Two-stage hybrid flow shop scheduling with preventive maintenance using multi-objective tabu search method

References

• Allaoui, H., and A. Artiba. 2006. “Scheduling Two-stage Hybrid Flow Shop with Availability Constraints.” Computers & Operations Research 33: 1399–1419.
   Web of Science ®Google Scholar
• Allaoui, H., and A. Artiba. 2009. “Johnson’s Algorithm: A Key to Solve Optimally or Approximately Flow Shop Scheduling Problems with Unavailability Periods.” International Journal of Production Economics 121: 81–87.
   Web of Science ®Google Scholar
• Al-Turki, U., C. Fedjki, and A. Andijani. 2001. “Tabu Search for a Class of Single-machine Scheduling Problems.” Computers & Operations Research 28: 1223–1230.
   Web of Science ®Google Scholar
• Armentano, V. A., and J. E. C. Arroyo. 2004. “An Application of a Multi-objective Tabu Search Algorithm to a Bicriterial Flowshop Problem.” Journal of Heuristics 10: 463–481.
   Web of Science ®Google Scholar
• Baykasoglu, A., S. Owen, and N. Gindy. 1999. “A Taboo Search based Approach to Find the Pareto Optimal Set in Multiple Objective Optimization.” Journal of Engineering Optimization 31: 731–748.
   Web of Science ®Google Scholar
• Baykasoglu, A., L. Ozbakir, and A. Sonmez. 2004. “Using Multiple Objective Tabu Search and Grammars to Model and Solve Multi-objective Flexible Job Shop Scheduling Problems.” Journal of Intelligent Manufacturing 15: 777–785.
   Web of Science ®Google Scholar
• Ben Ali, M., M. Sassi, M. Gossa, and Y. Harrath. 2011. “Simultaneous Scheduling of Production and Maintenance Tasks in the Job Shop.” International Journal of Production Research 49: 3891–3918.
   Web of Science ®Google Scholar
• Berrichi, A., L. Amodeo, F. Yalaoui, E. Châtelet, and M. Mezghiche. 2009. “Bi-objective Optimization Algorithms for Joint Production and Maintenance Scheduling: Application to the Parallel Machine Problem.” Journal of Intelligent Manufacturing 20: 389–400.
   Web of Science ®Google Scholar
• Berrichi, A., F. Yalaoui, L. Amodeo, and M. Mezghiche. 2010. “Bi-objective Ant Colony Optimization Approach to Optimize Production and Maintenance Scheduling.” Computers & Operations Research 37: 1584–1596.
   Web of Science ®Google Scholar
• Cassady, C. R., and E. Kutanoglu. 2005. “Integrating Preventive Maintenance Planning and Production Scheduling for a Single Machine.” IEEE Transactions on Reliability 54: 304–309.
   Web of Science ®Google Scholar
• Choobineh, F. F., E. Mohebbi, and H. Khoo. 2006. “A Multi-objective Tabu Search for a Single-machine Scheduling Problem with Sequence-dependent Setup Times.” European Journal of Operational Research 175: 318–337.
   Web of Science ®Google Scholar
• Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II.” IEEE Transactions on Evolutionary Computation 6: 182–197.
   Web of Science ®Google Scholar
• Ebeling, C. E. 1997. An Introduction to Reliability and Maintainability Engineering. New York: McGraw-Hill.
   Google Scholar
• Gen, M., and R. Cheng. 2000. Genetic Algorithms and Engineering Optimization. New York: John Wiley Sons.
   Google Scholar
• Glover, F. 1986. “Future Path for Integer Programming and Links to Artificial Intelligence.” Computers and Operations Research 5: 533–549.
   Web of Science ®Google Scholar
• Glover, F. 1989. “Tabu Search-Part I.” ORSA Journal on Computing 1: 190–206.
   Google Scholar
• Glover, F. 1990. “Tabu Search-Part II.” ORSA Journal on Computing 2: 4–32.
   Google Scholar
• Gupta, J. N. D., and E. A. Tunc. 1991. “Schedules for a Two-stage Hybrid Flowshop with Parallel Machines at the Second Stage.” International Journal of Production Research 29: 1489–1502.
   Web of Science ®Google Scholar
• Gupta, J. N. D., A. M. A. Hariri, and C. N. Potts. 1997. “Scheduling a Two-stage Hybrid Flow Shop with Parallel Machines at the First Stage.” Annals of Operations Research 67: 171–191.
   Web of Science ®Google Scholar
• Haouari, M., L. Hidri, and A. Gharbi. 2006. “Optimal Scheduling of a Two-stage Hybrid Flow Shop.” Mathematical Methods of Operations Research 64: 107–124.
   Web of Science ®Google Scholar
• Hmida, A. B., M. Haouari, M. J. Huguet, and P. Lopez. 2011. “Solving Two-stage Hybrid Flow Shop using Climbing Depth-bounded Discrepancy Search.” Computers and Industrial Engineering 60: 320–327.
   Web of Science ®Google Scholar
• Ishibuchi, H., T. Yoshida, and T. Murata. 2003. “Balance between Genetic Search and Local Search in Memetic Algorithms for Multiobjective Permutation Flowshop Scheduling.” IEEE Transactions on Evolutionary Computation 7: 204–223.
   Web of Science ®Google Scholar
• Jin, Y. L., Z. H. Jiang, and W. R. Hou. 2008. “Multi-objective Integrated Optimization Research on Preventive Maintenance Planning and Production Scheduling for a Single Machine.” International Journal of Advanced Manufacturing Technology 39: 954–964.
   Web of Science ®Google Scholar
• Jones, D. F., S. K. Mirrazavi, and M. Tamiz. 2002. “Multi-objective Meta-heuristics: An Overview of the Current State-of-the-Art.” European Journal of Operational Research 137: 1–9.
   Web of Science ®Google Scholar
• Konak, A., D. W. Coit, and A. E. Smith. 2006. “Multi-objective Optimization using Genetic Algorithms: A Tutorial.” Reliability Engineering and System Safety 91: 992–1007.
   Web of Science ®Google Scholar
• Lee, G. C., and Y. D. Kim. 2004. “A Branch-and-bound Algorithm for a Two-stage Hybrid Flowshop Scheduling Problem Minimizing Total Tardiness.” International Journal of Production Research 42: 4731–4743.
   Web of Science ®Google Scholar
• Lei, D. M. 2013. “Multi-objective Artificial Bee Colony for Interval Job Shop Scheduling with Flexible Maintenance.” International Journal of Advanced Manufacturing Technology 66: 1835–1843.
   Web of Science ®Google Scholar
• Lei, D. M. 2009. “Multi-objective Production Scheduling: A Survey.” International Journal of Advanced Manufacturing Technology 43: 926–938.
   Web of Science ®Google Scholar
• Li, J. Q., Q. K. Pan, and Y. C. Liang. 2010. “An Effective Hybrid Tabu Search Algorithm for Multi-objective Flexible Job Shop Scheduling Problems.” Computers and Industrial Engineering 59: 647–662.
   Web of Science ®Google Scholar
• Linn, R., and W. Zhang. 1999. “Hybrid Flow Shop Scheduling: A Survey.” Computers and Industrial Engineering 37: 57–61.
   Web of Science ®Google Scholar
• Mirabi, M., S. M. T. Fatemi Ghomi, and F. Jolai. 2013. “A Two-stage Hybrid Flowshop Scheduling Problem in Machine Breakdown Condition.” Journal of Intelligent Manufacturing 24: 193–199.
   Web of Science ®Google Scholar
• Moradi, E., S. M. T. Fatemi Ghomi, and M. Zandieh. 2011. “Bi-objective Optimization Research on Integrated Fixed Time Interval Preventive Maintenance and Production for Scheduling Flexible Job-shop Problem.” Expert Systems with Applications 38: 7169–7178.
   Web of Science ®Google Scholar
• Moradi, E., and M. Zandieh. 2010. “Minimizing the Makespan and the System Unavailability in Parallel Machine Scheduling Problem: A Similarity-based Genetic Algorithm.” International Journal of Advanced Manufacturing Technology 51: 829–840.
   Web of Science ®Google Scholar
• Oguz, C., and M. F. Ercan. 2005. “A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks.” Journal of Scheduling 8: 323–351.
   Web of Science ®Google Scholar
• Pinedo, M. 2008. Scheduling: Theory, Algorithms, and Systems. 3rd ed. New York: Prentice Hall.
   Google Scholar
• Ribas, I., R. Leisten, and J. M. Framinan. 2010. “Review and Classification of Hybrid Flow Shop Scheduling Problems from a Production System and a Solutions Procedure Perspective.” Computers & Operations Research 37: 1439–1454.
   Web of Science ®Google Scholar
• Ruiz, R., and J. A. Vazquez-Rodriguez. 2010. “The Hybrid Flow Shop Scheduling Problem.” European Journal of Operational Research 205: 1–18.
   Web of Science ®Google Scholar
• Sun, Y., C. Y. Zhang, L. Gao, and X. J. Wang. 2011. “Multi-objective Optimization Algorithms for Flow Shop Scheduling Problem: A Review and Prospects.” International Journal of Advanced Manufacturing Technology 55: 723–739.
   Web of Science ®Google Scholar
• Zitzler, E., and L. Thiele. 1999. “Multiobjective Evolutionary Algorithms: A Comparative Study and Strength Pareto Approach.” IEEE Transactions on Evolutionary Computation 3: 257–271.
   Web of Science ®Google Scholar