預算限制下連鎖藥局分店設立與重置問題之探討

A Study for opening and closing of branches of chain pharmacy under budget constrained

吳文智(Wen-Chih Wu)

指導教授 ： 曾信超

共同指導教授 ： 莊立民

長榮大學/管理學院/經營管理研究所(博)/碩士(2006年)

https://doi.org/10.6833/CJCU.2006.00037

摘要

為了降低醫療成本並期待能提高醫療方面的品質，民國八十二年我國藥事法第一百零二條明訂，在全民健保實施後兩年，開始實施醫藥分業制度。民國八十四年三月全民健保正式實施，然而，由於醫師與藥師針對藥劑權的歸屬爭論不休，再加上衛生署觀望與政策搖擺不定，一直到李登輝總統於民國八十六年元月在藥師節慶祝大會上支持推動醫藥分業的致詞，政府才開始實施分區與分階段雙軌制醫藥分業。
在醫藥分業的實施之下，開始有了健保特約藥局的產生。健保特約藥局必須經過政府嚴格挑選，必須聘任符合資格的藥事人員親自執業，且依照優良藥劑作業規範調劑藥品，並必須符合全民健康保險特約藥局特約要點規定，服務品質必須受肯定。於是越來越多的診所開始與健保特約藥局合作。
近幾年來，企業的經營模式逐漸走向以顧客為導向，公司或分公司所設立的位置必須靠近消費中心，以增加服務的機會藉以提高顧客的滿意度。與診所合作的健保特約藥局也面臨同樣的狀況，其設立的位置，必須靠近合作的診所，期盼提高取藥病人的滿意度。
本篇論文，主要是以南部某連鎖藥局為例，由於醫藥分業與健保特約藥局的出現，其主要顧客由原有的不特定消費者轉變成由合作的診所取得處方箋的病人，顧客需求發生了改變，它們決定開設新的分店，並關閉某些分店，而且開或關都會有成本的發生，而這些成本必須在預算內執行(意即在預算限制內執行此項計劃)，目標函數是希望顧客從診所到分店的總移動距離最小。在上述這些條件下，決定新分店的設置點及那些舊分店該給予關閉。最後本篇論文將利用基因演算法(GA)與禁忌搜尋法(TS)這兩種啟發式方法來求解，並比較其求解之效率。
根據本篇論文的研究結果，解決此類問題，TS和GA的效率都不差，而在速度上，TS明顯比GA來的快。而且，TS產生的解也明顯比GA好，在問題小的情況下，TS能保證產生最佳解，但GA並不是每次都產生最佳解，而在問題大的情況下，TS跟GA都無法保證產生最佳解，但是，TS的解比GA的解錯誤率低。
目前台灣醫藥分業尚未落實，未來倘若醫藥分業可以徹底實行，應用本論文所採用的方法，將可解決實務上藥局地理位置配置上的決策問題。

關鍵字

醫藥分業 ； 健保特約藥局 ； 基因演算法 ； 禁忌演算法 ； 預算限制 ； 設施配置問題

並列摘要

In order to decrease the medical cost and increase the medical quality, the Pharmaceutical Affairs Law No.120 of Taiwan regulates that the Government implemented the National Health Insurance in 1995, and the system of Separation of Medicine and Pharmacy in 1997. However, the separation caused both sides of doctor and pharmacist to ignite a vigorous war in their right. For the reason, the system was not implemented until 1997.
Under the implementation of the system of Separation of Medicine and Pharmacy, registered National Health Insurance pharmacies occurred. The Government verified those pharmacies strictly. The pharmacies have to employ pharmacists who are certificated and observe the regulations of lows. Their services have to be approved. Therefore, more and more clinics started to cooperate with registered National Health Insurance pharmacies.
Recently, the strategy of the operation of the business becomes customer-oriented strategy. In order to get more service opportunities and increase service quality, the locations of the facilities have to be close to customers. The registered National Health Insurance pharmacies face the same situation. They have to locate its branch near the customers.
In this paper, we study a case of a registered National Health Insurance pharmacy at south Taiwan. Due to the change of the medical environment, its major customers changed form unspecific customers to the patients of the clinics that cooperated with the pharmacy. Motivations for the problem stem from applications where, due to a change in the distribution of customer demand, the existing facility system no longer provides adequate service, the pharmacy has to relocate its branches. The objective is to minimize the total weighted travel distance for customers subject to a constraint on the budget for opening and/or closing facilities and a constraint on the total number of open facilities desired. For this problem, we develop two heuristic algorithms (Genetic algorithm and Tabu search) for it.
Based on the results of numerical experiments we conclude that the performance of the two heuristics is very good. We also show the tradeoff between time and solution quality. The TS approach is faster than the GA and the solutions generated by the TS approach are better than the GA. Further, the two approaches can not guarantee the precision of solution while the numbers of facilities are many (large problems). However, the error rate of the TS is lower than the GA.
The system of Separation of Medicine and Pharmacy is not completely implemented now. If it can be implemented absolutely in the future, the approaches that we develop will solve the problem of the facilities relocation in practice.

並列關鍵字

Separation of Medicine and Pharmacy ； Registered National Health Insurance pharmacies ； Genetic Algorithm ； Tabu search ； Budget Constraint ； Facility location

參考文獻

2. Balinski ML. Integer programming: methods, uses, computation. Management Science 1965;12:253–313.

3. Berman O., Simchi-Levi D. Conditional location problems on networks. Transportation Science 1990;24:77–8.

4. Captivo ME. Fast primal and dual heuristic for the p-median location problem. European Journal of Operational Research 1991;52:65–74.

5. Chhajed D., Lowe TJ. An O(nm) algorithm for a special case of the multi-median location problem on a tree. European Journal of Operational Research 1992;63:222–30.

6. Cornuejols G., Fisher ML., Nemhauser GL. Location of bank accounts to optimize Uoat: an analytical study of exact and approximate algorithms. Management Science 1977;23:789–810.