Using A Modified Genetic Algorithm to Solve the Scheduling of Physician’s Shifts
以電腦運算自動產生醫師值班表。值班表須符合 1.公平性；2. 可因個人需要而 調整值班日期；3.無連續值班，或是隔日值班的情形。
第一種稱為互斥法( repelling)；第二種稱為鍵結法( bonding)。另外以四種不同類 型的排班需求為測試題，來檢驗傳統的基因演算法與二種改良後的演算法在最佳 解、收斂速度、和執行時間上的差異。
實驗結果：每種方法在每個題目上各進行十次，然後求其平均。結果發現互斥法 比傳統演算法而言，最佳解進步了 7%；收斂速度快了 3.5%；執行時間則慢了 7.6%，而鍵結法比傳統演算法而言，最佳解進步了 30%；收斂速度快了 22%；
For the sake of patient safety, the residents’ work hours have got a lot of attentions. It is important to prevent resident physicians working for more than 24 hours to reduce the possibility of accompanying medical errors.
Purpose: To generate an on-duty timetable automatically with computers. This timetable should satisfy 1. fairness; 2. individual needs; 3. no consecutive shifts or shifts of every other day.
Methods: On the basis of the classical genetic algorithms, two newly devised
crossover operators have been proposed. The first one is repelling method; the second one is bonding methods. We tested the two methods with four different kinds of problems. The tests were done on a platform of personal computer and the genetic algorithm
Results: Each problem had been tested for each genetic algorithm methods. The results are the average of 10 independent runs. The repelling method is 7% better than classical one in the best individuals; 3.5% quicker in convergent speed; lags 7.6 % in running time. The bonding method is 30% better than classical one in the best
individuals; 22% quicker in convergent speed; lags 2.7% in running time.
Conclusion: The bonding method overtook both classical and repelling methods in the search of global optima and convergent speed. This may attribute to its effective crossing points selection or its 2-point crossover nature. In the future, we can test multiple-point crossover or uniform crossover and try to solve the more complex scheduling problems.