Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
Computers and Operations Research
Journal of Global Optimization
Computers and Operations Research
Design and Analysis of Experiments
Design and Analysis of Experiments
Computers and Industrial Engineering
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
The two-stage assembly scheduling problem to minimize total completion time with setup times
Computers and Operations Research
A variable neighborhood search for job shop scheduling with set-up times to minimize makespan
Future Generation Computer Systems
Flexible job-shop scheduling with parallel variable neighborhood search algorithm
Expert Systems with Applications: An International Journal
The distributed permutation flowshop scheduling problem
Computers and Operations Research
Computers and Operations Research
An adaptive genetic algorithm with dominated genes for distributed scheduling problems
Expert Systems with Applications: An International Journal
A high performing metaheuristic for job shop scheduling with sequence-dependent setup times
Applied Soft Computing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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In this paper, a novel distributed two stage assembly flowshop scheduling problem (DTSAFSP) is addressed. The objective is to assign jobs to several factories and schedule the jobs in each factory with the minimum total completion time (TCT). In view of the NP-hardness of the DTSAFSP, we develop heuristics method to deal with the problem and propose three hybrid meta-heuristics (HVNS, HGA-RVNS, and HDDE-RVNS). The parameters of HGA-RVNS and HDDE-RVNS are tuned by using the Taguchi method and that of HVNS is done by using the single factor ANOVA method. Computational experiments have been conducted to compare the performances of the proposed algorithms. The analyses of computational results show that, for the instances with small numbers of jobs, HDDE-RVNS obtains better performances than HGA-RVNS and HVNS; whereas for the instances with large numbers of jobs, HGA-RVNS is the best one in all the proposed algorithms. Computational results indicate that the performances of the HDDE-RVNS and HGA-RVNS are not much affected by the number of machines at the first stage and factories. The experimental results also show that the RVNS-based local search steps in both HGA-RVNS and HDDE-RVNS are efficient and effective.