Mobile sensors cover more area over a period of time than the same number of stationary sensors. However, the quality of coverage achieved by mobile sensors depends on the velocity, mobility pattern, number of mobile ...
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(纸本)1595932860
Mobile sensors cover more area over a period of time than the same number of stationary sensors. However, the quality of coverage achieved by mobile sensors depends on the velocity, mobility pattern, number of mobile sensors deployed and the dynamics of the phenomenon being sensed. The gains attained by mobile sensors over static sensors and the optimal motion strategies for mobile sensors are not well understood. In this paper we consider the problem of event capture using mobile sensors. The events of interest arrive at certain points in the sensor field and fade away according to arrival and departure time distributions. An event is said to be captured if it is sensed by one of the mobile sensors before it fades away. For this scenario we analyze how the quality of coverage scales with the velocity, path and number of mobile sensors. We characterize the cases where the deployment of mobile sensors has no advantage over static sensors and find the optimal velocity pattern that a mobile sensor should adopt. We also present algorithms for two motion planning problems: (i) for a single sensor, what is the minimum speed and sensor trajectory required to satisfy a bound on event loss probability and (ii) for sensors with fixed speed, what is the minimum number of sensors required to satisfy a bound on event loss probability. When events occur only along a line or a closed curve our algorithms return optimal velocity for the minimum velocity problem. For the minimum sensor problem, the number of sensors used is within a factor two of the optimal solution. For the case where the events occur at arbitrary points on a plane we present heuristic algorithms for the above motion planning problems and bound their performance with respect to the optimal. The results of this paper have wide range of applications in areas like surveillance, wildlife monitoring, hybrid sensor networks and under-water sensor networks. Copyright 2006 ACM.
A stochastic linear optimization approach for studying demand uncertainty in the aggregate production planning problem is proposed. To realize the integrative decision of production planning and inventory policy, inve...
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The design of product recovery network has received growing attention since the past decades. Due to the high number of processing activities involved and the high variability in the value of the components, the high ...
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The design of product recovery network has received growing attention since the past decades. Due to the high number of processing activities involved and the high variability in the value of the components, the high level of uncertainty is one of the important characteristics of such problem. This paper proposes a stochastic programming based approach by which a deterministic model for product recovery network design can be extended to explicitly account for the uncertainties. Since almost all the existing approaches for solving such problem are either restricted to deterministic situations or can only deal with a modest number of scenarios for the uncertain problem parameters, a solution approach integrating a recently proposed sampling method with an acceleration strategy is developed in this research. A computational study involving a large-scale product recovery network is presented to demonstrate the significance of the developed stochastic model as well as the efficiency of the proposed solution approach.
Because of its applicability, as well as its generality, research in the area of simulation-optimization continues to attract significant attention. These methods, most of which rely on the statistically motivated sea...
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This paper is concerned with algorithms for the logical generalisation of probabilistic temporal models from examples. The algorithms combine logic and probabilistic models through inductive generalisation. The induct...
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The aim of this research is to introduce a new type of stochastic optimal topology design method. The paper presents topology design procedure and compares the achieved results with optimal topologies obtained on dete...
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In over-subscription planning (OSP), the set of goals is not achievable jointly, and the task is to find a plan that attains the best feasible subset of goals given resource constraints. Recent classical OSP algorithm...
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In over-subscription planning (OSP), the set of goals is not achievable jointly, and the task is to find a plan that attains the best feasible subset of goals given resource constraints. Recent classical OSP algorithms ignore the uncertainly inherent in many natural application domains where OSPs arise. And while modeling stochastic OSP problems as MDPs is easy, the resulting models are too large for standard solution approaches. Fortunately OSP problems have a natural twotiered hierarchy, and in this paper we adapt and extend tools developed in the hierarchical reinforcement learning community in order to effectively exploit this hierarchy and obtain compact, factored policies. Typically, such policies are suboptimal, but under certain assumptions that hold in our planetary exploration domain, our factored solution is, in fact, optimal. Our algorithms work by repeatedly solving a number of smaller MDPs, while propagating information between them. We evaluate a number of variants of this approach on a set of stochastic instances of a planetary rover domain, showing substantial performance gains.
For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been ...
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For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been developing in various,ways. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. For such problems, as a fusion of these two approaches, after incorporating fuzzy goals of the decision maker for the objective functions, we propose an interactive fuzzy satisficing method for the expectation model to derive a satisficing solution for the decision maker. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method. (C) 2002 Elsevier Science B.V. All rights reserved.
This paper addresses Operating Rooms (ORs) planning problem with elective and emergency surgery demands. The planning problem is considered as a stochastic optimization problem in order to minimize overtime costs and ...
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This paper addresses Operating Rooms (ORs) planning problem with elective and emergency surgery demands. The planning problem is considered as a stochastic optimization problem in order to minimize overtime costs and patients’ related costs. An “almost” exact method combining Monte Carle simulation and mixed integer programming is presented, and convergence properties are investigated. Several heuristics and meta-heuristics are then proposed. Numerical experimentation is conduced to compare different optimization methods.
In this paper we study two stage problems of stochastic convex programming. Solving the problems is very hard. A L-shaped method for it is given. The implement of the algorithm is simple, so less computation work is n...
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In this paper we study two stage problems of stochastic convex programming. Solving the problems is very hard. A L-shaped method for it is given. The implement of the algorithm is simple, so less computation work is needed. The result of computation shows that the algorithm is effective.
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