Integrating combinatorial optimization with queueing theory for dynamic scheduling

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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.