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Purpose: The paper formulates a bi-objective mixed-integer nonlinear programming model, aimed at minimizing the total labor hours and the workload unfairness for the multi-skilled worker assignment problem in Seru production system (SPS). Design/methodology/approach: Three approaches, namely epsilon-constraint method, non-dominated sorting genetic algorithm 2 (NSGA-II) and improved strength Pareto evolutionary algorithm (SPEA2), are designed for solving the problem. Findings: Numerous experiments are performed to assess the applicability of the proposed model and evaluate the performance of algorithms. The merged Pareto-fronts obtained from both NSGA-II and SPEA2 were proposed as final solutions to provide useful information for decision-makers. Practical implications: SPS has the flexibility to respond to the changing demand for small amount production of multiple varieties products. Assigning cross-trained workers to obtain flexibility has emerged as a major concern for the implementation of SPS. Most enterprises focus solely on measures of production efficiency, such as minimizing the total throughput time. Solutions based on optimizing efficiency measures alone can be unacceptable by workers who have high proficiency levels when they are achieved at the expense of the workers taking more workload. Therefore, study the tradeoff between production efficiency and fairness in the multi-skilled worker assignment problem is very important for SPS. Originality/value: The study investigates a new mixed-integer programming model to optimize worker-to-seru assignment, batch-to-seru assignment and task-to-worker assignment in SPS. In order to solve the proposed problem, three problem-specific solution approaches are proposed. [ABSTRACT FROM AUTHOR]

• Zero assignment to make the transfer function matrix (TFM) from the disturbance to the residual rank-deficient at the disturbance frequencies. • Complex coefficient gain in residual evaluation is proposed to exploit the unique feature of rank-deficiency of the disturbance TFM. • A new optimization objective function based on the difference of the maximum singular value of the transfer function matrix is proposed. [Display omitted] Aiming at early detection of faults in dynamic systems subject to external periodic disturbances, this paper proposes a new generalized proportional-integral observer (GPIO) fault detection scheme with zero-pole joint optimization and novel complex coefficient gain (CCG) of residual evaluation. The focus of the proposed scheme is to reduce the adverse impacts caused by the semi-stationary periodic disturbance whose spectrum is uneven, with most energy being at some dominant frequencies. The proposed GPIO with a complex coefficient gain is designed in a two-stage procedure. In the first stage of zero assignment and pole optimization, the additional zeros introduced by the GPIO's integration action are allocated to near the disturbance frequency. The gain of the transfer function matrix relating from the disturbances to the fault indicator signals is minimized by pole optimization. In the second stage of designing complex coefficient gain in residual evaluation, the unique feature of rank-deficient caused by the additional zeros assigned in stage one is further exploited to cancel the disturbances in the fault indicator signals (which is also referred to as the fault detection residual in this article). It is proved that, for an arbitrary periodic disturbance with a specific spectrum, the remnant components of the disturbance in the indicator signals generated by the GPIO can cancel each other by a complex gain vector, which can be determined by the zero eigenvalue's left eigenvector of the rank-deficient of the disturbance transfer function matrix. The sufficient conditions for the convergence of the proposed fault detection filter are also given. Numerical examples illustrate the proposed method's better performance in detecting minor faults. [ABSTRACT FROM AUTHOR]

This paper presents a novel minimum entropy filter design for a class of stochastic nonlinear systems, which are subjected to non-Gaussian noises. Motivated by stochastic distribution control, an output entropy model is developed using a radial basis function neural network, while the parameters of the model can be identified by the collected data. Based upon the presented model, the filtering problem has been investigated, while the system dynamics have been represented. As the model output is the entropy of the estimation error, the optimal nonlinear filter is obtained based on the Lyapunov design, which makes the model output minimum. Moreover, the entropy assignment problem has been discussed as an extension of the presented approach. To verify the presented design procedure, a numerical example is given, which illustrates the effectiveness of the presented algorithm. The contributions of this paper can be summarized as follows: 1) an output entropy model is presented using a neural network; 2) a nonlinear filter design algorithm is developed as the main result; and 3) a solution of the entropy assignment problem is obtained, which is an extension of the presented framework. [ABSTRACT FROM AUTHOR]

This paper proposes a bound-based simulation budget allocation (BSBA) procedure for solving ranking and selection (R&S) problems in simulation optimization. For many practical applications, strict bounds on system performances can be obtained through empirical and theoretical approaches. These bounds provide additional information which may help solve R&S problems. In this paper, a new method of objective function estimation is proposed using both bound information and simulation outputs. This new estimation method is demonstrated to be particularly effective. To solve R&S problems, several asymptotic optimal allocation rules are also derived. Using these allocation rules, a BSBA procedure is proposed to achieve high efficiency in identifying the best design. Numerical experiments are provided to examine the performance of the proposed BSBA procedure. The computational results show that BSBA outperforms three compared allocation procedures, especially when bounds are tight or the simulation budget is small. [ABSTRACT FROM AUTHOR]

Valuation algebras abstract a large number of formalisms for automated reasoning and enable the definition of generic inference procedures. Many of these formalisms provide some notion of solution. Typical examples are satisfying assignments in constraint systems, models in logics or solutions to linear equation systems. Dynamic programming algorithms rely on low treewidth decompositions and can be understood as particular cases of a single algorithmic scheme for finding solutions in a valuation algebra. The most encompassing description of this algorithmic scheme to date has been proposed by Pouly and Kohlas together with sufficient conditions for its correctness. Unfortunately, the formalization relies on a theorem for which we provide a counterexample. In spite of that, the mainline of Pouly and Kohlas' theory is correct, although some of the necessary conditions have to be revised. In this paper we analyze the impact that the counterexample has on the theory, and rebuild the theory providing correct sufficient conditions for the algorithms. Furthermore, we also provide necessary conditions for the algorithms, allowing for a sharper characterization of when the algorithmic scheme can be applied. The contribution of the paper is not limited to correcting Pouly and Kohlas' theory. First, we generalize their results from discrete tuples to tuple systems. This allows us to cover some classical algorithms, such as sparse Cholesky factorization, as well as more recently proposed algorithms, such as Snowball (sparse all-pairs shortest path problems), as particular cases of the generic valuation-based algorithms presented here. For the particular case of valuation algebras induced by selective semirings, Pouly and Kohlas presented an algorithm for finding all solutions without requiring any additional condition for correctness. We characterize a necessary and a sufficient condition for it. Furthermore, we also introduce a new algorithm that requires no necessary condition. [ABSTRACT FROM AUTHOR]

Task assignment is the fundamental problem of crowdsourcing, which addresses the matching between service providers and demanders in urban services. As crowdsourcing has been receiving more attention, the focus of task assignment has expanded from one-sided or two-sided task assignment to three-sided task assignment. Recent research on the three-sided version aims at the online case or stable case, which only partially reflects the real scenario. In this paper, we consider a hybrid case that contains the aforementioned cases. Concretely, we propose a three-sided online stable task assignment problem. We first show the fundamental notations of task assignment. Then, we analyze and present the matching conditions and formulate the problem. As a warm-up, we propose three iconic algorithms as baselines and analyze their performance in the three-sided scenario. Inspired by two suggested matching technique in a two-sided task assignment problem, we present a new algorithm that divides the arrival sequence into three parts in which we utilize different strategies for different arrival intervals. It balances efficiency and effectiveness. In the experiment, all the algorithms are verified in size, memory, and time and tested on a real and a synthetic dataset. The results show that our algorithm maintains efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

To deal with group decision making (GDM) with interval multiplicative preference relations (IMPRs), this paper proposes a novel method based on multiplicative data envelopment analysis (DEA) cross-efficiency and stochastic weight space acceptability analysis. We first develop a multiplicative DEA model to evaluate the relative efficiency of all alternatives of a given multiplicative preference relation (MPR). Then, we present a method, free from consistency adjustment, to derive a priority vector using the multiplicative DEA cross-efficiency with respect to the given MPR. For GDM with IMPRs, we consider the decision makers' weights as a uniform distribution for acceptability analysis. A modified unacceptability index is further defined to measure the unlikeliness for a particular alternative in a particular rank. Finally, we develop an assignment problem model to achieve an optimal ranking by minimizing the total rank unacceptability, and to compute the expected priority vector of all alternatives. Numerical examples are provided to show the applicability and justifications of the proposed GDM method. [ABSTRACT FROM AUTHOR]

Many problems of interest for cyber-physical network systems can be formulated as mixed-integer linear programs in which the constraints are distributed among the agents. In this paper, we propose a distributed algorithmic framework to solve this class of optimization problems in a peer-to-peer network with no coordinator and with limited computation and communication capabilities. At each communication round, agents locally solve a small linear program, generate suitable cutting planes, and communicate a fixed number of active constraints. Within the distributed framework, we first propose an algorithm that, under the assumption of integer-valued optimal cost, guarantees finite-time convergence to an optimal solution. Second, we propose an algorithm for general problems that provides a suboptimal solution up to a given tolerance in a finite number of communication rounds. Both algorithms work under asynchronous, directed, unreliable networks. Finally, through numerical computations, we analyze the algorithm scalability in terms of the network size. Moreover, for a multi-agent multi-task assignment problem, we show, consistently with the theory, its robustness to packet loss. [ABSTRACT FROM AUTHOR]

This paper studies pole assignment of a class of high-order (HO) linear systems, which contains HO time-derivatives of both the state vector and the input vector, and can be used to model some practical control systems directly. Based on an equivalent first-order linear system, whose state vector contains HO time-derivatives of both the state vector and the input vector of the original HO linear system, it is shown that the pole assignment problem is solvable if and only if solutions to a linear matrix equation, which takes a similar form as the original HO linear system, are such that a matrix is nonsingular. Meanwhile, all the feedback gains are characterized by solutions to the linear matrix equation. Explicit solutions to the linear matrix equations are then proposed by using right-coprime factorization of the original HO linear system. Explicit feedback gains are also established for some special cases. A numerical example is worked out to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Semisupervised text classification has attracted much attention from the research community. In this paper, a novel model, the semisupervised sequential variational autoencoder (SSVAE), is proposed to tackle this problem. By treating the categorical label of unlabeled data as a discrete latent variable, the proposed model maximizes the variational evidence lower bound of the data likelihood, which implicitly derives the underlying label distribution for the unlabeled data. Analytical work indicates that the autoregressive nature of the sequential model is the crucial issue that renders the vanilla model ineffective. To remedy this, two types of decoders are investigated in the SSVAE model and verified. In addition, a reweighting approach is proposed to circumvent the credit assignment problem that occurs during the reconstruction procedure, which can further improve performance for sparse text data. Experimental results show that our method significantly improves the classification accuracy compared with other modern methods. [ABSTRACT FROM AUTHOR]

This paper studies the high-order moment control problem for discrete-time Markov jump linear systems (MJLSs) with certain dynamic response performance and disturbance rejection specifications. An appropriate cumulant generating function is employed to express the original stochastic system in high-order component form. This facilities the high-order moment stabilization of MJLSs. Moreover, a pole region assignment approach is utilized to ensure desired dynamic response specifications with a certain attenuation rate. An arithmetic and geometric inequality approach is utilized to extract sufficient conditions ensuring the designed controller existence. These conditions ensure the high-order moment steady-state property and certain dynamic specifications for the MJLSs. The effectiveness of the proposed method is demonstrated through numerical and practical examples. [ABSTRACT FROM AUTHOR]

This paper addresses the generalized covariance intersection (GCI) fusion for labeled random finite sets, avoiding the label inconsistency sensitivity problem and the information loss for target identities. In this paper, the label assignment is considered from the perspective of space and time. First, to solve the label inconsistency sensitivity problem, the joint label space for the support of fused labeled random finite sets is proposed to represent the target identity and the assignments of labels of different local label spaces, followed by obtaining the multitarget density over the joint label space via the GCI fusion. To avoid the information loss for target identities so as to improve the performance of the track continuity, we utilize all the label information to establish the assignment of targets across the time steps. Specifically, the identity distribution for each target is extracted from the global multitarget density and then utilized for computing the Cauchy-Schwarz divergence (CSD) of targets across the time steps. The assignment between the target identities across the time steps is obtained by formulating a linear assignment problem, which can be effectively solved by the Hungarian algorithm. Simulation results exhibit the effectiveness of the proposed approach in challenging scenarios. [ABSTRACT FROM AUTHOR]

The location estimation of multiple simultaneously active sources in an acoustic sensor network is quite challenging because the correct combination of measurements received from different microphone arrays is not usually known, generally termed the data association problem. Existing techniques generally formulate the data association problem as a multidimensional assignment and solve them using classical optimal techniques. Although these approaches show effectiveness for a limited amount of active sources, the computational time and complexity of these techniques increase significantly when the microphones or acoustic sources increase. In this paper, a learning approach is proposed that solves the multidimensional assignment using a deep neural network. Initially, the features that are employed for the association are formulated for each of the detected sources in all the array nodes, and a multidimensional assignment problem is formulated. Subsequently, a deep multidimensional assignment network is devised to extract the correspondence probability of the measurements received from the microphone arrays. Specifically, a data-driven differentiable approach is presented for multidimensional assignments that is computationally efficient. The proposed methodology is validated under realistic conditions for both speech and urban signals. The methodology is compared to state-of-the-art methods for showing the performance gain in terms of accuracy of association and location estimation with a reduced computational time. [ABSTRACT FROM AUTHOR]

Purpose - In many decision-making problems under parameter uncertainty, the most popular stochastic approach is not used because of its serious drawbacks. The purpose of this paper is to present another approach, which copes with the uncertainty of parameters. It uses a precise criterion evaluating a decision with respect to uncertain parameters. This precision by the maximum operator is performed on a term based on the criterion and called the relative regret. The approach is applied to the allocation problems in a complex of operations. Design/methodology/approach - The resource allocation problems in a complex of operations of independent and dependent structures to minimize a total execution time of all operations are investigated. Then, the results are extended for the problem of a task allocation in the complex of independent operations. The case is considered when the parameters in the functional models of the operations are uncertain, and their values belong to the intervals of known bounds. The solution algorithms for the uncertain problems are based on known solution algorithms for the corresponding deterministic problems. The solution algorithms for the latter problems are outlined in the paper. Findings - The main contribution of the paper consists in presenting the property that it is possible for the uncertain problems considered to replace the solution of the uncertain allocation problems by solving a number of corresponding deterministic problems. Research limitations/implications - The useful and interesting property of the solution algorithm for the allocation problems, in general, cannot be applied to the other decision-making problems under uncertainty. As an example of such a problem, a simple routing-scheduling problem is presented for which, however, a number of possible parameter scenarios can be substantially limited. Practical implications - The allocation problems addressed in the paper have a variety of applications in computer systems and in manufacturing systems. Moreover, a lack of crisp values for the parameters in models of individual operations is rather common. Originality/value - The paper extends previous results for the allocation problems in a complex of operations. [ABSTRACT FROM AUTHOR]

Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the computing power of virtual machines (VMs) and network status can greatly affect the completion time of data intensive tasks. However, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task completion time and keep good load balance of physical hosts at the same time. [ABSTRACT FROM AUTHOR]

The storage allocation problem of outbound containers in container yard is usually optimized by two successive stages, yard-bay allocation problem (YBAP) and space allocation problem (SAP), but they cannot obtain global optimal solution. This paper proposes an outer–inner cellular automaton algorithm (CAOI) to solve this problem, using YBAP and SAP as an integrated optimization process. Our work can be divided into several parts: (1) The outer cellular automaton (CAO) model is established to solve YBAP, and the inner cellular automaton (CAI) model is established to solve SAP. (2) In CAO model, an End–Front–Front–End (EFFE) transition rule is designed to make a cell change its state more instructive. (3) In CAI model, cells are initialized through a branch and bound method based on least-cost priority queue (LCBB). LCBB makes initial CAI cell space in an approximate optimal state. (4) An integrated strategy for CAO and CAI model is proposed, by which CAO and CAI execute co-evolution under the control of CAOI. Extensive experiments show CAOI achieves a better performance in solving storage allocation problem than other state-of-the-art methods, which synchronously optimizes the container transportation distance, the number of allocated yard-bays and rehandles. [ABSTRACT FROM AUTHOR]

This paper focuses on fast algorithm for computing the assignment reduct in inconsistent incomplete decision systems. It is quite inconvenient to judge the assignment reduct directly according to its definition. We propose the judgment theorem for the assignment reduct in the inconsistent incomplete decision system, which greatly simplifies judging this type reduct. On such basis, we derive a novel attribute significance measure and construct the fast assignment reduction algorithm (F-ARA), intended for computing the assignment reduct in inconsistent incomplete decision systems. Finally, we make a comparison between F-ARA and the discernibility matrix-based method by experiments on 13 University of California at Irvine (UCI) datasets, and the experimental results prove that F-ARA is efficient and feasible. [ABSTRACT FROM AUTHOR]

Abstract: Code equivalence is a basic concept in coding theory. The well-known theorem by MacWilliams gives a sufficient condition for code equivalence. Recently the MacWilliams theorem has been generalized, by Fan, Liu and Puig, making use of the generalized Hamming weights (GHWs). In this paper, we will present a further generalization of the MacWilliams theorem. Our result extends both the MacWilliams theorem and the result by Fan, Liu and Puig. We will first define “relative subcodes” of a linear code, based on the relative generalized Hamming weights (RGHWs) which is a generalization of the GHWs; and then establish a method based on finite projective geometry to characterize relative subcodes. Using this method, we will prove our main result. [Copyright &y& Elsevier]

Abstract: In hierarchical organizations, hierarchical structures naturally correspond to nested sets. That is, we have a collection of sets such that for any two sets, either one of them is a subset of the other, or they are disjoint. In other words, a nested set system forms a hierarchy in the form of a tree structure. The task assignment problem on such hierarchical organizations is a real life problem. In this paper, we introduce the tree-like weighted set packing problem, which is a weighted set packing problem restricted to sets forming tree-like hierarchical structure. We propose a dynamic programming algorithm with cubic time complexity. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of stochastic resource allocation problems where resources assigned to a task may fail probabilistically to complete assigned tasks. Failures to complete a task are observed before new resource allocations are selected. The resulting temporal resource allocation problem is a stochastic control problem, with a discrete state space and control space that grow in cardinality exponentially with the number of tasks. We modify this optimal control problem by expanding the admissible control space, and show that the resulting control problem can be solved exactly by efficient algorithms in time that grows nearly linear with the number of tasks. The approximate control problem also provides a bound on the achievable performance for the original control problem. The approximation is used as part of a model predictive control (MPC) algorithm to generate resource allocations over time in response to information on task completion status. We show in computational experiments that, for single resource class problems, the resulting MPC algorithm achieves nearly the same performance as the optimal dynamic programming algorithm while reducing computation time by over four orders of magnitude. In multiple resource class experiments involving 1000 tasks, the model predictive control performance is within 4% of the performance bound obtained by the solution of the expanded control space problem. [ABSTRACT FROM AUTHOR]

This paper proposes a marginal multi-object Bayesian filter with multiple hypotheses to track multiple objects in the presence of object appearing and object disappearing, missed detection and clutter. This filter delivers the probability of existence and probability density function of each object. A mathematical model for searching K-best hypotheses is set up by the maximization of the generalized joint likelihood ratios of hypotheses, which results in a 2-dimensional assignment problem. The K-best hypotheses can be acquired by using the Murty algorithm to solve the 2-dimensional assignment problem. According to the K-best hypotheses, the existence probabilities and probability density functions of objects are formed. Furthermore, an implementation of this filter for a linear Gaussian system is developed and is extended to nonlinear observations. Experimental result demonstrates that the proposed filter outperforms other available filters at various numbers of clutter and different detecting probabilities. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we introduce the incremental assignment problem. In this problem, a new pair of vertices and their incident edges are added to a weighted bipartite graph whose maximum-weighted matching is already known, and the maximum-weighted matching of the extended graph is sought. We propose an algorithm for the problem. [Copyright &y& Elsevier]