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With an ever increasing complexity, the verification of critical embedded systems is a challenging and expensive task. Among the available formal methods, model checking offers a high level of automation and would thus lower the cost of this process. But, the scalability of this technique is hindered by the state-space explosion problem, which fuelled the research community since its inception. To address this challenge, in the case of real size systems, the theoretical, the methodological and the algorithmic axes have to be integrated. The context-aware verification (CaV) strives to do this by focusing on the identification, the isolation and the reification of the environment surrounding the studied system. It enables the use of specific algorithms with a major, positive, impact on the scalability of model checking. In this paper, we apply this technique to study a Landing Gear System ( LGS) in the presence of failures. The problem has been decomposed in 885 independent verification units (called contexts). The analysis of 163 of these contexts on a 64 GB computer unraveled a 20 TB state space with more than 2.2 billion states. Moreover, using this approach arbitrarily long scenarios have been analysed using less than 10 GB of memory. [ABSTRACT FROM AUTHOR]

This paper discusses characteristics unique in the two-phase high-frequency-testing (HFT) environment in semiconductor manufacturing. We believe this paper is the first to define, formalize, and analyze the decision making problem associated with the two-phase HFT. Specifically, this paper defines the problem of minimizing the total HFT capacity usage by systematically finding the optimal number of preliminary HFT bin-1 chips subject to the main HFT, with the existence of the target service rate. We also propose a heuristic algorithm that exploits the special structure of the problem for efficiently obtaining a near-optimal solution. Finally, a numerical analysis and a case study have been conducted to gain more insights on the problem structure and the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a flexible nonmonotone filter method for solving nonlinear constrained optimization problems which are common models in industry. This new method has more flexibility for the acceptance of the trial step compared to the traditional filter methods, and requires less computational costs compared with the monotone-type methods. Moreover, we use a self-adaptive parameter to adjust the acceptance criteria, so that Maratos effect can be avoided a certain degree. Under reasonable assumptions, the proposed algorithm is globally convergent. Numerical tests are presented that confirm the efficiency of the approach. [ABSTRACT FROM AUTHOR]

To facilitate the implementation of machine monitoring algorithms on the shop floor, signal processing and decision-making strategies must be developed, which account for the difficulties associated with monitoring a machine in an industrial environment. Therefore, this paper focuses on introducing a new method for dresser contact detection, which takes into account sensor and data acquisition system costs, computational limitations of an embedded detection system, and noise robustness issues associated with shop floor implementation. This paper also discusses the way in which the monitoring algorithm directly interfaces with the machine control in real time in order to ensure that system faults are avoided. Furthermore, the new algorithm is compared with more traditional methods for change detection by applying each algorithm to the signal output from a dresser horsepower sensor. In an industrial application, the new algorithm is shown to provide zero missed detections, zero false alarms, and a sufficiently fast response time. [ABSTRACT FROM AUTHOR]

In this paper, we consider a two parallel machine problem where each machine has a fixed and known unavailable period. The objective is to minimize the makespan. In the literature, the problem has been considered for the case in which there is only one unavailable period on either one of the machines. This paper extends the results to two unavailable periods, one for each machine. For each of the different cases of the problem, an algorithm is developed to find the optimal solution. Although all the proposed algorithms have exponential time complexities, they are quite efficient in solving large problems. Computational results for problems with up to 1000 jobs are reported. [ABSTRACT FROM AUTHOR]

This paper proposes a modified discrete firefly algorithm (MDFA) to solve the problem of assembly sequence planning. Firstly, to improve the performance of the firefly algorithm (FA), we proposed a MDFA by endowing the fireflies with the capability of changeable visual range. The computing case shows the proposed algorithm is more effective and robust than standard FA, genetic algorithm and particle swarm optimization algorithm. Secondly, a method of how to set parameters for FA and MDFA is proposed. This method is practical in the application of FA to solve discrete problem. Thirdly, to make the sequences more closer to real industrial requirements, a so called process precedence relations (PPR) evaluation function is presented, which not only considering the interference between parts, assembly tools and clamps, but also regarding the assembly order between parts and their reference parts. Finally, the evolution performance of the MDFA is investigated, and the performance of the proposed approach to solve ASP is verified through two cases study. [ABSTRACT FROM AUTHOR]

The cell formation problem determines decomposition of the manufacturing cells of a production system. Machines are assigned to the cells to process one or more part families so that each cell is operated independently and the inter-cellular movements are minimized. This paper proposes a new algorithm for grouping problems (bin packing, graph coloring, scheduling, etc.) which is a grouping version of an almost new algorithm (league championship algorithm (LCA)), and we used it to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 35 most widely used benchmark problem instances from literature and compare the performance of grouping LCA (GLCA) and several well-known algorithms published. The proposed algorithm can reach the best solution for 29 of the 35 benchmark problems and differs with the best-known solution of three benchmark problems only with 0.7 % average gap. We also used a new method to find the number of initial cells. The results show that GLCA may hopefully be a new approach for such kinds of difficult-to-solve problems. Moreover, a real-world industrial case is provided to show how the proposed algorithm works. Considering the performance of the GLCA algorithm on all test problems, the proposed algorithm should thus be useful to both practitioners and researchers. [ABSTRACT FROM AUTHOR]

Efficient solution of the single source shortest path (SSSP) problem on road networks is an important requirement for numerous real-world applications. This paper introduces an algorithm for the SSSP problem using compression method. Owning to precomputing and storing all-pairs shortest path (APSP), the process of solving SSSP problem is a simple lookup of a little data from precomputed APSP and decompression. APSP without compression needs at least 1TB memory for a road network with one million vertices. Our algorithm can compress such an APSP into several GB, and ensure a good performance of decompression. In our experiment on a dataset about Northwest USA (with 1.2 millions vertices), our method can achieve about three orders of magnitude faster than Dijkstra algorithm based on binary heap. [ABSTRACT FROM AUTHOR]

Reducing expensive raw material waste is an important goal in the industry. In this paper, two-dimensional irregular cutting stock problem-a nesting problem that differs from other in their irregular shape of the pieces-with demand is studied, in which the required pieces has to be produced from large rectangular sheet minimizing material waste. Structure of this problem made it intractable for practical applications such that exact algorithms are not able to solve it in a reasonable time. Greedy randomized adaptive search procedure (GRASP) meta-heuristic algorithm is adapted to tackle the problem by providing high-quality solution in an appropriate time. The algorithm does not depend on the shape (convexity and regularity) of pieces and is able to deliver an optimum solution for instances up to 30 pieces of 7 different types. In addition, computational results are provided for different test problems from the related literature. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study a viscosity iterative algorithm for finding a common element of the set of solutions of a general variational inequality problem for two inverse strongly accretive operators and the set of fixed points of a δ-strict pseudocontraction in a real q-uniformly smooth Banach space. Some strong convergence theorems are obtained under appropriate conditions. As an application, we prove some strong convergence theorems for fixed point problems and variational inequality problems or equilibrium problems in Hilbert spaces. These results improve and extend the corresponding results announced by many others. [ABSTRACT FROM AUTHOR]

In this paper we prove the strong convergence of an iterative sequence for finding a common element of the fixed points set of a strictly pseudocontractive mapping and the solution set of the constrained convex minimization problem for a convex and continuously Fréchet differentiable functional in a real Hilbert space. We apply our result to solving the split feasibility problem and the convexly constrained linear inverse problem involving the fixed point problem for a strictly pseudocontractive mapping in a real Hilbert space. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present accelerations of the Mann and CQ algorithms. We first apply the Picard algorithm to the smooth convex minimization problem and point out that the Picard algorithm is the steepest descent method for solving the minimization problem. Next, we provide the accelerated Picard algorithm by using the ideas of conjugate gradient methods that accelerate the steepest descent method. Then, based on the accelerated Picard algorithm, we present accelerations of the Mann and CQ algorithms. Under certain assumptions, we show that the new algorithms converge to a fixed point of a nonexpansive mapping. Finally, we show the efficiency of the accelerated Mann algorithm by numerically comparing with the Mann algorithm. A numerical example is provided to illustrate that the acceleration of the CQ algorithm is ineffective. [ABSTRACT FROM AUTHOR]

During many decades, engineers relied on experimental abaci to design and optimize mechanical structures. Virtual charts are based on numerical computations and aim at making the design and optimization of structures faster and cheaper as it requires less (or no) experimentation. The parametric problems are solved offline and the associated charts are used for online design. However, in this context, the cost associated to the resolution of parametric problems can be extremely prohibitive. Model reduction techniques are an answer to circumvent this issue. This paper provides the developments of an algorithm for solving nonlinear parametric problems, based on the LATIN method for the treatment of the nonlinear aspects and the PGD for the parameter dependency. A full time-space-parameter decomposition of the solution is introduced into the LATIN algorithm, numerical examples on academic problems are given so as to point out the advantages of this extension and leads on possible improvements to the strategy are given. [ABSTRACT FROM AUTHOR]

This paper presents a semi-analytic solution for multiple cracks beneath a half-space surface under elastohydrodynamic lubrication (EHL) contact. The solution not only takes into account the fluid-structure interactions in the EHL contact but also the interactions among all the cracks. In developing the governing equation, each crack of mixed modes I and II is modeled as a continuous distribution of climb and glide dislocations with unknown densities. With such a treatment, the original problem for materials with multiple cracks is converted into two subproblems: a homogeneous EHL contact problem with the fluid pressure distribution and the lubricant film thickness to be determined, and a half-space problem with unknown dislocation densities, which are iteratively obtained by a modified conjugate gradient method. A numerical algorithm is developed to integrate the two problems. The computational process is performed until the convergence of the displacements, which are the sum of the displacements due to the fluid pressure and the subsurface cracks. Sample cases are presented to analyze the pressure and lubricant film thickness profiles of EHL contact and the subsurface stresses for cracked materials. [ABSTRACT FROM AUTHOR]

In a recent paper by Fattahi et al. (Int J Adv Manuf Technol 65:787-802, ), a mathematical model is presented for a two-stage assembly scheduling problem containing a hybrid flow shop and an assembly stage. Some constraints in the model are not valid. This note shows that some new constraints are proposed for the mathematical model. An example is proposed to present and prove this new model by executing mathematical software. [ABSTRACT FROM AUTHOR]

By ignoring some cell overlaps, global placement computes the best position for each cell to minimize the wirelength. It is an important stage in very large scale integration (VLSI) physical design, since circuit performance heavily depends on the placement results. In this paper, we propose an augmented Lagrangian method to solve the VLSI global placement problem. In the proposed method, a cautious dynamic density weight strategy is used to balance the wirelength objective and the density constraints, and an adaptive step size is used to obtain a trade-off between runtime and solution quality. The proposed method is tested on the IBM mixed-size benchmarks and the International Symposium on Physical Design 2006 placement contest benchmarks. Experimental results show that our global placement method outperforms the state-of-the-art placement approaches in terms of solution quality on most of the benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notion of efficiency (consistency) and examine some asymptotic properties of Markov chain Monte Carlo methods. We apply these results to the data augmentation (DA) procedure for independent and identically distributed observations. More precisely, we show that if both the sample size and the running time of the DA procedure tend to infinity, the empirical distribution of the DA procedure tends to the posterior distribution. This is a local property of the DA procedure, which may be, in some cases, more helpful than the global properties to describe its behavior. The advantages of using the local properties are the simplicity and the generality of the results. The local properties provide useful insight into the problem of how to construct efficient algorithms. [ABSTRACT FROM AUTHOR]

A new convex feasibility problem, the split equality problem (SEP), has been proposed by Moudafi and Byrne. The SEP was solved through the ACQA and ARCQA algorithms. In this paper the SEPs are extended to infinite-dimensional SEPs in Hilbert spaces and we established the strong convergence of a proposed algorithm to a solution of general split equality problems (GSEPs). [ABSTRACT FROM AUTHOR]

In this paper, we present a simultaneous subgradient algorithm for solving the multiple-sets split feasibility problem. The algorithm employs two extrapolated factors in each iteration, which not only improves feasibility by eliminating the need to compute the Lipschitz constant, but also enhances flexibility due to applying variable step size. The convergence of the algorithm is proved under suitable conditions. Numerical results illustrate that the new algorithm has better convergence than the existing one. [ABSTRACT FROM AUTHOR]

The airline's ability to offer flight schedules that provide service to passengers at desired times in competitive markets, while matching demand with an aircraft fleet of suitable size and composition, can significantly impact its profits. In this spirit, optional flight legs can be considered to construct a profitable schedule by optimally selecting among such alternatives in concert with assigning the available aircraft fleet to all the scheduled legs. Examining itinerary-based demands as well as multiple fare-classes can effectively capture network effects and realistic demand patterns. In addition, allowing flexibility on the departure times of scheduled flight legs can increase connection opportunities for passengers, hence yielding robust schedules while saving fleet assignment costs within the framework of an integrated model. Airlines can also capture an adequate market share by balancing flight schedules throughout the day, and recapture considerations can contribute to more realistic accepted demand realizations. We therefore propose in this paper a model that integrates the schedule design and fleet assignment processes while considering flexible flight times, schedule balance, and recapture issues, along with optional legs, path/itinerary-based demands, and multiple fare-classes. A polyhedral analysis is conducted to generate several classes of valid inequalities, which are used along with suitable separation routines to tighten the model representation. Solution approaches are designed by applying Benders decomposition method to the resulting tightened model, and computational results are presented using real data obtained from United Airlines to demonstrate the efficacy of the proposed procedures. [ABSTRACT FROM AUTHOR]

The m-machine no-wait flowshop scheduling problem, with the objective of minimizing total completion time subject to the constraint that makespan has to be less than or equal to a certain value, is addressed in this paper. First, an algorithm is proposed to find an upper bound on the makespan in case the upper bound is not given or unknown. Given the upper bound on makespan, a proposed algorithm ( PAL) with five versions L (1, 5, 10, 15, and 20) and a genetic algorithm ( GA) are utilized for solving the problem. Furthermore, a dominance relation is established for the case of four machines. The five versions of PAL and GA are evaluated on randomly generated problems with different number of jobs and number of machines. Computational experiments show that the errors of PA10, PA15, and PA20 are much smaller than that of GA while the CPU times of PA10, PA15, and PA20 are significantly smaller than that of GA. Therefore, the algorithms PA10, PA15, and PA20 are superior to the GA algorithm. [ABSTRACT FROM AUTHOR]

The Alternating Nonnegative Least Squares (ANLS) method is commonly used for solving nonnegative tensor factorization problems. In this paper, we focus on algorithmic improvement of this method. We present a Proximal ANLS (PANLS) algorithm to enforce convergence. To speed up the PANLS method, we propose to combine it with a periodic enhanced line search strategy. The resulting algorithm, PANLS/PELS, converges to a critical point of the nonnegative tensor factorization problem under mild conditions. We also provide some numerical results comparing the ANLS and PANLS/PELS methods. [ABSTRACT FROM AUTHOR]

As a strongly NP-hard problem, the hybrid flow-shop problem with multiprocessor tasks (HFSPMT) has gained increasing attention due to its academic significance and application value. In this paper, an effective algorithm based on the shuffled frog leaping algorithm (SFLA) is proposed for solving the HFSPMT. First, three decoding methods are used together to decode a solution to a better schedule. Especially, the forward scheduling decoding method is employed, aiming at narrowing the idle time between consecutive operations in the processor as well as increasing the flexibility in selecting processors to schedule the following operations. Second, a bilevel crossover is designed to make each individual share the good “meme” within each memeplex and exchange the good “meme” between different memeplexes. Third, multiple local search operators are used in an effective way by employing the meta-Lamarckian learning strategy to enhance the local exploitation ability. Meanwhile, the use of crossover and local search together in the SFLA can enrich the memetic search behavior and balance the exploration and exploitation abilities. In addition, the effect of parameter setting of the algorithm is investigated based on the Taguchi method of design of experiment, and suitable values are suggested for the parameters. Extensive testing results based on two types of well-known benchmarks are provided. And the effectiveness of the proposed algorithm is demonstrated by the comparisons with some existing algorithms. [ABSTRACT FROM AUTHOR]

This paper considers construction algorithms for the topological 2-D drawing of a graph. These algorithms allow to store, describe and modify the existing information on the drawing of a graph. Finally, we introduce necessary notions and structures to solve the problem of the topological 2-D drawing of a graph. [ABSTRACT FROM AUTHOR]

Most of real problems are modeling as multi-objective problems (MOPs) with imprecise coefficients. Coefficients or parameters are commonly imprecise because information is incomplete or unavailable. One category of these problems is multi-objective possibilistic problem (MOPP). Possibility theory is favorable for modeling incomplete information expressed by fuzzy propositions. On the other hand, MOPPs are a class of MOPs with imprecise parameters. Solving of MOPPs is usually difficult. Therefore, we should design approaches or extend exciting algorithms for solving them. This paper is a pre-processing stage for solving MOPPs. In other words, it discusses simple and useful integrated approach for reducing the number of objectives in multi-objective possibilistic problems. For this purpose, fuzzy principal component analysis (FPCA) can be used. The approach consists of a two-stage algorithm that in the first stage, we use FPCA for reducing objectives. In the second stage, we use results of previous stage for solving reduced problem. The new objective functions cover maximum variances of primal functions. We show that the proposed approach can generate efficient solutions without added extra constraints and variables than the general problems found in the literatures. An example illustrates steps of the procedure. [ABSTRACT FROM AUTHOR]

This article presents a multi-domain fast multipole hybrid boundary node method for composite materials in 3D elasticity. The hybrid boundary node method (hybrid BNM) is a meshless method which only requires nodes constructed on the surface of a domain. The method is applied to 3D simulation of composite materials by a multi-domain solver and accelerated by the fast multipole method (FMM) in this paper. The preconditioned GMRES is employed to solve the final system equation and precondition techniques are discussed. The matrix-vector multiplication in each iteration is divided into smaller scale ones at the sub-domain level and then accelerated by FMM within individual sub-domains. The computed matrix-vector products at the sub-domain level are then combined according to the continuity conditions on the interfaces. The algorithm is implemented on a computer code written in C + +. Numerical results show that the technique is accurate and efficient. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid algorithm combining particle swarm optimization (PSO) and tabu search (TS) is proposed to solve the job shop scheduling problem with fuzzy processing time. The object is to minimize the maximum fuzzy completion time, i.e., the fuzzy makespan. In the proposed algorithm, PSO performs the global search, i.e., the exploration phase, while TS conducts the local search, i.e., the exploitation process. The global best particle is used to direct other particles to optimal search space. Therefore, in the proposed algorithm, TS-based local search approach is applied to the global best particle to conduct find-grained exploitation. In order to share information among particles, one-point crossover operator is embedded in the hybrid algorithm. The proposed algorithm is tested on sets of the well-known benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed algorithm is shown against the best performing algorithms from the literature. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new smoothing function and show that it is coercive under suitable assumptions. Based on this new function, we propose a smoothing Newton method for solving the second-order cone complementarity problem (SOCCP). The proposed algorithm solves only one linear system of equations and performs only one line search at each iteration. It is shown that any accumulation point of the iteration sequence generated by the proposed algorithm is a solution to the SOCCP. Furthermore, we prove that the generated sequence is bounded if the solution set of the SOCCP is nonempty and bounded. Under the assumption of nonsingularity, we establish the local quadratic convergence of the algorithm without the strict complementarity condition. Numerical results indicate that the proposed algorithm is promising. [ABSTRACT FROM AUTHOR]

In this paper, the berth allocation problem with stochastic vessel handling times is formulated as a bi-objective problem. To solve the resulting problem, an evolutionary algorithm-based heuristic and a simulation-based Pareto front pruning algorithm is proposed. Computational examples show that the proposed approach provides solutions superior to the ones where the expected value of the vessel handling times is used. [ABSTRACT FROM AUTHOR]

In spite of the classic approaches of solution of dynamic facility layout problem, which only material handling and rearrangement costs are considered as objective function, these problems are the multi-objective problems. In this paper, a mixed integer linear programming formulation is presented for multi-objective dynamic facility layout problem concerning flexible bay structure. In addition, three current objectives in dynamic facility layout problems including minimizing material handling and rearrangement costs, maximizing adjacency rate, and minimizing shape ratio difference have been considered. Also, for solving this problem, two methods including the GAMS software and proposed parallel variable neighborhood search (PVNS) algorithm are used. So, it is worth mentioning that four test problems are solved by them, and the results show that the proposed PVNS algorithm is more efficient than the GAMS software. [ABSTRACT FROM AUTHOR]

This paper considers the single machine scheduling problem with a new version of time-dependent processing times. The processing time of a job is defined as a piecewise linear function of its start time. It is preferred that the processing of each job be started at a specific time which means that processing the job before or after that time implies additional effort to accomplish the job. The job-processing time is a nonmonotonic function of its start time. The increasing rate of processing times is job independent and the objective is to minimize the cycle time. We show that the optimal schedule is V shaped and propose an optimal polynomial time algorithm for the problem. [ABSTRACT FROM AUTHOR]

In this paper, the joint transceiver design issue is investigated for multi-cell systems with multiple mobile terminals (MTs). An optimization problem is formulated based on the criterion of mean squared error minimization for the joint design of the cooperative precoder of all multi-input multi-output (MIMO) cells and the cooperative decoder of all MIMO MTs. With a fixed decoder, the optimum precoder is first derived in a closed-form expression, where no exchange of data streams among all of the MIMO cells is required due to the limited capability of the backhaul. Subsequently, an optimum decoder is then obtained in an analytical expression with the fixed precoder. Based on these two expressions, an iterative algorithm with guaranteed convergence is finally developed to carry out the joint optimization of the cooperative precoders and decoders. Simulation results demonstrating the effectiveness of the proposed algorithm are also provided. [ABSTRACT FROM AUTHOR]

In this paper we present a new approach for solving elastoplastic problems as second order cone complementarity problems (SOCCPs). Specially, two classes of elastoplastic problems, i.e. the J plasticity problems with combined linear kinematic and isotropic hardening laws and the Drucker-Prager plasticity problems with associative or non-associative flow rules, are taken as the examples to illustrate the main idea of our new approach. In the new approach, firstly, the classical elastoplastic constitutive equations are equivalently reformulated as second order cone complementarity conditions. Secondly, by employing the finite element method and treating the nodal displacements and the plasticity multiplier vectors of Gaussian integration points as the unknown variables, we obtain a standard SOCCP formulation for the elastoplasticity analysis, which enables the using of general SOCCP solvers developed in the field of mathematical programming be directly available in the field of computational plasticity. Finally, a semi-smooth Newton algorithm is suggested to solve the obtained SOCCPs. Numerical results of several classical plasticity benchmark problems confirm the effectiveness and robustness of the SOCCP approach. [ABSTRACT FROM AUTHOR]

This paper addresses the multiple-route job shop scheduling problem to minimize makespan. The problem is recognized to be extremely difficult because of its combinatorial nature of integer optimization and the large size of the real problem. The goal is, thus, to obtain near-optimal schedules in a computationally efficient manner. Mathematical formulation of the problem is first presented. Then, an approach based on artificial immune algorithm is proposed. In order to evaluate the effectiveness of the proposed approach, 30 problems in small, medium, and large size are designed and solved using the proposed approach. Problems are also solved using Lingo software and the results are compared. The computational results show that the proposed approach generates high-quality schedules in a timely fashion. [ABSTRACT FROM AUTHOR]

In order to obtain efficiency and flexibility, assignment of machines' layout and determination of jobs' schedule on each machine are among the most important decisions. These decisions are interrelated and may impact each other but they are often treated separately or as a sequential decision in prior researches. In this paper, we propose a new approach to concurrently make the layout and scheduling decisions in a job shop environment. In other words, we consider an extension of the well-known job shop scheduling problem with transportation delay in which in addition to decisions made in the classic problem, the locations of machines have to be selected among possible sites. The only goal of the problem is the minimization of the makespan. A hybrid metaheuristic approach based on the scatter search algorithm is developed to tackle this problem. Using 43 randomly generated benchmark instances, the performance of the scatter search and its components are evaluated. We also applied our procedure to the classic job shop scheduling problems. Computational results show that our procedure is efficient. [ABSTRACT FROM AUTHOR]

In this paper, we focus on real-life settings that require the development of new models of flowshop scheduling problems, where job processing times can increase with the number of processed jobs due to the aging effect and decrease by the allocation of additional resource. We analyse the makespan minimization flowshop problem with such model and also with the aging effect only. We prove that the considered problems and their special cases are still polynomially solvable under given conditions, and on their basis, we provide optimal polynomial time solution algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the constitutive relations of the finite element method are constructed and used for solving two- and three-dimensional problems of the asymmetric theory of elasticity. Different variants of finite elements are considered. The numerical experiments are carried out to evaluate the reliability and computational efficiency of the finite element algorithm based on the comparison between the numerical and analytical solutions, numerical estimation of the convergence and checking of the degree of accuracy, to which the natural boundary conditions are satisfied. The obtained solutions to the two- and three-dimensional problems are interpreted from the viewpoint of their applicability to a design of experiments capable of revealing the facts of couple-stress effects in material under elastic deformation and identification of material constants for the asymmetric theory of elasticity. The capabilities of the finite element algorithm to interpret experimental data and estimate the errors occurring in real experiments have been tested by solving several example problems. [ABSTRACT FROM AUTHOR]

Part selection and machine loading are two major problems in the production planning phase of the flexible manufacturing systems. The problems have a combinatorial structure and usually, in practice, it is difficult to deal with this kind of problems using a mathematical programming model. In this paper, the above problems are formulated as a mixed-integer programming model which is handled sequentially and solved by a diversification-strategy-added version of the hybrid tabu search/simulated annealing algorithm of Zhang et al. ( Comput Oper Res 35:282-294, 2008) presented in 2008. The performance of the algorithm is tested on eight random-generated problems with different sizes. The results are compared with those of the mathematical programming model, the original version of Zhang et al.'s ( Comput Oper Res 35:282-294, 2008) algorithm and also a tabu search algorithm developed earlier by the authors. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimization of process planning in which various flexibilities are considered. The objective is to minimize total weighted sum of manufacturing costs. Various flexibilities, including process flexibility, sequence flexibility, machine flexibility, tool flexibility, and tool access direction (TAD) flexibility, generally exist in process planning and consideration of these flexibilities is essential for improving production efficiency and system flexibility. However, process planning is strongly NP-hard due to the existence of various flexibilities as well as complex machining precedence constraints. To tackle this problem, an imperialist competitive algorithm (ICA) is employed to find promising solutions with reasonable computational cost. ICA is a novel socio-politically motivated metaheuristic algorithm inspired by imperialist competition. It starts with an initial population and proceeds through assimilation, position exchange, imperialistic competition, and elimination. Computational experiments on three sets of process planning problem taken from literature are carried out, and comparisons with some existing algorithms developed for process planning are presented. The results show that the algorithm performs significantly better than existing algorithms like genetic algorithm (GA), simulated annealing (SA), tabu search (TS), and particle swarm optimization (PSO). [ABSTRACT FROM AUTHOR]

Virtual enterprise is a temporary network of independent companies or enterprises that can quickly bring together to fulfill a value-added task. With the development of Web service technologies, the enterprise business systems can be encapsulated as Web services. Establishing a virtual enterprise is actually a process of Web services composition. As more and more Web services are available, many Web services with different quality of service (QoS) can be found for tasks of the composite Web service. Thus, a significant research problem in Web service composition is how to select the correct Web services for tasks such that the composite Web service gives the best overall quality. In this paper, we propose an approach to achieve Web services selection efficiently with excellent comprehensive quality. Firstly, a comprehensive evaluation model based on generic QoS (GQoS) and domain QoS (DQoS) of composite Web service is established. Secondly, a novel optimization algorithm named culture max-min ant system (C-MMAS) is constructed to solve the problem of Web service selection based on DGQoS. Simulations show that when used in Web service selection, C-MMAS is better than ant colony optimization algorithm and MMAS in efficiency. [ABSTRACT FROM AUTHOR]

The job-shop scheduling problems with fuzzy processing time and fuzzy due date are investigated in this paper. The ranking concept among fuzzy numbers based on possibility and necessity measures which are developed in fuzzy sets theory is introduced. And on the basis of two consistent measures in this concept, several novel objective functions are proposed. The purpose of our research is to obtain the optimal schedules based on these objective functions. A modified DE algorithm will be designed to solve these objective functions. Several jop-shop scheduling problems with fuzzy processing time and fuzzy due date are experimented to show the efficiency and comparability of our approach. Through the experimental results, the potential application of the possibility and necessity theory in the real world is shown. [ABSTRACT FROM AUTHOR]

Modern motion control adopts acceleration/deceleration before interpolation (ADBI) motion planning to eliminate path command errors. However, the individual velocity profiles might not be continuous at the junction of the blocks. Acceleration/deceleration after interpolation (ADAI) method may provide an alternative for solving the discontinuous problems, but it causes path command errors. In this paper, an integrated acceleration/deceleration interpolation (IAD) scheme which integrates the ADBI and ADAI modules is proposed. The ADBI provides a look-ahead function which plans the feedrate profiles based on chord errors, command errors, curvatures, and acceleration limits. Within the look-ahead function, the command error equation is utilized to determine the feedrate at the junction of adjacent blocks. Then the ADBI performs non-uniform rational B-spline (NURBS) interpolation using the planned feedrate profile and outputs the position points to the ADAI module. The ADAI module processes the points by a digital convolution technique such that the continuity of the block junction velocity is ensured. Finally, the IAD is applied to the multi-block NURBS interpolation to validate its effectiveness. Simulations and experiments are conducted to demonstrate the IAD scheme. It is shown that the IAD scheme can reduce the acceleration significantly at the junctions of the blocks under the given tolerance of the command error. Furthermore, the proposed algorithm can improve tracking and contour accuracies as compared to the hybrid multi-blocks look-ahead approach. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid Pareto-based discrete artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each solution corresponds to a food source, which composes of two components, i.e., the routing component and the scheduling component. Each component is filled with discrete values. A crossover operator is developed for the employed bees to learn valuable information from each other. An external Pareto archive set is designed to record the non-dominated solutions found so far. A fast Pareto set update function is introduced in the algorithm. Several local search approaches are designed to balance the exploration and exploitation capability of the algorithm. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Presenting a more efficient mathematical model for the problem of integrating lot sizing, loading, and scheduling in capacitated flexible flow shops with sequence-dependent setups is the main novelty of this paper. In comparison to the former model, fewer variables and constraints of the current model makes it easier to be solved. Comparison between this model and the former one shows the superiority of this model. To validate the new model, an efficient lower bound has been provided. Mixed integer programming-based algorithms have been used to solve this model. [ABSTRACT FROM AUTHOR]

Generating schedules such that all operations are repeated every constant period of time is as important as generating schedules with minimum delays in all cases where a known discipline is desired or obligated by stakeholders. In this paper, a periodic job shop scheduling problem (PJSSP) based on the periodic event scheduling problem (PESP) is presented, which deviates from the cyclic scheduling. The PESP schedules a number of recurring events as such that each pair of event fulfills certain constraints during a given fixed time period. To solve such a hard PJSS problem, we propose a hybrid algorithm, namely PSO-SA, based on particle swarm optimization (PSO) and simulated annealing (SA) algorithms. To evaluate this proposed PSO-SA, we carry out some randomly constructed instances by which the related results are compared with the proposed SA and PSO algorithms as well as a branch-and-bound algorithm. In addition, we compare the results with a hybrid algorithm embedded with electromagnetic-like mechanism and SA. Moreover, three lower bounds (LBs) are studied, and the gap between the found LBs and the best found solutions are reported. The outcomes prove that the proposed hybrid algorithm is an efficient and effective tool to solve the PJSSP. [ABSTRACT FROM AUTHOR]

This paper presents a numerical algorithm based on a perturbation technique named asymptotic numerical method (ANM) to solve nonlinear problems with hyperelastic constitutive behaviors. The main advantages of this technique compared to Newton-Raphson are: (a) a large reduction of the number of tangent matrix decompositions; (b) in presence of instabilities or limit points no special treatment such as arc-length algorithms is necessary. The ANM uses high order series approximation with auto-adaptive step length and without need of any iteration. Introduction of this expansion into the set of nonlinear equations results into a sequence of linear problems with the same linear operator. The present work aims at providing algorithms for applying the ANM to the special case of compressible and incompressible hyperelastic materials. The efficiency and accuracy of the method are examined by comparing this algorithm with Newton-Raphson method for problems involving hyperelastic structures with large strains and instabilities. [ABSTRACT FROM AUTHOR]

This paper considers group scheduling problem in hybrid flexible flow shop with sequence-dependent setup times to minimize makespan. Group scheduling problem consists of two levels, namely scheduling of groups and jobs within each group. In order to solve problems with this context, two new metaheuristics based on simulated annealing (SA) and genetic algorithm (GA) are developed. A design procedure is developed to specify and adjust significant parameters for SA- and GA-based metaheuristics. The proposed procedure is based on the response surface methodology and two types of objective function are considered to develop multiple-objective decision making model. For comparing metaheuristics, makespan and elapsed time to obtain it are considered as two response variables representing effectiveness and efficiency of algorithms. Based on obtained results in the aspect of makespan, GA-based metaheuristic is recommended for solving group scheduling problems in hybrid flexible flow shop in all sizes and for elapsed time SA-based metaheuristic has better results. [ABSTRACT FROM AUTHOR]

In this paper, hybrid algorithms are developed for the multisource location-allocation problem in continuous space. Three hybrid algorithms are proposed to solve this problem that combine elements of several traditional metaheuristics (genetic algorithm and variable neighborhood search) and local searches to find near-optimal solutions. Many problems from the literature have been solved with these algorithms and the obtained results confirm the robustness of the proposed hybrid algorithms. Moreover, the results show that in comparison to the best methods in literature (GA and VNS), these algorithms provide some better solutions. [ABSTRACT FROM AUTHOR]

Self-Adapting Numerical Software (SANS) systems aim to automate some of the laborious human decision making involved in adapting numerical algorithms to problem data, network conditions, and computational platform. In this paper we describe the structure of a SANS system that tackles automatic algorithm choice, based on dynamic inspection of the problem data. We describe the various components of such a system, and their interfaces. [ABSTRACT FROM AUTHOR]

The tool switching problem is used to determine a job sequence and the tools to be loaded on a machine with the objective of minimising the total number of tool switches. In this paper, in order to develop a practical and efficient approach to solving this problem, a beam-search-based algorithm is introduced to formulate the solution space of the problem. The performance of the heuristic algorithm is compared with the heuristic developed by Bard. The proposed algorithm is then tested on some random test problems. The results show that the beam-search-based algorithm performs well in terms of computational efficiency and solution quality. [ABSTRACT FROM AUTHOR]