A fleet of unmanned aerial vehicles (UAVs) supported by logistics infrastructure, such as automated service stations, may be capable of long-term persistent operations. Typically, two key stages in the deployment of s...
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A fleet of unmanned aerial vehicles (UAVs) supported by logistics infrastructure, such as automated service stations, may be capable of long-term persistent operations. Typically, two key stages in the deployment of such a system are resource selection and scheduling. Here, we endeavor to conduct both of these phases in concert for persistent UAV operations. We develop a mixed integer linear program (MILP) to formally describe this joint design and scheduling problem. The MILP allows UAVs to replenish their energy resources, and then return to service, using any of a number of candidate service station locations distributed throughout the field. The UAVs provide service to known determin-istic customer space-time trajectories. There may be many of these customer missions occurring simultaneously in the time horizon. A customer mission may be served by several UAVs, each of which prosecutes a different segment of the customer mission. Multiple tasks may be conducted by each UAV between visits to the service stations. The MILP jointly determines the number and locations of resources (design) and their schedules to provide service to the customers. We address the computational complexity of the MILP formulation via two methods. We develop a branch and bound algorithm that guarantees an optimal solution and is faster than solving the MILP directly via CPLEX. This method exploits numerous properties of the problem to reduce the search space. We also develop a modified receding horizon task assignment heuristic that includes the design problem (RHTA(d)). This method may not find an optimal solution, but can find feasible solutions to problems for which the other methods fail. Numerical experiments are conducted to assess the performance of the RHTA(d) and branch and bound methods relative to the MILP solved via CPLEX. For the experiments conducted, the branch and bound algorithm and RHTA(d) are about 500 and 25,000 times faster than the MILP solved via CPLEX, respectively
In this study, the authors consider the application of a system architecture called cognitive radio (CR) with fibre-connected distributed antennas in IEEE 802.22 wireless regional area networks (WRANs) as it could bri...
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In this study, the authors consider the application of a system architecture called cognitive radio (CR) with fibre-connected distributed antennas in IEEE 802.22 wireless regional area networks (WRANs) as it could bring the benefits of much shorter wireless transmission distances, lower transmission power and the possibility of utilising multi-antenna transmission techniques. In this architecture, the authors study the secondary user (SU) access problem in uplink, where the SU to primary user (PU) link estimation is subject to random errors because PU could not assist link estimation of SU. This SU access problem is divided into two parts: antenna selection and access control. Thus, first antenna selection problem is modelled as a restless bandit problem, which is solved by the primal-dual index heuristic algorithm based on first order relaxation. In addition, the access control problem is modelled as a stochastic knapsack (SASK) problem with random weight, and then relaxed to be a deterministic second order cone programming problem. With the deduced upper bound, the access control problem is solved by the branch and bound algorithm, which yields the SU access based on SASK scheme. Simulation results illustrate the significant performance improvement of SASK scheme, compared with existing SU access methods.
Given a graph, in the maximum clique problem, one desires to find the largest number of vertices, any two of which are adjacent. A branch-and-bound algorithm for the maximum clique problem-which is computationally equ...
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Given a graph, in the maximum clique problem, one desires to find the largest number of vertices, any two of which are adjacent. A branch-and-bound algorithm for the maximum clique problem-which is computationally equivalent to the maximum independent (stable) set problem-is presented with the vertex order taken from a coloring of the vertices and with a new pruning strategy. The algorithm performs successfully for many instances when applied to random graphs and DIMACS benchmark graphs. (C) 2002 Elsevier Science B.V. All rights reserved.
This paper considers global optimization (maximization) problems. It emphasizes that for a generic function, it is inherently difficult to find the global optimum within a finite number of function evaluations. Theref...
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This paper considers global optimization (maximization) problems. It emphasizes that for a generic function, it is inherently difficult to find the global optimum within a finite number of function evaluations. Therefore, it is more realistic to talk about maximizing the expectation of the largest function value that can be obtained for a given number of function evaluations. Based on decision theoretic argument, we propose that the search region of the objective function be partitioned into certain number of subregions. Using the sampled function values from each subregion, estimators are derived to determine how ''promising'' each subregion is. The most promising subregion is then further partitioned. In some sense, the adaptive partitioned random search (APRS) proposed in this paper is a tree search type of algorithms like branch-and-boundalgorithms. The APRS, however, abandons the idea of finding the subregion where the global maximum is likely located in the first place. Instead it seeks the subregion where the largest improvement of the performance is most likely to be obtained if more function evaluations are taken. The APRS in general can provide a much better-than-average solution within a modest number of function evaluations. In fact, our various numerical experiments have shown that in comparison with the crude random search (CRS) in terms of number of function evaluations, the APRS can be at least hundreds of times more efficient. The simplicity and robustness of the APRS in terms of easy implementation and minimum assumptions on the objective function are also demonstrated.
This paper presents a branch, bound, and Remember (BB&R) exact algorithm using the Cyclic Best First Search (CBFS) exploration strategy for solving the scheduling problem, a single machine scheduling problem with ...
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This paper presents a branch, bound, and Remember (BB&R) exact algorithm using the Cyclic Best First Search (CBFS) exploration strategy for solving the scheduling problem, a single machine scheduling problem with sequence dependent setup times where the objective is to find a schedule with minimum total tardiness. The BB&R algorithm incorporates memory-based dominance rules to reduce the solution search space. The algorithm creates schedules in the reverse direction for problems where fewer than half the jobs are expected to be tardy. In addition, a branch and bound algorithm is used to efficiently compute tighter lower bounds for the problem. This paper also presents a counterexample for a previously reported exact algorithm in Luo and Chu (Appl Math Comput 183(1):575-588, 2006) and Luo et al. (Int J Prod Res 44(17):3367-3378, 2006). Computational experiments demonstrate that the algorithm is two orders of magnitude faster than the fastest exact algorithm that has appeared in the literature. Computational experiments on two sets of benchmark problems demonstrate that the CBFS search exploration strategy can be used as an effective heuristic on problems that are too large to solve to optimality.
We consider a production-inventory planning problem with time-varying demands, convex production costs and a warehouse capacity constraint. It is solved by use of the Lagrangian form of the maximum principle. The poss...
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We consider a production-inventory planning problem with time-varying demands, convex production costs and a warehouse capacity constraint. It is solved by use of the Lagrangian form of the maximum principle. The possible existence of strong decision and forecast horizons is demonstrated. When they exist, they permit the breaking up of the whole problem into a set of smaller problems which can be solved independently because optimal decisions up to a strong decision horizon are completely independent of demand data beyond the next forecast horizon. A forward branch and bound algorithm is developed to determine such horizons and to solve the whole problem.
This research addresses the problem of scheduling batches of parts in a flexible manufacturing system (FMS). Due to the use of serial access material-handling systems in many FMSs, the problem is modeled for a multi-c...
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This research addresses the problem of scheduling batches of parts in a flexible manufacturing system (FMS). Due to the use of serial access material-handling systems in many FMSs, the problem is modeled for a multi-cell FMS with flowshop characteristics. A branch and bound solution method is developed which exploits the special structure of the problem to develop strong lower bounds. Numerical computations show that the branch and bound algorithm solves large size problems in a reasonable time. Computational results are provided for a variety of test problems. (c) 2004 Elsevier B.V. All rights reserved.
Parallel implementations of a combined branch-and-bound algorithm for the knapsack problem with one constraint are considered. By the combined algorithm we mean an algorithm in which two methods of branching are imple...
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Parallel implementations of a combined branch-and-bound algorithm for the knapsack problem with one constraint are considered. By the combined algorithm we mean an algorithm in which two methods of branching are implemented, the method based on an estimate of the upper bound and the method of one-sided branching based on the vector. An approach combining parallel implementations of the brunch-and-bound method and the heuristic search is proposed and implemented.
Optimum multiuser detection for Code Division Multiple Access (CDMA) systems requires the solution of an NP-hard combinatorial optimization problem. It is well known that the computational complexity of the optimum mu...
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Optimum multiuser detection for Code Division Multiple Access (CDMA) systems requires the solution of an NP-hard combinatorial optimization problem. It is well known that the computational complexity of the optimum multiuser detector is exponential with the number of active users in the system. In order to reduce the complexity of the optimum multiuser detection, we propose a Reduced Complexity Maximum Likelihood (RCML) algorithm that includes a set of novel certain boundary rules and characteristics. We investigate the performance and complexity tradeoffs for the RCML algorithm by conducting a set of simulations;Maximum Likelihood (ML) detection as a reference for performance comparisons, and relaxation based Semidefinite Programming (SDPB) algorithm as a reference for complexity comparisons. We show that the RCML algorithm is a promising algorithm for its computational savings over relaxation based algorithms in lightly-to-moderately loaded CDMA systems, and for its optimality in highly loaded CDMA systems.
This paper presents a mathematical programming model for optimal highway pavement rehabilitation planning which minimizes the life-cycle cost for a finite horizon. It extends previous researches in this area by solvin...
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This paper presents a mathematical programming model for optimal highway pavement rehabilitation planning which minimizes the life-cycle cost for a finite horizon. It extends previous researches in this area by solving the problem of multiple rehabilitation activities on multiple facilities, with realistic empirical models of deterioration and rehabilitation effectiveness. The formulation is based on discrete control theory. A nonlinear pavement performance model and integer decision variables are incorporated into a mixed-integer nonlinear programming (MINLP). Two solution approaches, a branch-and-bound algorithm and a greedy heuristic, are proposed for this model. It is shown that the heuristic results provide a good approximation to the exact optima, but with much lower computational costs. (C) 2004 Elsevier Ltd. All rights reserved.
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