Abstract: In the era of resource sharing, cross-region medical treatment is an effective way to solve the contradiction between the growing demand of patients and the shortage of medical resources. Hospital collaboration has drawn much attention from government, industry and academia which has also become a vital topic for hospital management. With collaborative activities, the allied hospitals or medical facilities are enabled to improve healthcare quality and enhance patient satisfaction. The patients' cycle or waiting time might be significantly reduced and hospitals can be utilized fully if one proper hospital were recommended. Hence, hospital recommendation and patients scheduling in allied medical facilities are important for policy and decision makers.
The medical alliance is studied as the research object in this paper, and it is aimed to minimize the patient tardiness of medical diagnosis through the cross regional medical treatment with the premise of the key medical resources sharing. The patient referral mechanism among different hospitals is employed and patients are recommended to transfer to close hospitals if they are scheduled early. The commute time of patients and the equipment setup time dependent on the diagnosis type are considered, and the assignment and scheduling of patients are optimized with the objective of minimizing total patient tardiness.
To solve this problem, two new heuristic algorithms, namely EDD-ReAss1 and EDD-ReAss2, are proposed based on the Earliest Due Date rule (EDD) and patient's re-assignment. By combining with local search algorithm, the quality of medical treatment scheduling scheme is further improved and the waiting time for diagnosis/examination of patients is minimized. The experimental results show that the performance of the new heuristic algorithms EDD-ReAss1 and EDD-ReAss2 is significantly better than EDD, SPT and LPT rules, and the swap local search algorithm has the best performance in a short running time.

Key words: commute time, machine setup time, cross-region treatment, scheduling optimization