.
   Google Scholar
• Cruz-Chávez, M.A., & Frausto-Solís, J. (2006). A new algorithm that obtains an approximation of the critical path in the job shop scheduling problem. In Lecture notes in artificial intelligence (Vol. 4293, pp. 450–460). Berlin: Springer-Verlag. ISSN: 0302-9743.
   Google Scholar
• Cruz-Chávez, M.A., Frausto-Solís, J., & Ramos-Quintana, F. (2004). The problem of using the calculation of the critical path to solver instances of the job shop scheduling problem. International Journal of Computational Intelligence, 1(4), 334–337. ISSN(p): 1304-4508, ISSN(e): 1304-2386.
   Google Scholar
• Cruz-Chávez, M.A., Martínez-Oropeza, A., & Serna-Barqueda, S.A. (2010). Neighborhood hybrid structure for discrete optimization problems. In Electronics, Robotics and Automotive Mechanics Conference, CERMA2010 (pp. 108–113). Morelos, Mexico. ISBN: 978-0-7695-4204-1.
   Google Scholar
• Fisher, H., & Thompson, G.L. (1963). Probabilistic learning combinations of local job-shop scheduling rules. In J.F. Muth & G.L. Thompson (Eds.), Industrial scheduling (pp. 225–251). Englewood Cliffs, NJ: Prentice Hall.
   Google Scholar
• Gao, J., Sun, L., & Gen, M. (2008). A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers & Operations Research, 35, 2892–2907.
   Web of Science ®Google Scholar
• Garey, M.R., & Johnson, D.S. (1991). Computers and intractability: A guide to the theory of NP-completeness. New York, NY: W.H. Freeman. ISBN: 0-7167-1045-5.
   Google Scholar
• Goncalves, J.F., Magalhaes-Mendez, J.J., & Resendes, M.G.C. (2005). A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operational Research, 167, 77–95.
   Web of Science ®Google Scholar
• Hiller, F.S., & Lieberman, G.J. (1995). Introduction to operations research ( International editions). New York, NY: McGraw-Hill. ISBN: 0-07-113989-3.
   Google Scholar
• Jun-Qing, Li, Quan-Ke, P., Suganthan, P.N., & Chua, T.J. (2011). A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. International Journal of Advanced Manufacturing Technology, 52, 683–697.
   Web of Science ®Google Scholar
• Kolonko, M. (1999). Some new results on simulated annealing applied to the job shop scheduling problem. European Journal of Operational Research, 113, 123–136.
   Web of Science ®Google Scholar
• Kuo-Ling, H., & Ching-Jong, L. (2008). Ant colony optimization combined with taboo search for the job shop scheduling problem. Computers & Operations Research, 35, 1030–1046.
   Web of Science ®Google Scholar
• Lawrence, S. (1984). Resource constrained project scheduling: An experimental investigation of heuristic scheduling techniques (Supplement). Pittsburgh, PA: Graduate School of Industrial Administration, Carnegie-Mellon University.
   Google Scholar
• Mati, Y., Dauzre-Prs, S., & Lahlou, Ch. (2011). A general approach for optimizing regular criteria in the job-shop scheduling problem. European Journal of Operational Research, 212, 33–42.
   Web of Science ®Google Scholar
• Meeran, S., & Morshed, M.S. (2012). A hybrid genetic tabu search algorithm for solving job shop scheduling problems: A case study. Journal of Intelligent Manufacturing, 23, 1063–1078.
   Web of Science ®Google Scholar
• Nakano, R., & Yamada, T. (1991). Conventional genetic algorithm for job-shop problems. In M.K. Kenneth & L.B. Booker (Eds.), Proceedings of the 4th International Conference on Genetic Algorithms (pp. 474–479). San Mateo, CA: Morgan Kaufmann.
   Google Scholar
• Papadimitriou, C.H., & Steiglitz, K. (1998). Combinatorial optimization: Algorithms and complexity. New York, NY: Dover. ISBN: 0-486-40258-4.
   Google Scholar
• Roshanaei, V., Naderi, B., Jolai, F., & Khalili, M. (2009). A variable neighborhood search for job shop scheduling with set-up times to minimize makespan. Future Generation Computer Systems, 25, 654–661.
   Web of Science ®Google Scholar
• Roy, B., & Sussman, B. (1964). Les problemes d’ordonnancement avec contraintes disjonctives [Scheduling problems with disjunctive constraints] ( Note D.S. no. 9bis). Paris: SEMA.
   Google Scholar
• Sha, D.Y., & Hsu, C.-Y. (2006). A hybrid particle swarm optimization for job shop scheduling problem. Computers & Industrial Engineering, 51, 791–808.
   Web of Science ®Google Scholar
• Soon Chong, Ch., Hean Low, M.Y., Sivakumar, A.I., & Leng Gay, K. (2006). A bee colony optimization algorithm to job shop scheduling. Proceedings of the 2006 Winter Simulation Conference, pp. 1954–1961. Monterey, CA: IEEE.
   Google Scholar
• Steinhöfel, K., Albrecht, A., & Wong, C.K. (2003). An experimental analysis of local minima to improve neighborhood search. Computers & Operations Research, 30(14), 2157–2173.
   Web of Science ®Google Scholar
• Steinhöfel, U., & Der, K. (2001). A parallel implementation of job shop scheduling heuristics. In T. Sørevik, F. Manne, R. Moe, & A.H. Gebremedhin (Eds.), International Workshop on Applied Parallel Computing, LNCS 1947 (pp. 215–222). Berlin: Springer-Verlag.
   Google Scholar
• Taillard, E. (2013). Scheduling instances. Retrieved from http://mistic.heigvd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html
   Google Scholar
• Van Laarhooven, P.J.M., Aarts, E.H.L., & Lenstra, J.K. (1992). Job shop scheduling by simulated annealing. Operations Research, 40(1), 113–125.
   Web of Science ®Google Scholar
• Wang, T.Y., & Wu, K.B. (1999). An efficient configuration generation mechanism to solve job shop scheduling problem by the simulated annealing algorithm. International Journal of Systems Science, 30(5), 527–532.
   Web of Science ®Google Scholar
• Wang, T.Y., & Wu, K.B. (2000). A revised simulated annealing algorithm for obtaining the minimum total tardiness in job shop scheduling problems. International Journal of Systems Science, 31(4), 537–542.
   Web of Science ®Google Scholar
• Yamada, T., & Nakano, R. (1992). A genetic algorithm applicable to large-scale job-shop instances. In R. Manner & B. Manderick (Eds.), Parallel instance solving from nature 2 (pp. 281–290). Amsterdam: North-Holland.
   Google Scholar
• Zhang, C.Y., Li, P., Rao, Y., & Guan, Z. (2008). A very fast TS/SA algorithm for the job shop scheduling problem. Computers & Operations Research, 35, 282–294.
   Web of Science ®Google Scholar