The existing mobile hotel recommendation systems are usually subject to a difficult problem-travelers choose dominated hotels. This problem is difficult to resolve because there is no reason to recommend a hotel that ...
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The existing mobile hotel recommendation systems are usually subject to a difficult problem-travelers choose dominated hotels. This problem is difficult to resolve because there is no reason to recommend a hotel that is inferior to another in all aspects. To address this problem, an artificial dimension is added to each hotel to model unknown personal preferences. The possible values along the artificial dimension and the weight associated with it are derived by solving an integer nonlinear programming problem. Thus, the proposed methodology hybridizes objective and subjective weights. An illustrative example is provided to show the applicability of the proposed methodology. In addition, a field study was conducted in a small region of Seatwen District, Taichung City, Taiwan to evaluate the possible advantages of the proposed methodology over existing methods. The experimental results showed that the proposed methodology outperformed five existing methods in improving the successful recommendation rate, with the most significant advantage being up to 33 %. Furthermore, the recommendation results generated using the proposed methodology were found to be less risky.
This paper proposes a mathematical programming method to construct the membership functions of the fuzzy objective value of the cost-based queueing decision problem with the cost coefficients and the arrival rate bein...
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This paper proposes a mathematical programming method to construct the membership functions of the fuzzy objective value of the cost-based queueing decision problem with the cost coefficients and the arrival rate being fuzzy numbers. On the basis of Zadeh's extension principle, three pairs of mixed integernonlinear programs (MINLP) parameterized by the possibility level alpha are formulated to calculate the lower and upper bounds of the minimal expected total cost per unit time at alpha, through which the membership function of the minimal expected total cost per unit time of the fuzzy objective value is constructed. To provide a suitable optimal service rate for designing queueing systems, the Yager's ranking index method is adopted. Two numerical examples are solved successfully to demonstrate the validity of the proposed method. Since the objective value is completely expressed by a membership function rather than by a crisp value, it conserves the fuzziness of the input information, thus more information is provided for designing queueing systems. The successful extension of queueing decision models to fuzzy environments permits queueing decision models to have wider applications in practice. (c) 2006 Elsevier B.V. All rights reserved.
We present a Lagrangean decomposition to study integer nonlinear programming problems. Solving the dual Lagrangean relaxation we have to obtain at each iteration the solution of a nonlinearprogramming with continuous...
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We present a Lagrangean decomposition to study integer nonlinear programming problems. Solving the dual Lagrangean relaxation we have to obtain at each iteration the solution of a nonlinearprogramming with continuous variables and an integer linear programming. Decreasing iteratively the primal-dual gap we propose two algorithms to treat the integer nonlinear programming.
Multi-objective integer nonlinear programming (MOINLP) problems are multi-objective integerprogramming problems with at least one nonlinear objective function or constraint. To date, MOINLP problem has not been exact...
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Multi-objective integer nonlinear programming (MOINLP) problems are multi-objective integerprogramming problems with at least one nonlinear objective function or constraint. To date, MOINLP problem has not been exactly solved. Although traditional epsilon-constraint method can be used to solve MOINLP problem, the obtained solution may not be Pareto-optimal. To overcome this shortcoming, a basic epsilon-constraint method (BEM) is proposed to solve MOINLP problem exactly. However, the time complexity of BEM is as high as O((p-1)(MN)-N-2), where p, M, and N are the numbers of objectives, Pareto-optimal solutions, and feasible solutions, respectively. For this reason, an improved BEM (IBEM) is developed whose time complexity is O((MN)-N-2). That is, the time complexity of IBEM for solving MOINLP problem is equal to solving the single-objective one. Finally, to avoid using all the feasible solutions (N) in obtaining each Pareto-optimal solution, three methods to eliminate dominated solutions effectively are used before performing IBEM. The test results illustrate that our method can not only solve MOINLP problem exactly but also has high efficiency.
There are more and more data from Human Genome Project that we need better methods to deal with. Some mathematical models have been presented to analyze gene regulatory networks, such as Boolean networks Bayesian netw...
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ISBN:
(纸本)9781424467129
There are more and more data from Human Genome Project that we need better methods to deal with. Some mathematical models have been presented to analyze gene regulatory networks, such as Boolean networks Bayesian networks, differential equation, weight matrices, etc. Some of the models could describe some rules of the genetic regulatory relations properly, such as Bayesian networks, differential equation, but the rest did not work very well. It is thus necessary to consider some new mathematical models to estimate the gene regulatory relations according to some experimental data. Here we have utilized the entropy information from gene data of breast cancer metastasis to get the weights of all the genes, and set up a RNINLP model in which minimum error is regarded as objective function to search for the regulatory relation in the genes, and then sieved regulatory genes by coefficient correlation model. Using the programs of LINGO8.0 and MATLAB7.0, we got a gene regulatory network of the 27 genes related to the breast cancer metastasis. We found that there were 25 pairs of genes exiting regulatory relation and 11 pairs of genes being mutual promotion effect. RNINLP utilizes almost all the information of the data, so it can describe the regulatory relation of genes with coefficient correlation model, and the model can be extended to even more complicated gene regulatory networks.
In the present paper, we invoke a newly developed genetic hybrid algorithm (GHA) to solve the trim loss problem of a paper-converting mill. The genetic algorithm was specifically designed for nonconvex mixed integer n...
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In the present paper, we invoke a newly developed genetic hybrid algorithm (GHA) to solve the trim loss problem of a paper-converting mill. The genetic algorithm was specifically designed for nonconvex mixed integer nonlinear programming problems. The current problem is an integer non-convex nonlinearprogramming (INLP) problem involving bilinear constraints. As shown elsewhere, the problem can be written in expanded linear form and solved either as an integer linear programming (ILP) or as a mixed integer linear programming (MILP) problem. In each case, the formulation is a special case of MINLP and, therefore, directly solvable by the genetic hybrid algorithm. The example considered is taken from the family of real daily trim optimization problems encountered at a Finnish paper-converting mill with a yearly capacity of 100 000 t. In this paper, we present the genetic hybrid algorithm, the INLP-problem to be solved and compare the results with those obtained by a classical optimization method. (C) 1999 Elsevier Science Ltd. All rights reserved.
Several relevant optimization problems can be formulated as generalizations of Capacitated Covering Problems, by considering a cost function that combines a linear term with a nonlinear one. In this paper we introduce...
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Several relevant optimization problems can be formulated as generalizations of Capacitated Covering Problems, by considering a cost function that combines a linear term with a nonlinear one. In this paper we introduce the Staircase Capacitated Covering Problem, where the nonlinear term has a staircase shape, and we propose a framework based on a Metaheuristic algorithm for solving problems having this formulation. The performance of the Metaheuristic algorithm in solving the Staircase Capacitated Covering Problem is evaluated on a set of instances derived from an industrial application, and it is compared with a linearized formulation of the problem solved by CPLEX. In particular, the experiments show that the former produces better solutions in the same computing time.
In this paper, a multi-buyer multi-vendor supply chain problem is considered in which there are several products, each buyer has limited capacity to purchase products, and each vendor has warehouse limitation to store...
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In this paper, a multi-buyer multi-vendor supply chain problem is considered in which there are several products, each buyer has limited capacity to purchase products, and each vendor has warehouse limitation to store products. In this chain, the demand of each product is stochastic and follows a uniform distribution. The lead-time of receiving products from a vendor to a buyer is assumed to vary linearly with respect to the order quantity of the buyer and the production rate of the vendor. For each product, a fraction of the shortage is backordered and the rest are lost. The ordered product quantities are placed in multiple of pre-defined packets and there are service rate constraints for the buyers. The goal is to determine the reorder points, the safety stocks, and the numbers of shipments and packets in each shipment of the products such that the total cost of the supply chain is minimized. We show that the model of this problem is of an integer nonlinear programming type and in order to solve it a harmony search algorithm is employed. To validate the solution and to compare the performance of the proposed algorithm, a genetic algorithm is utilized as well. A numerical illustration and sensitivity analysis are given at the end to show the applicability of the proposed methodology in real-world supply chain problems. (C) 2011 Elsevier Inc. All rights reserved.
Assuming that maximum tolerable posterior risks are specified for both producer and consumer, an integer nonlinear programming problem is formulated and solved in order to determine the optimal defects-per-unit accept...
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Assuming that maximum tolerable posterior risks are specified for both producer and consumer, an integer nonlinear programming problem is formulated and solved in order to determine the optimal defects-per-unit acceptance sampling plan when lots found unacceptable may be resubmitted for reinspection. The number of nonconformities per inspected item follows a Poisson distribution. A computational algorithm is proposed to solve the underlying constrained minimization problem. The suggested procedure simplifies and quickens the determination of the inspection scheme for resubmitted lot acceptance with limited posterior risks that minimizes the expected number of sampled items per lot. An application to the manufacturing of paper is considered to illustrate the methodology developed. The generalized truncated gamma distribution is used to describe the prior uncertainty about the incoming defect rate per unit. The degree of similarity between the available previous information and the current study is 'also evaluated. Suitable ways are provided to assume a reduced parameter space for the defect rate and to update the prior model using past performance of the acceptance plan. The incorporation of lot resubmissions, as well as previous defect count data and expert opinions, into the decision process often yields appreciable savings in sampling effort. (C) 2016 Elsevier Inc. All rights reserved.
Today's growth in the volume of wireless devices coupled with the demand for data-intensive use cases has motivated the deployment of millimeter-wave (mmWave) small-cell networks. Although it is true that mmWave n...
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Today's growth in the volume of wireless devices coupled with the demand for data-intensive use cases has motivated the deployment of millimeter-wave (mmWave) small-cell networks. Although it is true that mmWave networks can carry a large volume of traffic, highly intermittent connectivity and the challenges related to installing many small-cell base stations (BSs) in urban geometry have impeded its progression into practical networks. To cope with these challenges, we present, in this paper, an approach to the mmWave BS deployment (site planning) problem, based on the minimum-deployment-cost criterion that is subject to user equipment (UE) outage constraints. Unlike the prior works, the proposed model captures the randomness of link blockage and signal-to-interference-plus-noise-ratio (SINR) statistics in mmWave networks. We formulate the minimum-cost deployment problem as large-scale integer nonlinear programming (INP). To deal with the coupled and combinatorial of the problem, the large-scale INP has approached to devise a suboptimal but efficient algorithm by decomposing it into two subproblems: (i) cell coverage optimization and (ii) minimum subset selection. We provide the solutions to each subproblem as well as theoretical justifications of them. Simulation results that illustrate UE outage guarantees of the proposed BS deployment method are presented. The results reveal that the proposed method uniquely distributes the macro-diversity orders that are distinct from other benchmarks.
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