使用改良型基因演算法解決醫師排班問題 Using A Modified Genetic Algorithm to Solve the Scheduling of Physician’s Shifts

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使用改良型基因演算法解決醫師排班問題

Using A Modified Genetic Algorithm to Solve the Scheduling of Physician’s Shifts

中文摘要

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。

材料和方法:以基因演算法為基礎,提出二種改良式交配子(crossover

operators),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%;

執行時間則慢了2.7%。

結論:鍵結法比傳統的基因演算法不論上收斂速度、最佳解上都有長足的進步,

而執行速度只差了一點。主要的原因在於交配子的精細運算,另外也可能得力於 二點交配。以後可進行更多點的交配分析及更複雜的排班題目研究。

英文摘要

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

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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.

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