Abstract
Abstract No-wait flowshop scheduling problems with the objective to minimize the total flow time is an important sequencing problem in the field of developing production plans and has a wide engineering background. Genetic algorithm (GA) has the capability of global convergence and has been proven effective to solve NP-hard combinatorial optimization problems, while simple heuristics have the advantage of fast local convergence and can be easily implemented. In order to avoid the defect of slow convergence or premature, a heuristic genetic algorithm is proposed by incorporating the simple heuristics and local search into the traditional genetic algorithm. In this hybridized algorithm, the structural information of no-wait flowshops and high-effective heuristics are incorporated to design a new method for generating initial generation and a new crossover operator. The computational results show the developed heuristic genetic algorithm is efficient and the quality of its solution has advantage over the best known algorithm. It is suitable for solving the large scale practical problems and lays a foundation for the application of meta-heuristic algorithms in industrial production.
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