Levelled bed occupancy and controlled waiting lists using Master surgical schedules

Scheduling surgical patients is one of the complex organizational tasks hospitals face daily. Master surgical scheduling is one way to optimize utilization of scarce resources and to create a more predictable outflow from the operating room towards subsequent hospital departments. The paper addresses two aims. First, we investigate the effect of the length of the planning horizon and other planning parameters in a master surgical scheduling approach on patients ́ waiting time, schedule stability and hospital efficiency. Second, the master surgical scheduling approach is compared with a standard operating room planning approach on levelled bed occupancy. The assignment of patients to a master surgical schedule is carefully described. Using real case data from a regional hospital i

Robust UAV Mission Planning

Unmanned Areal Vehicles (UAVs) can provide significant contributions to information gathering in military missions. UAVs can be used to capture both full motion video and still imagery of specific target locations within the area of interest. In order to improve the effectiveness of a reconnaissance mission, it is important to visit the largest number of interesting target locations possible, taking into consideration operational constraints related to fuel usage between target locations, weather conditions and endurance of the UAV. We model this planning problem as the well-known orienteering problem, which is a generalization of the traveling salesman problem. Given the uncertainty in the military operational environment, robust planning solutions are required. As such, our model takes into account uncertainty in the fuel usage between targets (for instance due to weather conditions) as well as uncertainty in the importance of visiting specific target locations. We report results using different uncertainty sets that specify the degree of uncertainty against which any feasible solution will be protected. We also compare the probability that a solution is feasible for the robust solution on one hand and the solution found with average fuel usage and expected value of information on the other. In doing so, we show how the sustainability of a UAV mission can be significantly improved

The Orienteering Problem under Uncertainty Stochastic Programming and Robust Optimization compared

The Orienteering Problem (OP) is a generalization of the well-known traveling salesman problem and has many interesting applications in logistics, tourism and defense. To reflect real-life situations, we focus on an uncertain variant of the OP. Two main approaches that deal with optimization under uncertainty are stochastic programming and robust optimization. We will explore the potentialities and bottlenecks of these two approaches applied to the uncertain OP. We will compare the known robust approach for the uncertain OP (the robust orienteering problem) to the new stochastic programming counterpart (the two-stage orienteering problem). The application of both approaches will be explored in terms of their suitability in practice

The cooperative ballistic missile defence game

The increasing proliferation of ballistic missiles and weapons of mass destruction poses new risks worldwide. For a threatened nation and given the characteristics of this threat a layered ballistic missile defence system strategy appears to be the preferred solution. However, such a strategy involves negotiations with other nations concerning the use of their defence systems as part of the layered defence system. This paper introduces the Cooperative Ballistic Missile Defense Game, CBMDG, to support the strategic negotiations between a threatened nation and the possible coalition nations. The model determines the assignment of ballistic missile interceptors to the coalition nations that minimizes the expected number of interceptors required to achieve the desired defence level in case of an attack. Simultaneously, it identifies the bargaining strength of each coalition of nations, in order to determine the compensation for participating in the layered defence system to protect the threatened nation

Online UAV Mission Planning

Unmanned Aerial Vehicles (UAVs) have become an essential asset for military and law enforcement operations. In particular their use for surveillance and reconnaissance tasks has been growing due to the quick developments in the areal systems themselves, sensor technology, and image processing techniques that enable near-realtime acquisition of information about activities and/or emergent situations in areas of interest. However, collecting information from all the required locations often is not possible due to the endurance of the UAV. This chapter will address the online UAV planning problem wherein operators have to plan UAV missions in the most efficient way based on available information on the performance of the UAV and additional requests for informatio

Robust UAV Mission Planning

Unmanned Areal Vehicles (UAVs) can provide significant contributions to information gathering in military missions. UAVs can be used to capture both full motion video and still imagery of specific target locations within the area of interest. In order to improve the effectiveness of a reconnaissance mission, it is important to visit the largest number of interesting target locations possible, taking into consideration operational constraints related to fuel usage between target locations, weather conditions and endurance of the UAV. We model this planning problem as the well-known orienteering problem, which is a generalization of the traveling salesman problem. Given the uncertainty in the military operational environment, robust planning solutions are required. As such, our model takes into account uncertainty in the fuel usage between targets (for instance due to weather conditions) as well as uncertainty in the importance of visiting specific target locations. We report results using different uncertainty sets that specify the degree of uncertainty against which any feasible solution will be protected. We also compare the probability that a solution is feasible for the robust solution on one hand and the solution found with average fuel usage and expected value of information on the other. In doing so, we show how the sustainability of a UAV mission can be significantly improved

A robust approach to the missile defence location problem

This paper proposes a model for determining a robust defence strategy against ballistic missile threat. Our approach takes into account a variety of possible future scenarios and different forms of robustness criteria, including the well-known absolute robustness criterion. We consider two problem variants. In the first, the number of ballistic missile interceptor systems is minimised, such that a predetermined defence level is achieved. In the second variant, the defence level is maximised for a given number of available interceptor systems. The solutions of both variants consist of a subset of all possible locations of the interceptor systems. We applied two solution approaches to this problem: a heuristic and an exact solution method. The heuristic method is based on simulated annealing and produces good results within a short amount of computation time. We also developed an integer programming formulation which can be solved to optimality using a standard solver. The computation time is higher, but because of the nice properties of the proposed IP-formulation, it can still be solved within reasonable amount of computation time. These two solution approaches were tested using a fictive, but realistic dataset. The results illustrate the effects of the predetermined defence levels and the availability of interceptor systems, as well as the robustness of the solutions produced. Finally, we used our dataset to illustrate the differences between both variants and their use in practice