Paper Title:
Genetic Algorithm for Parallel-Machine Batching and Scheduling to Minimize Total Weighted Tardiness
  Abstract

This paper considers parallel batch-processing machine problems with compatible job family, dynamic job arrivals, and non-identical job sizes to minimize total weighted tardiness. Given that the problem of interest is non-deterministic polynomial-time (NP) hard , we propose a hybrid genetic algorithm (HGA) that incorporates batching decision and batch scheduling. Moreover, HGA is compared with simulated annealing (SA) algorithms to assess the performance of the proposed algorithm. Computational results revealed that the proposed HGA outperformed in terms of the number of best solution found, and HGA is slightly better when comparing the average TWT value.

  Info
Periodical
Edited by
Qi Luo
Pages
1142-1147
DOI
10.4028/www.scientific.net/AMM.58-60.1142
Citation
F. D. Chou, H. M. Wang, "Genetic Algorithm for Parallel-Machine Batching and Scheduling to Minimize Total Weighted Tardiness", Applied Mechanics and Materials, Vols. 58-60, pp. 1142-1147, 2011
Online since
June 2011
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