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Algorithms, Volume 11, Issue 4 (April 2018)

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Open AccessArticle A Heuristic Approach to Solving the Train Traffic Re-Scheduling Problem in Real Time
Algorithms 2018, 11(4), 55; https://doi.org/10.3390/a11040055
Received: 28 February 2018 / Revised: 11 April 2018 / Accepted: 12 April 2018 / Published: 21 April 2018
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Abstract
Effectiveness in managing disturbances and disruptions in railway traffic networks, when they inevitably do occur, is a significant challenge, both from a practical and theoretical perspective. In this paper, we propose a heuristic approach for solving the real-time train traffic re-scheduling problem. This
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Effectiveness in managing disturbances and disruptions in railway traffic networks, when they inevitably do occur, is a significant challenge, both from a practical and theoretical perspective. In this paper, we propose a heuristic approach for solving the real-time train traffic re-scheduling problem. This problem is here interpreted as a blocking job-shop scheduling problem, and a hybrid of the mixed graph and alternative graph is used for modelling the infrastructure and traffic dynamics on a mesoscopic level. A heuristic algorithm is developed and applied to resolve the conflicts by re-timing, re-ordering, and locally re-routing the trains. A part of the Southern Swedish railway network from Karlskrona centre to Malmö city is considered for an experimental performance assessment of the approach. The network consists of 290 block sections, and for a one-hour time horizon with around 80 active trains, the algorithm generates a solution in less than ten seconds. A benchmark with the corresponding mixed-integer program formulation, solved by commercial state-of-the-art solver Gurobi, is also conducted to assess the optimality of the generated solutions. Full article
(This article belongs to the Special Issue Algorithms for Scheduling Problems) Printed Edition available
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Open AccessArticle Dual Market Facility Network Design under Bounded Rationality
Algorithms 2018, 11(4), 54; https://doi.org/10.3390/a11040054
Received: 18 February 2018 / Revised: 12 April 2018 / Accepted: 16 April 2018 / Published: 20 April 2018
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Abstract
A number of markets, geographically separated, with different demand characteristics for different products that share a common component, are analyzed. This common component can either be manufactured locally in each of the markets or transported between the markets to fulfill the demand. However,
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A number of markets, geographically separated, with different demand characteristics for different products that share a common component, are analyzed. This common component can either be manufactured locally in each of the markets or transported between the markets to fulfill the demand. However, final assemblies are localized to the respective markets. The decision making challenge is whether to manufacture the common component centrally or locally. To formulate the underlying setting, a newsvendor modeling based approach is considered. The developed model is solved using Frank-Wolfe linearization technique along with Benders’ decomposition method. Further, the propensity of decision makers in each market to make suboptimal decisions leading to bounded rationality is considered. The results obtained for both the cases are compared. Full article
(This article belongs to the Special Issue Algorithms for Scheduling Problems) Printed Edition available
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Open AccessArticle Linking and Cutting Spanning Trees
Algorithms 2018, 11(4), 53; https://doi.org/10.3390/a11040053
Received: 12 March 2018 / Revised: 11 April 2018 / Accepted: 11 April 2018 / Published: 19 April 2018
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Abstract
We consider the problem of uniformly generating a spanning tree for an undirected connected graph. This process is useful for computing statistics, namely for phylogenetic trees. We describe a Markov chain for producing these trees. For cycle graphs, we prove that this approach
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We consider the problem of uniformly generating a spanning tree for an undirected connected graph. This process is useful for computing statistics, namely for phylogenetic trees. We describe a Markov chain for producing these trees. For cycle graphs, we prove that this approach significantly outperforms existing algorithms. For general graphs, experimental results show that the chain converges quickly. This yields an efficient algorithm due to the use of proper fast data structures. To obtain the mixing time of the chain we describe a coupling, which we analyze for cycle graphs and simulate for other graphs. Full article
(This article belongs to the Special Issue Efficient Data Structures)
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Open AccessArticle Introduction to Reconfiguration
Algorithms 2018, 11(4), 52; https://doi.org/10.3390/a11040052
Received: 13 September 2017 / Revised: 17 April 2018 / Accepted: 17 April 2018 / Published: 19 April 2018
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Abstract
Reconfiguration is concerned with relationships among solutions to a problem instance, where the reconfiguration of one solution to another is a sequence of steps such that each step produces an intermediate feasible solution. The solution space can be represented as a reconfiguration graph
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Reconfiguration is concerned with relationships among solutions to a problem instance, where the reconfiguration of one solution to another is a sequence of steps such that each step produces an intermediate feasible solution. The solution space can be represented as a reconfiguration graph, where two vertices representing solutions are adjacent if one can be formed from the other in a single step. Work in the area encompasses both structural questions (Is the reconfiguration graph connected?) and algorithmic ones (How can one find the shortest sequence of steps between two solutions?) This survey discusses techniques, results, and future directions in the area. Full article
(This article belongs to the Special Issue Reconfiguration Problems)
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Open AccessArticle A Crowd Cooperative Spectrum Sensing Algorithm Using a Non-Ideal Channel
Algorithms 2018, 11(4), 51; https://doi.org/10.3390/a11040051
Received: 20 March 2018 / Revised: 13 April 2018 / Accepted: 16 April 2018 / Published: 18 April 2018
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Abstract
Spectrum sensing is the prerequisite of the realization of cognitive radio. So it is a significant part of cognitive radio. In order to stimulate the SUs to sense the spectrum, we combine the incentive mechanism of crowd-sensing with cooperative spectrum sensing effectively, and
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Spectrum sensing is the prerequisite of the realization of cognitive radio. So it is a significant part of cognitive radio. In order to stimulate the SUs to sense the spectrum, we combine the incentive mechanism of crowd-sensing with cooperative spectrum sensing effectively, and put forward a crowd cooperative spectrum sensing algorithm with optimal utility of secondary users (SUs) under non-ideal channel which we define SUs’ utility expectation functions related to rewards, sensing time and transmission power. Then, we construct the optimization problem of maximizing the utilities of SUs by optimizing the sensing time and the transmission power, and prove that this problem is a convex optimization problem. The optimal sensing time and transmission power are obtained by using the Karush-Kuhn-Tucker (KKT) conditions. The numerical simulation results show that the spectrum detection performance of algorithm, which we put forward, is improved. Full article
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Open AccessArticle Evaluating Typical Algorithms of Combinatorial Optimization to Solve Continuous-Time Based Scheduling Problem
Algorithms 2018, 11(4), 50; https://doi.org/10.3390/a11040050
Received: 22 February 2018 / Revised: 11 April 2018 / Accepted: 12 April 2018 / Published: 17 April 2018
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Abstract
We consider one approach to formalize the Resource-Constrained Project Scheduling Problem (RCPSP) in terms of combinatorial optimization theory. The transformation of the original problem into combinatorial setting is based on interpreting each operation as an atomic entity that has a defined duration and
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We consider one approach to formalize the Resource-Constrained Project Scheduling Problem (RCPSP) in terms of combinatorial optimization theory. The transformation of the original problem into combinatorial setting is based on interpreting each operation as an atomic entity that has a defined duration and has to be resided on the continuous time axis meeting additional restrictions. The simplest case of continuous-time scheduling assumes one-to-one correspondence of resources and operations and corresponds to the linear programming problem setting. However, real scheduling problems include many-to-one relations which leads to the additional combinatorial component in the formulation due to operations competition. We research how to apply several typical algorithms to solve the resulted combinatorial optimization problem: enumeration including branch-and-bound method, gradient algorithm, random search technique. Full article
(This article belongs to the Special Issue Algorithms for Scheduling Problems) Printed Edition available
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Open AccessArticle Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
Algorithms 2018, 11(4), 49; https://doi.org/10.3390/a11040049
Received: 9 March 2018 / Revised: 10 April 2018 / Accepted: 11 April 2018 / Published: 17 April 2018
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Abstract
With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the
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With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station. Full article
(This article belongs to the Special Issue Advanced Artificial Neural Networks)
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Open AccessArticle An Approach for Setting Parameters for Two-Degree-of-Freedom PID Controllers
Algorithms 2018, 11(4), 48; https://doi.org/10.3390/a11040048
Received: 19 March 2018 / Revised: 5 April 2018 / Accepted: 6 April 2018 / Published: 13 April 2018
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Abstract
In this paper, a new tuning method is proposed, based on the desired dynamics equation (DDE) and the generalized frequency method (GFM), for a two-degree-of-freedom proportional-integral-derivative (PID) controller. The DDE method builds a quantitative relationship between the performance and the two-degree-of-freedom PID controller
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In this paper, a new tuning method is proposed, based on the desired dynamics equation (DDE) and the generalized frequency method (GFM), for a two-degree-of-freedom proportional-integral-derivative (PID) controller. The DDE method builds a quantitative relationship between the performance and the two-degree-of-freedom PID controller parameters and guarantees the desired dynamic, but it cannot guarantee the stability margin. So, we have developed the proposed tuning method, which guarantees not only the desired dynamic but also the stability margin. Based on the DDE and the GFM, several simple formulas are deduced to calculate directly the controller parameters. In addition, it performs almost no overshooting setpoint response. Compared with Panagopoulos’ method, the proposed methodology is proven to be effective. Full article
(This article belongs to the Special Issue Algorithms for PID Controller)
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Open AccessArticle A Novel Dynamic Generalized Opposition-Based Grey Wolf Optimization Algorithm
Algorithms 2018, 11(4), 47; https://doi.org/10.3390/a11040047
Received: 12 March 2018 / Revised: 9 April 2018 / Accepted: 9 April 2018 / Published: 13 April 2018
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Abstract
To enhance the convergence speed and calculation precision of the grey wolf optimization algorithm (GWO), this paper proposes a dynamic generalized opposition-based grey wolf optimization algorithm (DOGWO). A dynamic generalized opposition-based learning strategy enhances the diversity of search populations and increases the potential
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To enhance the convergence speed and calculation precision of the grey wolf optimization algorithm (GWO), this paper proposes a dynamic generalized opposition-based grey wolf optimization algorithm (DOGWO). A dynamic generalized opposition-based learning strategy enhances the diversity of search populations and increases the potential of finding better solutions which can accelerate the convergence speed, improve the calculation precision, and avoid local optima to some extent. Furthermore, 23 benchmark functions were employed to evaluate the DOGWO algorithm. Experimental results show that the proposed DOGWO algorithm could provide very competitive results compared with other analyzed algorithms, with a faster convergence speed, higher calculation precision, and stronger stability. Full article
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Open AccessArticle Short-Run Contexts and Imperfect Testing for Continuous Sampling Plans
Algorithms 2018, 11(4), 46; https://doi.org/10.3390/a11040046
Received: 8 February 2018 / Revised: 5 April 2018 / Accepted: 7 April 2018 / Published: 12 April 2018
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Abstract
Continuous sampling plans are used to ensure a high level of quality for items produced in long-run contexts. The basic idea of these plans is to alternate between 100% inspection and a reduced rate of inspection frequency. Any inspected item that is found
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Continuous sampling plans are used to ensure a high level of quality for items produced in long-run contexts. The basic idea of these plans is to alternate between 100% inspection and a reduced rate of inspection frequency. Any inspected item that is found to be defective is replaced with a non-defective item. Because not all items are inspected, some defective items will escape to the customer. Analytical formulas have been developed that measure both the customer perceived quality and also the level of inspection effort. The analysis of continuous sampling plans does not apply to short-run contexts, where only a finite-size batch of items is to be produced. In this paper, a simulation algorithm is designed and implemented to analyze the customer perceived quality and the level of inspection effort for short-run contexts. A parameter representing the effectiveness of the test used during inspection is introduced to the analysis, and an analytical approximation is discussed. An application of the simulation algorithm that helped answer questions for the U.S. Navy is discussed. Full article
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Open AccessArticle Approximation Algorithms for the Geometric Firefighter and Budget Fence Problems
Algorithms 2018, 11(4), 45; https://doi.org/10.3390/a11040045
Received: 6 March 2018 / Revised: 3 April 2018 / Accepted: 10 April 2018 / Published: 11 April 2018
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Abstract
Let R denote a connected region inside a simple polygon, P. By building barriers (typically straight-line segments) in P\R, we want to separate from R part(s) of P of maximum area. All edges of the boundary of P are
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Let R denote a connected region inside a simple polygon, P. By building barriers (typically straight-line segments) in P \ R , we want to separate from R part(s) of P of maximum area. All edges of the boundary of P are assumed to be already constructed or natural barriers. In this paper we introduce two versions of this problem. In the budget fence version the region R is static, and there is an upper bound on the total length of barriers we may build. In the basic geometric firefighter version we assume that R represents a fire that is spreading over P at constant speed (varying speed can also be handled). Building a barrier takes time proportional to its length, and each barrier must be completed before the fire arrives. In this paper we are assuming that barriers are chosen from a given set B that satisfies certain conditions. Even for simple cases (e.g., P is a convex polygon and B the set of all diagonals), both problems are shown to be NP-hard. Our main result is an efficient ≈11.65 approximation algorithm for the firefighter problem, where the set B of allowed barriers is any set of straight-line segments with all endpoints on the boundary of P and pairwise disjoint interiors. Since this algorithm solves a much more general problem—a hybrid of scheduling and maximum coverage—it may find wider applications. We also provide a polynomial-time approximation scheme for the budget fence problem, for the case where barriers chosen from a set of straight-line cuts of the polygon must not cross. Full article
(This article belongs to the Special Issue Algorithms for Hard Problems: Approximation and Parameterization)
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Open AccessArticle Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
Algorithms 2018, 11(4), 44; https://doi.org/10.3390/a11040044
Received: 24 January 2018 / Revised: 21 March 2018 / Accepted: 5 April 2018 / Published: 9 April 2018
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Abstract
The A* algorithm has been widely investigated and applied in path planning problems, but it does not fully consider the safety and smoothness of the path. Therefore, an improved A* algorithm is presented in this paper. Firstly, a new environment modeling method is
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The A* algorithm has been widely investigated and applied in path planning problems, but it does not fully consider the safety and smoothness of the path. Therefore, an improved A* algorithm is presented in this paper. Firstly, a new environment modeling method is proposed in which the evaluation function of A* algorithm is improved by taking the safety cost into account. This results in a safer path which can stay farther away from obstacles. Then a new path smoothing method is proposed, which introduces a path evaluation mechanism into the smoothing process. This method is then applied to smoothing the path without safety reduction. Secondly, with respect to path planning problems in complex terrains, a complex terrain environment model is established in which the distance and safety cost of the evaluation function of the A* algorithm are converted into time cost. This results in a unification of units as well as a clarity in their physical meanings. The simulation results show that the improved A* algorithm can greatly improve the safety and smoothness of the planned path and the movement time of the robot in complex terrain is greatly reduced. Full article
(This article belongs to the Special Issue Metaheuristics for Rich Vehicle Routing Problems)
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Open AccessArticle Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling
Algorithms 2018, 11(4), 43; https://doi.org/10.3390/a11040043
Received: 28 February 2018 / Revised: 2 April 2018 / Accepted: 4 April 2018 / Published: 6 April 2018
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Abstract
The number of just-in-time jobs maximization in a permutation flow shop scheduling problem is considered. A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with
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The number of just-in-time jobs maximization in a permutation flow shop scheduling problem is considered. A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with up to 10 jobs and five machines are solved by the mathematical model in an acceptable running time (3.3 min on average) while the enumeration method consumes, on average, 1.5 s. The 10 constructive heuristics proposed show they are practical especially for large-scale instances (up to 100 jobs and 20 machines), with very good-quality results and efficient running times. The best two heuristics obtain near-optimal solutions, with only 0.6% and 0.8% average relative deviations. They prove to be better than adaptations of the NEH heuristic (well-known for providing very good solutions for makespan minimization in flow shop) for the considered problem. Full article
(This article belongs to the Special Issue Algorithms for Scheduling Problems) Printed Edition available
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Open AccessArticle Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method
Algorithms 2018, 11(4), 42; https://doi.org/10.3390/a11040042
Received: 28 February 2018 / Revised: 2 April 2018 / Accepted: 3 April 2018 / Published: 4 April 2018
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Abstract
The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning
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The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order) spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE) method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them. Full article
(This article belongs to the Special Issue Monte Carlo Methods and Algorithms)
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Open AccessArticle A Distributed Indexing Method for Timeline Similarity Query
Algorithms 2018, 11(4), 41; https://doi.org/10.3390/a11040041
Received: 10 February 2018 / Revised: 27 March 2018 / Accepted: 29 March 2018 / Published: 30 March 2018
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Abstract
Timelines have been used for centuries and have become more and more widely used with the development of social media in recent years. Every day, various smart phones and other instruments on the internet of things generate massive data related to time. Most
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Timelines have been used for centuries and have become more and more widely used with the development of social media in recent years. Every day, various smart phones and other instruments on the internet of things generate massive data related to time. Most of these data can be managed in the way of timelines. However, it is still a challenge to effectively and efficiently store, query, and process big timeline data, especially the instant recommendation based on timeline similarities. Most existing studies have focused on indexing spatial and interval datasets rather than the timeline dataset. In addition, many of them are designed for a centralized system. A timeline index structure adapting to parallel and distributed computation framework is in urgent need. In this research, we have defined the timeline similarity query and developed a novel timeline index in the distributed system, called the Distributed Triangle Increment Tree (DTI-Tree), to support the similarity query. The DTI-Tree consists of one T-Tree and one or more TI-Trees based on a triangle increment partition strategy with the Apache Spark. Furthermore, we have provided an open source timeline benchmark data generator, named TimelineGenerator, to generate various timeline test datasets for different conditions. The experiments for DTI-Tree’s construction, insertion, deletion, and similarity queries have been executed on a cluster with two benchmark datasets that are generated by TimelineGenerator. The experimental results show that the DTI-tree provides an effective and efficient distributed index solution to big timeline data. Full article
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Open AccessFeature PaperArticle Connectivity and Hamiltonicity of Canonical Colouring Graphs of Bipartite and Complete Multipartite Graphs
Algorithms 2018, 11(4), 40; https://doi.org/10.3390/a11040040
Received: 12 February 2018 / Revised: 23 March 2018 / Accepted: 24 March 2018 / Published: 29 March 2018
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Abstract
A k-colouring of a graph G with colours 1,2,,k is canonical with respect to an ordering π=v1,v2,,vn of the vertices of G if adjacent vertices
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A k-colouring of a graph G with colours 1 , 2 , , k is canonical with respect to an ordering π = v 1 , v 2 , , v n of the vertices of G if adjacent vertices are assigned different colours and, for 1 c k , whenever colour c is assigned to a vertex v i , each colour less than c has been assigned to a vertex that precedes v i in π . The canonical k-colouring graph of G with respect to π is the graph Can k π ( G ) with vertex set equal to the set of canonical k-colourings of G with respect to π , with two of these being adjacent if and only if they differ in the colour assigned to exactly one vertex. Connectivity and Hamiltonicity of canonical colouring graphs of bipartite and complete multipartite graphs is studied. It is shown that for complete multipartite graphs, and bipartite graphs there exists a vertex ordering π such that Can k π ( G ) is connected for large enough values of k. It is proved that a canonical colouring graph of a complete multipartite graph usually does not have a Hamilton cycle, and that there exists a vertex ordering π such that Can k π ( K m , n ) has a Hamilton path for all k 3 . The paper concludes with a detailed consideration of Can k π ( K 2 , 2 , , 2 ) . For each k χ and all vertex orderings π , it is proved that Can k π ( K 2 , 2 , , 2 ) is either disconnected or isomorphic to a particular tree. Full article
(This article belongs to the Special Issue Reconfiguration Problems)
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Open AccessArticle On Hierarchical Text Language-Identification Algorithms
Algorithms 2018, 11(4), 39; https://doi.org/10.3390/a11040039
Received: 7 February 2018 / Revised: 23 March 2018 / Accepted: 23 March 2018 / Published: 27 March 2018
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Abstract
Text on the Internet is written in different languages and scripts that can be divided into different language groups. Most of the errors in language identification occur with similar languages. To improve the performance of short-text language identification, we propose four different levels
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Text on the Internet is written in different languages and scripts that can be divided into different language groups. Most of the errors in language identification occur with similar languages. To improve the performance of short-text language identification, we propose four different levels of hierarchical language identification methods and conducted comparative tests in this paper. The efficiency of the algorithms was evaluated on sentences from 97 languages, and its macro-averaged F1-score reached in four-stage language identification was 0.9799. The experimental results verified that, after script identification, language group identification and similar language group identification, the performance of the language identification algorithm improved with each stage. Notably, the language identification accuracy between similar languages improved substantially. We also investigated how foreign content in a language affects language identification. Full article
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Open AccessArticle Combinatorial GVNS (General Variable Neighborhood Search) Optimization for Dynamic Garbage Collection
Algorithms 2018, 11(4), 38; https://doi.org/10.3390/a11040038
Received: 20 November 2017 / Revised: 7 March 2018 / Accepted: 23 March 2018 / Published: 27 March 2018
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Abstract
General variable neighborhood search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. We propose a novel extension of the conventional GVNS. Our approach incorporates ideas and techniques from the field of quantum computation during
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General variable neighborhood search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. We propose a novel extension of the conventional GVNS. Our approach incorporates ideas and techniques from the field of quantum computation during the shaking phase. The travelling salesman problem (TSP) is a well known NP-hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes via the Global Positioning System (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of TSP instances, which are shown in a series of figures and tables, allow us to conclude that the novel GVNS algorithm can provide an efficient solution for this type of geographical problem. Full article
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Open AccessArticle Generalized Kinetic Monte Carlo Framework for Organic Electronics
Algorithms 2018, 11(4), 37; https://doi.org/10.3390/a11040037
Received: 31 January 2018 / Revised: 16 March 2018 / Accepted: 22 March 2018 / Published: 26 March 2018
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Abstract
In this paper, we present our generalized kinetic Monte Carlo (kMC) framework for the simulation of organic semiconductors and electronic devices such as solar cells (OSCs) and light-emitting diodes (OLEDs). Our model generalizes the geometrical representation of the multifaceted properties of the organic
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In this paper, we present our generalized kinetic Monte Carlo (kMC) framework for the simulation of organic semiconductors and electronic devices such as solar cells (OSCs) and light-emitting diodes (OLEDs). Our model generalizes the geometrical representation of the multifaceted properties of the organic material by the use of a non-cubic, generalized Voronoi tessellation and a model that connects sites to polymer chains. Herewith, we obtain a realistic model for both amorphous and crystalline domains of small molecules and polymers. Furthermore, we generalize the excitonic processes and include triplet exciton dynamics, which allows an enhanced investigation of OSCs and OLEDs. We outline the developed methods of our generalized kMC framework and give two exemplary studies of electrical and optical properties inside an organic semiconductor. Full article
(This article belongs to the Special Issue Monte Carlo Methods and Algorithms)
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Open AccessArticle A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem
Algorithms 2018, 11(4), 36; https://doi.org/10.3390/a11040036
Received: 22 January 2018 / Revised: 11 March 2018 / Accepted: 20 March 2018 / Published: 25 March 2018
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Abstract
In this paper, a gradient-based cuckoo search algorithm (GCS) is proposed to solve a reservoir-scheduling problem. The classical cuckoo search (CS) is first improved by a self-adaptive solution-generation technique, together with a differential strategy for Lévy flight. This improved CS is then employed
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In this paper, a gradient-based cuckoo search algorithm (GCS) is proposed to solve a reservoir-scheduling problem. The classical cuckoo search (CS) is first improved by a self-adaptive solution-generation technique, together with a differential strategy for Lévy flight. This improved CS is then employed to solve the reservoir-scheduling problem, and a two-way solution-correction strategy is introduced to handle variants’ constraints. Moreover, a gradient-based search strategy is developed to improve the search speed and accuracy. Finally, the proposed GCS is used to obtain optimal schemes for cascade reservoirs in the Jinsha River, China. Results show that the mean and standard deviation of power generation obtained by GCS are much better than other methods. The converging speed of GCS is also faster. In the optimal results, the fluctuation of the water level obtained by GCS is small, indicating the proposed GCS’s effectiveness in dealing with reservoir-scheduling problems. Full article
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Open AccessFeature PaperArticle Entropy-Based Algorithm for Supply-Chain Complexity Assessment
Algorithms 2018, 11(4), 35; https://doi.org/10.3390/a11040035
Received: 28 February 2018 / Revised: 20 March 2018 / Accepted: 21 March 2018 / Published: 24 March 2018
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Abstract
This paper considers a graph model of hierarchical supply chains. The goal is to measure the complexity of links between different components of the chain, for instance, between the principal equipment manufacturer (a root node) and its suppliers (preceding supply nodes). The information
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This paper considers a graph model of hierarchical supply chains. The goal is to measure the complexity of links between different components of the chain, for instance, between the principal equipment manufacturer (a root node) and its suppliers (preceding supply nodes). The information entropy is used to serve as a measure of knowledge about the complexity of shortages and pitfalls in relationship between the supply chain components under uncertainty. The concept of conditional (relative) entropy is introduced which is a generalization of the conventional (non-relative) entropy. An entropy-based algorithm providing efficient assessment of the supply chain complexity as a function of the SC size is developed. Full article
(This article belongs to the Special Issue Algorithms for Scheduling Problems) Printed Edition available
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Open AccessArticle Failure Mode and Effects Analysis Considering Consensus and Preferences Interdependence
Algorithms 2018, 11(4), 34; https://doi.org/10.3390/a11040034
Received: 4 February 2018 / Revised: 15 March 2018 / Accepted: 15 March 2018 / Published: 21 March 2018
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Abstract
Failure mode and effects analysis is an effective and powerful risk evaluation technique in the field of risk management, and it has been extensively used in various industries for identifying and decreasing known and potential failure modes in systems, processes, products, and services.
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Failure mode and effects analysis is an effective and powerful risk evaluation technique in the field of risk management, and it has been extensively used in various industries for identifying and decreasing known and potential failure modes in systems, processes, products, and services. Traditionally, a risk priority number is applied to capture the ranking order of failure modes in failure mode and effects analysis. However, this method has several drawbacks and deficiencies, which need to be improved for enhancing its application capability. For instance, this method ignores the consensus-reaching process and the correlations among the experts’ preferences. Therefore, the aim of this study was to present a new risk priority method to determine the risk priority of failure modes under an interval-valued Pythagorean fuzzy environment, which combines the extended Geometric Bonferroni mean operator, a consensus-reaching process, and an improved Multi-Attributive Border Approximation area Comparison approach. Finally, a case study concerning product development is described to demonstrate the feasibility and effectiveness of the proposed method. The results show that the risk priority of failure modes obtained by the proposed method is more reasonable in practical application compared with other failure mode and effects analysis methods. Full article
(This article belongs to the Special Issue Algorithms for Decision Making)
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