To curb outbreaks of contagious diseases, county health departments must set up and operate clinics to dispense medications and vaccines. Carefully planning these clinics in advance of such an event is difficult and important. We developed and implemented operations research models to improve clinic planning for the Montgomery County (Maryland) Public Health Services. They include discrete-event simulation models and capacity-planning and queueing-system models. We validated these models using data that we collected during full-scale simulations of disease outbreaks. We also developed guidelines for the physical design of clinics based on general queueing principles and our own experiences.
Abstract-Robust discrete optimization is a technique for structuring uncertainty in the decision-making process. The objective is to find a robust solution that has the best worst-case performance over a set of possible scenarios. However, this is a difficult optimization problem. This paper proposes a two-space genetic algorithm as a general technique to solve minimax optimization problems. This algorithm maintains two populations. The first population represents solutions. The second population represents scenarios. An individual in one population is evaluated with respect to the individuals in the other population. The populations evolve simultaneously, and they converge to a robust solution and its worst-case scenario. Since minimax optimization problems occur in many areas, the algorithm will have a wide variety of applications. To illustrate its potential, we use the two-space genetic algorithm to solve a parallel machine scheduling problem with uncertain processing times. Experimental results show that the two-space genetic algorithm can find robust solutions.
In dynamic, stochastic manufacturing systems, production planners and manufacturing engineers can benefit from understanding how rescheduling strategies affect system performance. This knowledge will help these experts design and operate better manufacturing planning and control systems. This paper presents new analytical models that can predict the performance of rescheduling strategies and quantify the trade-offs between different performance measures. In the parallel machine systems under consideration, jobs of different types arrive dynamically, and setups occur when production changes from one job type to another. Three rescheduling strategies are studied: periodic, hybrid, and event-driven based on the queue size. The scheduling algorithm groups jobs of the same type in batches to eliminate unnecessary setups. The analytical models require less computational effort than simulation models, and experimental results show that they accurately estimate important performance measures like average flow time, machine utilization, and setup frequency.
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