A note on the total completion time problem in a permutation flowshop with a learning effect
The concept of learning process plays a key role in production environments. However, it is relatively unexplored in the flowshop setting. In this short note, we consider a permutation flowshop scheduling problem with a learning effect where the objective is to minimize the sum of completion times or flowtime. A dominance rule and several lower bounds are established to speed up the search for the optimal solution. In addition, the performances of several well-known heuristics are evaluated when the learning effect is present.
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- Mosheiov, Gur & Sidney, Jeffrey B., 2003. "Scheduling with general job-dependent learning curves," European Journal of Operational Research, Elsevier, vol. 147(3), pages 665-670, June.
- Framinan, J. M. & Leisten, R., 2003. "An efficient constructive heuristic for flowtime minimisation in permutation flow shops," Omega, Elsevier, vol. 31(4), pages 311-317, August.
- Rajendran, Chandrasekharan & Ziegler, Hans, 1997. "An efficient heuristic for scheduling in a flowshop to minimize total weighted flowtime of jobs," European Journal of Operational Research, Elsevier, vol. 103(1), pages 129-138, November.
- Chung, Chia-Shin & Flynn, James & Kirca, Omer, 2002. "A branch and bound algorithm to minimize the total flow time for m-machine permutation flowshop problems," International Journal of Production Economics, Elsevier, vol. 79(3), pages 185-196, October.
- Biskup, Dirk, 1999. "Single-machine scheduling with learning considerations," European Journal of Operational Research, Elsevier, vol. 115(1), pages 173-178, May.
- Mosheiov, Gur, 2001. "Scheduling problems with a learning effect," European Journal of Operational Research, Elsevier, vol. 132(3), pages 687-693, August.
- Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
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