The optimal sensor placement problem consists of determining the number, types, and locations of sensors satisfying inhomogeneous coverage requirements while minimising a specified cost function. The cost function can...
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The optimal sensor placement problem consists of determining the number, types, and locations of sensors satisfying inhomogeneous coverage requirements while minimising a specified cost function. The cost function can reflect various factors such as the actual cost of the sensors, their total number, and energy consumption. A strict and general formulation of the problem is described here for sensors characterised by probability of detection at some specified probability of false alarm. The formulation includes non-uniform coverage preferences and realistic, non-line-of-sight detection accounting on signal propagation effects. The optimisation is expressed as a solution to a binary linear programming problem. While exact solution of this problem is typically prohibitive, a fast greedy algorithm is presented that yields a near-optimal solution. It can also be successfully applied to improve coverage of an existing sensor network. This approach compares very favourably to an alternative heuristic strategy based on placing sensors one-by-one in the previously worst-covered location.
Operational planning within public transit companies has been extensively tackled but still remains a challenging area for operations research models and techniques. This phase of the planning process comprises vehicl...
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Operational planning within public transit companies has been extensively tackled but still remains a challenging area for operations research models and techniques. This phase of the planning process comprises vehicle-scheduling, crew-scheduling and rostering problems. In this paper, a new integer mathematical formulation to describe the integrated vehicle-crew-rostering problem is presented. The method proposed to obtain feasible solutions for this binary non-linear multi-objective optimization problem is a sequential algorithm considered within a preemptive goal programming framework that gives a higher priority to the integrated vehicle-crew-scheduling goal and a lower priority to the driver rostering goals. A heuristic approach is developed where the decision maker can choose from different vehicle-crew schedules and rosters, while respecting as much as possible management's interests and drivers' preferences. An application to real data of a Portuguese bus company shows the influence of vehicle-crew-scheduling optimization on rostering solutions.
In this paper we study an actual problem proposed by an agricultural cooperative devoted to harvesting corn and grass. The cooperative uses harvesters for harvesting the crop and trucks for carrying it from the smallh...
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In this paper we study an actual problem proposed by an agricultural cooperative devoted to harvesting corn and grass. The cooperative uses harvesters for harvesting the crop and trucks for carrying it from the smallholdings to the landowners' silos. The goal is to minimize the total working time of the machinery. Therefore, the cooperative needs to plan both the harvesters and trucks routing. This routing problem simultaneously incorporates the following characteristics: time windows, nested decisions, processing times required to service each facility and the fact that facilities must be visited in clusters. A binary integer linearprogramming model is proposed to solve this problem. However, since approaches dealing directly with such formulation lead to considerable computation times, we propose a heuristic alternative solution approach for the problem. The heuristic is applied to the case of the cooperative "Os Irmandios" with a large number of landowners and smallholdings. We report on extensive computational tests to show that the proposed heuristic approach can solve large problems effectively in reasonable computing time.
This work considers a decision problem about orders of owners and routes of smallholdings for a harvester in an agricultural cooperative in which each owner has a proposal about the instant time in which he would like...
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This work considers a decision problem about orders of owners and routes of smallholdings for a harvester in an agricultural cooperative in which each owner has a proposal about the instant time in which he would like that the machine starts the activity in his land and the different smallholdings of each owner should be processed as a block. A binary linear programming model is introduced in order to reducing costs. Solving the model for actual size instances is computationally burdensome. Hence, we introduce and implement two heuristic algorithms to reduce the computational time. The heuristics are applied to the real case of the cooperative "Os Irmandios" with a large number of owners and smallholdings. The numerical results show that the heuristics can solve large instances effectively with reasonable computational effort.
Using agricultural preservation priorities derived from an analytical hierarchy process by 23 conservation experts from 18 agencies in the state of Delaware, this research uses weighted benefit measures to evaluate th...
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Using agricultural preservation priorities derived from an analytical hierarchy process by 23 conservation experts from 18 agencies in the state of Delaware, this research uses weighted benefit measures to evaluate the historical success of Delaware's agricultural protection fund, which spent nearly $100 million in its first decade. This research demonstrates how these operation research techniques can be used in concert to address relevant conservation questions. Results suggest that the state's sealed-bid-offer auction, which determines the yearly conservation selections, is superior to benefit-targeting approaches frequently employed by conservation organizations, but is inferior to the optimization technique of binary linear programming that could have provided additional benefits to the state, such as 12,000 additional acres worth an estimated $25 million. Copyright 2010 Northeastern Agricultural and Resource Economics Association.
Determination of an optimal configuration (numbers, types, and locations) of a sensor network is an important practical problem. In most applications, complex signal propagation effects and inhomogeneous coverage pref...
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ISBN:
(纸本)9780819481580
Determination of an optimal configuration (numbers, types, and locations) of a sensor network is an important practical problem. In most applications, complex signal propagation effects and inhomogeneous coverage preferences lead to an optimal solution that is highly irregular and nonintuitive. The general optimization problem can be strictly formulated as a binary linear programming problem. Due to the combinatorial nature of this problem, however, its strict solution requires significant computational resources (NP-complete class of complexity) and is unobtainable for large spatial grids of candidate sensor locations. For this reason, a greedy algorithm for approximate solution was recently introduced [S. N. Vecherin, D. K. Wilson, and C. L. Pettit, "Optimal sensor placement with terrain-based constraints and signal propagation effects," Unattended Ground, Sea, and Air Sensor Technologies and Applications XI, SPIE Proc. Vol. 7333, paper 73330S (2009)]. Here further extensions to the developed algorithm are presented to include such practical needs and constraints as sensor availability, coverage by multiple sensors, and wireless communication of the sensor information. Both communication and detection are considered in a probabilistic framework. Communication signal and signature propagation effects are taken into account when calculating probabilities of communication and detection. Comparison of approximate and strict solutions on reduced-size problems suggests that the approximate algorithm yields quick and good solutions, which thus justifies using that algorithm for full-size problems. Examples of three-dimensional outdoor sensor placement are provided using a terrain-based software analysis tool.
Channel allocation is an important area of research in open spectrum networks which asserts a significant impact on the spectrum utilization and the fairness among users. This paper studies the optimization of channel...
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ISBN:
(纸本)9781424427932
Channel allocation is an important area of research in open spectrum networks which asserts a significant impact on the spectrum utilization and the fairness among users. This paper studies the optimization of channel allocation, considering multiple objectives. For each objective, a binaryprogramming model is described. Then a new optimization objective called fairness constrained maximum throughput is proposed. To achieve this optimization objective, a unified binary linear programming (UBLP) model is constructed which is then solved by the simplex method and branch-and-bound search. The solution to this model satisfies a bandwidth requirement for each user, e.g., the bandwidth for each user is equal to or larger than a per-user bandwidth minimum, and the solution also maximizes the network throughput. We prove that given different per-user bandwidth minimum, the optimal solution to the UBLP model achieves specific optimization objectives, such as the maximum network throughput and the max-min fairness. For the proportional fairness objective, the solution to the UBLP model proves to be within a bound of the optimal solution.
Economic theory asserts that to achieve maximum conservation benefits land acquisition needs to be cost effective. Yet the most common planning technique used by land conservation organizations is 'benefit-targeti...
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Economic theory asserts that to achieve maximum conservation benefits land acquisition needs to be cost effective. Yet the most common planning technique used by land conservation organizations is 'benefit-targeting' that focuses only on acquiring parcels with the highest benefits and ignores costs. Unlike most of the literature which focuses on covering problems, this research applies optimization techniques to achieve maximum aggregate conservation benefits for an ongoing land acquisition effort in the Catoctin Mountain Region in central Maryland. For this case study, optimization yields additional conservation benefits worth an estimated $3.1-$3.9 million or achieves the same level of conservation benefits but at a cost savings ranging from $0.9 to $3.5 million, depending on the initial budget size. Finally, the highest efficiencies are achieved in low budget scenarios, like those most prevalent in conservation efforts. (c) 2005 Elsevier Ltd. All rights reserved.
In assembly line balancing problems, parallel execution of assembly operations is often advocated because of its enhanced flexibility and minimum lead-time. Although the theoretical maximum number of possible assembly...
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In assembly line balancing problems, parallel execution of assembly operations is often advocated because of its enhanced flexibility and minimum lead-time. Although the theoretical maximum number of possible assembly sequences combinatorially explodes with the number of components in a product, graphical representations can depict these sequences in a surveyable way. The AND/OR graph representation is an appropriate basis for optimum sequence selection, which can be achieved via heuristic, metaheuristic, and exact methods. The exact method, based on binary linear programming, is described. To arrive at the appropriate model, a novel approach for AND/OR graph generation, based on subassembly detection, is presented. The method is demonstrated with simple cases and next extended to increasingly complex products. A modification of the optimization method is applied, which enables a search for sequences with maximum parallelism. (c) 2004 Elsevier B.V. All rights reserved.
Fuzzy binary linear programming (FBLP) problems are very essential in many fields such as assignment and assembly line balancing problems in operational research, multiple projects, locations, and candidates selection...
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Fuzzy binary linear programming (FBLP) problems are very essential in many fields such as assignment and assembly line balancing problems in operational research, multiple projects, locations, and candidates selection cases in management science, as well as representing and reasoning with prepositional knowledge in artificial intelligence. Although FBLP problems play a significant role in human decision environment, not very much research has focused on FBLP problems. This work first proposes a simple means of expressing a triangular fuzzy number as a linear function with an absolute term. A method of linearizing absolute terms is also presented. The developed goal programming (GP) model weighted by decision-makers' (DMs) preference aims to optimize the expected objective function and minimize the sum of possible membership functions' deviations. After presented a novel way of linearizing product terms, the solution algorithm is proposed to generate a crisp trade-off promising solution that is also an optimal solution in a certain sense. Three examples, equipment purchasing choice, investment project selection, and assigning clients to project leaders, illustrate that the proposed algorithm can effectively solve generalized FBLP problems. (C) 2001 Elsevier Science B.V. All rights reserved.
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