No-wait flowshop scheduling problems with the objective to minimize the total flow time is an important se-quencing problem in the field of developing production plans and has a wide engineering background. Genetic al...No-wait flowshop scheduling problems with the objective to minimize the total flow time is an important se-quencing problem in the field of developing production plans and has a wide engineering background. Genetic algo-rithm (GA) has the capability of global convergence and has been proven effective to solve NP-hard combinatorial op-timization problems,while simple heuristics have the advantage of fast local convergence and can be easily imple-mented. In order to avoid the defect of slow convergence or premature,a heuristic genetic algorithm is proposed by in-corporating 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 ge-netic 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 in-dustrial production.展开更多
基金Project 60304016 supported by the National Natural Science Foundation of China
文摘No-wait flowshop scheduling problems with the objective to minimize the total flow time is an important se-quencing problem in the field of developing production plans and has a wide engineering background. Genetic algo-rithm (GA) has the capability of global convergence and has been proven effective to solve NP-hard combinatorial op-timization problems,while simple heuristics have the advantage of fast local convergence and can be easily imple-mented. In order to avoid the defect of slow convergence or premature,a heuristic genetic algorithm is proposed by in-corporating 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 ge-netic 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 in-dustrial production.
