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This paper systematically constructs a multi-project and a multi-objective human resource scheduling mathematical model in construction projects and puts forward an improved genetic algorithm to solve it by aiming at the problems existing in the process of human resource scheduling and optimization. Specifically, first, the basic mathematical models of GPRs and resource-constrained construction project human resource scheduling problem (RCWSP/GPRs) are established, and the multi-project equilibrium problem is extended. Then, an improved inter-cluster separation (ICS) algorithm is proposed and used to solve the RCWSP/GPRs problem. Finally, on this basis, the mathematical model of multi-project and multi-objective human resource scheduling problem and the solution method based on multi-objective-integrated circuit are proposed. At the same time, the resource-constrained multi-project and multi-skill human resource scheduling problem and the integer programming mathematical model under the generalized priority relationship are proposed. Also, the simulation results verify the accuracy of the proposed algorithm and model. [ABSTRACT FROM AUTHOR]

In this paper, we study the tangential touch and C¹ regularity of the free boundary near the fixed boundary of the double obstacle problem for Laplacian and fully nonlinear operator. The main idea to have the properties is regarding the upper obstacle as a solution of the single obstacle problem. Then, in the classification of global solutions of the double problem, it is enough to consider only two cases for the upper obstacle, a/2 xn² or a/2 xn² + bxnx1 for some b ∈ ℝ, b ≠ 0. The second one is a new type of upper obstacle, which does not exist in the study of local regularity of the free boundary of the double problem. Thus, in this paper, a new type of difficulties that come from the second type upper obstacle is mainly studied. [ABSTRACT FROM AUTHOR]

In this paper, we conduct a convergence rate analysis of the augmented Lagrangian method with a practical relative error criterion designed in Eckstein and Silva [Mathematical Programming, 141, 319–348 (2013)] for convex nonlinear programming problems. We show that under a mild local error bound condition, this method admits locally a Q-linear rate of convergence. More importantly, we show that the modulus of the convergence rate is inversely proportional to the penalty parameter. That is, an asymptotically superlinear convergence is obtained if the penalty parameter used in the algorithm is increasing to infinity, or an arbitrarily Q-linear rate of convergence can be guaranteed if the penalty parameter is fixed but it is sufficiently large. Besides, as a byproduct, the convergence, as well as the convergence rate, of the distance from the primal sequence to the solution set of the problem is obtained. [ABSTRACT FROM AUTHOR]

Image stitching among images that have significant illumination changes will lead to unnatural mosaic image. An image stitching algorithm based on histogram matching and scale-invariant feature transform (SIFT) algorithm is brought out to solve the problem in this paper. First, histogram matching is used for image adjustment, so that the images to be stitched are at the same level of illumination, then the paper adopts SIFT algorithm to extract the key points of the images and performs the rough matching process, followed by RANSAC algorithm for fine matches, and finally calculates the appropriate mathematical mapping model between two images and according to the mapping relationship, a simple weighted average algorithm is used for image blending. The experimental results show that the algorithm is effective. [ABSTRACT FROM AUTHOR]

Optimization is an important and decisive task in science. Many optimization problems in science are naturally too complicated and difficult to be modeled and solved by the conventional optimization methods such as mathematical programming problem solvers. Meta-heuristic algorithms that are inspired by nature have started a new era in computing theory to solve the optimization problems. The paper seeks to find an optimization algorithm that learns the expected quality of different places gradually and adapts its exploration-exploitation dilemma to the location of an individual. Using birds' classical conditioning learning behavior, in this paper, a new particle swarm optimization algorithm has been introduced where particles can learn to perform a natural conditioning behavior towards an unconditioned stimulus. Particles are divided into multiple categories in the problem space and if any of them finds the diversity of its category to be low, it will try to go towards its best personal experience. But if the diversity among the particles of its category is high, it will try to be inclined to the global optimum of its category. We have also used the idea of birds' sensitivity to the space in which they fly and we have tried to move the particles more quickly in improper spaces so that they would depart these spaces as fast as possible. On the contrary, we reduced the particles' speed in valuable spaces in order to let them explore those places more. In the initial population, the algorithm has used the instinctive behavior of birds to provide a population based on the particles' merits. The proposed method has been implemented in MATLAB and the results have been divided into several subpopulations or parts. The proposed method has been compared to the state-of-the-art methods. It has been shown that the proposed method is a consistent algorithm for solving the static optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a mathematical programming model for solving a staff scheduling problem based on one-day duties (task patterns) of individual staff members. The model can accommodate various service types, management policies, and staff preferences. We first enumerate all feasible one-day duties and propose an iterated local search approach that incorporates various methodologies, including a size-reduction method and a very large-scale neighborhood search. For the very large-scale neighborhood search, we design a dynamic programming method that aims to find the most improved schedule and can be used in the rescheduling stage. Computational results show that the model and the proposed algorithm perform well for real-world instances in Japan. [ABSTRACT FROM AUTHOR]

In this paper, we study the stability of stochastic second-order programming when the probability measure is perturbed. Under the Lipschitz continuity of the objective function and metric regularity of the feasible set-valued mapping, the outer semicontinuity of the optimal solution set and Lipschitz continuity of optimal values are demonstrated. Moreover, we prove that, if the constraint non-degeneracy condition and strong second-order sufficient condition hold at a local minimum point of the original problem, there exists a Lipschitz continuous solution path satisfying the Karush–Kuhn–Tucker conditions. [ABSTRACT FROM AUTHOR]

This paper studies a two-agent single-machine scheduling problem with sum-of-processing-times-based learning consideration. The goal is to find an optimal schedule to minimize the total late work of the first agent subject to the restriction that the maximum lateness of the second agent has an upper bound. For this problem, a branch-and-bound algorithm along with several dominances and a lower bound is developed to find the optimal solution, and a tabu algorithm with several improvements is proposed to find the near-optimal solution. Computational experiments are provided to further measure the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In the present paper, we introduce a pair of multiobjective second-order symmetric variational control programs over cone constraints and derive weak, strong and converse duality theorems under second-order F -convexity assumption. Moreover, self-duality theorem is also discussed. Our results extend some of the known results in literature. [ABSTRACT FROM AUTHOR]

Nowadays, the management of soil resources for global food security is one of the strategic purposes of the countries. In this regard, creating soil nutrition's supply chain network for the fertility of agricultural fields is one of the most critical priorities of the government. In the above strategic network, we are faced with uncertain parameters that show a complex behavior under time, geography, and market changes. This parametric uncertainty is aggravated by the occurrence of disruptions such as sanctions and natural disasters, with devastating effects on strategic, tactical, and operational decisions. In this paper, by adopting a set of reactive and preventive strategies, the resilient supply chain network of dependent petrochemical industries is presented, consistent with the structure of its production cycle in Iran. To cope with the uncertainty, a robust programming approach was extended into flexible-possibilistic programming. The final model controls the uncertainty and risk-aversion of output decisions and effectively confronts the adverse effects of disruptions. Finally, using a real industrial study, the effectiveness and applicability of the final model are explained. Furthermore, by providing efficient solutions, mean cost and minimum standard deviations were achieved. [ABSTRACT FROM AUTHOR]

In decision making with interval-valued intuitionistic fuzzy preference relations (IVIFPRs), the consistency and its improvement are key issues. The aim of this paper is to investigate a new multiplicative consistency of IVIFPRs based on interval arithmetic, and propose goal programming approach to improve consistency and consensus. Based on analysis of the drawbacks of the existing consistency definitions of IVIFPR, new definition for multiplicative consistent IVIFPR is introduced. Based on the proposed multiplicative consistency and analysis of the relationship between interval-valued intuitionistic fuzzy weights and multiplicative consistent IVIFPR, goal programming-based model is developed for deriving interval-valued intuitionistic fuzzy weights from IVIFPR. To measure the multiplicative consistency degree of IVIFPRs, a consistency index is introduced and then an acceptable consistency is defined. For an IVIFPR with unacceptable consistency, a mathematical programming approach is proposed to improve its consistency. By considering the experts' subjective and consensus weights, the experts' comprehensive weights are determined. Some numerical examples illustrate the proposed models. [ABSTRACT FROM AUTHOR]

Owing to more vague concepts frequently represented in decision data, intuitionistic fuzzy sets (IFSs) are more flexibly used to model real-life decision situations. At the same time, with ever increasing complexity in many decision situations in reality, there are often some challenges for a decision maker to provide complete attribute preference information, i.e., the weights may be completely unknown or partially known. The aim of this paper is to develop an effective method for solving intuitionistic fuzzy multi-attribute decision making (MADM) problems with incomplete weight information. In this method, ratings of alternatives on attributes are expressed with IFSs. The multi-objective programming models are established to calculate unknown weights by using weight information partially known a priori. The derived minimum weighted Minkowski distance power models are used to determine the unknown weights and to generate the ranking order of the alternatives simultaneously. The proposed models are easily extended to intuitionistic fuzzy MADM problems with different weight information structures. An example of the supplier selection problem is examined to demonstrate applicability and flexibility of the proposed models and method. [ABSTRACT FROM AUTHOR]

This paper develops a multi-objective multi-layer location-pricing (MLLP) model with congested facilities in which the facilities act like a classic queuing system. The customers who arrive to this system receive service at all layers in a predetermined order to fulfill their demands. The goal is to determine (1) optimal number of the facilities required at each layer, (2) optimal allocation of customers to facilities, and (3) optimal price of providing service at each layer. The objective functions are to maximize the total profit of the system and to minimize the sum of travel and waiting times, simultaneously. The problem is formulated as a multi-objective nonlinear integer mathematical programming model. Since the problem is hard to be solved analytically, we present a multi-objective meta-heuristic algorithm (MHA) based on an electromagnetism-like mechanism (ELM) as a solution for multi-objective MLLP. This algorithm used an elitist mechanism to strengthen the structure of search engine in order to find better quality solutions. The results indicate the efficiency and effectiveness of the proposed algorithm in comparison with the traditional ELM. [ABSTRACT FROM AUTHOR]

In recent years, several convergent variants of the multi-block alternating direction method of multipliers (ADMM) have been proposed for solving the convex quadratic semidefinite programming via its dual, which is inherently a 3 -block separable convex optimization problem with coupled linear constraints. Among these multi-block ADMM-type algorithms, the modified 3 -block ADMM in [Chang, XK, SY Liu and X Li (2016). Modified alternating direction method of multipliers for convex quadratic semidefinite programming. Neurocomputing, 214, 575–586] bears a peculiar feature that the augmented Lagrangian function is not necessarily to be minimized with respect to the block-variable corresponding to the quadratic term in the objective function. In this paper, we lay the theoretical foundation of this phenomenon by interpreting this modified 3 -block ADMM as a special implementation of the Davis–Yin 3 -operator splitting [Davis, D and WT Yin (2017). A three-operator splitting scheme and its optimization applications. Set-Valued and Variational Analysis, 25, 829–858]. Based on this perspective, we are able to extend this modified 3 -block ADMM to a generalized 3 -block ADMM, in the sense of [Eckstein, J and DP Bertsekas (1992). On the Douglas–Rachford splitting method and the proximal point algorithm for maximal monotone operators. Mathematical Programming, 55, 293–318], which not only applies to the more general convex composite quadratic programming problems but also admits the flexibility of achieving even better numerical performance. [ABSTRACT FROM AUTHOR]

Wavelets are a relatively recent development in applied mathematics. Motivated by the recent paper [On accumulated spectrograms, Trans. Amer. Math. Soc. 368 (2016) 3629-3649] by Abreu, Gröchenig and Romero, we will prove in the present paper related results in the context of wavelets and time-scale analysis. [ABSTRACT FROM AUTHOR]

FP-Growth algorithm is an algorithm of association rules that does not generate a set of candidate, so it has very high practical value in face of the rapid growth of data volume in wisdom medical treatment. Because FP-Growth is a memory-resident algorithm, it will appear to be powerless when it is used for massive data sets. The paper combines Hadoop and FP-Growth algorithm and through the actual analysis of traditional Chinese medicine (TCM) data compares the performance in two different environments of stand-alone and distributed. The experimental results show that FP-Growth algorithm has a great advantage in the processing and execution of massive data after the MapReduce parallel model, so that it will have better development prospects for intelligent medical treatment. [ABSTRACT FROM AUTHOR]

Despite the effectiveness of the decomposition-based multi-objective evolutional algorithm (MOEA/D-M2M) in solving continuous multi-objective optimization problems (MOPs), its performance in addressing 0/1 multi-objective knapsack problems (MOKPs) has not been fully explored. In this paper, we use MOEA/D-M2M with an improved greedy repair strategy to solve MOKPs. It first decomposes an MOKP into a number of simple optimization subproblems and solves them in a collaborative way. Each subproblem has its own subpopulation, and then an improved greedy strategy is introduced to improve the performance of the proposed algorithm on MOKPs. Therein, a weight vector chosen randomly from a corresponding subpopulation is utilized to repair infeasible individuals or improve feasible individuals to have a better fitness, which improves the convergence of the population. Experimental studies on a set of test instances indicate that the MOEA/D-M2M with the improved greedy strategy is superior to MOGLS and MOEA/D in terms of finding better approximations to the Pareto front. [ABSTRACT FROM AUTHOR]

Customer self-pickup, offered as an option at most distribution centers, can provide flexible service times and save operational costs. Customers can either choose to self-pickup their demand or to have it delivered by a traditional way. At each customer point, the delivery demand is split, with the amount depending on the service and personal characteristics. In this situation, how to efficiently locate distribution centers and route deliveries becomes a vital problem for express companies that has not been studied in the literature. In this paper, for the first time, we propose a mathematical programming model for optimizing the location-routing problem with split demand (LRP-SD), together with a delivery ratio analysis model to predict self-pickup and delivery demand. To adapt the model to real-world cases, two heuristics as used in large-scale simulation-based optimization are devised and implemented. One is biogeography-based optimization (BBO) for solution speed, and the other is an adaptive large neighborhood search (ALNS) for solution quality. The two algorithms are compared using real data from a Shanghai-based express delivery company. [ABSTRACT FROM AUTHOR]

In the discrete-valued input control of dynamical systems, quantizers, which convert continuous-valued signals into discrete-valued ones, play an important role. This paper focuses on the design problem of dynamic quantizers with fixed-structures such as a fixed-order and a decentralized structure. It is known to be difficult to solve the problem since it is NP-hard. Therefore, in order to solve the problem in practical steps, we adopt differential evolution (DE) which is one of the evolutionary computation algorithms. We first propose a DE-based design method of quantizers with fixed structures. Then, the effectiveness of the proposed method is verified by examples in the discrete-valued input control of dynamical systems. Moreover, we compare the performance of the proposed DE-based method with that of a particle swarm optimization (PSO)-based method, and show that DE is an easy-to-use and reliable tool for quantizer design problem. [ABSTRACT FROM AUTHOR]

Research in optimization under uncertainty is alive. It assumes different shapes and forms, all concurring to the general goal of designing effective and efficient tools for handling imprecision in an Optimization setting. In this paper we present a new approach for dealing with multiobjective programming problems with fuzzy objective functions. Similar to many approaches in the literature, our approach relies on the deffuzification of involved fuzzy quantities. Our improvement stem from the choice of a deffuzification operator that captures essential features of fuzzy parameters at hand rather than those that yield single values, leading to a loss of many useful information. Two oracles play a pivotal role in the proposed method. The first one returns a near interval approximation to a given fuzzy number. The other one delivers a Pareto Optimal solution of the resulting multiobjective program with interval coefficient. A numerical example is also provided for the sake of illustration. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of managing the traffic flow at traffic-light controlled junctions adopting a mixed, nonlinear programming model. More precisely, we adopt an adaptive, acyclic control system based on the genetic algorithm (GA), a system that allows to accurately decide in real time on the light switching pattern that minimizes the traffic waiting time. Equally importantly, the inherent crossroads constraints, namely, the security of their users and the structure thereof, also referred to as the antagonism principle, are considered in the proceeding of this method. The implementation of this model on two crossroads significantly decreases the waiting time and regulates the traffic flow. Therefore, the adaptive control system based on the GA proves efficient compared to the nonadaptive system. [ABSTRACT FROM AUTHOR]

Hotel competency is inherently intangible and multivariate. It involves a multiple-criteria decision-making problem. Particularly in the currently rapidly shrinking hotel market in Taiwan, what determines domestic chain hotel groups' (DCHGs') competence and survival involves more complex and multiple factors. A practical and effective tool is urgently required for making appropriate decisions. This paper thus proposes a combined consistent fuzzy preference relations (CFPR) and the VIKOR model, aiming to prioritize criteria and solution alternatives for hotel managers. In contrast to prior studies that have used mathematical programming, the model here is also tested using real-world hotel management cases and expert consultation. First, based upon the resource-based view, we propose 21 criteria and six dimensions as the determinants of DCHG competencies. Then, VIKOR is applied to produce the most appropriate alternatives with the corresponding weights obtained using the CFPR method. The combined method successfully manages the problems of linguistic ambiguity and consistency, determines the relative weights of the different factors and provides a ranking priority. The result is compared with some similar methods and is shown to be more useful and reliable. Finally, the verified model can be used to produce strategies. A decision-maker can make selection(s) from the solution formula. Our study may thus contribute to the hotel industry with the efficient decision-making tool of resource-based view (RSV) and two-phase methodologies. [ABSTRACT FROM AUTHOR]

Scheduling and resource allocation problems are widespread in many areas of today's technology and management. Their different forms and structures appear in production, logistics, software engineering, computer networks, project and human resources management, services, etc. The literature (problem classification, scheduling and resource allocation models, solutions) is vast and exhaustive. In practice, however, classical scheduling problems with fixed structures and standard constraints (precedence, disjoint, etc.) are rare. Practical scheduling problems include also logical and nonlinear constraints, and they use nonstandard criteria of schedule evaluations. Indeed, in many cases, decision makers are interested in the feasibility and/or optimality of a given schedule for specified conditions formulated as general and/or specific questions. Thus, there is a need to develop a programming framework that will facilitate the modeling and solving of a variety of diverse scheduling problems. The framework should be able to (a) model any types of constraints, (b) ask questions/criteria relating to the schedule execution mode and (c) be highly effective in finding solutions (schedule development). This paper proposes such a constraint-based declarative programming framework for modeling and solving scheduling problems which satisfies the assumptions above. It was built with the Constraint Logic Programming (CLP) environment and supported with Mathematical Programming (MP). The functionality and effectiveness of this framework are presented with the use of an illustrative example for the resource-constrained scheduling problem with additional resources. [ABSTRACT FROM AUTHOR]

Very recently Günther et al. [E. Günther, O. Maurer, N. Megow and A. Wiese (2013). A new approach to online scheduling: Approximating the optimal competitive ratio. In Proc. 24th Annual ACM-SIAM Symp. Discrete Algorithms (SODA).] initiate a new systematic way of studying online problems by introducing the competitive ratio approximation scheme (simplified as competitive schemes in this paper), which is a class of algorithms {Aϵ|ϵ > 0} with a competitive ratio at most ρ*(1 + ϵ), where ρ* is the best possible competitive ratio over all online algorithms. Along this line, Günther et al. [E. Günther, O. Maurer, N. Megow and A. Wiese (2013). A new approach to online scheduling: Approximating the optimal competitive ratio. In Proc. 24th Annual ACM-SIAM Symp. Discrete Algorithms (SODA).] provide competitive schemes for several online over time scheduling problems like Qm|rj, (pmtn)|∑wjcj, while the running times are polynomial if the number of machines is a constant. In this paper, we consider the classical online scheduling problems, where jobs arrive in a list. We present competitive schemes for Rm‖Cmax and Rm‖ ∑iCip , where the running times are polynomial if the number of machines is a constant. Specifically, we are able to derive a competitive scheme for P‖Cmax which runs in polynomial time even if the number of machines is an input. Our method is novel and more efficient than that of Günther et al. [E. Günther, O. Maurer, N. Megow and A. Wiese (2013). A new approach to online scheduling: Approximating the optimal competitive ratio. In Proc. 24th Annual ACM-SIAM Symp. Discrete Algorithms (SODA).] Indeed, by utilizing the standard rounding technique for the off-line scheduling problems, we reformulate the online scheduling problem into a game on an infinite graph, through which we arrive at the following key point: Assuming that the best competitive ratio is ρ*, for any online algorithm there exists a list of a polynomial number of jobs showing that its competitive ratio is at least ρ* - O(ϵ). Interestingly such a result is achieved via a dynamic programming algorithm. Our framework is also applicable to other online problems. [ABSTRACT FROM AUTHOR]

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling. [ABSTRACT FROM AUTHOR]

Influence maximization is a well-known problem in the social network analysis literature which is to find a small subset of seed nodes to maximize the diffusion or spread of information. The main application of this problem in the real-world is in viral marketing. However, the classic influence maximization is disabled to model the real-world viral marketing problem, since the effect of the marketing message content and nodes' opinions have not been considered. In this paper, a modified version of influence maximization which is named as "opinion-aware influence maximization" (OAIM) problem is proposed to make the model more realistic. In this problem, the main objective is to maximize the spread of a desired opinion, by optimizing the message content, rather than the number of infected nodes, which leads to selection of the best set of seed nodes. A nonlinear bi-objective mathematical programming model is developed to model the considered problem. Some transformation techniques are applied to convert the proposed model to a linear single-objective mathematical programming model. The exact solution of the model in small datasets can be obtained by CPLEX algorithm. For the medium and large-scale datasets, a new genetic algorithm is proposed to cope with the size of the problem. Experimental results on some of the well-known datasets show the efficiency and applicability of the proposed OAIM model. In addition, the proposed genetic algorithm overcomes state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm to automatically detect meaningful modes in a histogram. The proposed method is based on the behavior of local minima in a scale-space representation. We show that the detection of such meaningful modes is equivalent in a two classes clustering problem on the length of minima scale-space curves. The algorithm is easy to implement, fast and does not require any parameter. We present several results on histogram and spectrum segmentation, grayscale image segmentation and color image reduction. [ABSTRACT FROM AUTHOR]

Using the central value operator and the semi-dispersion measure of fuzzy number, this paper proposes the definitions of the lower and upper semi-variances. A general multiperiod fuzzy portfolio optimization model with return demand on the portfolio at each period is proposed with the objectives of maximizing both terminal wealth and the cumulative diversification degree of portfolios over the whole investment horizon, and minimizing terminal risk. A fuzzy multiobjective nonlinear programming technique is applied to convert the proposed model into a single-objective model. A genetic algorithm (GA) is given to solve it. Besides, a numerical example is given to illustrate the application of the proposed model and the effectiveness of the designed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel stereo matching algorithm to solve environment sensing problems. It integrates a non-convex optical flow and Viterbi process. The non-convex optical flow employs a new adaptive weighted non-convex Total Generalized Variation (TGV) model, which can obtain sharp disparity maps. Structural similarity, total variation constraint, and a specific merging strategy are combined with the 4 bi-directional Viterbi process to improve the robustness. In the fusion of the optical flow and Viterbi process, a new occlusion processing method is incorporated in order to get more sharp disparity and more robust result. Extensive experiments are conducted to compare this algorithm with other state-of-the-art methods. Experimental results show the superiority of our algorithm. [ABSTRACT FROM AUTHOR]

Mathematical programming representations (MPRs) are discrete-event simulation models represented using math programming. In this paper, we first introduce an MPR formulation for single-server queueing systems based on idle times. We use this formulation to conduct a perturbation analysis to study the effect of imposing a constraint on idle times. We apply the formulation to obtain a linear programming-based gradient estimator for idle times. We demonstrate the integration of optimization into MRP and identify properties of the optimal solution to facilitate finding the optimal solution. [ABSTRACT FROM AUTHOR]

In this paper, we present a bilinear second-order cone programming safe approximation for the distributionally robust chance constrained program (DRCCP), assuming that the support of the uncertain parameters, and the first and second marginal moments of the probability with respect to the interval constraint imposed on the sum of the uncertain parameters are given. If we further know the covariance matrix, we can obtain a bilinear semi-definite programming safe approximation. Preliminary numerical tests indicate that the proposed models are competitive. [ABSTRACT FROM AUTHOR]

A reduction transforms a binary picture only by changing some black points to white ones, which is referred to as deletion. Sequential reductions traverse the black points of a picture, and consider a single point for possible deletion, while parallel reductions can delete a set of black points simultaneously. Two reductions are called equivalent if they produce the same result for each input picture. A deletion rule is said to be equivalent if it yields a pair of equivalent parallel and sequential reductions. This paper introduces a class of equivalent deletion rules that allows us to establish a new sufficient condition for topology-preserving parallel reductions in arbitrary binary pictures. In addition we present a method of verifying that a deletion rule given by matching templates is equivalent, a necessary and sufficient condition for order-independent deletion rules, and a sufficient criterion for order-independent and translation-invariant parallel subfield-based algorithms. [ABSTRACT FROM AUTHOR]

This study surveys the use of fuzzy numbers in classic optimization models, and its effects on making decisions. In a wide sense, mathematical programming is a collection of tools used in mathematical optimization to make good decisions. There are many sectors of economy that employ it. Finance and government, logistics and manufacturing, the distribution of the electrical power are worth to be first mentioned. When real life problems are modeled mathematically, there is always a trade-off between model's accuracy and complexity. By this survey, we aim to present in a concise form some mathematical models from the literature together with the methods to solve them. We will focus mainly on fuzzy fractional programming problems. We will also refer to but not describe in detail the multi-criteria decision-making problems involving fuzzy numbers and linear fractional programming models. [ABSTRACT FROM AUTHOR]

This paper presents two semi-definite programming (SDP) based methods to solve the Key Player Problem (KPP). The KPP is to identify a set of nodes (i.e., key players) from a social network of size such that the number of nodes connected to these nodes is maximized. The KPP has applications in social diffusion and products adoption as it helps maximizing information diffusion and impact. We first formulate the KPP as an integer program (IP) and then convert it into an SDP formulation, which can be solved efficiently and produce a set of high quality candidate solutions. We develop an IP-based algorithm and a stochastic search (greedy) algorithm to find the final solution for the KPP. We compare our algorithms with existing methods in small and large networks with different network structures, including random graph, scale-free network, and community-based scale-free network (CSN). Computational results show that our algorithms are more efficient in solving the KPP in all networks. In addition, we examine how the network structure influences the nodes coverage. It is found that CSNs allow the highest nodes coverage due to their community and scale-free structure. [ABSTRACT FROM AUTHOR]

In this paper, signed distance of Symmetrical Intuitionistic Fuzzy Numbers (SIFNs) is introduced. Based on this signed distance and the crisp ranking system on real numbers, a new ranking system for SIFNs is defined, which seems to be very realistic. To illustrate the applicability and suitability of the proposed ranking method and to deal with ambiguity and imprecision, one of the vital mathematical programming problem viz. Solid Transportation Problem (STP) is formulated in intuitionistic fuzzy environment. A new method has been proposed to compute initial basic feasible solution for the same. Also the significance of the proposed approach over existing methods is illustrated. Finally numerical examples are solved to demonstrate the efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

Swarm intelligence algorithms are amongst the most efficient approaches toward solving optimization problems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments. However, numerous real-world problems are dynamic which could not be solved using static approaches. In this paper, a novel approach based on artificial fish swarm algorithm (AFSA) has been proposed for optimization in dynamic environments in which changes in the problem space occur in discrete intervals. The proposed algorithm can quickly find the peaks in the problem space and track them after an environment change. In this algorithm, artificial fish swarms are responsible for finding and tracking peaks and several behaviors and mechanisms are employed to cope with the dynamic environment. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of tested dynamic environments modeled by moving peaks benchmark. [ABSTRACT FROM AUTHOR]

Motivated by a variety of applications in sharing economy, we study an identical parallel machine scheduling problem with due date-to-deadline window by jointly considering machine sharing and the time value of money. A factory owns a set of parallel identical machines and processes a set of production orders within a finite time period. In the sharing setting, the factory may also rent external machines to handle a part of orders by paying some extra cost. The factory aims to determine the sharing policy of the production orders and the scheduling rule of machines, to maximize its total future value of profits by satisfying the orders. To the best of our knowledge, there are no previous results for this problem. In this work, a mathematical programming model is derived, and a problem-specific genetic algorithm and a heuristic are proposed to solve large-scale instances. Numerical experiments using randomly generated instances are carried out to evaluate the effectiveness and efficiency of the proposed solution methods. [ABSTRACT FROM AUTHOR]

Supplier selection is a practical problem in supply chain management and quality is the most important criterion in supplier selection. In this study, we developed a supplier selection model based on process quality, in which the Six Sigma quality index Q p k is used as a tool to assess the process quality provided by suppliers. Note that index estimation based on sample data is prone to uncertainty in the assessment of process quality. Therefore, we derived the confidence interval of Q p k via mathematical programming to reduce the likelihood of assessment miscalculations, and then used this interval to perform a pairwise comparison of suppliers. Our goal was to identify criteria that can be used to select the optimal suppliers for long-term collaborations and sustainable partnerships. A case study is also presented to demonstrate the practical implementation of the proposed method. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a mathematical programming approach with widespread applications in productivity and efficiency analysis. Compared with traditional DEA models, two-stage DEA models show the performance of each process and make available more information for decision making. In an article by Kao and Liu, models were proposed for combining a two-stage process to achieve overall fuzzy efficiency measures. Their method follows the simple geometric average approach and uses the product of two efficiencies. The present article applies a different angle for efficiency analysis in the two-stage fuzzy DEA. We suggest that the overall efficiency score of a decision-making unit (DMU) is defined as total weight of stage efficiencies, not as the simple product of their efficiency. Moreover, the proposed fuzzy DEA models are different from the model by Kao and Liu for fuzzy data in that our models are linear without the need for additional changes in variables and use the same set of constraints to measure the efficiency of DMUs with fuzzy input and output data. While the models by Kao and Liu are a nonlinear optimization problem that need additional changes in variables, and use different sets of constraints to measure fuzzy efficiencies. Additionally, our proposed approach evaluates the performance of DMUs from both optimistic and pessimistic viewpoints. Finally, using the proposed approach, the Taiwanese non-life insurance company problem will be investigated. [ABSTRACT FROM AUTHOR]

For a system of two tandem queues with a finite intermediate buffer, we study the asymptotically maximal throughput as the number of servers in each station grows to infinity. First, we study the system with only dedicated servers, and then we examine the system with both dedicated and flexible servers. We assume that travel times between the two stations are negligible and that each server can only work on one customer at a time. We model the system as a birth–death Markov process, derive a closed form solution for the stationary distribution, and quantify the maximal asymptotic normalized throughput as the number of servers grows to infinity. We show that flexibility is more favorable for small systems, and as the number of servers grows, the benefits of flexibility decrease. Furthermore, we prove that when the number of servers goes to infinity, there is no need of flexibility at all, as the maximum value of the throughput is obtained. However, flexibility still has a secondary beneficial effect — a little flexibility (on the order of the square root of the number of dedicated servers at each station) guarantees that all dedicated servers are busy and results in faster convergence to the maximum throughput. [ABSTRACT FROM AUTHOR]

There are several factors that need to be considered in fleet management when it is necessary to resolve disturbances which necessitate aircraft re-assignment due to flight cancellations, making this a multiple-criteria decision-making problem. A change in the type of aircraft assigned can lead to additional alterations as well as directly affecting connecting flights and interconnected schedules serving specific flight segments. Decision-making is crucial and involves the consideration of complex cost effects, with possible disruptive actions evaluated according to the priorities of airline management and the available resources. Airline managers require a practical and flexible tool to help them make appropriate decisions in a rapidly changing and highly competitive environment. Differing from prior studies using mathematical programming, we propose a hybrid model based on the decision-making trial and evaluation laboratory method and the concepts of analytic network process (DANP) to aid in the decision-making process. We also recommend using the VIKOR method to select the most appropriate alternatives, with the corresponding weights obtained using the DANP method. The efficiency and effectiveness of the proposed method is demonstrated by testing it on a real-world flight cancellation case and in consultation with experts. The results show that this hybrid model is an effective resource that airline managers can use to address and resolve aircraft re-assignment irregularities. [ABSTRACT FROM AUTHOR]

A firm sets up his facilities including manufacturing/remanufacturing plants and distribution/collection centers, incorporating an existing closed-loop supply chain (CLSC) network. The entering firm has to compete with the existing firms in the existing network. The entering firm behaves as the leader of a Stackelberg game while the existing firms in the existing network are followers. We assume that the entering firm can anticipate the existing firms' reaction to his potential location decision before choosing his optimal policy. We use a CLSC network equilibrium model in which the decision makers are faced with multiple objectives to capture the existing firms' reaction. A mathematical programming model with equilibrium constraints is developed for this competitive CLSC network design problem by taking into account the market competition existing in the decentralized CLSC network. A solution method is developed by integrating Genetic algorithm with an inexact logarithmic-quadratic proximal augmented Lagrangian method. Finally, numerical examples and the related results are studied for illustration purpose. [ABSTRACT FROM AUTHOR]

In JPEG2000 standard, the number of bit planes of wavelet coefficients to be used in encoding is dependent on the compression ratio as well as subbands. These significant wavelet bit planes can be utilized to embed bits of secret data as they are retained in the final bit stream after Tier-2 encoding. In proposed techniques, the above mentioned concept have been utilized to embed secret data bits in lowest significant bit planes of the quantized wavelet coefficients of a cover image. In first technique, secret data is converted into a series of symbols using multiple bases notational system. These bases are selected by using the degree of local variation of coefficient values of the cover image so that coefficient of a complex region can potentially carry more secret data bits as compared to coefficients of smooth region. Symbols of secret data are embedded into bit planes of significant quantized wavelet coefficients by using EMD approaches. In second technique, the secret data bits are embedded into significant quantized wavelet coefficients by using modified EMD. Experimental results show that these proposed techniques provide large embedding capacity and better visual quality stego images than existing steganography techniques applicable to JPEG2000 compressed images. It has also been shown that modified EMD-based technique is better than EMD with multiple bases notational system (MBNS)-based technique. [ABSTRACT FROM AUTHOR]

A sparse Bayesian linear regression model is proposed that generalizes the Bayesian Lasso to a class of Bayesian models with scale mixtures of normal distributions as priors for the regression coefficients. We assume a hierarchical Bayesian model with a binary indicator for whether a predictor variable is included in the model, a generalized normal prior distribution for the coefficients of the included variables, and a Student- t error model for robustness to heavy tailed noise. Our model out-performs other popular sparse regression estimators on synthetic and real data. [ABSTRACT FROM AUTHOR]

We discuss the construction of designs for estimation of nonparametric regression models with autocorrelated errors when the mean response is to be approximated by a finite order linear combination of dilated and translated versions of the Daubechies wavelet bases with four vanishing moments. We assume that the parameters of the resulting model will be estimated by weighted least squares (WLS) or by generalized least squares (GLS). The bias induced by the unused components of the wavelet bases, in the linear approximation, then inflates the natural variation of the WLS and GLS estimates. We therefore construct our designs by minimizing the maximum value of the average mean squared error (AMSE). Such designs are said to be robust in the minimax sense. Our illustrative examples are constructed by using the simulated annealing algorithm to select an optimal -point design, which are integers, from a grid of possible values of the explanatory or design variable . We found that the integer-valued designs we constructed based on GLS estimation, have smaller minimum loss when compared to the designs for WLS or ordinary least squares (OLS) estimation, except when the correlation parameter approaches 1. [ABSTRACT FROM AUTHOR]

The first-order and the second-order directional derivatives of singular values are used to characterize the tangent cone, the normal cone and the second-order tangent set of the epigraph of the nuclear norm of matrices. Based on the variational geometry of the epigraph, the no gap second-order optimality conditions for the optimization problem, whose constraint is defined by the matrix cone induced by the nuclear norm, are established. [ABSTRACT FROM AUTHOR]

The identification of fingerprints and palmprints is considered a challenging research line in Biometrics. Nowadays, the accuracy of these techniques highly depends on the quality of the involved impressions, specially if the matching is performed in latent cases. In the present work, a new algorithm that reports a high accuracy in both cases is presented. This proposal requires a minimum amount of manually marked information in latent impressions and deals successfully with problems of missing and spurious minutiae. Moreover, we improved a verification algorithm based on a previously introduced feature model. The algorithm uses a strategy for finding adaptable local matches between substructures obtained from images. The experimental results show that our proposal achieves high accuracy for all cases, despite the major differences that exist between a palmprint and a fingerprint. For latent fingerprint identification our approach shows its robustness in retrieving 258 latents from different size scale of the background dataset, achieving a rank-1 identification rate over 57% in all cases. Carrying out a similar experimentation for latent palmprint identification, our approach achieved a rank-1 identification rate over 75%. [ABSTRACT FROM AUTHOR]