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Internet of Things, low energy consumption, and intelligent and functionally integrated urban infrastructure construction are crucial elements in the development of smart cities. Distributed generation (DG) and electric vehicle charging infrastructure play a vital role in the planning and construction of smart cities. However, the uncertainty associated with the power output of distributed generation significantly impacts the planning of distribution networks. To address this issue, this paper proposes a site-selection recommendation algorithm that leverages urban multimedia data and improved generative adversarial networks. The proposed algorithm begins by modeling the uncertainty of wind power and photovoltaic (PV) generation using an enhanced conditional generative adversarial network model. To generate multimedia datasets with time-series characteristics for wind power and PV generation scenarios, monthly multimedia data labels are incorporated into the model. These multimedia datasets, representing a wide range of scenarios, are then clustered using the K-means clustering method. Furthermore, a distributed generation planning model is established, aiming to minimize the annual integrated cost. The planning problem is efficiently solved using CPLEX, a mathematical programming solver. In the simulation experiments, the proposed scheme is compared with alternative schemes. The results demonstrate that the proposed scheme achieves a significant total cost saving of 21.95% compared to the comparison scheme. Moreover, the experimental comparison reveals that the proposed scheme exhibits higher stability. Additionally, in terms of algorithm efficiency, the proposed algorithm outperforms the other three algorithms tested in terms of the number of iterations and speed. The experimental results highlight the effectiveness of the proposed planning model in improving the economy and stability of the distribution network. Furthermore, it enhances the computational efficiency of the planning problem associated with distributed power supply and electric vehicle charging stations. The findings of this research hold substantial research significance for the site selection planning of distributed power supply. [ABSTRACT FROM AUTHOR]

With the development of modern science and technology, the field of UAV has also entered the era of high-tech exploration. Among them, the task planning, allocation, path exploration, and algorithm optimization of heterogeneous multi UAV technology are our main concerns. Based on the above situation, this paper proposes a heterogeneous multi UAV task planning technology based on ant colony algorithm powered BP neural network. The planning, research, and design are mainly carried out according to the actual situation of the UAV flight test, and the mathematical programming model is established according to the UAV load degree and maximum flight distance as constraints. This paper focuses on the contribution of the ant colony optimization algorithm to benefit maximization and task minimization. The experimental results show that the BP neural network optimized by the ant colony algorithm can improve the number of iterations and training time. Compared with some comparative algorithms, its performance is better. [ABSTRACT FROM AUTHOR]

Timetable optimization techniques offer opportunity for saving energy and hence reducing operational costs for high-speed rail services. The existing energy-saving timetable optimization is mainly concentrated on the train running state adjustment and the running time redistribution between two stations. Not only the adjustment space of timetables is limited, but also it is hard for the train to reach the optimized running state in reality, and it is difficult to get feasible timetable with running time redistribution between two stations for energy-saving. This paper presents a high-speed railway energy-saving timetable based on stop schedule optimization. Under the constraints of safety interval and stop rate, with the objective of minimizing the increasing energy consumption of train stops and the shortest travel time of trains, the high-speed railway energy-saving timetable optimization model is established. The fuzzy mathematics programming method is used to design an efficient algorithm. The proposed model and algorithm are demonstrated in the actual operation data of Beijing-Shanghai high-speed railway. The results show that the total operating energy consumption of the train is reduced by 3.7%, and the total travel time of the train is reduced by 11 minutes. [ABSTRACT FROM AUTHOR]

In the emerging modern technology of additive manufacturing, the need for optimization can be found in literature in many places. Additive manufacturing (AM) is making an object layer by layer directly from digital data. Previous works of literature have classified additive manufacturing processes into seven types. However, there is a lack of comprehensive review describing the optimization challenges and opportunities in the material extrusion process (polymer technology) and also the need for FDM polymer materials application in impeller making. In this review paper, a specific optimization method called multicriteria decision-making (MCDM) from the mathematical programming technique used in additive manufacturing polymer technology (AMPT) is discussed. The other topics such as different types of optimization techniques, applications of different MCDM tools and their applications in different fields including AM, and the optimization challenges and opportunities in AMPT particularly impeller application are discussed. [ABSTRACT FROM AUTHOR]

The two-stage data envelopment analysis models are among the widely used mathematical programming approaches to evaluate the performance of two-stage structures. In this paper, a two-stage structure with shared inputs and feedback is studied. To reduce undesirable outputs, an additive slack-based measure model is proposed to evaluate the stages and overall efficiencies, while undesirable outputs are weakly disposable. As it does not require determining the weights for combining stages' efficiencies, all Pareto optimal stages' efficiencies can be gained. In addition, the proposed approach can identify desirable outputs from undesirable outputs, thereby avoiding the need for weighting. This advantage from the aspect of multiobjective programming helps internal evaluation of the network model to match priorities of managers. The proposed nonlinear model is reformulated as a second-order cone program, which is a convex optimization problem that can be solved to global optimality. This is a computational improvement over the parametric models in the literature. Furthermore, the proposed model is applied for country-wise and area-wise performance evaluations of a real industrial application dataset in mainland China. Results show that the efficiency of the overall system relies between the efficiencies of the two stages and for all DMUs, the first stage's efficiency scores are always higher than the second stage ones in both evaluations. Also, the Pearson correlation coefficient test results show that the overall efficiency is more correlated with the waste disposal stage. Finally, to show the effect of the decision maker's preference, a detailed sensitivity analysis is performed. [ABSTRACT FROM AUTHOR]

Due to the growing use of Plug-in Electric vehicles (PEVs) in transportation networks, the charge/discharge scheduling of PEVs in Electric Vehicles Parking Lots (EVPLs) can be effective on the distribution network's (DN) resiliency. This paper presents a bilevel optimization model to improve the resiliency of the DN taking into account the interaction between the DN islanding problem and the charge/discharge scheduling of PEVs in the energized EVPLs. In the Upper-Level (UL) problem, regarding the electrical loads and managing the charge/discharge of PEVs, the islands' boundaries are determined with the aim of maximizing the amount of restored load. Knowing the islands' boundaries and the energized EVPLs from the UL problem, the changes in travels characteristic including destination EVPLs are determined in the Lower-Level (LL) problem to identify the nearest energized EVPL to the out-of-service destination EVPL. The number of PEV drivers that change their deenergized destination depends on the distance between the nearest energized EVPL to the destination. A combination of mathematical programming and evolutionary algorithm is applied to reach the final solution. The proposed model is implemented by applying several concurrent faults to the 118-bus active DN, which is coupled with a 25-node traffic network. The results confirm the efficiency of the proposed model for improving the resiliency of DNs with managing the charge/discharge of PEVs in the restored EVPLs. [ABSTRACT FROM AUTHOR]

Multiple sources localization based on time difference of arrival (TDOA) measurements is investigated in this paper. Different from the traditional methods, a novel and practical multisource localization algorithm is proposed by adopting a priori information of relative distance among emitting sources. Since the maximum likelihood (ML) cost function for multisource estimation is highly nonconvex, the semidefinite relaxation (SDR) is utilized to reformulate the ML cost function. A robust estimator is obtained, which can be solved by semidefinite programming (SDP). Moreover, the constrained Cramér-Rao bound is also derived as a benchmark by considering the range constraints between sources. Simulation results verify the superior performance of the proposed algorithm over the traditional methods. [ABSTRACT FROM AUTHOR]

This paper aims to develop a data envelopment analysis (DEA) based model for allocating input resources and deciding output targets in organizations with a centralized decision-making environment, for example, banks, police stations, and supermarket chains. The central decision-maker has an interest in maximizing the total output production and at the same time minimizing the total input consumption. Traditionally, all decision-making units (DMUs) can be easily projected to the efficient frontier, which is a mathematical feasibility; however, it does not guarantee the managerial feasibility during the planning period. In this paper, we will take potential limitations of input-output changes into account by building a difficulty coefficient matrix of modifying their production in the current production possibility set so that the solution guarantees managerial feasibilities. Three objectives, namely, maximizing aggregated outputs, minimizing the consumption of input resources, and minimizing the total difficulty coefficient, are proposed and incorporated into the formation of resource allocation and target setting scheme. Building on this, we combine DEA and multiobjective programming to solve the resource allocation and target setting problem. In the end, we apply our proposed approach to a real-world problem of sixteen chain hotels to illustrate the efficacy and usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

An optimal dimensioning of the room and pillars of the square or rectangular section is finding adequate transversal section support for the pillar; in that sense, all forces acting on the pillar must be evaluated. The pillar has its ends subjected to normal (σ) and shear stresses (τ). In addition, the pillar can also be subjected to dynamic stresses from the seismic waves generated by rock blasting. Thus, when calculating the dimensions of a mine pillar, the stresses imposed by seismic waves must be considered. The combination of lithostatic and dynamic stresses generated by blasting can affect the pillar, generating regions, or areas that will exhibit the formation of cracks, microcracks, chipping, and other detrimental processes for its stability. In that regard, it is appropriate to assess the dynamic effects of the energy delivered in each wave pulse so that these dynamic quantities can be included in the dimensioning of the pillars. This paper presents the physical and mathematical formulation describing the dimensioning issues of room and pillars and its solution using numerical modeling. The numerical modeling is performed using nonlinear mathematical programming and simulation with FLAC 3D (Fast Lagrangian Analysis of Continua in 3D). In this study, it was proven that the stress generated by the seismic wave represents a dynamic stress of nearly 5.5 MPa. 'Dimensioned pillars considering wave stresses allows it to recover an average 1.6% less when it is compared with pillars only under overlying rock mass'. [ABSTRACT FROM AUTHOR]

During the evolution of the twenty-first century, the increase in people's material life level has also brought an increase in mental stress. For college students, not only need to bear heavy academic pressure, but also face the market competition after entering the society. Therefore, the psychological fitness instruction for undergraduate students has become an inseparable part of college education. However, in the current stage of mental health education, the quality of teaching is often difficult to guarantee. For the purpose of promoting the in-depth implementation of mental health education in colleges and universities and improving teaching effectiveness, one needs to analyze the quality of teaching in great details and understand the factors affecting the quality of teaching. In this paper, the mathematical programming algorithm is used to analyze the quality of college students' Psychological Wellness Instruction. It deeply studies the development status of psychological wellness training and the basic composition and algorithm principle of mathematical programming algorithm. It integrates it into the practice of teaching quality analysis on the basis of linear regression algorithm theory. The experimental results show that the highest confidence interval of teachers' teaching organization, cognitive level, and students' participation level in the mental health course has reached more than 80.00%. This shows the importance of these three factors to the improvement of teaching quality. In order to enhance the quality of college students' psychological wellness training, it is necessary to innovate the form of classroom organization and the degree of interaction between students and the classroom. [ABSTRACT FROM AUTHOR]

The paper deals with composition design of self-compacting concrete containing blast furnace granulated slag and polycarboxylate type superplasticizer in a wide range of concrete strength classes. Optimal superplasticizer content, cement, and blast furnace slag consumptions are obtained from the viewpoint of minimum water demand and water-cement ratio, maximum self-compacting concrete mixture workability retention, and concrete strength. A significant effect of joint influence of superplasticizer and blast furnace slag is demonstrated. The possibility of minimizing the cement consumption at optimal superplasticizer content and slag consumption of 100–120 kg/m3 is shown. Dependences for calculating SCC strength at 1, 7, and 28 days vs. the water-cement ratio are obtained. It is found that the nature of correlation dependences for SCC splitting and compression strengths when using the blast furnace slag and the superplasticizer is linear. Optimization of SCC compositions using mathematical programming enables to obtain compositions with a lowest possible cost while providing a set of specified parameters. [ABSTRACT FROM AUTHOR]

This paper investigates the C-logit stochastic user equilibrium (SUE) problem on a bimodal transportation network with road and rail travel modes. The C-logit model captures the overlapping effect among the different paths via commonality factors; sequentially, it has ability to obtain a more realistic traffic flow distribution pattern. In this paper, when we redefine the link travel cost functions and employ a binary Logit model for the mode split, the bimodal C-logit SUE model can be simplified into an unconstrained nonlinear mathematical programming formulation. Such model is verified to satisfy the bimodal C-logit SUE conditions at its stationary point and can be solved by existing algorithms. So, the simplified model can be convenient to be used on the general bimodal transportation network. [ABSTRACT FROM AUTHOR]

This paper studies the energy efficiency optimization problem for coordinated multipoint (CoMP)-enabled and backhaul-constrained ultra-dense small-cell networks (UDNs). Energy efficiency is an eternal topic for future wireless communication networks; however, taking actual bottleneck of the backhaul link and the coordinated network architecture into consideration, it is difficult to find an effective way to improve the energy efficiency of the network. Aiming at this problem, we propose to combine cell association, subchannel allocation, backhaul resource allocation, and sleep/on of the cells together to develop an optimization algorithm for energy efficiency in UDN and then solve the formulated energy efficiency optimization problem by means of improved modified particle swarm optimization (IMPSO) and linear programming in mathematics. Simulation results indicate that nearly 13 % energy cost saving and 21 % energy efficiency improvement can be obtained by combining IMPSO with linear programming, and the backhaul link data rate can be improved by 30 % as the number of small cells increases. From the results, it can be found that by combining IMPSO with linear programming, the proposed algorithm can improve the network energy efficiency effectively at the expense of limited complexity. [ABSTRACT FROM AUTHOR]

This paper studies joint algorithms of order picking and distribution in new retail enterprises. The problem will consider many factors, such as the type of goods, picking time, batch capacity of distribution, distribution time, and distribution cost. First of all, the research problems are summarized as mathematical programming problems. Then, a genetic algorithm and comparison algorithms are proposed. Finally, the rationality of the model and the effectiveness of the algorithms are verified by computational experiments, and management enlightenments are revealed. [ABSTRACT FROM AUTHOR]

The passenger transport demands at a given junction station fluctuate obviously in different time periods, which makes the rail departments unable to establish an even train operation schedule. This paper considers an optimization problem for train operations at the junction station in passenger dedicated lines. A satisfaction function of passengers is constructed by means of analyzing the satisfaction characteristics and correlative influencing factors. Through discussing the passengers' travel choice behavior, we formulate an optimization model based on maximum passenger satisfaction for the junction and then design a heuristic algorithm. Finally, a numerical example is provided to demonstrate the application of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

As a result of the heterogeneity of decision-makers, it is more suitable to use multi-granular linguistic information to express assessing information over alternatives for decision-makers. Decision-makers may only provide partial preference information as the limitation of knowledge over the alternatives. Thus, the incomplete multi-granular 2-tuple fuzzy linguistic preference relations (IMGFLPRs) can be applied to manage the GDM problems in complex environments. In this paper, we propose a novel group decision-making (GDM) model based on mathematical programming with IMGFLPRs. It is more important that the proposed mathematical programming can directly deal with IMGFLPRs and does not need to be converted into a uniform form. By this means, we construct a consensus reaching process based on IMGFLPRs, considering simultaneously dynamic adjustment of experts’ weight. Finally, an emergency plan selection problem is solved by the proposed model, which shows the result of GDM is effectiveness. [ABSTRACT FROM AUTHOR]

A class of cubic systems with two invariant straight lines dx/dt = y(1 - x²), dy/dt = x + δy + nx² + mxy + ly² + bxy². is studied. It is obtained that the focal quantities of O (0, 0) are W0 = δ; if W0 = 0, the W1 = m(n + l); if W0 = W1 = 0, the W2 = nm(b+1) if W0 = W1 = W2 = 0, then O is a center, and it has been proved that the above mentioned cubic system has at most one limit cycle surrounding weak focal O (0, 0). This paper also aims to solve the remaining issues in the work of Zheng and Xie (2009). [ABSTRACT FROM AUTHOR]

Graceful labeling is one of the best known labeling methods of graphs. Despite the large number of papers published on the subject of graph labeling, there are few particular techniques to be used by researchers to gracefully label graphs. In this paper, first a new approach based on the mathematical programming technique is presented to model the graceful labeling problem. Then a "branching method" is developed to solve the problem for special classes of graphs. Computational results show the efficiency of the proposed algorithm for different classes of graphs. One of the interesting results of our model is in the class of trees. The largest tree known to be graceful has at most 27 vertices but our model can easily solve the graceful labeling for trees with 40 vertices. [ABSTRACT FROM AUTHOR]

The development of customized service is an important way to transform and upgrade China's mining industry. However, in practice, there remain problems, such as the slow market response speed of service providers and the contradiction between the large-scale development of service providers and the personalized service needs of service demanders. This paper uses the theory and method of service modular design to solve these problems and explores the process-based service modular design method. Service modular design depends largely on the determination of the relationship between service activities and the reasonable division of modules. However, previous research has rarely made use of modular design methods and modeling tools in the mining service context. At the same time, evaluations of the relationship between service activities relying on knowledge and those relying on experience have been inconclusive. Therefore, this paper proposes a service modularization design method based on the fuzzy relation analysis of a design structure matrix (DSM) that solves the optimal module partition scheme. Triangular fuzzy number and fuzzy evidence theory are used to evaluate and fuse the multidimensional and heterogeneous relationship between service activities, and the quantitative processing of the comprehensive relationship between service activities is carried out. On this basis, the service module structure is divided, followed by the construction of the mathematical programming model with the maximum sum of the average cohesion degree in the module and the average coupling degree between modules as the driving goal. The genetic algorithm is used to solve the problem, and the optimal module division result is obtained. Finally, taking the service modular design of SHD coal production enterprises in China as an example, the feasibility of the proposed method is verified. [ABSTRACT FROM AUTHOR]

The purchase cost of chain convenience stores accounts for a large proportion of the total cost. Enterprises are facing the problem of how to reasonably reduce the purchase cost while ensuring the quality of service. This paper first considers the uniqueness of chain convenience stores, draws on existing research results, and establishes an evaluation index system for chain convenience store suppliers through field research. Then, we use the principal component analysis method of selection to determine the weight of each index and make a preliminary ranking of the importance of chain convenience store suppliers. Secondly, according to the relevant weights determined by the network hierarchy method, the collected data are substituted into the multiobjective mathematical programming model to analyze the distribution of supplier procurement and calculate the procurement cost. The level of procurement expenditures accounted for by suppliers confirms the importance of suppliers. The results show that through the evaluation and ranking of suppliers and the effective management of suppliers according to the ranking results, not only the goal of cost minimization is achieved but also the reasonable service level is guaranteed, which is scientific. [ABSTRACT FROM AUTHOR]

This paper proposes to address the problem of the simultaneous optimization of the liner shipping route and ship schedule designs by incorporating port time windows. A mathematical programming model was developed to minimize the carrier's total operating cost by simultaneously optimizing the port call sequence, ship arrival time per port of call, and sailing speed per shipping leg under port time window constraints. In view of its structure, the nonlinear nonconvex optimization model is further transformed into a mixed-integer linear programming model that can be efficiently solved by extant solvers to provide a global optimal solution. The results of the numerical experiments performed using a real-world case study indicated that the proposed model performs significantly better than the models that handle the design problems separately. The results also showed that different time windows will affect the optimal port call sequence. Moreover, port time windows, bunker price, and port efficiency all affect the total operating cost of the designed shipping route. [ABSTRACT FROM AUTHOR]

In this paper, the two-stage orienteering problem with stochastic weights is studied, where the first-stage problem is to plan a path under the uncertain environment and the second-stage problem is a recourse action to make sure that the length constraint is satisfied after the uncertainty is realized. First, we explain the recourse model proposed by Evers et al. (2014) and point out that this model is very complex. Then, we introduce a new recourse model which is much simpler with less variables and less constraints. Based on these two recourse models, we introduce two different two-stage robust models for the orienteering problem with stochastic weights. We theoretically prove that the two-stage robust models are equivalent to their corresponding static robust models under the box uncertainty set, which indicates that the two-stage robust models can be solved by using common mathematical programming solvers (e.g., IBM CPLEX optimizer). Furthermore, we prove that the two two-stage robust models are equivalent to each other even though they are based on different recourse models, which indicates that we can use a much simpler model instead of a complex model for practical use. A case study is presented by comparing the two-stage robust models with a one-stage robust model for the orienteering problem with stochastic weights. The numerical results of the comparative studies show the effectiveness and superiority of the proposed two-stage robust models for dealing with the two-stage orienteering problem with stochastic weights. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce some generalized contractions and prove certain new fixed point results for self-mappings satisfying these contractions in the setting of F -metric space. As an application of our results, we investigate the problem of dynamic programming related to the multistage process which formulates the problems of computer programming and mathematical optimization. We also provide an example to support the validity of our main results. [ABSTRACT FROM AUTHOR]

This paper introduces the taxi route network design problem (TXRNDP) for a fixed-route taxi service operating in Iran and, in similar form, in various other developing countries. The service operates fairly similar to regular transit services in that vehicles are only permitted to follow a certain predetermined route on the network. The service is provided with small size vehicles and main features are that vehicles only depart if full and that there are no intermediate boarding stops. In Iran the service attracts a high modal share but requires better coordination which is the main motivation for the present study. We develop a mathematical programming model to minimize the total travel time experienced by passengers while constraining the number of taxi lines, the trip transfer ratio and the length of taxi lines. A number of assumptions are introduced in order to allow finding an exact rather than heuristic solution. We further develop a linear programming solution to minimize the number of taxis required to serve the previously found fixed-route taxi network. Results of a case study with the city of Zanjan, Iran, illustrate the resulting taxi flows and suggest the capability of the proposed model to reduce the total travel time, the total waiting time and the number of taxi lines compared to the current taxi operation. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

It is proposed to use fuzzy similarity in fuzzy decision-making approach to deal with the supplier selection problem in supply chain system. According to the concept of fuzzy TOPSIS earlier methods use closeness coefficient which is defined to determine the ranking order of all suppliers by calculating the distances to both fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) simultaneously. In this paper we propose a new method by doing the ranking using similarity. New proposed method can do ranking with less computations than original fuzzy TOPSIS. We also propose three different cases for selection of FPIS and FNIS and compare closeness coefficient criteria and fuzzy similarity criteria. Numerical example is used to demonstrate the process. Results show that the proposed model is well suited for multiple criteria decision-making for supplier selection. In this paper we also show that the evaluation of the supplier using traditional fuzzy TOPSIS depends highly on FPIS and FNIS, and one needs to select suitable fuzzy ideal solution to get reasonable evaluation. [ABSTRACT FROM AUTHOR]

Steelmaking continuous casting (SCC)—hot rolling (HR) is a key process in the production of steel products. It is also a process with large energy consumption. Energy saving has always been an important goal of production scheduling of this process. In this paper, aiming at integrated scheduling optimization for SCC-HR processes, energy saving objective is converted to minimize waiting time of slabs in slab yard, so as to reduce slab temperature loss and achieve the goal of saving energy. An integrated two-stage mathematical programming model is established for the above problems, and a hybrid algorithm of genetic algorithm and linear programming is designed for the integrated model. The correctness of the model and the validity of the algorithm are verified by computational experiments using simulated instances. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective mathematical programming (MMP) model to day-ahead market clearing of joint energy and reserves auctions by integrating a thyristor-controlled series compensator (TCSC) device with the optimization problem developed under normal and contingency cases. The proposed market clearing framework includes minimization of energy and reserves offer cost, congestion rent, TCSC cost, expected interruption cost, line overload, voltage deviation, and loadability limit. A new index called congestion efficiency index is proposed for the best placement of TCSC under network contingency conditions. Traditional MMP methods such as direction scalarization and ε-constraint methods scalarize the objective vector into a single objective. Those cases are time-consuming and require a number of runs equal to the number of desired efficient solutions. In this paper, the non-dominated sorting genetic algorithm II (NSGA-II), which is integrated with an adaptive neuro-fuzzy inference system (ANFIS), is proposed to find the solution of the optimal schedule of the units energy and reserves by which the parameters of NSGA-II (probabilities of crossover and mutation) are dynamically set, according to a training process. The proposed methodology is developed on the IEEE 30-bus test system, and the results are compared with fuzzy-based and ordinary NSGA-II methods. These comparisons confirm the efficiency of the developed method. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a general Transient Stability Estimation Index (TSEI) is introduced, which can be used in different network without any data training step. The TSEI determines the stability status based on quantifying the impact of parameters, which affect transient stability problem. To quantify the parameters, some networks are simulated off-line, and these parameters are randomly changed in their typical range. Then, the impact of these parameters is analyzed using critical clearing time of simulated short circuits. In this paper, IEEE 14-bus and New England 39-bus networks are used to provide off-line learning sets. Unlike the previous works which have used decision tree algorithm as a direct security assessment tool, here, decision tree is used for analyzing of the parameters impact. To validate the result of the data processing, they are compared with the classical transient stability solution. Then, the TSEI is proposed, which draws significantly low processing burden, which is suitable for online transient stability assessment. To test the generality of the proposed index, IEEE 162-bus network is used without any previous training data. In a real power system, using wide area measurements, first the required parameters are measured or estimated, and then the proposed TSEI is calculated for large disturbances. Simulation results show that the proposed index can acceptably estimate the stability, even in new networks. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we consider a multistage feedback control strategy for producing 1,3-propanediol in microbial continuous fermentation. Both the dilution rate and the concentration of glycerol in the input feed are used as control variables, and these variables are further assumed to be in the form of a linear combination of biomass and glycerol concentrations. Unlike the general form of linear feedback control, the coefficients of linear combination are continuous functions with respect to time. Inspired by the control parameterization method, we use the piecewise-constant functions to approximate the coefficient functions; then we get the multistage feedback control law by solving nonlinear mathematical programming problems. Numerical results indicate the flexibility and effectiveness of our strategy. [ABSTRACT FROM AUTHOR]

This paper presents output-only observer/Kalman filter identification (O3KID), an effective method for the identification of the dynamic model of a structure and its underlying modal parameters using only output time histories measured on the field. The method is suitable for structural health monitoring based on modal parameters, in particular, for those civil infrastructures whose excitation is random in nature and in the way that it is applied to the structure (e.g., wind and traffic) and therefore is difficult to measure. O3KID is based on a linear-time-invariant state-space model and is derived from an established and successful approach for input-output system identification, known as observer/Kalman filter identification. The paper rigorously proves the applicability of the approach to the output-only case, presents the resulting new algorithms, and demonstrates them via examples on both numerical and experimental data. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Cache-enabled heterogeneous cellular networks (HCNs) have been investigated extensively to alleviate backhaul congestion and reduce content delivery delay. In this paper, we jointly optimize content placement and user association to minimize the average content delivery delay in cache-enabled HCNs based on flow-level models. This formulation considers (1) different timescales of content placement and content delivery, (2) locality of content popularity, and (3) the heterogeneity of spatial traffic distribution, which are often neglected in existing researches. The joint optimization problem is formulated as a mixed integer nonlinear programming problem in load-non-coupled and load-coupled models, respectively. We decouple this problem into two interrelated subproblems and resolve them individually. For the user association problem under a given content placement situation, we propose a content-level selective association algorithm, which allows the requests for different contents at the same location to connect to different base stations (BSs). In addition, we propose a greedy content caching algorithm to add contents to the caches of BSs in an iterative manner. These two algorithms are alternately executed until the caches of all the BSs are filled to capacity. Simulation results show that the proposed algorithm achieves better performance in terms of average delay and backhaul usage compared with traditional content placement and user association approaches. [ABSTRACT FROM AUTHOR]

In this paper we propose a fuzzy co-clustering algorithm via modularity maximization, named MMFCC. In its objective function, we use the modularity measure as the criterion for co-clustering object-feature matrices. After converting into a constrained optimization problem, it is solved by an iterative alternative optimization procedure via modularity maximization. This algorithm offers some advantages such as directly producing a block diagonal matrix and interpretable description of resulting co-clusters, automatically determining the appropriate number of final co-clusters. The experimental studies on several benchmark datasets demonstrate that this algorithm can yield higher quality co-clusters than such competitors as some fuzzy co-clustering algorithms and crisp block-diagonal co-clustering algorithms, in terms of accuracy. [ABSTRACT FROM AUTHOR]

This paper reviews the effect of applying optimization methods on the collection process of solid waste, with particular interest in mathematical programming and geographic information system approaches in developing countries. Mathematical programming approaches maximize or minimize an objective function for improvement in procedure, to ensure operational efficiency and also serve as decision support tools. They however provide partial solutions when implemented in reality and cannot fully handle road network constraints. Geographic information system approaches allow processing of additional considerations, often ignored in other methods, such as the street network modeling. Incorporating environmental pollution consideration is very challenging in this approach, the vehicle routing problem solver encountering limits for large data. For enhanced efficiency of the vehicle routing systems, studies should further focus on incorporating all network constraints, environmental pollution considerations, and impact of land use changes on routing. [ABSTRACT FROM AUTHOR]

Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method has been extended in previous literature to consider the situation with interval input data. However, the weights associated with criteria are still subjectively assigned by decision makers. This paper develops a mathematical programming model to determine objective weights for the implementation of interval extension of TOPSIS. Our method not only takes into account the optimization of interval-valued Multiple Criteria Decision Making (MCDM) problems, but also determines the weights only based upon the data set itself. An illustrative example is performed to compare our results with that of existing literature. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic user equilibrium (SUE) assignment model for a schedule-based transit network with capacity constraint. We consider a situation in which passengers do not have the full knowledge about the condition of the network and select paths that minimize a generalized cost function encompassing five components: (1) ride time, which is composed of in vehicle and waiting times, (2) overload delay, (3) fare, (4) transfer constraints, and (5) departure time difference. We split passenger demands among connections which are the space-time paths between OD pairs of the network. All transit vehicles have a fixed capacity and operate according to some preset timetables. When the capacity constraint of the transit line segment is reached, we show that the Lagrange multipliers of the mathematical programming problem are equivalent to the equilibrium passenger overload delay in the congested transit network. The proposed model can simultaneously predict how passengers choose their transit vehicles to minimize their travel costs and estimate the associated costs in a schedule-based congested transit network. A numerical example is used to illustrate the performance of the proposed model. [ABSTRACT FROM AUTHOR]

In the setting of Ben-Tal's generalized algebraic operations, this paper deals with Mond-Weir type dual theorems of multiobjective programming problems involving generalized invex functions. Two classes of functions, namely, (h,φ)-pseudoinvex and (h,φ)-quasi-invex, are defined for a vector function. By utilizing these two classes of functions, some dual theorems are established for conditionally proper efficient solution in (h,φ)-multiobjective programming problems. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to present a minmax programming model for the optimal dispatch of Traffic and Patrol Police Service Platforms with single traffic congestion. The objective is to minimize the longest time of the dispatch for Traffic and Patrol Police Service Platforms. Some numerical experiments are carried out, and the optimal project is given. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive framework model of a distribution company with security and reliability considerations. A probabilistic wind farm, which is a renewable energy resource, is modeled in this work. The requirement energy of distribution company can be either provided by distribution company's own distributed generations or purchased from power market. Two reliability indices as well as DC load flow equations are also considered in order to satisfy reliability and security constraints, respectively. Since allocating proper spinning reserve improves reliability level, the amount of spinning reserve will be calculated iteratively. In this work, all equations are expressed in a linear fashion in which unit commitment formulation depends on binary variables associated with only on/off of units. The benders decomposition method is used to solve security-based unit commitment. [ABSTRACT FROM AUTHOR]

The paper presents a new methodology for optimizing the design of DC-DC converters. The magnitudes that we take into account are efficiency, ripples, bandwidth, and RHP zero placement. We apply a geometric programming approach, because the variables are positives and the constraints can be expressed in a posynomial form. This approach has all the advantages of convex optimization. We apply the proposed methodology to a boost converter. The paper also describes the optimum designs of a buck converter and a synchronous buck converter, and the method can be easily extended to other converters. The last example allows us to compare the efficiency and bandwidth between these optimal-designed topologies. [ABSTRACT FROM AUTHOR]

We focus our study on a discussion of duality relationships of a minimax fractional programming problem with its two types of second-order dual models under the second-order generalized convexity type assumptions. Results obtained in this paper naturally unify and extend some previously known results on minimax fractional programming in the literature. [ABSTRACT FROM AUTHOR]

Paknejad et al.'s model is considered in this paper. Itemwise multiobjective models for both exponential and uniform lead-time demand are taken and the results are compared numerically both in fuzzy optimization and intuitionistic fuzzy optimization techniques. Objective of this paper is to establish that intuitionistic fuzzy optimizaion method is better than usual fuzzy optimization technique as expected annual cost of this inventory model is more minimized in case of intuitionistic fuzzy optimization method. As a single objective stochastic inventory model where the lead-time demand follows normal distribution andwith varying defective rate, expected annual cost is also measured. Finally the model considers for fuzzy cost components, which make the model more realistic, and numerical values for uniform, exponential, and normal leadtime demand are compared. Necessary graphical presentations are also given besides numerical illustrations. [ABSTRACT FROM AUTHOR]

This paper proposes a cluster partitioning technique to calculate improved upper bounds to the optimal solution of maximal covering location problems. Given a covering distance, a graph is built considering as vertices the potential facility locations, and with an edge connecting each pair of facilities that attend a same client. Coupling constraints, corresponding to some edges of this graph, are identified and relaxed in the Lagrangean way, resulting in disconnected subgraphs representing smaller subproblems that are computationally easier to solve by exact methods. The proposed technique is compared to the classical approach, using real data and instances from the available literature. [ABSTRACT FROM AUTHOR]

This paper introduces habit-forming preferences in a Barro-type endogenous growth model with productive public services. Government expenditure, which may be subject to congestion, is financed by distortionary income taxation. Different from the standard time-separable model, the presence of habits makes the economy feature transitional dynamics, which are solved in closed form. Setting the income tax so as to equate the elasticity of public services in production is shown to maximize both long-run growth and welfare as in the standard model. This second-best solution coincides with the first-best outcome only in the presence of proportional congestion. [ABSTRACT FROM AUTHOR]

A novel generation method of chaotic attractor is introduced in this paper. The underlying mechanism involves a simple three-dimensional time-varying system with simple time functions as control inputs. Moreover, it is demonstrated by simulation that various attractor patterns are generated conveniently by adjusting suitable system parameters. The largest Lyapunov exponent of the system has been obtained. [ABSTRACT FROM AUTHOR]

Rough interval (RI) is an appropriate generalization of the crisp interval and is very much useful to express uncertain parameters or partially unknown variables when they contain dual‐layer information. In real‐life problems, there are many situations where players of a matrix game cannot assess their pay‐offs by using fuzzy sets/intuitionistic fuzzy sets or ordinary intervals. RIs are used as an excellent tool to handle such situations. This paper explores matrix games with RI pay‐offs and investigates two different solution methodologies to solve such a game. In the first approach, a pair of auxiliary linear programming problems with RI coefficients for the players has been constructed. Then each of the two auxiliary programming problems is converted into two linear programming problems with interval coefficients (LPPICs) using lower and upper approximation intervals of the RI. Finally from each LPPIC, two classical linear programming problems (LPPs) are constructed. In the second approach, the expected value technique for RI is used for transforming auxiliary mathematical programming models under the RI environment to crisp LPP. A case study on the telecom market share problem is considered to show the applicability of the proposed approaches and results are compared and analyzed with an existing method. [ABSTRACT FROM AUTHOR]

As a useful tool, probabilistic hesitant fuzzy set is an enhanced version for hesitant fuzzy set. It could be used to model the uncertainty very effectively. However, in probabilistic hesitant fuzzy risky multiple attribute decision making problems, the occurrence probabilities of elements in a probabilistic hesitant fuzzy element and the probability of risk status are often difficult to obtain by subjective evaluation of a decision maker. This paper aims to propose two nonlinear programming models for calculating the probabilities of elements in a probabilistic hesitant fuzzy element and the probability of risk status respectively. First, a nonlinear programming model using maximum entropy principle is established for determining the probabilities of elements in a probabilistic hesitant fuzzy element. Second, by introducing the water‐filling theory, we put forward its extension and design a novel mathematical programming model to determine the probability of risk status. Moreover, we have proved that both the two mathematical programming models are convex programming models and their global optimal solutions can be found. Thirdly, the collective overall expected values of alternatives are calculated and the ranking order can be derived. Then, the selection of investment project is investigated, and comparison analysis shows the superiority of the presented approach. [ABSTRACT FROM AUTHOR]

An introduction is presented in which discusses articles within the issue on topics including local search and mathematical programming; enhancing optimization problems by integrating an Ant algorithm; and migrating birds optimization when solving machine-part cell formation problems.

A focused crawler is topic-specific and aims selectively to collect web pages that are relevant to a given topic from the Internet. However, the performance of the current focused crawling can easily suffer the impact of the environments of web pages and multiple topic web pages. In the crawling process, a highly relevant region may be ignored owing to the low overall relevance of that page, and anchor text or link-context may misguide crawlers. In order to solve these problems, this paper proposes a new focused crawler. First, we build a web page classifier based on improved term weighting approach (ITFIDF), in order to gain highly relevant web pages. In addition, this paper introduces an evaluation approach of the link, link priority evaluation (LPE), which combines web page content block partition algorithm and the strategy of joint feature evaluation (JFE), to better judge the relevance between URLs on the web page and the given topic. The experimental results demonstrate that the classifier using ITFIDF outperforms TFIDF, and our focused crawler is superior to other focused crawlers based on breadth-first, best-first, anchor text only, link-context only, and content block partition in terms of harvest rate and target recall. In conclusion, our methods are significant and effective for focused crawler. [ABSTRACT FROM AUTHOR]

This paper mainly studies the optimization design of toll plaza, including the determination of the number of tollbooths and the design of the shape and size of the toll plaza. The optimization objectives are the construction cost of toll plaza, the throughput, and the accident rate. Through the analysis on the structure of toll plaza and the traffic flow, we determine the impact factors for optimization targets and select the number of tollbooths, the length of entrance and exit queue area, and the ingress and egress angles as decision variables and then build the function relationship between construction cost, accident rate, throughput, and decision variables. Then based on those functions to build the mathematical programming model, so as to get the optimal design plan. [ABSTRACT FROM AUTHOR]