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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 the sensitivity analysis of weakly efficient extreme solutions in multiple objective linear programming (MOLP). The aim of the paper is to compute the set of the parameters (corresponding to one coefficient) for which a given extreme point is a weakly efficient solution. We also focus on the properties of the parameters set by proving convexity and closeness of this set. Moreover, we compare the results of the sensitivity analysis of efficiency and of weak efficiency in MOLP. [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]

The considered problem in this paper is a 0-1 Multi-Quadratic Fractional Programming Problem (0-1MQFP) without the restriction of positive denominators. The two important contributions of the paper are: (a) it proposes a linearization technique to solve any type of 0-1MQFP problem; and (b) when applied to the 0-1MQFP problem with the restriction of positive denominators, it requires less number of constraints and variables as compare with the available techniques in the literature. The problem is proved to be NP-hard. The linearization process is summarized with the help of an algorithm and flow chart, and further illustrated by examples. Codes for linearizing 0-1MQFPP is given in the APPENDIX. [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 presents an algorithm to generate ensemble classifier by joint optimization of accuracy and diversity. It is expected that the base classifiers in an ensemble are accurate and diverse (i.e., complementary iii terms of errors) among each other for the ensemble classifier to be more accurate. We adopt a multi-objective evolutionary algorithm (MOEA) for joint optimization of accuracy and diversity on our recently developed nonuniform layered cluster oriented ensemble classifier (NULCOEC). In NULCOEC, the data set is partitioned into a variable number of clusters at different layers. Base classifiers are then trained on the clusters at different layers. The performance of NULCOEC is a function of the vector of the number of layers and clusters. The research presented in this paper investigates the implication of applying MOEA to generate NULCOEC. Accuracy and diversity of the ensemble classifier is expressed as a function of layers and clusters. A MOEA then searches for the combination of layers and clusters to obtain the nondominated set of (accuracy, diversity). We have obtained the results of single objective optimization (i.e., optimizing either accuracy or diversity) and compared them with the results of MOEA on sixteen UCI data sets. The results show that the MOEA can improve the performance of ensemble classifier. [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]

In this paper we propose an approach for multiobjective programming problems with fuzzy number coefficients. The main idea behind our approach is to approximate involved fuzzy numbers by their respective nearest interval approximation counterparts. An algorithm that returns a nearest interval approximation to a given fuzzy number, plays a pivotal role in the proposed method. Our approach contrasts markedly with those based on deffuzification operators which replace a fuzzy set by a single real number leading to a loss of many important information. A numerical example is also provided for the sake of illustration. [ABSTRACT FROM AUTHOR]

This paper examines the allocation of yard cranes and blocks for yard activities in container terminals. In this paper, the yard cranes are confined to rail mounted gantry cranes (RMGC), which are characterized by the restricted traveling range on a pair of rails. Since RMGCs and yard blocks are tightly bound to each other, when allocating them, we should make sure that the RMGCs allocated for a yard activity are able to together cover the blocks allocated for the corresponding yard activity. In addition, considering that there are four basic activities occurring in the yard which compete with each other for the scarce resources and have different requirements and priorities in the allocation of blocks and yard cranes, we treat them in a single model rather than in multiple independent models as were generally done in literature. A mixed integer programming model is constructed, and an iterative decomposition solution procedure is proposed for the problem. Based on the solution procedure, a decision support system is developed and implemented for a terminal in Tianjin seaport. Using the actual data, the numerical experiments show the effectiveness and efficiency of the decision support system. [ABSTRACT FROM AUTHOR]

The aim of this paper is to use the concept of extended linguistic variables to develop a new methodology for solving multiattribute group decision making problems under linguistic assessments. In this methodology, using the generalized induced ordered weighted averaging operator and extended linguistic variables, linguistic ratings of each alternative on all attributes for each decision maker are aggregated into a comprehensive assessment, which is not always a linguistic term in the linguistic term set designated a priori. Comprehensive assessments of each alternative on all decision makers are similarly aggregated into a comprehensive assessment of the alternative for the group. An optimal linguistic term in the linguistic term set which approaches the comprehensive assessment of each alternative for the group as close as possible is computed and an extended 2-tuple fuzzy linguistic representation is obtained for the alternative. The ranking order of alternatives for the group is generated according to the extended 2-tuple fuzzy linguistic representations. A numerical example is examined to demonstrate applicability and implementation process of the methodology proposed in this paper. [ABSTRACT FROM AUTHOR]

Points out that this special issue consists of seven papers covering various important areas of evolutionary optimization. Main reasons for using evolutionary computation in solving optimization problems; Techniques for solving discrete constrained optimization problems; Brief survey of research on network design problems, fixed charge transportation problem, minimum spanning tree problem, centralized network design, local area network design and optimal scheduling problems.

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 this paper, we extend a class of merit functions proposed by Kanzow et al. (1997) for linear/nonlinear complementarity problems to Symmetric Cone Complementarity Problems (SCCP). We show that these functions have several interesting properties, and establish a global error bound for the solution to the SCCP as well as the level boundedness of every merit function under some mild assumptions. Moreover, several functions are demonstrated to enjoy these properties. [ABSTRACT FROM AUTHOR]

In this paper, we introduce new concepts of ε-weak efficient solutions and ε-efficient solutions for a nonconvex multiobjective fractional programming problem involving n-set functions. Using an ε-parametric approach and a new theorem of alternative for nonconvex n-set functions, some necessary and sufficient conditions for ε-approximate solutions axe derived. [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]

This paper deals with the problem of optimum design of a foundation for rotating machinery with a view to minimize vibration transmission from a machine source to its supporting structure. The problem of analysis and optimization of the installation systems of machinery has been extensively researched on the assumption of a rigid supporting structure. The design based on a rigid supporting structure model is reasonable for the installation of machinery in many real engineering situations. However, this rigid support based model may not be appropriate for the problem studied herein where the machinery is to be installed on a relatively flexible supporting plate. Thus, a generalized mathematical model of mobility power flow is developed in this paper, with a rotating machine as vibration source, resilient mounts as isolator, and a flexible sup-porting plate as receiver. The objective of minimizing vibration transmission is realized by optimization of stiffness coefficients of resilient mounts with a constraint on the vibration level of the machine. The design objective is chosen as the minimization of the power flow transmitted to the flexible supporting plate through the resilient mounts at the excitation frequency of the machinery. Both a single excitation frequency and a range of excitation frequencies are considered. A gradient-based mathematical programing method is selected for its advantage of efficiently solving the current type of optimization problem with multiple mounts and multi-degree-of-freedom vibration transmission. The sensitivities of the objective and the constraint functions with respect to the design variables are derived analytically. The design and per-formance of the optimized machinery foundation is illustrated and discussed using several numerical examples. The optimized mounting system is suitable for the installation case of a rotating machine with a low or medium service speed on a flexible supporting plate structure. [ABSTRACT FROM AUTHOR]

For solving complex problems, in many cases, software alone might not be sufficient and we need hybrid systems of software and humans in which humans not only direct the software performance but also perform computing and vice versa. Therefore, we advocate constructing "social computers" which combine software and human services. However, to date, human capabilities cannot be easily programmed into complex applications in a similar way like software capabilities. There is a lack of techniques to conceptualize and program human and software capabilities in a unified way. In this paper, we explore a new way to virtualize, provision and program human capabilities using cloud computing concepts and service delivery models. We propose novel methods for conceptualizing and modeling clouds of human-based services (HBS) and combine HBS with software-based services (SBS) to establish clouds of hybrid services. In our model, we present common APIs, similar to well-developed APIs for software services, to access individual and team-based compute units in clouds of HBS. Based on that, we propose a framework for utilizing SBS and HBS to solve complex problems. We present several programming primitives for hybrid services, also covering forming hybrid solutions consisting of software and humans. We illustrate our concepts via some examples of using our cloud APIs and existing cloud APIs for software. [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 considers the general Spatial Price Equilibrium (SPE) problem under constraints of supply-guarantee at demand markets and, in the meantime, protecting the scarce resources at supply markets. We first formulate the equilibrium problem as a variational inequality (VI) problem with partially unknown mapping. Then, we propose a progressive equilibration algorithm for solving the problem under consideration. The global convergence of the proposed method is proved under suitable assumptions. Some preliminary numerical results demonstrate the reliability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a load balancing mechanism based on cell breathing was proposed for the hybrid EPON-WiMAX network. The hybrid EPON-WiMAX network integrates the emerging Ethernet Passive Optical Network (EPON) and Worldwide Interoperability for Microwave Access (WiMAX) technologies to support high bandwidth, broadband access, mobility, and Quality of Service (QoS) guarantee. Load balancing is required due to uneven distribution of traffic among different WiMAX base stations (BS) as well as mobility of WiMAX Subscriber Station (SS). Cell breathing is a well-known load balancing technique adopted in wireless networks. By changing the transmission powers of the base stations, the cell breathing technique is able to adjust the traffic, both uplink and downlink, load of base stations. In the integrated EPON-WiMAX network, transmission powers of WiMAX base stations can be optimally allocated at the EPON optical networking unit (ONU). In this paper, we formulate the cell breathing-based load balancing problem into a linear programming problem. Our goal is to find the best power adjustment that maximizes system throughput. To alleviate the time complexity of solving the linear programming problem, we propose a Heaviest Load First Algorithm (HLFA) to obtain the near optimal solution. The solution obtained by the HLFA algorithm will not waste any transmission power to make the entire system energy-efficiency. We demonstrate the performance of HLFA via extensive simulations. The simulation results show that the HLFA can provide the best solution to achieve load balancing and enhance the system throughput as compared to existing solutions. Furthermore, HLFA maintains good performance even when the system has multiple overloaded ONU-BSs. [ABSTRACT FROM AUTHOR]

In this paper, some new results on the l1 exact penalty function method are presented. A simple optimality characterization is given for the nonconvex differentiable optimization problems with inequality constraints via the l1 exact penalty function method. The equivalence between sets of optimal solutions in the original mathematical programming problem and its associated exact penalized optimization problem is established under suitable r-invexity assumption. The penalty parameter is given, above which this equivalence holds. Furthermore, the equivalence between a saddle point in the considered nonconvex mathematical programming problem with inequality constraints and a minimizer in its penalized optimization problem with the l1 exact penalty function is also established. [ABSTRACT FROM AUTHOR]

In this paper a multi-product, multi-echelon location–allocation model for the optimization of a supply chain design is proposed. This model integrated inventory decisions into distribution network design with stochastic market demands. The goal is to select the optimum numbers, locations, and capacities of the opening warehouses so that all customer demands to be satisfied at minimum total costs of the distribution network. We develop a nonlinear mixed-integer model and propose an efficient heuristic solution procedure for the problem. The solution approach is based on Lagrangian relaxation, improved with efficient heuristic to solve complex sub-problems. Computational results indicate that the proposed method yields good solutions with high quality within a reasonable computational time for various real-size problems. [ABSTRACT FROM AUTHOR]

Many researches have been carried out in fuzzy linear regression since the past three decades. In this paper, a fuzzy linear regression model based on goal programming is proposed. The proposed model takes into account the centers of fuzzy data as an important feature as well as their spreads. Furthermore, the model can deal with both symmetric and non-symmetric data. To show the efficiency of proposed model, it is compared with some earlier methods based on simulation studies and numerical examples. Moreover, the sensitivity of the model to outliers is discussed. [ABSTRACT FROM AUTHOR]

Weighted additive models are well known for dealing with multiple criteria decision making problems. Fuzzy goal programming is a branch of multiple criteria decision making which has been applied to solve real life problems. Several weighted additive models are introduced to handle fuzzy goal programming problems. These models are based on two approaches in fuzzy goal programming namely goal programming and fuzzy programming techniques. However, some of these models are not able to solve all kinds of fuzzy goal programming problems and some of them that appear in current literature suffer from a lack of precision in their formulations. This paper focuses on weighed additive models for fuzzy goal programming. It explains the oversights within some of them and proposes the necessary corrections. A new improved weighted additive model for solving fuzzy goal programming problems is introduced. The relationships between the new model and some of the existing models are discussed and proved. A numerical example is given to demonstrate the validity and strengths of the new model. [ABSTRACT FROM AUTHOR]

Fuzzy mathematics is a generalization in which fuzzy numbers replace real numbers and fuzzy arithmetic replaces real arithmetic. It is an excellent scope for modeling vague and uncertain aspects of the actual environments. In this important area, Dubois and Prade1 defined a fuzzy matrix as a rectangular array of fuzzy numbers. They have also defined the LR type fuzzy numbers with some useful approximate arithmetic operators. The aim of this paper is to extend some useful aspects of linear algebra e.g. determinant, norm and eigenvalue for fuzzy matrices with LR fuzzy number entries by the use of fuzzy arithmetic. Finally, applications in fuzzy analytical hierarchy process (AHP) are investigated. [ABSTRACT FROM AUTHOR]

There exists little investigation on multiattribute decision making under intuitionistic fuzzy environments although both crisp and fuzzy multiattribute decision making have achieved a great progress. In this paper, multiattribute decision making problems using intuitionistic fuzzy sets are investigated and the TOPSIS is further extended to develop one new methodology for solving such problems. In this methodology, an interval fractional programming model is constructed on the basis of the relative closeness coefficient using the TOPSIS. Comprehensive evaluation of each alternative, which may be described as an intuitionistic fuzzy set or interval number, is calculated using two auxiliary mathematical programming problems derived from the interval fractional programming model proposed in this paper. Optimal degrees of membership for alternatives are calculated to determine their ranking order using the concept of likelihood based on the ranking method of interval numbers. Implementation process of the method proposed in this paper is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

This paper establishes a theory framework of a class of nonlinear Lagrangians for solving nonlinear programming problems with inequality constraints. A set of conditions are proposed to guarantee the convergence of nonlinear Lagrangian algorithms, to analyze condition numbers of nonlinear Lagrangian Hessians as well as to develop the dual approaches. These conditions are satisfied by well-known nonlinear Lagrangians appearing in literature. The convergence theorem shows that the dual algorithm based on any nonlinear Lagrangian in the class is locally convergent when the penalty parameter is less than a threshold under a set of suitable conditions on problem functions and the error bound solution, depending on the penalty parameter, is also established. The paper also develops the dual problems based on the proposed nonlinear Lagrangians, and the related duality theorem and saddle point theorem are demonstrated. Furthermore, it is shown that the condition numbers of Lagrangian Hessians at optimal solutions are proportional to the controlling penalty parameters. We report some numerical results obtained by using nonlinear Lagrangians. [ABSTRACT FROM AUTHOR]