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We have developed a methodology for allocating operating room capacity to specialties. Our methodology consists of a finite-horizon mixed integer programming (MIP) model which determines a weekly operating room allocation template that minimizes inpatients' cost measured as their length of stay. A number of patient type priority (eg emergency over non-emergency patient) and clinical constraints (eg maximum number of hours allocated to each specialty, surgeon, and staff availability) are included in the formulation. The optimal solution from the analytical model is inputted into a simulation model that captures some of the randomness of the processes (eg surgery time, demand, arrival time, and no-show rate of the outpatients) and non-linearities (eg the MIP assumes proportional allocation of demand satisfaction (output) with room allocation (input)). The simulation model outputs the average length of stay for each specialty and the room utilization. On a case example of a Los Angeles County Hospital, we show how the hospital length of stay pertaining to surgery can be reduced. doi:10.1057/palgrave.jors.2602596 Keywords: mixed integer programming; surgery; operating room capacity; block time scheduling; simulation

There is an increasing trend in the chemical process industry to operate flexible batch plants because of their capability to manufacture multiple products simultaneously by sharing the same process resources. In this paper, the scheduling of multi-purpose batch chemical plants with junction (header) constraints is considered. A mixed-integer non-linear model for the scheduling of multi-purpose batch chemical plants is formulated that considers the connection between equipment sets, transfer times, variable batch sizes, alternative process plans, and batch merging. Because of the computational time complexity of the batch scheduling problem, a heuristic scheduling algorithm that minimizes the total tardiness is developed to solve the model. [ABSTRACT FROM AUTHOR]

A distributed control approach based on multiagent negotiation is presented, wherein stops and buses act as agents that communicate in real-time to achieve dynamic coordination of bus dispatching at various stops. The negotiation between a Bus Agent and a Stop Agent is conducted based on marginal cost calculations. We present optimality conditions for the formulated problem, using a negotiation algorithm, which we derive, to coordinate bus holding at various stops. A comparison between the negotiation algorithm and other simple bus control strategies such as on-schedule and even-headway strategies made through simulations verifies the robustness and efficiency of our negotiation strategy to different transit environments, involving both stationary passenger arrivals as well as a variety of nonstationary passenger arrivals. [ABSTRACT FROM PUBLISHER]

Presentation of two integer programming formulations which account for traditional integer programming model formulations for job-shops and flow-shops. Characteristics common to high-technology manufacturing; Premises on which the approach is based; Detailed information on the models.