Showing papers in "Computers & Operations Research in 2005"

TL;DR: This paper investigates the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index and proposes a combining model by integrating SVM with the other classification methods.

TL;DR: The purpose of this paper is to give an overview of all recent exact solution approaches, and to show that the knapsack problem still is hard to solve for these algorithms for a variety of new test problems.

TL;DR: This work hybridizes the solution construction mechanism of ACO with beam search, which is a well-known tree search method, and calls this approach Beam-ACO.

TL;DR: Two data mining methodologies-Artificial Neural Networks and Support Vector Machine and two encoding methods-simple frequency-based scheme and tfi?idf scheme are used to detect potential system intrusions in this study.

TL;DR: The generalization ability of the neural network ensemble is found to be superior to the single best model for three real world financial decision applications.

TL;DR: This research shows that as the uncertainty in the travel time information increases, a dynamic routing strategy that takes the real-time traffic information into account becomes increasingly superior to a static one.

TL;DR: This paper presents problem instances with as many as 1200 customers along with estimated solutions and introduces the variable-length neighbor list as a tool to reduce the number of unproductive computations.

TL;DR: Network design problems concern the selection of arcs in a graph in order to satisfy, at minimum cost, some flow requirements, usually expressed in the form of origin-destination pair demands.

TL;DR: A computerized nurse-scheduling model is developed that is adapted to Riyadh Al-Kharj hospital Program (in Saudi Arabia) to improve the current manual-made schedules and is considered to perform reasonably well.

TL;DR: The findings reveal that the nonlinear ensemble model proposed here can be used as an alternative forecasting tool for exchange rates to achieve greater forecasting accuracy and improve prediction quality further.

TL;DR: Results from computational experiments show that the proposed tabu search heuristic consistently produces high-quality solutions and outperforms other published heuristics tbr the TOP.

TL;DR: This paper establishes an economic production quantity (or EPQ) model for deteriorating items when the demand rate depends not only the on-display stock level but also the selling price per unit.

TL;DR: A new and effective metaheuristic algorithm, active guided evolution strategies, for the vehicle routing problem with time windows, which combines the strengths of the well-known guided local search and evolution strategies metaheuristics into an iterative two-stage procedure.

TL;DR: This paper surveys applications of combinatorial optimization to multicast routing and discusses the most important problems considered in this area, as well as their models.

TL;DR: In this paper, a preference aggregation method is developed for ranking alternative courses of actions by combining preference rankings of alternatives given on individual criteria or by individual decision makers.

TL;DR: This study uses artificial neural networks to predict stock price movement for firms traded on the Shanghai stock exchange, and shows that neural networks outperform the linear models compared.

TL;DR: A multicriteria approach for combining prioritization methods within the analytic hierarchy process (AHP) is proposed, with the leading assumption that for each particular decision problem and related hierarchy, AHP must not necessarily employ only one prioritization method.

TL;DR: This research attempts to minimize total weighted tardiness on parallel batch machines with incompatible job families and unequal ready times of the jobs by developing variations of a time window heuristic based on a decision theory approach for forming and sequencing the batches on a single machine.

TL;DR: The results obtained on an extended set of test problems show that the parallel procedure achieves linear accelerations and identifies solutions of comparable quality to those obtained by the best methods in the literature.

TL;DR: The GRA model can be incorporated into the NN model to help designers focus on the most influential elements in form design of mobile phones, suggesting that theN model should be used to help product designers determine the best combination of form elements for achieving a desirable product image.

TL;DR: This paper presents a multi-period inventory lot-sizing scenario, where there are multiple products and multiple suppliers, and an enumerative search algorithm and a heuristic are presented to address the problem.

TL;DR: An auxiliary network is defined and the LP solution to the considered model provides an initial lower bound and is also used in a rounding procedure that provides the initial solution for a Tabu search heuristic.

TL;DR: This problem is formulated as a non-linear 0-1 programming model in which the distance between the machines is sequence dependent, and a technique is proposed to efficiently implement the proposed algorithm.

TL;DR: The experimental work demonstrates that even for larger problems the results of the GA-based method significantly surpass those of the related work with respect to both solution quality and the CPU time to obtain a solution.

TL;DR: In this paper, the authors proposed an enhanced model incorporating robustness to handle these linking constraints and compared two Benders decomposition methods-one with the aircraft routing problem as the master problem and one with the crew pairing problem.

TL;DR: This paper develops and compares different local search algorithms for the no-wait flow-shop problem with makespan criterion (Cmax) and presents several variants of descending search and tabu search algorithms that are relatively more effective in finding better quality solutions than existing algorithms.

TL;DR: This work first linearizes the model, and then provides a Lagrangean heuristic that finds high-quality solutions within reasonable computational time that provides new and realistic insights into the hub-and-spoke network design problem.

TL;DR: A heuristic for solving large problem instances is developed and is more effective when there are many more products than there are planning periods, and the average deviation of the heuristic solution from the corresponding exact solution depends on the size of the problem.

TL;DR: An approach based on linear programming that estimates the weights for a pairwise comparison matrix generated within the framework of the analytic hierarchy process makes sense for a number of reasons, which are discussed.

TL;DR: It is illustrated that the proposed TV-ANN fares well, both for training and holdout samples, when compared to the traditional backpropagation artificial neural network (ANN) and the statistical linear discriminant analysis.