Integrating combinatorial optimization with queueing theory for dynamic scheduling

各种各样的行业和服务都面临着如何将一系列新的工作分配给一组复杂的资源的问题。例如，为了递送化疗，医院需要考虑以下事实：患者可能提前或延迟到达他们的预约，药物方案的不同保质期和特异性对准时药物制备和/或储存产生挑战，以及时间、设备（例如，床、椅子）、房间和治疗患者所需的工作人员可以基于患者的状况而改变。类似的问题出现在制造业中，其中生产、维护和交付操作必须被调度;在电信中，其中带宽必须被分配以满足用户需求;以及在运输应用中，例如跑道调度。 所有这些问题的共同点是随机到达的一个不同的请求，必须满足分配复杂的资源组合。

A wide variety of industries and services are faced with making decisions about the assignment of a stream of incoming jobs to a complex set of resources. For example, to deliver chemotherapy, a hospital needs to take into account the fact that patients may arrive early or late for their appointments, that differing shelf-lives and specificities of drug regimens create a challenge for on-time drug preparation and/or storage, and that the time, equipment (e.g., beds, chairs), rooms, and staff required for the treatment of a patient may change based on the patient's condition. Similar problems arise in manufacturing, where production, maintenance, and delivery operations must be scheduled; in telecommunications where bandwidth must be allocated to fulfill user needs; and in transportation applications such as runway scheduling. Common among all these problems is the stochastic arrival of a varying requests which must be met by the allocation complex combinations of resources.