This paper focuses on the train service design problem within a given period in an urban rail transit line, where multiple either full-length or short-turn service routes can be operated, and each service route can ut...
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This paper focuses on the train service design problem within a given period in an urban rail transit line, where multiple either full-length or short-turn service routes can be operated, and each service route can utilize one of several different train compositions. The problem lies on determining the turn-back stations, train composition and frequency of each service route operated on the line. Considering the interests of operators and passengers, we decompose the problem as two subproblems namely train service configuration and passenger assignment. The first subproblem is formulated as an integer linear programming model with the objective of minimizing operators' cost. Given a train service scheme, the second subproblem is modelled as a capacitated continuous multi-commodity flow model to minimize passengers' waiting time cost and transfer cost. The optimal strategy is extended to determine the behaviour of passengers and capture the extra waiting time of passengers under capacity constraint. The two sub-models are weighted and integrated into a mixed integer nonlinear programming model, which is further transformed into a mixed integer linear programming model using a novel linearization method. By exploiting the special characteristics of the model, a tailored and easy to implement local search algorithm is developed to solve large-scale instances. Starting from the operator-optimum solution which can be easily obtained, the algorithm solves the two sub-models iteratively to search better solutions within a precalculated search range which is smaller than the complete feasible domain. Finally, different sizes of instances constructed from two urban rail transit lines are utilized to demonstrate the performance and practicability of the proposed approaches.
The paper proves that data-independent neighborhood functions with the smooth property (all strict local optima are global optima) for maximum 3-satisfiability (MAX 3-SAT) must contain all possible solutions for large...
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The paper proves that data-independent neighborhood functions with the smooth property (all strict local optima are global optima) for maximum 3-satisfiability (MAX 3-SAT) must contain all possible solutions for large instances. Data-independent neighborhood functions with the smooth property for 0-1 knapsack are shown to have size with the same order of magnitude as the cardinality of the solution space. Data-independent neighborhood functions with the smooth property for traveling salesman problem (TSP) are shown to have exponential size. These results also hold for certain polynomially solvable sub-problems of MAX 3-SAT, 0-1 knapsack and TSP. (C) 2004 Elsevier B.V. All rights reserved.
A new circle and ellipse detector is presented in this paper. The proposed method adopts a hybrid scheme which consists of a genetic algorithm (GA) phase and a localsearch phase. In the GA phase, an efficient fitness...
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A new circle and ellipse detector is presented in this paper. The proposed method adopts a hybrid scheme which consists of a genetic algorithm (GA) phase and a localsearch phase. In the GA phase, an efficient fitness evaluation procedure and specific genetic operators are proposed for this application. The candidates with fitness values above a threshold are added into a candidate list. In the localsearch phase, the members of the candidate list are locally improved and their fitness values fall into two groups. The candidates belonging to the group with higher fitness values are output. The experimental results show that the proposed method can detect circles and ellipses correctly, and the computation and storage cost is very little. (C) 1999 Elsevier Science B.V. All rights reserved.
作者：
Bernardino, RaquelPaias, AnaUniv Lisbon
Fac Ciencias DEIO C6Piso 4 P-1749016 Lisbon Portugal Univ Lisbon
Fac Ciencias Ctr Matemat Aplicacoes Fundamentais & Invest Oper C6Piso 1 P-1749016 Lisbon Portugal
In this article, we address the family traveling salesman problem (FTSP), an NP-hard problem that may be seen as a generalization of the traveling salesman problem. In the FTSP, the set of cities is partitioned into s...
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In this article, we address the family traveling salesman problem (FTSP), an NP-hard problem that may be seen as a generalization of the traveling salesman problem. In the FTSP, the set of cities is partitioned into several families and one wants to find the minimum cost route that visits a given number of cities in each family. We propose two metaheuristics, a population-based method and a localsearch method, and a hybrid algorithm, which combines a branch-and-cut algorithm with a localsearch procedure. All the proposed methods improve the best known upper bounds from the literature. The localsearch method and the hybrid algorithm improve the best known upper bounds for all the benchmark instances with unknown optimal value, while the population-based method improves the best known upper bounds for all instances, except two. Furthermore, we developed an instance generator to create additional test instances with different visit patterns. These newly generated instances were considered in the computational experiment and, thus, we provide optimal values or upper bounds for each instance. Additionally, we created a website dedicated to the FTSP, where this information is made available to the scientific community ().
This article explains a battery-based control method to reduce the photovoltaic output power fluctuations, which, in turn, will reduce the frequency deviations of the power system. The battery control method introduce...
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This article explains a battery-based control method to reduce the photovoltaic output power fluctuations, which, in turn, will reduce the frequency deviations of the power system. The battery control method introduced here will maintain the state of charge near 50%. Therefore, it will increase the lifetime of the battery, as well as decreasing the maintenance cost. A local search algorithm is provided with this battery control method to search the optimal capacity of the battery required for smoothing the photovoltaic output power fluctuations. Then, a cost comparison is provided to realize the performance of the searchalgorithm. To verify the effectiveness of the proposed method, simulation results are presented using the actual insolation and load data.
This paper is concerned with automated classification of Combinatorial Optimization Problem instances for instance-specific parameter tuning purpose. We propose the CluPaTra Framework, a generic approach to CLUster in...
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This paper is concerned with automated classification of Combinatorial Optimization Problem instances for instance-specific parameter tuning purpose. We propose the CluPaTra Framework, a generic approach to CLUster instances based on similar PAtterns according to search TRAjectories and apply it on parameter tuning. The key idea is to use the search trajectory as a generic feature for clustering problem instances. The advantage of using search trajectory is that it can be obtained from any local-search based algorithm with small additional computation time. We explore and compare two different search trajectory representations, two sequence alignment techniques (to calculate similarities) as well as two well-known clustering methods. We report experiment results on two classical problems: Travelling Salesman Problem and Quadratic Assignment Problem and industrial case study.
During the urban distribution process, unexpected events may frequently result in disruptions to the current distribution plan, which need to be handled in real-time vehicle routing. In this paper, a knowledge-based m...
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During the urban distribution process, unexpected events may frequently result in disruptions to the current distribution plan, which need to be handled in real-time vehicle routing. In this paper, a knowledge-based modeling approach, PAM (disruption-handling Policies, local search algorithms and object-oriented Modeling), is developed, which combines the scheduling knowledge of experienced schedulers with the optimization knowledge concerning models and algorithms in the field of Operations Research to obtain an effective solution in real time. Experienced schedulers can respond to different disruptions promptly with heuristic adjustment based on their experience, but their solutions may be inaccurate, inconsistent, or even infeasible. This method is limited when the problem becomes large-scale. The model-algorithm method can handle large-scale problems, but it has to predefine a specific disruption and a specific distribution state for constructing a model and algorithm, which is inflexible, time-consuming and consequently unable to promptly obtain solutions for responding to different disruptions in real time. PAM modeling approach combines the advantages and eliminates the disadvantages of the two methods aforementioned. Computational experiments show that solutions achieved by this modeling approach are practical and the speed of achieving the solutions is fast enough for responding to disruptions in real time. (C) 2012 Elsevier B.V. All rights reserved.
We consider mathematical programming problems with the so-called piecewise convex objective functions. A solution method for this interesting and important class of nonconvex problems is presented. This method is base...
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We consider mathematical programming problems with the so-called piecewise convex objective functions. A solution method for this interesting and important class of nonconvex problems is presented. This method is based on Newton's law of universal gravitation, multicriteria optimization and Helly's theorem on convex bodies. Numerical experiments using well known classes of test problems on piecewise convex maximization, convex maximization as well as the maximum clique problem show the efficiency of the approach.
Background: Native structures of proteins are formed essentially due to the combining effects of local and distant (in the sense of sequence) interactions among residues. These interaction information are, explicitly ...
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Background: Native structures of proteins are formed essentially due to the combining effects of local and distant (in the sense of sequence) interactions among residues. These interaction information are, explicitly or implicitly, encoded into the scoring function in protein structure prediction approaches-threading approaches usually measure an alignment in the sense that how well a sequence adopts an existing structure;while the energy functions in Ab Initio methods are designed to measure how likely a conformation is near-native. Encouraging progress has been observed in structure refinement where knowledge-based or physics-based potentials are designed to capture distant interactions. Thus, it is interesting to investigate whether distant interaction information captured by the Ab Initio energy function can be used to improve threading, especially for the weakly/distant homologous templates. Results: In this paper, we investigate the possibility to improve alignment-generating through incorporating distant interaction information into the alignment scoring function in a nontrivial approach. Specifically, the distant interaction information is introduced through employing an Ab Initio energy function to evaluate the "partial" decoy built from an alignment. Subsequently, a local search algorithm is utilized to optimize the scoring function. Experimental results demonstrate that with distant interaction items, the quality of generated alignments are improved on 68 out of 127 query-template pairs in Prosup benchmark. In addition, compared with state-to-art threading methods, our method performs better on alignment accuracy comparison. Conclusions: Incorporating Ab Initio energy functions into threading can greatly improve alignment accuracy.
In this article we provide an algorithm, where to escape from a local maximum y of convex function f over D, we (locally) solve piecewise convex maximization max{min{f (x) - f (y), p (y) (x)} | x is an element of D} w...
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In this article we provide an algorithm, where to escape from a local maximum y of convex function f over D, we (locally) solve piecewise convex maximization max{min{f (x) - f (y), p (y) (x)} | x is an element of D} with an additional convex function p (y) (center dot). The last problem can be seen as a strictly convex improvement of the standard cutting plane technique for convex maximization. We report some computational results, that show the algorithm efficiency.
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