L. Evers (Lanah)
http://repub.eur.nl/ppl/26109/
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http://repub.eur.nl/
RePub, Erasmus University RepositoryOnline stochastic UAV mission planning with time windows and time-sensitive targets
http://repub.eur.nl/pub/65602/
Wed, 01 Oct 2014 00:00:01 GMT<div>L. Evers</div><div>A.I. Barros</div><div>H. Monsuur</div><div>A.P.M. Wagelmans</div>
In this paper we simultaneously consider three extensions to the standard Orienteering Problem (OP) to model characteristics that are of practical relevance in planning reconnaissance missions of Unmanned Aerial Vehicles (UAVs). First, travel and recording times are uncertain. Secondly, the information about each target can only be obtained within a predefined time window. Due to the travel and recording time uncertainty, it is also uncertain whether a target can be reached before the end of its time window. Finally, we consider the appearance of new targets during the flight, so-called time-sensitive targets, which need to be visited immediately if possible. We tackle this online stochastic AV mission planning problem with time windows and time-sensitive targets using a re-planning approach. To this end, we introduce the Maximum Coverage Stochastic Orienteering Problem with Time Windows (MCS-OPTW). It aims at constructing a tour with maximum expected profit of targets that were already known before the flight. Secondly, it directs the planned tour to predefined areas where time-sensitive targets are expected to appear. We have developed a fast heuristic that can be used to re-plan the tour, each time before leaving a target. In our computational experiments we illustrate the benefits of the MCS-OPTW planning approach with respect to balancing the two objectives: the expected profits of foreseen targets, and expected percentage of time-sensitive targets reached on time. We compare it to a deterministic planning approach and show how it deals with uncertainty in travel and recording times and the appearance of time-sensitive targets.A two-stage approach to the orienteering problem with stochastic weights
http://repub.eur.nl/pub/74030/
Wed, 01 Jan 2014 00:00:01 GMT<div>L. Evers</div><div>K.M. Glorie</div><div>S. van der Ster</div><div>A.I. Barros</div><div>H. Monsuur</div>
The Orienteering Problem (OP) is a routing problem which has many interesting applications in logistics, tourism and defense. The aim of the OP is to find a maximum profit path or tour, which is feasible with respect to a capacity constraint on the total weight of the selected arcs. In this paper we consider the Orienteering Problem with Stochastic Weights (OPSWs) to reflect uncertainty in real-life applications. We approach this problem by formulating a two-stage stochastic model with recourse for the OPSW where the capacity constraint is hard. The model takes into account the effect that stochastic weights have on the expected total profit value to be obtained, already in the modeling stage. Since the expected profit is in general non-linear, we introduce a linearization that models the total profit that can be obtained for a given tour and a given scenario of weight realizations. This linearization allows for the application of Sample Average Approximation (SAA). The SAA solution asymptotically converges to the optimal solution of the two-stage model, but is computationally expensive. Therefore, to solve large instances, we developed a heuristic that exploits the problem structure of the OPSW and explicitly takes the associated uncertainty into account. In our computational experiments, we evaluate the benefits of our approach to the OPSW, compared to both a standard deterministic approach, and a deterministic approach that is extended with utilization of real-time information.The cooperative ballistic missile defence game
http://repub.eur.nl/pub/53504/
Sun, 01 Dec 2013 00:00:01 GMT<div>L. Evers</div><div>A.I. Barros</div><div>H. Monsuur</div>
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.Robust UAV mission planning
http://repub.eur.nl/pub/61342/
Wed, 12 Dec 2012 00:00:01 GMT<div>L. Evers</div><div>T.A.B. Dollevoet</div><div>A.I. Barros</div><div>H. Monsuur</div>
Unmanned Aerial 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, 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. Therefore, our model takes into account uncertainty in the fuel usage between targets, for instance due to weather conditions. We report results for 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 solutions on one hand and the solution found with average fuel usage on the other. These probabilities are assessed both by simulation and by derivation of problem specific theoretical bounds on the probability of constraint feasibility. In doing so, we show how the sustainability of a UAV mission can be significantly improved. Additionally, we suggest how the robust solution can be operationalized in a realistic setting, by complementing the robust tour with agility principles.The Orienteering Problem under Uncertainty Stochastic Programming and Robust Optimization compared
http://repub.eur.nl/pub/37193/
Fri, 07 Sep 2012 00:00:01 GMT<div>L. Evers</div><div>K.M. Glorie</div><div>S. van der Ster</div><div>A.I. Barros</div><div>H. Monsuur</div>
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.Robust UAV Mission Planning
http://repub.eur.nl/pub/22802/
Fri, 25 Feb 2011 00:00:01 GMT<div>L. Evers</div><div>T.A.B. Dollevoet</div><div>A.I. Barros</div><div>H. Monsuur</div>
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.Levelled bed occupancy and controlled waiting lists using Master surgical schedules
http://repub.eur.nl/pub/21241/
Mon, 01 Nov 2010 00:00:01 GMT<div>L. Evers</div><div>J.M. van Oostrum</div><div>A.P.M. Wagelmans</div>
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 in the Netherlands a simulation study is performed. The approach is applicable to any other hospital.
Results show that only the planning horizon has substantial influence on outcome parameters waiting time, schedule stability and hospital efficiency. We found that increasing the planning horizon increases patients’ waiting time on the one hand, but also increases schedule stability and hospital efficiency on the other hand. Regarding our second aim, we found that using an MSS substantially decreases variability in bed occupancy levels.