本研究中探討運用遺傳演算法求解二機開放工場中工作具有工作?接性現象的排程問題（即O2|Blocking|Cmax 問題）：假定工作在?機台的處?時間均已給定，本研究的目標為將n 個具有工作?接性的工作排入此二機開放工場排程中，求其最短之製距(Makespan)。依據本研究所得的結果，我們建議運用遺傳演算法求解O2|Blocking|Cmax 問題時，可使用LOX(Linear OrderCrossover)為交配運算子，採用PBM(Position Based Mutation)作為突變運算子。同時本研究也在遺傳演算法求解時??之設定，如：族群?目、交配機?、突變機?及後代?等提供明確之建議。此外，本研究運用600 個隨機範?的?據結果，觀察工作?目及工作處?時間的變?程?(標準差)等實驗因子，對遺傳演算法求解之品質及其執?時間的影響。本?文的研究成果，可提供決策者欲運用遺傳演算法求解O2|Blocking|Cmax 問題時，得有完整決策環境的?考，並事先得知可能影響決策品質的注意事項。 In this paper, we consider a two-machine scheduling problem in an openshop with blocking jobs. We are given the processing times of n blocking jobs on both machines, and the objective is to minimize the makespan. Symbolically, we are dealing with the problem O2|Blocking|Cmax. The results from our numerical experiments suggest that one should use LOX (Linear Order Crossover) and PBM (Position-Based Mutation) as the genetic operators if one would like use the genetic algorithm (GA) to solve the O2|Blocking|Cmax problem. And, we state useful guidelines for setting the parameters in GA, for instance, population size, crossover rate, mutation rate and number of generations, etc. From the 600 random examples, we also observe that the number of jobs and the variance of the processing time for the jobs significantly affect the performance of the GA. Indeed, our study provides valuable decision support information for the decision makers who attempts to use the GA to solve the O2|Blocking|Cmax problem.
