In a surveillance mission, the task of Unmanned Aerial Vehicles (UAV) path planning can in some cases be addressed using Sequential Monte Carlo (SMC) simulation. If sufficient a priori information about the target and the environment is available an assessment of the future state of the target is obtained by the SMC simulation. This assessment is used in a set of "what-if" simulations to compare different alternative UAV paths. In a static environment this simulation can be conducted prior to the mission. However, if the environment is dynamic, it is required to run the "what-if" simulations on-line i.e. in real-time.In this paper the details of this on-line simulation approach in UAV path planning is studied and its performance is compared with two other methods: an off-line simulationaided path planning and an exhaustive search method. The conducted simulations indicate that the on-line simulation has generally a higher performance compared with the two other methods.11th IEEE Symposium on Distributed Simulation and Real-Time Applications 1550-6525/07 $25.00
The Assignment Problem is a classical problem in the field of combinatorial optimization, having a wide range of applications in a variety of contexts. In general terms, the Assignment Problem consists of determining the best assignment of tasks to agents according to a predefined objective function. Different variants of the Assignment Problem have been extensively investigated in the literature in the last 50 years. In this work, we introduce and analyze the problem of optimizing a business process model with the objective of finding the most beneficial assignment of tasks to agents. Despite similarities, this problem is distinguished from the traditional Assignment Problem in that we consider tasks to be part of a business process model, being interconnected according to defined rules and constraints. In other words, assigning a business process to agents is a more complex form of the Assignment Problem. Two main categories of business processes, assignment-independent and assignment-dependent, are distinguished. In the first category, different assignments of tasks to agents do not affect the flow of the business process, while processes in the second category contain critical tasks that may change the workflow, depending on who performs them. In each category several types of processes are studied. Algorithms for finding optimal and near-optimal solutions to these categories are presented. For the first category, depending on the type of process, the Hungarian algorithm is combined with either the analytical method or simulation to provide an optimal solution. For the second category, we introduce two algorithms. The first one finds an optimal solution, but is feasible only when the number of critical tasks is small. The second algorithm is applicable to large number of critical tasks, but provides a near-optimal solution. In the second algorithm a hill-climbing heuristic method is combined with the Hungarian algorithm and simulation to find an overall near-optimal solution. A series of tests is conducted which demonstrates that the proposed algorithms efficiently find optimal solutions for assignment-independent and near-optimal solutions for assignment-dependent processes.
Abstract-In this paper we present the problem of optimizing a business process model with the objective of finding the most beneficial assignment of tasks to agents, without modifying the structure of the process itself. The task assignment problem for four types of processes are distinguished and algorithms for finding optimal solutions to them are presented: 1) a business process with a predetermined workflow, for which the optimal solution is conveniently found using the well-known Hungarian algorithm. 2) a Markovian process, for which we present an analytical method that reduces it to the first type. 3) a nonMarkovian process, for which we employ a simulation method to obtain the optimal solution. 4) the most general case, i.e. a nonMarkovian process containing critical tasks. In such processes, depending on the agents that perform critical tasks the workflow of the process may change. We introduce two algorithms for this type of processes. One that finds the optimal solution, but is feasible only when the number of critical tasks is few. The second algorithm is even applicable to large number of critical tasks but provides a near-optimal solution. In the second algorithm a hill-climbing heuristic method is combined with Hungarian algorithm and simulation to find an overall near-optimal solution for assignments of tasks to agents. The results of a series of tests that demonstrate the feasibility of the algorithms are included.
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