An adaptable scheduling algorithm for flexible flow lines
Operations Research
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Computers and Operations Research
Assessing the performance of the simulated annealing algorithm using information theory
Assessing the performance of the simulated annealing algorithm using information theory
Simulated annealing: past, present, and future
WSC '95 Proceedings of the 27th conference on Winter simulation
Computational experience with a branch-and-cut algorithm for flowshop scheduling with setups
Computers and Operations Research
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
A Simulated Annealing Approach to Bicriteria Scheduling Problems on a Single Machine
Journal of Heuristics
Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines
Computers and Operations Research
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints
Computers and Industrial Engineering
A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A two-stage hybrid flowshop scheduling problem in machine breakdown condition
Journal of Intelligent Manufacturing
Computers and Industrial Engineering
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One of the scheduling problems with various applications in industries is hybrid flow shop. In hybrid flow shop, a series of n jobs are processed at a series of g workshops with several parallel machines in each workshop. To simplify the model construction in most research on hybrid flow shop scheduling problems, the setup times of operations have been ignored, combined with their corresponding processing times, or considered non sequence-dependent. However, in most real industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacturing, hybrid flow shop problems have sequence-dependent setup times (SDST). In this research, the problem of SDST hybrid flow shop scheduling with parallel identical machines to minimize the makespan is studied. A novel simulated annealing (NSA) algorithm is developed to produce a reasonable manufacturing schedule within an acceptable computational time. In this study, the proposed NSA uses a well combination of two moving operators for generating new solutions. The obtained results are compared with those computed by Random Key Genetic Algorithm (RKGA) and Immune Algorithm (IA) which are proposed previously. The results show that NSA outperforms both RKGA and IA.