A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Hybrid flow shop scheduling: a survey
Computers and Industrial Engineering
Scheduling with Finite Capacity Output Buffers
Operations Research
Total completion time minimization in a computer system with a server and two parallel processors
Computers and Operations Research
A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks
Journal of Scheduling
Minimizing total completion time in two-machine flow shops with exact delays
Computers and Operations Research
Complexity and algorithms for two-stage flexible flowshop scheduling with availability constraints
Computers & Mathematics with Applications
Minimum deviation algorithm for two-stageno-wait flowshops with parallel machines
Computers & Mathematics with Applications
Scheduling two-stage hybrid flow shop with availability constraints
Computers and Operations Research
Computers in Industry - Special issue: Application of genetics algorithms in industry
Computers and Operations Research
A planning and scheduling problem for an operating theatre using an open scheduling strategy
Computers and Industrial Engineering
Solving two-stage hybrid flow shop using climbing depth-bounded discrepancy search
Computers and Industrial Engineering
Two-stage hybrid flow shop with precedence constraints and parallel machines at second stage
Computers and Operations Research
Hi-index | 0.01 |
Considering the practical application and the computational complexity of the two-stage no-wait hybrid flow shop scheduling problem, this paper proposes a genetic algorithm (GA). Based on the description of the problem and its properties, some constructive heuristics are first proposed to obtain the upper bound. Then the implementation details of the proposed GA are illustrated, in which the results of heuristics are employed into the initial population. Next, a preliminary computational test with factorial design is conducted to tune the key parameters of four versions of the proposed genetic algorithms resulting from combinations of different crossover and mutation operators. With the tuned parameters, the performance of the proposed genetic algorithms is evaluated in terms of the mean percentage deviation of the solution with respect to the lower bound value, through an extensive computational experiment. The results with different problem configurations demonstrate the effectiveness and efficiency of the proposed genetic algorithm and also demonstrate that the GA performs relatively better when the LOX (two-point linear order crossover) operator and the swap mutation operator are used.