Convergence of an annealing algorithm
Mathematical Programming: Series A and B
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Design and Analysis of Experiments
Design and Analysis of Experiments
Computers and Operations Research
A combinatorial particle swarm optimisation for solving permutation flowshop problems
Computers and Industrial Engineering
A high performing metaheuristic for job shop scheduling with sequence-dependent setup times
Applied Soft Computing
Computers and Industrial Engineering
Genetic algorithms for coordinated scheduling of production and air transportation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A simulated annealing algorithm based approach for balancing and sequencing of mixed-model U-lines
Computers and Industrial Engineering
Hi-index | 12.06 |
In this communication, we strive to apply a novel simulated annealing to consider scheduling hybrid flowshop problems to minimize both total completion time and total tardiness. To narrow the gap between the theory and the practice of the hybrid flowshop scheduling, we integrate two realistic and practical assumptions which are sequence-dependent setup and transportation times into our problem. We apply a metaheuristic based on simulated annealing (SA) which strikes a compromise between intensification and diversification mechanisms to augment the competitive performance of our proposed SA. A comprehensive calibration of different parameters and operators are done. We employ Taguchi method to select the optimum parameters with the least possible number of experiments. For the purpose of performance evaluation of our proposed algorithm, we generate a benchmark against which the adaptations of high performing algorithms in the literature are brought into comparison. Moreover, we investigate the impacts of increase of number of jobs on the performance of our algorithm. The efficiency and effectiveness of our hybrid simulated annealing are inferred from all the computational results obtained in various situations.