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Simulating multi-body dynamics with both rigid and flexible parts and with frictional contacts is a hard problem. We solve this by expressing the couplings between the bodies as position level constraints. The implicit treatment of the constraint directions gives us improved stability over velocity based methods. Then by employing regularization of nonlinear constraints and a convex minimization formulation, we bridge constraint-based methods to traditional force-based methods. In fact, the former are just a dual variables formulation of the latter. We solve this dual problem using position based dynamics (PBD). We show how PBD is a completely valid modeling technique and we extend it with an accurate contact and Coulomb friction model. We further show for the first time how the same solver can be used to simulate both rigid and deformable solids with two way coupling. For the soft bodies we introduce a novel form of linear finite elements expressed as constraints, that is more accurate than PBD mass-spring systems. More of our results include the energy conserving Newmark integrator and the accelerated Jacobi solver suitable for parallel architectures. Note that this paper is an extended and revised version of the conference paper published in [1]. [ABSTRACT FROM AUTHOR]

In this paper, four meshless local weak form methods such as direct meshless local Petrov–Galerkin method (DMLPG), meshless local Petrov–Galerkin method (MLPG), local weak radial point interpolation method (LWRPIM) and local weak moving kriging method (LWMK) are applied to find the numerical solution of coupled non-linear advection–diffusion–reaction system arising in the prevention of groundwater contamination problem. A comparison between these methods is done from the perspective of accuracy and computational efficiency. An efficient fourth-order exponential time differencing Runge–Kutta method is utilized for the time discretization. The computational efficiency is the most significant advantage of the DMLPG method in comparison with the other meshless local weak form methods, because DMLPG reduces the computational costs, significantly. This is due to the fact that DMLPG shifts the numerical integrations over low-degree polynomials rather than over complicated shape functions. The main aim of this paper is to show that the meshless local weak form methods can be used for solving the system of non-linear partial differential equations especially coupled non-linear advection–diffusion–reaction system. The numerical results confirm the good efficiency of the proposed methods for solving our model. [ABSTRACT FROM AUTHOR]

This paper focuses on coupling two different computational approaches, namely finite element method (FEM) and meshless finite difference method (MFDM), in one domain. The coupled approach is applied in solving thermomechanical initial–boundary value problem where the heat transport in the domain is non-stationary. In this method, the domain is divided into two subdomains for FEM and MFDM, respectively. Contrary to other coupling techniques, the approach presented in this paper is defined in terms of mathematical problem formulation rather than at the approximation level. In the weak form of thermomechanical initial–boundary value problem (variational principle), the appropriate additional coupling integrals are defined a-priori. Subsequently, the FEM and the MFDM approximations, which may differ from each other, are provided to the formulation. It is assumed that there exists a very thin layer of material between the subdomains, which is not spatially discretized. The width of this layer may be considered the coupling parameter and it is the same for both, thermal and mechanical parts. Similar approach is applied to essential boundary conditions (e.g. prescribed temperature and displacements). Consequently, the consistent formulation of the mixed problem for the coupled FEM–MFDM method is derived. The analysis is illustrated with two- and three-dimensional examples of mechanical and thermomechanical problems. [ABSTRACT FROM AUTHOR]

Three-valued abstraction is an established technique in software model checking. It proceeds by generating a state space model over the values true, false and unknown, where the latter value is used to represent the loss of information due to abstraction. Temporal logic properties can then be evaluated on such models. In case of an unknown result, the abstraction is iteratively refined until a definite result can be obtained. In this paper, we present and extend our work on parameterised three-valued model checking (PMC). In our parameterised three-valued models, unknown parts can be either associated with the constant value unknown or with expressions over boolean parameters. Our parameterisation is an alternative way to state that the truth value of certain predicates or transitions is actually not known and that the checked property has to yield the same result under each possible parameter instantiation. A specific feature of our approach is that it allows for establishing logical connections between parameters: While unknown parts in pure three-valued models are never related to each other, our parameterisation approach enables to represent facts like ‘a certain pair of transitions has unknown but complementary truth values’, or ‘the value of a predicate is unknown but remains unchanged along all states of a certain path’. We demonstrate that such facts can be automatically derived from the system to be verified and that covering these facts in an abstract model can be crucial for the success and the efficiency of checking temporal logic safety and liveness properties. Parameterisation enhances the precision of three-valued models without increasing their state space, but it leads to an exponential increase in time complexity, since any property of interest must be checked for each possible parameter instantiation. In this extended paper, we introduce a novel algorithm for direct parameterised three-valued model checking that straightly explores the parameterised state space and thus avoids to construct all instantiations explicitly. We present example verification tasks where the application of our direct algorithm considerably reduces the time effort of PMC. [ABSTRACT FROM AUTHOR]

Highlights • We model a new quantity discounts supply chain planning problem. • The model is very hard to solve using leading commercial solvers. • We develop MIP-based local search algorithms for our problem. • We apply our model to a realistic food supply chain. • We show the efficiency of our algorithms in getting high quality solutions quickly. Abstract Supplier selection with quantity discounts has been an active research problem in the literature. In this paper, we focus on a new real-world quantity discounts scheme, where suppliers are selected in the beginning of a strategic planning period (e.g., 5 years). Monthly orders are placed from the selected suppliers, but the quantity discounts are based on the aggregated annual order quantities. We incorporate this type of cost structure in a multi-period, multi-product, multi-echelon supply chain planning problem, and develop a mixed integer linear programming (MIP) model for it. Our model is highly intractable; leading commercial solvers cannot construct high quality feasible solutions for realistic instances even after multiple hours of solution time. We develop an algorithm that constructs an initial feasible solution and a large neighborhood search method that combines two customized iterative algorithms based on MIP-based local search and improves such solution. We report numerical results for a food supply chain application and show the efficiency of using our methodology in getting very high quality primal solutions quickly. [ABSTRACT FROM AUTHOR]

Abstract We study in this paper the accuracy and stability of partially and fully implicit schemes for phase field modeling. Through theoretical and numerical analysis of Allen–Cahn and Cahn–Hilliard models, we investigate the potential problems of using partially implicit schemes, demonstrate the importance of using fully implicit schemes and discuss the limitation of energy stability that are often used to evaluate the quality of a numerical scheme for phase-field modeling. In particular, we make the following observations: 1. a convex splitting scheme (CSS in short) can be equivalent to some fully implicit scheme (FIS in short) with a much different time scaling and thus it may lack numerical accuracy; 2. most implicit schemes are energy-stable if the time-step size is sufficiently small; 3. a traditionally known conditionally energy-stable scheme still possesses an unconditionally energy-stable physical solution; 4. an unconditionally energy-stable scheme is not necessarily better than a conditionally energy-stable scheme when the time step size is not small enough; 5. a first-order FIS for the Allen–Cahn model can be devised so that the maximum principle will be valid on the discrete level and hence the discrete phase variable satisfies | u h (x) | ≤ 1 for all x and, furthermore, the linearized discretized system can be effectively preconditioned by discrete Poisson operators. [ABSTRACT FROM AUTHOR]

Highlights • We introduced a mathematical model for the fueling line of fuel cycle modeling to minimize the tritium inventory level. • We obtained the tritium working inventory in the Isotope Separation System during one burn and dwell cycle under different tokamak operation modes. • The model provides insights on determining the optimum scheduling results for minimizing tritium working inventory. Abstract This paper proposes an optimization model for the scheduling of the fuel cycle of a fusion power plant. The objective of the problem is to minimize the tritium working inventory in the Isotope Separation System under periodic demand, thereby solving problems associated with high inventories. The model is formulated as a mixed integer nonlinear programming problem based on the state-task network approach. The proposed model is applied to inductive, hybrid, and non-inductive tokamak operation modes. With proposed case studies, the model is expected to provide many useful insights to determine the optimum scheduling results providing information on flow pattern, flow rate, and flow timing between equipment while minimizing tritium working inventory. [ABSTRACT FROM AUTHOR]

Solving a decision-making problem about a brand-new product might include preferences from a high number of potential customers (e.g., followers of a company on social media) and managerial constraints (or preferences) given by corporate managers with regard to different aspects (i.e., economical, technical, environmental, etc.) over multiple criteria (e.g., weight, capacity, color, or usefulness of a product). These give us some new insights on fusing preferences given by persons having different perspectives (e.g., economical, technical, environmental, etc.), including decision-makers, and aimed to be suitable for different organizational structures (e.g., multilevel structures). Herein, a proper representation is needed to merge preferences from each perspective, enabling their propagation, throughout an organizational structure until the level in which a decision is made. This representation is presented as a decision-making unit (DMU) , and is used as the primary component of our decision-making model. In this paper, we propose a novel decision-making model that recursively merges the preferred criteria from different DMUs using the logic scoring of preference (LSP) method . An illustrative example demonstrating the applicability of the proposed model, in the context of a new product design, is included in the paper. [ABSTRACT FROM AUTHOR]

This paper considers the k -sink location problem in dynamic path networks. A dynamic path network consists of an undirected path with positive edge lengths, uniform edge capacity, and positive vertex supplies. A path can be considered as a road, edge lengths as the distance along a road segment and vertex supplies as the number of people at a location. The edges all have a fixed common capacity , which limits the number of people that can enter that edge in a unit of time. The problem is to find the optimal location of k sinks (exits) on the path such that each evacuee is sent to one of the k sinks. The existence of capacities causes congestion, which can slow evacuation down in unexpected ways. Let x be a vector denoting the location of the k sinks. The optimal evacuation policy for x is ( k − 1 ) -dimensional vector d , called ( k − 1 ) -divider . Each component of d corresponds to a boundary dividing all evacuees between adjacent two sinks into two groups, i.e., all supplies to the right of the boundary evacuate to the right sink and all the others to the left sink. In this paper, we consider optimality defined by two different criteria, the minimax criterion and the minisum one. We prove that the minimax problem can be solved in O ( k n ) time and the minisum problem in O ( n 2 ⋅ min ⁡ { k log ⁡ n + log ⁡ n , 2 log ⁡ k log ⁡ log ⁡ n } ) time, where n is the number of vertices in the given network. [ABSTRACT FROM AUTHOR]

In this paper we solve the inventory routing problem (IRP) occurring in industrial gas distribution where liquefied industrial gases are distributed to customers that have cryogenic tanks to store the gases on-site. We consider a multi-period inventory routing problem with multiple products assuming deterministic demand rates and the proposed model is formulated as a linear mixed-integer program. We propose an incremental approach based on decomposing the set of customers in the original problem into sub-problems. The smallest sub-problem consists of the customer that needs to be delivered most urgently along with a set of its neighbors. We solve each sub-problem with the number of customers growing successively by providing the solution of the previously solved sub-problem as an input. Each sub-problem is then solved with a randomized local-search heuristic method. We also propose an objective function that drives the local-search heuristics toward a long-term optimal solution. The main purpose of this paper is to develop a solution methodology appropriate for large-scale real-life problem instances particularly in industrial gas distribution. [ABSTRACT FROM AUTHOR]

Given a set of n weighted points in the plane, a set of non-negative (ordered) weights and a connected polygonal region S , the weighted anti-ordered median straight-line location problem consists in finding a straight line intersecting S and maximizing the sum of ordered weighted distances to the points. In this paper we show how to find such a straight line in O ( n 4 ) time when the Euclidean distance is considered. As a consequence of the results given in the paper the weighted Euclidean anti-median straight-line problem can be solved in O ( n 2 ) time. [ABSTRACT FROM AUTHOR]

This paper considers the problem of hybrid flowshop scheduling. First, we review the shortcoming of the available model in the literature. Then, four different mathematical models are developed in form of mixed integer linear programming models. A complete experiment is conducted to compare the models for performance based on the size and computational complexities. Besides the models, the paper proposes a novel hybrid particle swarm optimization algorithm equipped with an acceptance criterion and a local search heuristic. The features provide a fine balance of diversification and intensification capabilities for the algorithm. Using Taguchi method, the algorithm is fine tuned. Then, two numerical experiments are performed to evaluate the performance of the proposed algorithm with three particle swarm optimization algorithms available in the scheduling literature and one well-known iterated local search algorithm in the hybrid flowshop literature. All the results show the high performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k -labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of randomly choosing subsets, we select the minimum required subsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correlations. Construction of the cover is achieved by formulating the subset selection as a minimum set covering problem (SCP) and solving it by using approximation algorithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may be applied to the classification of any given multi-label dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. First, we introduce SCP as a general framework for constructing label covers while allowing the user to incorporate cover construction constraints. We demonstrate the effectiveness of this framework by proposing two construction constraints whose enforcement produces covers that improve the prediction performance of random selection by achieving better coverage of labels and inter-label correlations. Second, we provide theoretical bounds that quantify the probabilities of random selection to produce covers that meet the proposed construction criteria. The experimental results indicate that the proposed methods improve multi-label classification accuracy and stability compared to the RAKEL algorithm and to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new metaheuristic algorithm named the Hybrid Average Subtraction and Standard Deviation based Optimizer (HASSO) to solve the optimization problem. The proposed algorithm uses the amount of information gathered from averages, subtraction, standard deviation, and hybrid population members to explore the search space in order to reach the near-optimal or quasi-optimal solution. The mathematical modeling of the proposed algorithm has been presented in detail. We solve twenty-three well-known important functions containing the unimodal and multimodal functions to obtain their quasi-optimal solution. The results obtained for unimodal and multimodal functions indicate the high exploitation ability of HASSO in converging towards the global optima. The convergence plot of the algorithm shows that the use of five phases in the algorithm makes the algorithm tend towards the optimal or quasi-optimal solution in fewer iterations. In addition to this, the obtained results are compared with nine classical algorithms, and the sensitivity of the algorithm to the population size and number of iterations is also evaluated. Our algorithm is also statistically analyzed for superiority over other algorithms. From this analysis, HASSO's superiority over the other nine classical algorithms in providing a highly accurate solution with a lesser number of iterations is clearly shown. • A Hybrid Average Subtraction and Standard Deviation based Optimizer is proposed. • The proposed algorithm uses various statistical features to explore the search space. • The proposed algorithm is tested on the standard 23 benchmark functions. • The results are compared with other algorithms and the sensitivity is checked. • The superiority of the algorithm over the other classical algorithms is verified. [ABSTRACT FROM AUTHOR]

We are promoting the use of variational principles as some generalizing idea that allows us to create consistent approximations of mathematical models. In this paper, we formulate a computing technology to solve direct and inverse problems of atmospheric dynamics and chemistry. For the implementation, we use functional decomposition methods, splitting techniques, and integrating factors designed as the solutions of some adjoint problems. One of the main purposes of the paper is to relate the theory of approximation with the concept of Eulerian integrating factors that plays the fundamental role in the theory of differential equations. We demonstrate the utility and versatility of the concept applying it to solve the systems of differential equations of convection–diffusion (with dominant convection) and stiff systems of kinetic equations. To identify the prospects of our approach, we briefly introduce how to use integrating factors in the case of unstructured grids. [Copyright &y& Elsevier]

The main market studies depict a future in which all one-aisle aeroplanes will mount engines composed of elements with complex surfaces. Among the new generation of tools, the barrel-shaped tools were released to the market for the machining of such types of surfaces. However, there are no evidences reported in the literature about how do these tools work. This work presents for the first time a mathematical model based on the numerical discretization of barrel cutters to predict the cutting forces. The effects of helix angle, runout and lead and tilt angles were included and the model was validated for a range of conditions in a 5-axis machine. In this line, this paper enables future mathematical developments including more details and aiming at other complex problems. [ABSTRACT FROM AUTHOR]

This paper proposes an optimisation algorithm called Grasshopper Optimisation Algorithm (GOA) and applies it to challenging problems in structural optimisation. The proposed algorithm mathematically models and mimics the behaviour of grasshopper swarms in nature for solving optimisation problems. The GOA algorithm is first benchmarked on a set of test problems including CEC2005 to test and verify its performance qualitatively and quantitatively. It is then employed to find the optimal shape for a 52-bar truss, 3-bar truss, and cantilever beam to demonstrate its applicability. The results show that the proposed algorithm is able to provide superior results compared to well-known and recent algorithms in the literature. The results of the real applications also prove the merits of GOA in solving real problems with unknown search spaces. [ABSTRACT FROM AUTHOR]

This paper presents a new one-fluid method for simulating formation and the collapse of cavitation regions in water during an isothermal process. In this method, the fluid phase changes are included into the wave pattern of an exact Riemann solver. The model behavior is assessed by comparing the numerical results with the other numerical models for several 1D Riemann problems. One-dimensional water hammer problems with vast creation and collapsing of cavitation zones are simulated as well—and the numerical results are compared to experimental results. The new model results are in very good agreement with accepted results reported in the literature. The presented results clearly show that the new model is able to capture the various behaviors of water during the phase change in the saturation dome and the vapor state, which was neglected in previous studies. Finally, the new model is adopted to an ALE method on an adaptive triangular grid to simulate an underwater explosion phenomenon inside a rigid cylinder—and the results are compared with other simulations. [ABSTRACT FROM AUTHOR]

Two-phase flows in pipelines occur in a variety of processes in the nuclear, petroleum and gas industries. Because of the practical importance of accurately predicting steady and unsteady flows along the line, two-fluid models have been extensively used in numerical simulations. These models are usually written as a system of non-linear partial-differential equations. A reliable prediction of such flows is a difficult task to accomplish due to the numerous sources of uncertainties, such as the basic two-phase flow model, the flow-pattern models, the initial and boundary conditions and the numerical method used to solve the initial-boundary-value problem. Several numerical methods, conservative or not, may be used to discretize the problem. In this paper we analyze the global performance of the numerical model, obtained when we couple a classical one-dimensional single-pressure four-equation two-fluid model to a numerical discretization based on the flux-corrected transport (FCT) method, to solve the stratified-pattern two-phase flow that occurs in typical gas-gathering offshore pipelines. The gas phase is considered to be compressible and the liquid phase is incompressible, whereas the flow is assumed to be isothermal. We show that the FCT method is second-order in space and that our numerical kernel produces accurate simulations for steady, as well as unsteady, simulations. We also analyze the hyperbolicity property of the two-fluid model and the adequate prescription of the boundary conditions. The results show that hyperbolicity is lost in part of the area of the stratified-pattern region of a classical flow-pattern diagram. Moreover, the eigenvalues alternate signs at the boundaries, which has implications on how to impose the boundary conditions. Numerical simulations of typical offshore gas pipeline flows show very good agreement when compared to a commercial software. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to optimize the trading strategies of a proactive distribution company (PDISCO) in the real-time market by mobilizing the demand response. Each distribution-level demand is considered as an elastic one. To capture the interrelation between the PDISCO and the real-time market, a bi-level model is presented for the PDISCO to render continuous offers and bids strategically. The upper-level problem expresses the PDISCO’s profit maximization, while the lower-level problem minimizes the operation cost of the transmission-level real-time market. To solve the proposed model, a primal-dual approach is used to translate this bi-level model into a single-level mathematical program with equilibrium constraints. Results of case studies are reported to show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient adaptive mesh method for solving phase-field model of a mixture of two-phase incompressible fluid flows. The adaptive mesh is generated by a coordinate transformation that is determined as the solution of the classical problem of the optimal mass transportation, also known as Monge–Kantorovich problem (MKP). The numerical solution of the MKP is computed by taking the gradient of the steady state solution of a parabolic Monge–Ampère equation (PMAE). Several numerical experiments are conducted to demonstrate the performance of the PMAE method for solving phase-field models. The numerical results illustrate that the adaptive mesh computed with PMAE method captures well the moving interface of the phase-field function. [ABSTRACT FROM AUTHOR]

In this paper, we will introduce and discuss new parameterizations to solve elastoplasticity problems by using the Asymptotic Numerical Method (ANM). The elastic–plastic transition and the elastic unloading are taken into account by using the regularization technique proposed in Assidi et al. (2009) [1] and Zahrouni et al. (2005) [2] . The ANM is a family of algorithms for path following problems; each ANM step is based on the computation of truncated vectorial series with respect to a path parameter “ a ” (Cochelin et al., 1994 [3] ). We present and discuss different parameterizations in ANM algorithm for solving elastoplasticity problems, namely the definition of the path parameter “ a ”; two concepts of parameterization are introduced and compared: a Riks type parameterization which is a combination of both load parameter and time and a parameterization based on the minimization of a rest (Mottaqui et al., 2010 [4,5] ). We will also discuss and compare the definitions of the step length in the case of elastoplasticity. Aiming to analyze the quality of the solutions, we will compute and study the residue of all the equations for different values of tolerance parameters of the ANM continuation. To illustrate the performance of these proposed parameterizations and step length definitions, we will give numerical comparisons on structural elastoplasticity problems with the Newton–Raphson method. [ABSTRACT FROM AUTHOR]

At present, there are some linearly homomorphic signatures in the standard model, whose security is based on the RSA assumption and Diffie–Hellman assumption. But there are still no lattice-based linearly homomorphic signature in the standard model. How to construct it? It is proposed as a open problem by Freeman [12] . In this paper, we propose the first lattice-based linearly homomorphic signature in the standard model, which settle this open problem. [ABSTRACT FROM AUTHOR]

Single floor facility layout problem (FLP) is related to finding the arrangement of a given number of departments within a facility; while in multi-floor FLP, the departments should be imbedded in some floors inside the facility. The significant influence of layout design on the effectiveness of any organization has turned FLP into an important issue. This paper presents a three- (two-) stage mathematical programming method to find competitive solutions for multi- (single-) floor problems. At the first stage, the departments are assigned to the floors through a mixed integer programming model (the single floor version does not require this stage). At the second stage, a nonlinear programming model is used to specify the relative position of the departments on each floor; and at the third stage, the final layouts within the floors are determined, through another nonlinear programming model. The multi-floor version is studied in the states in which the locations of the elevators are either formerly specified or not. Computational results show that this framework can find a wide variety of high quality layouts at competitive cost (up to 43% reduction) within a short amount of time for small and especially large size problems, compared to the existing methods in the literature. Also, the proposed method is flexible enough to accommodate the complicated and real-world problems, because of using mathematical programming model and solving it directly. [ABSTRACT FROM AUTHOR]

In this paper, we prove the existence portion of the Bertram–Feinberg–Mukai Conjecture for an infinite family of new cases using degeneration technique. This not only leads to a substantial improvement of known results but also develops finer tools for analyzing the moduli of rank two limit linear series which should be useful for applications to other higher-rank Brill–Noether problems. [ABSTRACT FROM AUTHOR]

Given a weighted graph G = ( V , E ) , the broadcasting problem is to find a broadcast center such that the maximum communication time from the center to all vertices is minimized. For this problem, Slater et al. [29] proposed an O ( n ) -time algorithm in unweighted trees and Koh and Tcha [23] designed an O ( n log ⁡ n ) -time algorithm in unweighted trees under the telephone model. In this paper, we strengthen the results of Slater et al. by showing an O ( n ) -time algorithm in weighted trees under the postal model. The algorithm computes the set of broadcast centers, determines the broadcast time of the broadcast centers, and an optimal broadcast scheme from one of the centers to all vertices in the tree. We further show that the broadcast time of any vertex in the tree can also be determined in O ( n ) time. [ABSTRACT FROM AUTHOR]

Hub location problems have applications in a variety of fields including cargo delivery systems and telecommunication network design. Hub location problems deal with locating a set of hub nodes and allocating non-hub nodes to the located hubs. This paper presents a new bi-objective model for a multi-modal hub location problem under uncertainty considering congestion in the hubs. The objective functions attempt to minimize the total transportation cost as well as minimize the maximum transportation time between each pair of Origin-Destination (O-D) nodes in the network. To cope with the computational complexity of the problem, a well-known meta-heuristic algorithm, namely differential evolution (DE), is developed to obtain near-optimal Pareto solutions. Furthermore, several computational experiments and sensitivity analyses are provided to demonstrate the efficiency and applicability of the presented model and solution algorithm. Finally, the conclusion is presented. [ABSTRACT FROM AUTHOR]

In hub location problems, a decision-maker may encounter hybrid uncertain environments where randomness and fuzziness are in the state of affairs. The purpose of this paper is to develop a new two-stage uncapacitated hub location (UHL) problem with recourse, in which uncertain parameters are characterized by both probability and possibility distributions. When demands are the only uncertain parameters, we show that the proposed two-stage UHL model is equivalent to a static optimization problem subject to equilibrium constraint. In the case that the randomness of uncertain demands follows normal distributions, we reduce the equilibrium constraint to its equivalent credibility constraint. Furthermore, when the fuzziness of uncertain demands follows triangular distributions, we discuss the convexity of equilibrium objective function, and establish the equivalent deterministic programming model of the original UHL problem. In general case, we adopt fuzzy simulation (FS) method to approximate uncertain parameters. To solve the proposed hub location problem, we design a hybrid heuristic algorithm by integrating genetic algorithm (GA), variable neighborhood search (VNS) and FS. We conduct some numerical experiments and compare the computational results obtained by the VNS-based GA and standard GA. The computational results together with convergence analysis demonstrate that the VNS-based GA achieves the better performance than standard GA. Finally, we carry out the sensitivity analysis to recognize the most significant parameter of the proposed optimization model. [ABSTRACT FROM AUTHOR]

Re-combining parts of existing 3D object models is an interesting and efficient technique to create novel shape collections. However, due to the lack of direct parts’ correspondence across different shape families, such data-driven modeling approaches in literature are mostly limited to the synthesis of in-class shapes only. To address the problem, this paper proposes a novel approach to create 3D shapes via re-combination of cross-category object parts from an existing database of different model families. In our approach, a reference shape containing multi-functional constituent parts is pre-specified by users, and its design style is then reused to guide the creation process. To this end, the functional substructures are first extracted for the reference shape. After that, we explore a series of category pairs which are potential replacements for the functional substructures of the reference shape to make interesting variations. We demonstrate our ideas using various examples, and present a user study to evaluate the usability and effectiveness of our technique. [ABSTRACT FROM AUTHOR]

Many applications require manipulation and visualization of complex and highly detailed models at realtime. In this paper, we present a new mesh process and rendering method for realtime high quality rendering. The basic idea is to send a simplified mesh to hardware pipeline, while use the online tessellation on the GPU to facilitate the rendering of complex geometric details. We formulate it into an inverse tessellation problem that first computes the simplified mesh, and then optimizes the tessellated mesh with geometric details to approximate the original mesh. To solve this problem, we propose a two-stage algorithm. In the first stage, we employ an iterative surface simplification technique, where we take the requirement of hardware tessellation into consideration to obtain an optimal simplified mesh. In the second stage, to better utilize the hardware tessellation, we propose a moving vertex strategy to approximate the tessellated mesh to the original mesh. Results show that our method achieves 2–4 times faster at rendering but still retains high quality geometrical details. [ABSTRACT FROM AUTHOR]

In this paper we present an extended Truncated Hierarchical Catmull–Clark Subdivision (eTHCCS) method, which improves the efficiency of local refinement in Truncated Hierarchical Catmull–Clark Subdivision (THCCS). Based on the Stam’s method, we first build a set of basis functions over arbitrary quadrilateral meshes and apply them to THCCS. Then, a new basis-function-insertion scheme is developed with the aid of the truncation mechanism, which refines one-ring neighboring elements rather than two-ring neighborhoods. Therefore, eTHCCS significantly improves the efficiency of local refinement compared with THCCS, as demonstrated by one benchmark problem and several complex models. Moreover, eTHCCS is also proved to preserve the input geometry and produce nested spaces. [ABSTRACT FROM AUTHOR]

The total variation (TV) based iterative regularization method has been utilized to recover images degraded by blur and impulse noise. It is well-known that the TV regularization model preserves the edges well in the restored images while suffers from staircase effect. In this paper, we consider a high-order total variation minimization model which removes undesired artifacts for restoring blurry and noisy images. Then a primal-dual splitting algorithm is developed to solve the high-order minimization problem. The convergence of the proposed method is guaranteed. Numerical results illustrate that the proposed method is competitive with the state-of-the-art methods in terms of the peak signal-to-noise (PSNR) and the structural similarity index measurement (SSIM). [ABSTRACT FROM AUTHOR]

This review paper presents the state of the art on the problems, approaches and analytical models for assembly and transfer line design and balancing that addresses explicitly cost and profit oriented objectives. The discussions aim to facilitate identifying open problems and research areas that have wide practical applications and that necessitate further investigations. Moreover, they might serve as a foundation for developing decision support systems (DSS) that aid managers in planning and designing profitable or cost efficient assembly and transfer lines. [ABSTRACT FROM AUTHOR]

In this paper, an inventory model for a deteriorating item with time dependent deterioration has been developed in imprecise environment. For an order more than a fixed label, the supplier offers a credit period for payments per order to his/her retailers, but an interest is charged by the supplier if the payment period exceeds the credit period. The supplier also offers a partial permissible delay in payments even if the order quantity is less than the said fixed label, i.e., the credit period is offered on a fraction of the amount purchased. Due to these facilities, the retailer also offers a fixed partial credit period to his/her customers to boost the demand. Obviously, demand depends on the duration on which the credit period is offered. Depending upon the different types of imprecise inventory costs (fuzzy, rough or fuzzy-rough), different models are developed. Models are formulated as a profit maximization problem with respect to the retailer and depending upon different imprecise parameters, different models are solved. As the optimization of imprecise objectives are not well defined, their expected values are optimized to find an optimal decision for the decision maker (DM). Non-linear optimization problems for different models are solved using a gradient based optimization technique – Generalized Reduced Gradient (GRG) method (using LINGO software). Numerical experiments are performed to illustrate the models and some parametric studies are made. [ABSTRACT FROM AUTHOR]

Many traditional facility location models have sought optimal solutions in two-dimensional space. However, certain locations in reality need to be modeled in three-dimensional space. This paper addresses a location problem in three-dimensional space from the application perspective. Specifically, we introduce a 3D coverage location model of Wi-Fi access points (APs) in an indoor environment. To do this, we first incorporate a generic radio propagation assuming typical obstructions of a building into the standard coverage model, using 3D Euclidean distance characterizing radio signal penetration. The suggested model is then applied for an efficient AP placement plan in a multi-story building at a university campus. The empirical results present 3D representations of the solutions for multi-floor deployment of Wi-Fi APs. This research is expected to provide a useful modeling framework for combining facility location model with wireless network design in a 3D environment. [ABSTRACT FROM AUTHOR]

We introduce a number of problems regarding edge-color modifications in edge-colored graphs and digraphs. Consider a property π , a c -edge-colored graph G c not satisfying π , and an edge-recoloring cost matrix R = [ r i j ] c × c where r i j ≥ 0 denotes the cost of changing color i of edge e to color j . Basically, in this kind of problem the idea is to change the colors of one or more edges of G c in order to construct a new edge-colored graph such that the total edge-recoloring cost is minimized and property π is satisfied. We also consider the destruction of potentially undesirable structures with the minimum edge-recoloring cost. In this paper, we are especially concerned with the construction and destruction of properly edge-colored and monochromatic paths, trails and cycles in graphs and digraphs. Some related problems and future directions are presented. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the problem of designing a new observer for bioreactor models. The main idea is to construct a nonlinear observer with linear errors, which has an adjustable and robust convergence. Simulation results are presented using a model of Chemostat and a model of an anaerobic digestion process for the treatment of wastewater. [ABSTRACT FROM AUTHOR]

Physical topology design of optical networks is frequently accomplished by using evolutionary approaches. However, fitness evaluation for this type of problems is time consuming and the overall optimization process presents a huge execution time. In this paper we propose a new method that uses a multi-objective evolutionary approach to handle the design of all-optical networks. We focused on the simultaneous optimization of the network topology and the device specifications in order to minimize both the capital expenditure of the network and the network performance. Our method uses surrogate models to accelerate the fitness evaluation and a novel network generative model based on preferential attachment to generate the seeds for the evolutionary process. Our approach can provide high quality solutions with a very small execution time when compared to the previous approaches. In order to assess our proposal we performed a set of simulations aiming to analyze the convergence ability and the diversity of the generated solutions for scenarios considering uniform and non-uniform traffic matrices. From our results, we obtained an evolutionary approach that presents better solutions than previous proposals for all analyzed scenarios. Our proposal presents an execution time that is up to 84% and 88% lower than the execution time needed by the previous approaches for uniform and non-uniform traffic, respectively. [ABSTRACT FROM AUTHOR]

The central problem in case based reasoning (CBR) is to infer a solution for a new problem-instance by using a collection of existing problem–solution cases. The basic heuristic guiding CBR is the hypothesis that similar problems have similar solutions. Recently, some attempts at formalizing CBR in a theoretical framework have been made, including work by Hüllermeier who established a link between CBR and the probably approximately correct (PAC) theoretical model of learning in his ‘case-based inference’ (CBI) formulation. In this paper we develop further such probabilistic modelling, framing CBI it as a multi-category classification problem. We use a recently-developed notion of geometric margin of classification to obtain generalization error bounds. [ABSTRACT FROM AUTHOR]

The dictionary matching with gaps problem is to preprocess a dictionary D of total size | D | containing d gapped patterns P 1 , … , P d over an alphabet Σ , where each gapped pattern P i is a sequence of subpatterns separated by bounded sequences of don't cares. Then, given a query text T of length n over Σ , the goal is to output all locations in T in which a pattern P i ∈ D , 1 ≤ i ≤ d , ends. There is a renewed current interest in the gapped matching problem stemming from cyber security. In this paper we solve the problem where all patterns in the dictionary have one gap or a few gaps with at least α and at most β don't cares, where α and β are given parameters. Specifically, we show that the dictionary matching with a single gap problem can be solved in either O ( d log ⁡ d + | D | ) preprocessing time and O ( d log ε ⁡ d + | D | ) space, and query time O ( n ( β − α ) log ⁡ log ⁡ d log 2 ⁡ | D | + occ ) , where occ is the number of patterns found, or preprocessing time and space: O ( d 2 + | D | ) , and query time O ( n ( β − α ) + occ ) , where occ is the number of patterns found. We also show that the dictionary matching with k gaps problem, where k ≥ 1 , can be solved in preprocessing time: O ( | D | log ⁡ | D | ) , space: O ( | D | + d ( c 1 log ⁡ d ) k k ! ) , and query time: O ( ( β − α ) k ( n + ( c 2 log ⁡ d ) k k ! log ⁡ log ⁡ | D | ) + occ ) , where c 1 , c 2 > 1 are constants and occ is the number of patterns found. As far as we know, these are the best solutions for this setting of the problem, where many overlaps may exist in the dictionary. [ABSTRACT FROM AUTHOR]

The frequency assignment problem asks for assigning frequencies to transmitters in a wireless network and includes a variety of specific subproblems. In particular, the packing coloring problem comes from the regulations concerning the assignment of broadcast frequencies to radio stations. The problem is placed on a graph-theoretical footing and modeled as the packing chromatic number χ ρ ( G ) of a graph G . The packing chromatic number of G is the smallest integer k such that the vertex set V ( G ) can be partitioned into disjoint classes X 1 , … , X k , with the condition that vertices in X i have pairwise distance greater than i . The determination of the packing chromatic number is known to be NP -hard. This paper presents an integer linear programming model and a satisfiability test model for the packing coloring problem. The proposed models were applied for studying the packing chromatic numbers of Cartesian products of paths and cycles and for the packing chromatic numbers of some distance graphs. [ABSTRACT FROM AUTHOR]

Efficient solutions in Multi-Objective Integer Linear Programming (MOILP) problems are categorized into two distinct types, supported and non-supported. Many researchers try to gain some conditions to determine whether a feasible solution is efficient, nevertheless there is no attempt to identify the efficiency status of a given efficient solution, i.e. supported and non-supported. In this paper, we first verify the relationships between Data Envelopment Analysis (DEA) and MOILP and then design two distinct practical procedures: the first one specifies whether or not an arbitrary feasible solution is efficient, meanwhile the second one, as the main aim of this study, determines the efficiency status of an efficient solution. Finally, as a contribution of the suggested approach, we illustrate the drawback of Chen and Lu’s methodology (Chen and Lu, 2007) which is developed for solving an extended assignment problem. [ABSTRACT FROM AUTHOR]

With symbolic computation, Bell-polynomial scheme and bilinear method are applied to a two-dimensional Korteweg–de Vries (KdV) model, which is firstly proposed with Lax pair generating technique. Bell-polynomial expression with one auxiliary independent variable is derived and transformed into bilinear form. According to the coupled two-field conditions between the primary and replica fields, Bell-polynomial-typed Bäcklund transformations (BTs) are constructed and converted into the bilinear ones. Finally, soliton solutions of the two-dimensional KdV model are obtained (via solving the bilinear representation and BT, respectively) and compared. Such associated integrable properties as bilinear representation, BT (especially auxiliary-independent-variable-involved Bell-polynomial-typed ones constructed in this paper) and soliton solutions (especially the multi-soliton ones) may be useful for further study on other two-dimensional KdV and KdV-typed models. [ABSTRACT FROM AUTHOR]

A famous theorem of Thue asserts that there exist arbitrarily long words without repetitions (identical adjacent blocks) over a 3-letter alphabet. In this paper we study a game-theoretic variant of this result, where a word is constructed jointly by two players who alternately append letters to the end of existing word. One of the players (Ann) takes care of avoiding repetitions, while the other one (Ben) tries to force them. We provide an explicit strategy for Ann to avoid non-trivial repetitions over a 9-letter alphabet. This solves an open problem posed by Pegden [13] . We also consider a similar game for overlaps in words (identical overlapping blocks). We give an explicit strategy for Ann to avoid overlaps over a 4-letter alphabet, even if the players are placing letters on arbitrarily chosen positions in constructed word (provided its final length is even). Additionally we prove that the bound of 4 in this result is optimal. [ABSTRACT FROM AUTHOR]

The cloud computing allows to use virtually infinite resources, and seems to be a new promising opportunity to solve scientific computing problems. The MapReduce parallel programming model is a new framework favoring the design of algorithms for cloud computing. Such framework favors processing of problems across huge datasets using a large number of heterogeneous computers over the web. In this paper, we are interested in evaluating how the MapReduce framework can create an innovative way for solving operational research problems. We proposed a MapReduce-based approach for the shortest path problem in large-scale real-road networks. Such a problem is the cornerstone of any real-world routing problem including the dial-a-ride problem (DARP), the pickup and delivery problem (PDP) and its dynamic variants. Most of efficient methods dedicated to these routing problems have to use the shortest path algorithms to construct the distance matrix between each pair of nodes and it could be a time-consuming task on a large-scale network due to its size. We focus on the design of an efficient MapReduce-based approach since a classical shortest path algorithm is not suitable to accomplish efficiently such task. Our objective is not to guarantee the optimality but to provide high quality solutions in acceptable computational time. The proposed approach consists in partitioning the original graph into a set of subgraphs, then solving the shortest path on each subgraph in a parallel way to obtain a solution for the original graph. An iterative improvement procedure is introduced to improve the solution. It is benchmarked on a graph modeling French road networks extracted from OpenStreetMap. The results of the experiment show that such approach achieves significant gain of computational time. [ABSTRACT FROM AUTHOR]

In this paper, the viewpoint of non-linear complementarity problem (NCP) is adopted to derive a system of index-one differential algebraic equations (DAEs) for elastoplastic models, by recasting the complementary trio to an algebraic equation through the Fischer–Burmeister NCP-function. Then, we develop a novel algorithm based on the Lie-group GL ( n , R ) to iteratively solve the resultant DAEs at each time marching step. The Lie-group differential algebraic equations (LGDAE) method is convergent very fast, rendering efficient numerical schemes which can long-term preserve the yield-surface for plasticity models, without resorting on two-phase equations and on-off switching criteria. Several examples, including two non-linear elastoplastic oscillators whose restoring forces are modeled by elastoplastic constitutive equations, are used to assess the performance of the presently developed index-one formulation of elastoplasticity and test the efficiency and accuracy of LGDAE. [ABSTRACT FROM AUTHOR]

In the paper a power-aware problem of scheduling preemptable jobs on parallel identical machines to minimize the schedule length is considered. Exact approaches utilizing the idea of grouping jobs are presented and compared from the viewpoint of the size of an appropriate non-linear programming problem. [ABSTRACT FROM AUTHOR]

In this paper a class of combinatorial optimization problems with uncertain costs is discussed. The uncertainty is modeled by specifying a discrete scenario set containing K distinct cost scenarios. The Ordered Weighted Averaging (OWA for short) aggregation operator is applied to choose a solution. Some well known criteria used in decision making under uncertainty such as the maximum, minimum, average, Hurwicz and median are special cases of OWA. Furthermore, by using OWA, the traditional robust (min–max) approach to combinatorial optimization problems with uncertain costs can be generalized. The computational complexity and approximability of the problem of minimizing OWA for the considered class of problems are investigated and some new positive and negative results in this area are provided. These results remain valid for many basic problems, such as network or resource allocation problems. [ABSTRACT FROM AUTHOR]

Decisions on the quay crane assignment problem and the quay crane scheduling problem are typically made independently. However, the efficiency of container terminals can be increased when these decisions are made simultaneously due to the interrelation between quay crane assignment and scheduling. A mathematical formulation for the integrated quay crane assignment and scheduling problem (QCASP) is developed in this paper. Practical considerations are incorporated in the model, such as quay crane (QC) interference. A Lagrangian relaxation is proposed for the model. Feasible solutions are then obtained from the proposed heuristic. Computational results are provided for the proposed Lagrangian relaxation. [ABSTRACT FROM AUTHOR]

This paper deals with the computational modeling and numerical simulation of contact problems at finite deformations using the finite element method. Quasi-static and dynamic problems are considered and two particular frictional conditions, full stick friction and frictionless cases, are addressed. Lagrange multipliers and regularized formulations of the contact problem, such as penalty or augmented Lagrangian methods, are avoided and a new direct elimination method is proposed. Conserving algorithms are also introduced for the proposed formulation for dynamic contact problems. An assessment of the performance of the resulting formulation is shown in a number of selected benchmark tests and numerical examples, including both quasi-static and dynamic contact problems under full stick friction and frictionless contact conditions. Conservation of key discrete properties exhibited by the time stepping algorithm used for dynamic contact problems is also shown in an example. [ABSTRACT FROM AUTHOR]