A fast taboo search algorithm for the job shop problem
Management Science
Scheduling multiprocessor tasks in a two-stage flow-shop environment
Proceedings of the 21st international conference on Computers and industrial engineering
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms
A new approach to solve hybrid flow shop scheduling problems by artificial immune system
Future Generation Computer Systems - Special issue: Computational science of lattice Boltzmann modelling
A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks
Journal of Scheduling
Tsp-based scheduling in a batch-wise hybrid flow-shop
Robotics and Computer-Integrated Manufacturing
Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints
Computers and Industrial Engineering
Scheduling two-stage hybrid flow shop with availability constraints
Computers and Operations Research
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
A novel algorithm for dynamic task scheduling
Future Generation Computer Systems
Genetic algorithms for a two-agent single-machine problem with release time
Applied Soft Computing
A hybrid intelligent model for order allocation planning in make-to-order manufacturing
Applied Soft Computing
Improved bounds for hybrid flow shop scheduling with multiprocessor tasks
Computers and Industrial Engineering
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The hybrid flow shop scheduling with multiprocessor task (HFSMT) problem is a substantial production scheduling problem for minimizing the makespan, and there exist many difficulties in solving large scale HFSMT problems which include many jobs, machines and tasks. The HFSMT problems known as NP-hard and the proposal of an efficient genetic algorithm (GA) were taken into consideration in this study. The numerical results prove that the computational performance of a GA depends on the factors of initial solution, reproduction, crossover, and mutation operators and probabilities. The implementation details, including a new mutation operator, were described and a full factorial experimental design was determined with our GA program by using the best values of the control parameters and the operators. After a comparison was made with the studies of Oguz [1], Oguz and Ercan [2] and Kahraman et al. [3] related to the HFSMT problems, the computational results indicated that the proposed genetic algorithm approach is very effective in terms of reduced total completion time or makespan (C"m"a"x) for the attempted problems.