We consider the problem of jointly maximizing the time-to-first-failure (TTFF), defined as the time till the first node in the network runs out of battery energy, and minimizing the total power in energy constrained s...
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We consider the problem of jointly maximizing the time-to-first-failure (TTFF), defined as the time till the first node in the network runs out of battery energy, and minimizing the total power in energy constrained static wireless networks. It is shown in A.K. Das et al. (2004) that simply optimizing the TTFF criterion may not provide the ideally optimum solution. Besides maximizing the TTFF, the ideally optimum solution guarantees that the lifetimes of all nodes are at least as high as in other trees which provide the same TTFF. A composite objective function involving the TTFF and the sum of the transmitter powers is therefore proposed in A.K. Das et al. (2004), along with a discussion of an optimal solution methodology using mixed integer linear programming. In this paper, we discuss a 2-step heuristic-procedure for the joint optimization problem. The first step is a greedy iterative algorithm which provides an optimal solution, but with respect to the TTFF criterion only. The second step is a tree-improvement technique which is used to refine the TTFF-optimal tree such that the total transmitter power is minimized, without affecting the optimal TTFF. Recent work has shown that the power consumed in the receiver circuitry can be almost comparable to the transmit power, especially in short-range networks. Our algorithms are therefore designed to take into account both the transmitter side and receiver side power expenditures. Simulation results are presented to validate the performance of the algorithm
We present some proposals to approximately solve a period vehicle routing problem used to model the extraction of oil from a set of onshore oil wells in Brazil. This problem differs from the well-known period vehicle ...
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We present some proposals to approximately solve a period vehicle routing problem used to model the extraction of oil from a set of onshore oil wells in Brazil. This problem differs from the well-known period vehicle routing problem in several aspects. One major difference between them, responsible for increasing the complexity of the problem, is that, in the proposed problem, the number of visits required by a customer during the period is not previously determined. We developed some pure GRASP heuristics and some other heuristics that include the use of memory in GRASP. Experimental results illustrate the effectiveness of GRASP with adaptive memory over pure GRASP heuristics
e-Procurement is an Internet based business process for sourcing direct or indirect materials. This paper considers a procurement scenario of a buyer procuring large quantities of a single good. The suppliers submit n...
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e-Procurement is an Internet based business process for sourcing direct or indirect materials. This paper considers a procurement scenario of a buyer procuring large quantities of a single good. The suppliers submit nonconvex piecewise linear supply curves as their bids. Such bids enable the suppliers to effectively express their economies of scale and transportation constraints. The buyer imposes a business rule of limiting the winning suppliers within a preferred range. The bid evaluation problem faced by the buyer is to select the winning suppliers and their trading quantities, such that the cost of procurement is minimized while satisfying the supply, demand, and business constraints. The bid evaluation problem is NP-hard even for a simple special case. In this paper, the problem is formulated as a mixed integer linear programming problem and we propose a Lagrangian relaxation based heuristic to find a near optimal solution. The computational experiments performed on representative data sets show that the proposed heuristic produces a feasible solution with negligible optimality gap.
This paper presents the methodology and the results of the must offer waiver process as applied in the California electricity market. The goal of this cost saving process is to minimize the market operation costs by d...
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This paper presents the methodology and the results of the must offer waiver process as applied in the California electricity market. The goal of this cost saving process is to minimize the market operation costs by deploying an optimal unit commitment approach. This procedure replaces the "first come, first served" must offer waiver process that is currently in place. It uses a security constrained unit commitment application to commit enough units to meet the reliability requirement and minimize the total start up and minimum load costs over the specified time period. A post must offer waiver procedure utilizing the security constrained unit commitment is also established to calculate and allocate the incremental costs associated with the local reliability requirement. The key components of the new must offer waiver procedure, the methodology and computer algorithm of security constrained unit commitment, and the methodology of the incremental cost calculation and allocation are addressed in this paper. The paper finally presents computational results based on actual data to illustrate the benefits of the new approach
This paper presents the application of a machine learning technique (neural networks) to the problem of optimising the traffic flows (label switched path placement) within telecommunications networks using multi-proto...
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This paper presents the application of a machine learning technique (neural networks) to the problem of optimising the traffic flows (label switched path placement) within telecommunications networks using multi-protocol label switching (MPLS). It is shown here that the machine learning technique achieves very fast computation of accurate solutions to the problem of placing label switched paths in order to maximise the performance of the network. Results are presented which compare the accuracy of the neural network solutions with the optimal solutions produced by the standard mixed integer linear programming technique, and the time taken to produce the solutions. As well, network simulation results are presented which show the improvement in network performance, in terms of packet loss, that can be achieved when using such a technique.
Throughput is an important performance consideration for multihop wireless networks. In this paper, we study the joint link scheduling and power control problem, focusing on maximizing the network throughput. We formu...
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Throughput is an important performance consideration for multihop wireless networks. In this paper, we study the joint link scheduling and power control problem, focusing on maximizing the network throughput. We formulate the maximum throughput link scheduling with power control (MATH-SPC) problem, and present a mixed integer linear programming (MILP) formulation to provide optimal solutions. However, simply maximizing the throughput leads to a severe bias on bandwidth allocation among all links. In order to enhance both throughput and fairness, we define a new parameter, the demand satisfaction factor (DSF), to characterize the fairness of bandwidth allocation. We formulate the maximum throughput fair link scheduling with power control (MATA-SPC) problem and present an MILP formulation for this problem. We also present an effective polynomial time heuristic algorithm, namely, the serial LP rounding (SLPR) heuristic. Our numerical results show that bandwidth can be fairly allocated among all links/flows by solving our MATA-SPC formulation or using our heuristic algorithm at the cost of a minor reduction of network throughput
A probabilistic finite receding horizon approach for trajectory planning of autonomous air vehicles is presented. The approach is based on mixed integer linear programming (MILP) techniques. The risk areas are modelle...
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A probabilistic finite receding horizon approach for trajectory planning of autonomous air vehicles is presented. The approach is based on mixed integer linear programming (MILP) techniques. The risk areas are modelled by dynamic boundaries to direct the vehicle towards the target. Various constraints are formulated to avoid radar zones and collisions, etc. These constraints are extended to be both hard and soft so as to alleviate the infeasibility problem usually encountered. The finite receding horizon approach is numerically stable and can be applied to centralized trajectory planning of a fleet of UAVs in real time. The MILP problem is solved using commercially available software AMPL/CPLEX. Finally the approach is applied to different scenarios with upper and lower bounds on the speed and acceleration of each UAV.
In service oriented architectures, complex applications are composed from a variety of functionally equivalent Web services, which may differ for quality parameters. Under this scenario, applications are defined as hi...
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In service oriented architectures, complex applications are composed from a variety of functionally equivalent Web services, which may differ for quality parameters. Under this scenario, applications are defined as high level business processes and service composition can be implemented dynamically by identifying the best set of services available at run time. In this paper, we model the service composition problem as a mixedintegerlinear problem where both local constraints and global constraints can be specified.
We consider the problem of maximizing the lifetime of a given multicast connection in wireless networks that use directional antennas and have limited energy resources. We present a constraint formulation for the MLM ...
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We consider the problem of maximizing the lifetime of a given multicast connection in wireless networks that use directional antennas and have limited energy resources. We present a constraint formulation for the MLM (multicast lifetime maximization) problem in terms of MILP (mixed integer linear programming) for wireless ad hoc networks, which we can use to evaluate and compare the realistic performance of different heuristic algorithms.
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