scholarly journals Weighted Tardiness Minimization in Job Shops with Setup Times by Hybrid Genetic Algorithm

2011 ◽  
pp. 363-372 ◽  
Author(s):  
Miguel A. González ◽  
Camino R. Vela ◽  
Ramiro Varela
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Setup Times ◽  
Job Shops ◽  
Weighted Tardiness

Genetic Algorithm for Parallel-Machine Batching and Scheduling to Minimize Total Weighted Tardiness

2011 ◽  
Vol 58-60 ◽  
pp. 1142-1147
Author(s):  
Fuh Der Chou ◽  
Hui Mei Wang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Genetic Algorithm ◽  
Parallel Machine ◽  
Batch Scheduling ◽  
Total Weighted Tardiness ◽  
Computational Results ◽  
Family Dynamic ◽  
Weighted Tardiness ◽  
Batch Processing Machine

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.

Solving parallel machine scheduling problem with release dates using genetic algorithm

2018 ◽  
Vol 7 (2.29) ◽  
pp. 91
Author(s):  
Yasothei Suppiah ◽  
Ajitha Angusamy ◽  
Goh Wei Wei ◽  
Noradzilah Bt Ismail
Keyword(s):  
Genetic Algorithm ◽  
Setup Times ◽  
Total Weighted Tardiness ◽  
Release Dates ◽  
Scheduling Problem ◽  
Weighted Tardiness ◽  
Research Questions ◽  
Sequence Dependent Setup ◽  
Dependent Setup Times ◽  
Sequence Dependent Setup Times

This research deals with a scheduling problem for parallel machines environment to minimize total weighted tardiness with the consideration of sequence dependent setup times and release dates. There are two research questions that need to be addressed: 1) How to allocate jobs on machines ?  2) How to sequence jobs on each machine? Therefore, this research aims to find an efficient solution method that answers the research questions with the goal of minimizing the total weighted tardiness with the presence of sequence dependent setup times. Due to the complexity of the problem at hand, the authors have developed genetic algorithm to find a solution to this problem. Furthermore, various dispatching rules were used to enhance the performance of the genetic algorithm in terms of the total weighted tardiness value. 

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