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The quay crane scheduling problem (QCSP) is one of the most important problems for the operations at container ports. The QCSP aims to decide a QC schedule for loading and unloading containers so as to minimize the vessel turnaround time. The QCSP is subject to various kinds of constraints, e.g., task precedence constraints and QC noninterference constraints. This paper extends the QCSP by taking into consideration the stability constraints, which are crucial for the safety reason but often omitted in the existing literature. We provide a mathematical model for the QCSP with stability constraints (QCSPSCs). A bicriteria evolutionary algorithm is proposed to solve the QCSPSC. The algorithm consists of a sliding-window heuristic to fix the schedule, which violates the stability constraints. Extensive experiments are conducted to demonstrate the effectiveness of the algorithm. The computational results of the traditional QCSP and the QCSPSC are also compared and analyzed. Note to Practitioners—Stability is a vital factor for the safety of the ship and the goods inside. At a terminal, when a berthed container ship receives loading and unloading operations performed by quay cranes (QCs), it is required that the ship stability criterion does not exceed a certain threshold. In practice, ship operation managers are keen in minimizing the ship’s turnaround time via an efficient QC schedule for loading and unloading containers without leaning the ship to one side. The schedule is then implemented by automatic scheduling systems or simulation tools in the terminal routines. This paper presents some practical stability constraints and integrates them into the traditional QC scheduling model. The results show that by considering the stability constraints, the ship will become more stable, but the turnaround time will not be affected significantly. We believe that the introduction of ship stability constraints can not only fill in the gap between the research on QC scheduling and the real-world ship handling practices but also enrich the studies on logistics planning and port operations. [ABSTRACT FROM AUTHOR]

Based on the in-depth analysis of the essence and key features of vague phenomena, this paper focuses on establishing the axiomatical foundation of membership degree theory using for modeling vague phenomena, presents an axiomatic system to govern membership degrees and their interconnections. The concept of vague partition is introduced, on this basis, the concept of fuzzy set in Zadeh’s sense is redefined based on vague partition from the perspective of axiomatization. The thesis defended in this paper is that the mutual constraint relationship among vague attribute values in a vague partition should be the starting point to recognize and model vague phenomena by the quantitative analysis method. [ABSTRACT FROM AUTHOR]

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature. [ABSTRACT FROM PUBLISHER]

This article reports on the 10th International Web Rule Symposium, which was held at Stony Brook University in Stony Brook, New York, from July 6-9, 2016. [ABSTRACT FROM AUTHOR]

In reality, projects usually consume complex resources. Making good use of the various resources is vital for optimal project selection and maximum profit earning. This paper proposes a new project selection model from the perspective of complex resource constraints. In the model, the resources are divided into non-renewable and renewable categories, and some resources of the two categories can both be shared by different projects. In addition, the paper considers the situation where the company has resources in stock and can purchase them in the marketplace if they are out of stock. The paper proves that the proposed model which considers renewable resource and resource sharing produces higher profit than the ones that do not consider renewable resource and resource sharing. To solve the complex model problem, an improved genetic algorithm is presented. For the sake of illustration, a case study is provided. [ABSTRACT FROM PUBLISHER]

Purpose: Iterative image reconstruction algorithms are commonly used to optimize an objective function, especially when the objective function is nonquadratic. Generally speaking, the iterative algorithms are computationally inefficient. This paper presents a fast algorithm that has one backprojection and no forward projection. Methods: This paper derives a new method to solve an optimization problem. The nonquadratic constraint, for example, an edge-preserving denoising constraint is implemented as a nonlinear filter. The algorithm is derived based on the POCS (projections onto projections onto convex sets) approach. Awindowed FBP (filtered backprojection) algorithm enforces the data fidelity. An iterative procedure, divided into segments, enforces edge-enhancement denoising. Each segment performs nonlinear filtering. Results: The derived iterative algorithm is computationally efficient. It contains only one backprojection and no forward projection. Low-dose CT data are used for algorithm feasibility studies. The nonlinearity is implemented as an edge-enhancing noise-smoothing filter. Conclusions: The patient studies results demonstrate its effectiveness in processing low-dose x ray CT data. This fast algorithm can be used to replace many iterative algorithms. [ABSTRACT FROM AUTHOR]

The main goal of designing individualized products is to meet the specific requirements presented by the customer. Therefore, designers need to adapt the relevant components of the product for each customer, which is difficult to achieve efficiently with existing methods. In this paper, we propose an integrated approach that enables intuitive modelling of products and supports fast conversion of different customer requirements (CRs) to configuration solutions. Firstly, the product properties are decomposed, and a standardized CR expression template is established to enable the mapping of CR information to product properties change information. Secondly, the community structure in complex network theory is introduced to visualize and quantitatively describe the relationship between product parts. The community detection method based on fuzzy clustering is applied to module division, and the optimal result is obtained using the F -statistic and modularity as evaluation indexes. Finally, the individualized product configuration problem is transformed into a dynamic constraint satisfaction problem. According to CR information and product module division result, the hierarchical community structure is used to narrow the search space and quickly derive solutions with high customer satisfaction through case-based configuration. An automatic guided vehicle is used as an example to illustrate the effectiveness and practicality of this approach. [ABSTRACT FROM AUTHOR]

The graph burning problem is an NP-hard combinatorial optimization problem that helps quantify how vulnerable a graph is to contagion. This paper introduces three mathematical formulations of the problem: an integer linear program (ILP) and two constraint satisfaction problems (CSP1 and CSP2). Thanks to off-the-shelf optimization software, these formulations can be solved optimally over arbitrary graphs; this is relevant because the only algorithms designed to date for this problem are approximation algorithms and heuristics, which do not guarantee to find optimal solutions. We empirically compared the proposed formulations using random graphs and off-the-shelf optimization software. The results show that CSP1 and CSP2 tend to reach optimal solutions in less time than the ILP. Therefore, we executed them over some benchmark graphs of order at most 5908. The previously best-known solutions for some of these graphs were improved. We draw some empirical observations from the experimental results. For instance, we find the tendency: the larger the graph's optimal solution, the more difficult it is to find it. Finally, the resulting set of optimal solutions might be helpful as a benchmark dataset for the performance evaluation of non-exact algorithms. [ABSTRACT FROM AUTHOR]

ARX algorithms are a class of symmetric-key algorithms constructed by Addition, Rotation, and XOR. To evaluate the resistance of an ARX cipher against differential and impossible-differential cryptanalysis, the recent automated methods employ constraint satisfaction solvers to search for optimal characteristics or impossible differentials. The main difficulty in formulating this search is finding the differential models of the non-linear operations. While an efficient bit-vector differential model was obtained for the modular addition with two variable inputs, no differential model for the modular addition by a constant has been proposed so far, preventing ARX ciphers including this operation from being evaluated with automated methods. In this paper, we present the first bit-vector differential model for the n-bit modular addition by a constant input. Our model contains O (log 2 (n)) basic bit-vector constraints and describes the binary logarithm of the differential probability. We describe an SMT-based automated method that includes our model to search for differential characteristics of ARX ciphers including constant additions. We also introduce a new automated method for obtaining impossible differentials where we do not search over a small pre-defined set of differences, such as low-weight differences, but let the SMT solver search through the space of differences. Moreover, we implement both methods in our open-source tool ArxPy to find characteristics and impossible differentials of ARX ciphers with constant additions in a fully automated way. As some examples, we provide related-key impossible differentials and differential characteristics of TEA, XTEA, HIGHT, LEA, SHACAL-1, and SHACAL-2, which achieve better results compared to previous works. [ABSTRACT FROM AUTHOR]

In this paper, we design efficient algorithms to approximately count the number of edges of a given k-hypergraph, and to sample an approximately uniform random edge. The hypergraph is not given explicitly and can be accessed only through its colorful independence oracle: The colorful independence oracle returns yes or no depending on whether a given subset of the vertices contains an edge that is colorful with respect to a given vertex-coloring. Our results extend and/or strengthen recent results in the graph oracle literature due to Beame et al. [ACM Trans. Algorithms, 16 (2020), 52], Dell and Lapinskas [Proceedings of STOC, ACM, 2018, pp. 281-288], and Bhattacharya et al. [Proceedings of ISAAC, 2019]. Our results have consequences for approximate counting/sampling: We can turn certain kinds of decision algorithms into approximate counting/sampling algorithms without causing much overhead in the running time. We apply this approximate counting/sampling-to-decision reduction to key problems in fine-grained complexity (such as k-SUM, k-OV, and weighted k-Clique) and parameterized complexity (such as induced subgraphs of size k or weight-k solutions to constraint satisfaction problems). [ABSTRACT FROM AUTHOR]

Constraint Satisfaction Problems (CSP) are notoriously hard. Consequently, powerful decomposition methods have been developed to overcome this complexity. However, this poses the challenge of actually computing such a decomposition for a given CSP instance, and previous algorithms have shown their limitations in doing so. In this paper, we present a number of key algorithmic improvements and parallelisation techniques to compute so-called Generalized Hypertree Decompositions (GHDs) faster. We thus advance the ability to compute optimal (i.e., minimal-width) GHDs for a significantly wider range of CSP instances on modern machines. This lays the foundation for more systems and applications in evaluating CSPs and related problems (such as Conjunctive Query answering) based on their structural properties. [ABSTRACT FROM AUTHOR]

In this paper, Bayesian quickest change detection problems with sampling right constraints are considered. In particular, there is a sequence of random variables whose probability density function will change at an unknown time. The goal is to detect this change in a way such that a linear combination of the average detection delay and the false alarm probability is minimized. Two types of sampling right constrains are discussed. The first one is a limited sampling right constraint, in which the observer can take at most \(N\) observations from this random sequence. Under this setup, we show that the cost function can be written as a set of iterative functions, which can be solved by Markov optimal stopping theory. The optimal stopping rule is shown to be a threshold rule. An asymptotic upper bound of the average detection delay is developed as the false alarm probability goes to zero. This upper bound indicates that the performance of the limited sampling right problem is close to that of the classic Bayesian quickest detection for several scenarios of practical interest. The second constraint discussed in this paper is a stochastic sampling right constraint, in which sampling rights are consumed by taking observations and are replenished randomly. The observer cannot take observations if there are no sampling rights left. We characterize the optimal solution, which has a very complex structure. For practical applications, we propose a low complexity algorithm, in which the sampling rule is to take observations as long as the observer has sampling rights left and the detection scheme is a threshold rule. We show that this low complexity scheme is first order asymptotically optimal as the false alarm probability goes to zero. [ABSTRACT FROM AUTHOR]

A constraint satisfaction problem (CSP), \(\textsf {Max-CSP}(\mathcal {F})\) , is specified by a finite set of constraints \(\mathcal {F}\subseteq \lbrace [q]^k \rightarrow \lbrace 0,1\rbrace \rbrace\) for positive integers q and k. An instance of the problem on n variables is given by m applications of constraints from \(\mathcal {F}\) to subsequences of the n variables, and the goal is to find an assignment to the variables that satisfies the maximum number of constraints. In the (γ ,β)-approximation version of the problem for parameters 0 ≤ β ≤ γ ≤ 1, the goal is to distinguish instances where at least γ fraction of the constraints can be satisfied from instances where at most β fraction of the constraints can be satisfied. In this work, we consider the approximability of this problem in the context of sketching algorithms and give a dichotomy result. Specifically, for every family \(\mathcal {F}\) and every β < γ, we show that either a linear sketching algorithm solves the problem in polylogarithmic space or the problem is not solvable by any sketching algorithm in \(o(\sqrt {n})\) space. In particular, we give non-trivial approximation algorithms using polylogarithmic space for infinitely many constraint satisfaction problems. We also extend previously known lower bounds for general streaming algorithms to a wide variety of problems, and in particular the case of q=k=2, where we get a dichotomy, and the case when the satisfying assignments of the constraints of \(\mathcal {F}\) support a distribution on \([q]^k\) with uniform marginals. Prior to this work, other than sporadic examples, the only systematic classes of CSPs that were analyzed considered the setting of Boolean variables q = 2, binary constraints k =2, and singleton families \(|\mathcal {F}|=1\) and only considered the setting where constraints are placed on literals rather than variables. Our positive results show wide applicability of bias-based algorithms used previously by [47] and [41], which we extend to include richer norm estimation algorithms, by giving a systematic way to discover biases. Our negative results combine the Fourier analytic methods of [56], which we extend to a wider class of CSPs, with a rich collection of reductions among communication complexity problems that lie at the heart of the negative results. In particular, previous works used Fourier analysis over the Boolean cube to initiate their results and the results seemed particularly tailored to functions on Boolean literals (i.e., with negations). Our techniques surprisingly allow us to get to general q-ary CSPs without negations by appealing to the same Fourier analytic starting point over Boolean hypercubes. [ABSTRACT FROM AUTHOR]

Tree search algorithms, such as branch-and-bound, are the most widely used tools for solving combinatorial and non-convex problems. For example, they are the foremost method for solving (mixed) integer programs and constraint satisfaction problems. Tree search algorithms come with a variety of tunable parameters that are notoriously challenging to tune by hand. A growing body of research has demonstrated the power of using a data-driven approach to automatically optimize the parameters of tree search algorithms. These techniques use a training set of integer programs sampled from an application-specific instance distribution to find a parameter setting that has strong average performance over the training set. However, with too few samples, a parameter setting may have strong average performance on the training set but poor expected performance on future integer programs from the same application. Our main contribution is to provide the first sample complexity guarantees for tree search parameter tuning. These guarantees bound the number of samples sufficient to ensure that the average performance of tree search over the samples nearly matches its future expected performance on the unknown instance distribution. In particular, the parameters we analyze weight scoring rules used for variable selection. Proving these guarantees is challenging because tree size is a volatile function of these parameters: we prove that, for any discretization (uniform or not) of the parameter space, there exists a distribution over integer programs such that every parameter setting in the discretization results in a tree with exponential expected size, yet there exist parameter settings between the discretized points that result in trees of constant size. In addition, we provide data-dependent guarantees that depend on the volatility of these tree-size functions: our guarantees improve if the tree-size functions can be well approximated by simpler functions. Finally, via experiments, we illustrate that learning an optimal weighting of scoring rules reduces tree size. [ABSTRACT FROM AUTHOR]

Currently-used decision support solutions allow decision makers to estimate the cost of developing a new product, its production, and promotion, and compare the estimated cost to the target cost. However, these solutions are inadequate for supporting simulations of identifying conditions, by which the specific cost is reached. The proposed approach provides a framework for searching for possible variants towards reaching the target production cost. This paper is concerned with a prototyping problem of product development described in terms of a constraint satisfaction problem. The proposed method uses parametric estimation to identify relationships between variables, and constraint programming to search for project completion variants within the company's resources and project requirements. The results of an experiment indicate that constraint programming provides effective search strategies for finding admissible solutions. Consequently, the proposed approach allows decision makers to obtain alternative scenarios within the limits imposed by the production process. In this, it outperforms current methods dedicated to the support of evaluating the total cost of a new product. The declarative approach presented in this paper is used to model the production cost; however, it can be effortlessly extended to other aspects of product development (e.g., product reliability). [ABSTRACT FROM AUTHOR]

Computing a mapping between two process models is a crucial technique, since it enables reasoning and operating across processes, like providing a similarity score between two processes, or merging different process variants to generate a consolidated process model. In this paper we present a new flexible technique for process model mapping, based on the relaxation labeling constraint satisfaction algorithm. The technique can be instantiated so that different modes are devised, depending on the context. For instance, it can be adapted to the case where one of the mapped process models is incomplete, or it can be used to ground an adaptable similarity measure between process models. The approach has been implemented inside the open platform NLP4BPM, providing a visualization of the performed mappings and computed similarity scores. The experimental results witness the flexibility and usefulness of the technique proposed. [ABSTRACT FROM AUTHOR]

This paper presents an experimental analysis of a nonlinear model predictive controller combined with an integral sliding mode control component and an explicit dead-time compensator applied to the closed-loop control of FlatFish, an autonomous underwater vehicle (AUV). The proposed strategy is composed of three control instances that can be integrated to deal with constraints, disturbances, and dead time. The integral sliding mode control instance is considered to attenuate unmeasured disturbance effects, the filtered Smith predictor component is used to compensate for the dead-time effect, and the model predictive control layer aims at systematically optimizing closed-loop performance with constraint satisfaction. The discussion highlights that any of these control layers can be either included or removed from the control chain, which depends on the challenges presented by the AUV problem. The benchmark for such evaluation is the original control chain of FlatFish, which is based on a P cascade controller. Experimental tests were performed with FlatFish in a saltwater tank located in Bremen, Germany, and showed the superior performance of the proposed system. The experimental results show that the proposed control chain may be effectively used to provide improved tracking performance as expected from theoretical analysis. [ABSTRACT FROM AUTHOR]

Automatic 3D building reconstruction from laser scanning or photogrammetric point clouds has gained increasing attention in the past two decades. Although many efforts have been made, the complexity of buildings and incompletion of point clouds, i.e., data missing, still make it a challenging task for automatic 3D reconstruction of buildings in large-scale urban scenes with various architectural styles. This paper presents an innovative approach for automatic generation of 3D models of complex buildings from even incomplete point clouds. The approach first decomposes the 3D space into multiple space units, including 3D polyhedral cells, facets and edges, where the facets and edges are also encoded with topological-relation constraints. Then, the units and constraints are used together to approximate the buildings. On one hand, by extracting facets from 3D cells and further extracting edges from facets, this approach simplifies complicated topological computations. On the other hand, because this approach models buildings on the basis of polyhedral cells, it can guarantee that the models are manifold and watertight and avoid correcting topological errors. A challenging dataset containing 105 buildings acquired in Central, Hong Kong, was used to evaluate the performance of the proposed approach. The results were compared with two previous methods and the comparisons suggested that the proposed approach outperforms other methods in terms of robustness, regularity, and accuracy of the models, with an average root-mean-square error of less than 0.9 m. The proposed approach is of significance for automatic 3D modelling of buildings for urban applications. [ABSTRACT FROM AUTHOR]

Workflow management is a core component of modern Enterprise Information Systems (EISs) infrastructure that automates the execution of critical business processes. One of the particular interests of the security community is how to ensure the completion of the workflow execution in the presence of authorisation constraints. These constraints present some restrictions on the users or the roles that are authorised to execute the workflow tasks. The goal is to enforce the legal assignments of access privileges to the executors of the workflow tasks. Despite the variety of approaches proposed in this context, an approach dedicated to the inter-organisational workflows is still missing. In this paper, we take a step towards this goal by proposing a multi-agent-based model, named RPMInter-Work (task-Role assignment Planning Model for Inter-organisational Workflow). Our approach aims to perform the planning of the task-role assignments in inter-organisational workflow in presence of authorisation constraints that are related to task-role assignments. In our research work, this planning problem is formulated as a DisCSP (Distributed Constraint Satisfaction Problem). Our proposed contribution is based on the requirements of inter-organisational workflows, in particular, the autonomy of the participating organisations and the respect of their privacy. A prototype of RPMInter-Work is implemented using JADE (Java Agent DEvelopment) platform and some evaluation results of this prototype are exposed in this paper. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increased interest in processing fuzzy queries over XML data that is also possibly fuzzy. Attention has been paid to various extensions of XML query languages that introduce concepts of fuzzy theory. We propose an extension of the XQuery query language in an attempt to handle flexible queries that provide priority, threshold, and fuzzy expression as well as fuzzy linguistic labels allowing users great flexibility in customizing query constraints. We give a detailed description of an advanced query processing software system developed using GPFCSP (Generalized Prioritized Fuzzy Constraint Satisfaction Problem) as the theoretical background. The software, called FXI (Fuzzy XQuery Interpreter), was developed as a web application using Java, AngularJS, and eXist-db — an open source native XML database and it incorporates various advanced features such as fuzzy ordering operations and fuzzy compatibility calculations that includes priorities. The paper presents its design, the most important considerations related to implementation, as well as testing using realistic scenarios. [ABSTRACT FROM AUTHOR]

Cloud computing is a new computing paradigm which is gaining wide acceptance among scientific fraternity in the recent years. The services of cloud could be effectively used for running large-scale data and computation-intensive scientific workflow applications. Finding the optimal schedule for such workflows has been a major concern among the cloud users. In the present work, a novel approach of combining both optimization of the schedule along with the allocation of the virtual machines (VMs) based on security requirements is envisaged. This paper focuses on generating an optimized schedule for the complex workflow structures. The main objective of the schedule is to minimize the makespan of the schedule. In this paper, we design the scheduling heuristic based on the cost prediction matrix (CPM) for optimized cost calculation. The CPM will estimate the execution cost by considering the child's child task also. This leads to a prophetic estimation on the available VMs. In addition to this, we have used a fuzzy-based decision model for deciding the selection of the VMs based on security constraints in the cloud. This fuzzy model is combined with the optimized cost calculation from CPM for each and every task of the workflow. The proposed secured cost prediction-based scheduling (SCPS) algorithm then schedules the task in the best possible VM, so that the makespan is minimized. Our results show that the newly developed SCPS algorithm yields efficient schedule compared to other existing scheduling models in spite of the inclusion of security constraints besides scheduling. Nevertheless, this secured scheduling is done without much increase in the time complexity. [ABSTRACT FROM AUTHOR]

In this paper, we consider a MIMO wiretap system in wireless sensor networks (WSNs), where the confidential signal sent to the legitimate receive (Bob) may be eavesdropped by the eavesdropper (Eve). Assuming that only partial channel state information (CSI) can be obtained by the transmitter, we consider both worst case (WC) and outage-constrained (OC) robust secrecy optimizations. To solve the WC design, we propose to linearize these logarithmic determinant terms. After linearization, we tackle the CSI uncertainty using the Nemirovski lemma. Then, an alternating optimization (AO) algorithm is proposed to solve the reformulated problem. On the other hand, to solve the OC design, we transform the probabilistic constraint into safe and tractable reformulation by the Bernstein-type inequality (BTI) and large deviation inequality (LDI), and an AO algorithm is proposed. Numerical results are provided to demonstrate the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

The recently approved regulation about self-consumption in Spain will dramatically change the level of penetration of photovoltaic distributed generation combined with storage devices at residential level in short term. In this paper, we will study the impact of the massive installation of such technologies under the constraints imposed by the new regulation, over the terminal distribution network. As it will be demonstrated, this impact will be drastic in terms of energy demand, voltage profile, and load curves. [ABSTRACT FROM AUTHOR]

This paper proposes a constraint satisfaction problem algorithm based on implicit decision trees and dynamic programming for the design of multiple composite panels subjected to a set of blending rules and external forces. This decision tree is built incrementally on the 'fly', adding plies from a particular set, and does not require any guiding laminate or stacking sequence table for blending purposes. The upper bound algorithm complexity is provided in this work. The technique is applied to the design of a well-known composite multi-panel problem addressed in previous studies; the novel procedure exhibits high-quality solutions (in terms of structure weight) and low computational cost (in terms of laminate evaluations). The novel algorithm scalability is also checked showing that the node tree expansion is independent of the number of panels to be designed. This algorithm could therefore be a suitable choice for large-scale multi-panel design problems. [ABSTRACT FROM AUTHOR]

This paper presents a robust gain-scheduling controller design based on the parameter-space approach. The gain-scheduling control is of interest for systems with varying dynamics at different operating conditions. Requirements of robustness of stability and performance are also necessary if the system is subject to other parametric or model uncertainties. The parameter space approach is capable of projecting these requirements to the space of design parameters and facilitate the design of robust gain-scheduling control. This method is applied to automated vehicle lateral control to ensure its tracking accuracy and stability subject to uncertainties in vehicle load, speed and tire saturation. The proportional feedback gain and look-ahead distance are scheduled with respect to speed while sustaining robust stability, mixed sensitivity bound constraint and satisfaction of performance indices. Realistic hardware-in-the-loop (HIL) simulation with a validated vehicle model and road tests are conducted to demonstrate the robust performance of the designed gain-scheduling control in the presence of model uncertainty and disturbances. Another general robust gain-scheduling approach, the linear-matrix-inequality- (LMI-) design is also conducted for benchmarking purposes and the comparisons are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel 3T2R labelling robot with hybrid mechanism for the end-faces of round steels. The hybrid mechanism consists of a three degrees-of-freedom (DOFs) translation (3T) parallel mechanism and a two DOFs rotation (2R) mechanism. Based on the Jacobian matrix of the hybrid mechanism, the author analyzed the influence of each dimension parameter on three performance indices, namely, kinematics, transmission and stiffness of the mechanism, under the constraints of the workspace. Based on the Pareto frontier approach, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was adopted to find the optimal values of the three performance indices, and identify the dimension parameters that bring the best overall performance of the whole mechanism. The research results shed new light on the design of light weight labelling robot. [ABSTRACT FROM AUTHOR]

In this paper we study a variation of the random $k$ -SAT problem, called polarised random $k$ -SAT, which contains both the classical random $k$ -SAT model and the random version of monotone $k$ -SAT another well-known NP-complete version of SAT. In this model there is a polarisation parameter $p$ , and in half of the clauses each variable occurs negated with probability $p$ and pure otherwise, while in the other half the probabilities are interchanged. For $p=1/2$ we get the classical random $k$ -SAT model, and at the other extreme we have the fully polarised model where $p=0$ , or 1. Here there are only two types of clauses: clauses where all $k$ variables occur pure, and clauses where all $k$ variables occur negated. That is, for $p=0$ , and $p=1$ , we get an instance of random monotone $k$ -SAT. We show that the threshold of satisfiability does not decrease as $p$ moves away from $\frac{1}{2}$ and thus that the satisfiability threshold for polarised random $k$ -SAT with $p\neq \frac{1}{2}$ is an upper bound on the threshold for random $k$ -SAT. Hence the satisfiability threshold for random monotone $k$ -SAT is at least as large as for random $k$ -SAT, and we conjecture that asymptotically, for a fixed $k$ , the two thresholds coincide. [ABSTRACT FROM AUTHOR]

Nonlinear model predictive control (NMPC) is one of the few control methods that can handle complex nonlinear systems with multi‐objectives and various constraints. However, the performance of NMPC highly depends on the model accuracy and the deterministic solutions may suffer from conservatism, for example, robust MPC only considers the worst‐case scenario, which yields the NMPC not working efficiently in uncertain stochastic cases. To address these issues, a model‐and data‐driven predictive control approach using Gaussian processes (GP‐MDPC) is synthesized in this paper, which copes with the tracking control problems of stochastic nonlinear systems subject to model uncertainties and chance constraints. Because GP has high flexibility to capture complex unknown functions and it inherently handles measurement noise, GP models are employed to approximate the unknowns, the predictions and uncertainty quantification provided by the GPs are then exploited to propagate the uncertainties through the nonlinear model and to formulate a finite‐horizon stochastic optimal control problem (FH‐SOCP). Specifically, given the GP models, closed‐loop simulations are executed offline to generate Monte Carlo samples, from which the back‐offs for constraint tightening are calculated iteratively. The tightened constraints then guarantee the satisfaction of chance constraints online. A tractable GP‐MDPC framework using back‐offs for handling nonlinear chance constrained tracking control problems is yielded, whose advantages include fast online evaluation, consideration of closed‐loop behaviour, and achievable trade‐off between conservatism and constraint violation. Comparisons are carried out to verify the effectiveness and superiority of the proposed GP‐MDPC scheme with back‐offs. [ABSTRACT FROM AUTHOR]

Implementing inquiry-oriented instructional proposals at the university level collides with many constraints related to the current pedagogical paradigm based on the transmission of previously established works of knowledge. One such constraint comes from defining subjects' curricula by a set of topics to study, not questions to inquire. Our research team has been working for more than 15 years in designing and implementing an inquiry-oriented instructional format called 'study and research paths' (SRPs) within the Anthropological Theory of the Didactic. This paper focuses on the commonalities and specificities of the implemented SRPs to identify critical strategies and didactic devices that facilitate their integration in current university settings. The experimentations also reveal key issues for the sustainability of inquiry-oriented educational proposals related to the question that generates the inquiry and the types of contracts established to approach it. [ABSTRACT FROM AUTHOR]

Finding the optimal assignment of locomotives to fulfill freight train transportation schedule under time constraints is a complex problem, even on a linear section of the railway. In previous studies, a graph model was proposed that allows solving the assignment problem using a static graph algorithm. It was proved that finding the optimal assignment without constraints is equivalent to finding the minimal path cover of a specific acyclic graph. However, due to time constraints, the optimal solution may not be valid. This paper presents methods for modifying the found solution in order for it to satisfy the time constraints on locomotives. Two operations on path in the minimal path cover of a graph are introduced: crossover and changeover. They allow to rebuild the found solution without spoiling the admissible sequences of transportation, while the invalid ones are corrected. [ABSTRACT FROM AUTHOR]

Vehicle routing problems (VRP) are a kind of typical combinational optimization problem, particularly in the logistics industry. This paper proposes a constrained evolutionary optimization algorithm, called CEOA, for solving many-objective VRP with simultaneous delivery, pickup, and time windows (VRPSDPTW). Specifically, we first define the weight value vectors based on the constraint satisfaction situation, which can adaptively adjust according to the feedback of population solutions during the search process. Subsequently, based on the feedback from the weight value vectors, the environmental selection strategy is employed to identify promising solutions for both infeasible and feasible situations. Furthermore, considering the data characteristics of the problem at hand, the crossover and mutation operations are tailored to better align with the VRPSDPTW, which is further explained and illustrated in detail regarding solution construction. The experimental results demonstrate the effectiveness of the proposed algorithm for VRPSDPTW in comparison with other state-of-the-art methods. • This paper designs an adapting weight value vector to indicate infeasible solutions, which cannot compare together. • We use a constrained satisfaction strategy to select feasible solutions and select infeasible solutions, respectively. • The crossover and mutation operations are redesigned to make it more fit the real problem. [ABSTRACT FROM AUTHOR]

Project scheduling problem is to determine the schedule of allocating resources so as to balance the total cost and the completion time. This paper mainly introduce project scheduling problem with uncertain variables. In order to meet different management requirements, we put forward the number of workers constraint based on the activity duration times and total cost constraints. And activity duration times and the number of workers are assumed to be uncertain variables in this paper. Expected value model is an important tool for the solving of project scheduling problem. We established single-objective programming model and multi-objective programming model to meet different needs, respectively. [ABSTRACT FROM AUTHOR]

This paper investigates the correlation between sectoral environmental performance and firm growth, using a large data set of 61,219 Italian manufacturing firms and sector environmental-economic accounts. Specifically, the paper investigates the extent to which the correlation between (past) emissions intensity and environmental regulation influences firms' economic growth. Our results show a trade-off: structurally, higher emissions intensity gives firms more freedom and relaxes the constraints on growth. This is a trade-off which rejects our hypothesis that economic and environmental performance might dynamically be positively correlated. However, although lower emissions intensity does not pay in terms of promoting turnover, there seems to be a non-linear relation characterising the economic growth-environmental performance relationship. Transforming the prevalent trade-off in a possible joint dynamics in which poor environmental performances hampers firm growth and investment in greener technologies might be associated with positive economic performance, thus becomes possible. [ABSTRACT FROM AUTHOR]

This paper focuses on statistical optimization and, more specifically, timing yield (TY)-constrained optimization. For cell replacement in timing-constrained optimization, we need an indicator that examines whether or not a timing violation occurs and gives the available timing for a gate. In deterministic optimization, the timing slack is used for this indicator. Although there is an analogous concept of TY slack in statistical optimization, it has not been well utilized. This paper proposes an effective way to use the TY slack for successful statistical optimization. To achieve this, we present an efficient method to calculate the TY slacks of gates and a strategy that uses timing resources for effective statistical optimization. Based on this work, we propose a novel statistical leakage minimization method that uses the TY slack for a gate change metric. The use of TY-based metrics that are appropriate for statistical design ensures that our method has a better optimization performance at a higher speed. Experimental results on ISCAS-85 benchmark circuits show that the leakage minimization method reduces leakage by 25.2% compared to the statistical benchmark method. In addition, our method has a better runtime when the number of gates is high. [ABSTRACT FROM PUBLISHER]

Purpose The purpose of this practice-oriented paper is to look at a recent, late-phase development in public administration (PA) reform in Romania, specifically the drafting of the recently adopted national Strategy on Strengthening the Efficiency of Public Administration (2014-2020). In particular, the paper focuses on the opportunities and limits of outsourcing the building of the vision underlying the strategy and the prioritization of strategic objectives. The article’s story is also placed in the broader context of agencification literature and, more specifically, the involvement of executive agencies in policymaking.Design/methodology/approach The paper describes the vision-building exercise, developed according to a script already tested in several sectoral strategy-making processes, and the objectives and procedure of the online participatory consultation by using an adapted real-time Delphi format (similarly tested in the recent past).Findings The paper reports on the ways in which the output of the visioning process and of online consultations may be used to enhance a strategic process already underway.Originality/value PA reform in post-communist countries has been among the most hotly debated, intensely pursued, yet seemingly elusive policy objectives of the transition and post-transition periods. Among pre-accession and then European Union (EU) member states, the need to get in and then to get involved in European policymaking provided some impetus for such reforms and also set substantial constraints, without however always adding much predictability or significantly streamlining the public sector. The paper contributes to this debate by proving an innovative method of devising a reform strategy by outsourcing the strategy-building process to an agency with the necessary know-how and experience. [ABSTRACT FROM AUTHOR]

The work described in this paper is part of the development of a framework to support the joint execution of cooperative missions by a group of vehicles, in a simulated, augmented, or real environment. Such a framework brings forward the need for formal languages in which to specify the vehicles that compose a team, the scenario in which they will operate, and the mission to be performed. This paper introduces the Scenario Description Language (SDL) and the Team Description Language (TDL), two Extensible Markup Language based dialects that compose the static components necessary for representing scenario and mission knowledge. SDL provides a specification of physical scenario and global operational constraints, while TDL defines the team of vehicles, as well as team-specific operational restrictions. The dialects were defined using Extensible Markup Language schemas, with all required information being integrated in the definitions. An interface was developed and incorporated into the framework, allowing for the creation and edition of SDL and TDL files. Once the information is specified, it can be used in the framework, thus facilitating environment and team specification and deployment. A survey answered by practitioners and researchers shows that the satisfaction with SDL+TDL is elevated (the overall evaluation of SDL+TDL achieved a score of 4 out of 5, with 81%/78.6% of the answers ≥4); in addition, the usability of the interface was evaluated, achieving a score of 86.7 in the System Usability Scale survey. These results imply that SDL+TDL is flexible enough to represent scenarios and teams, through a user-friendly interface. [ABSTRACT FROM PUBLISHER]

Recently, there is a surge of interests on heterogeneous information network analysis, where the network includes different types of objects or links. As a newly emerging network model, heterogeneous information networks have many unique features, e.g., complex structure and rich semantics. Moreover, meta path, the sequence of relations connecting two object types, is widely used to integrate different types of objects and mine the semantics information in this kind of networks. The object ranking is an important and basic function in network analysis, which has been extensively studied in homogeneous networks including the same type of objects and links. However, it is not well exploited in heterogeneous networks until now, since the characteristics of heterogeneous networks introduce new challenges for object ranking. In this paper, we study the ranking problem in heterogeneous networks and propose the HRank method to evaluate the importance of multiple types of objects and meta paths. Since the traditional meta path coarsely embodies path semantics, we propose a constrained meta path to subtly capture the refined semantics through confining constraints on objects. Based on a path-constrained random walk process, HRank can simultaneously determine the importance of objects and constrained meta paths through applying the tensor analysis. Extensive experiments on three real datasets show that HRank can effectively evaluate the importance of objects and paths together. Moreover, the constrained meta path shows its potential on mining subtle semantics by obtaining more accurate ranking results. [ABSTRACT FROM AUTHOR]

The Physical Internet concept was developed to address the current unsustainability problem of logistic systems. The key elements are the encapsulation and the handling of world-standard smart green modular containers (π-containers) throughout an open global logistic infrastructure. Logistic processes such as partial loading/unloading or splitting/merging ofπ-containers play an important role for a successful Physical Internet. However, the large variety of transformation processes can introduce desynchronisation between the physical and informational flows, i.e. the real composition of a compositeπ-container (a set of stackedπ-containers) can differ of the information stored in the supply chain management system. To overcome this problem, this paper proposes a system to generate and maintain automatically a virtual three-dimensional layout reflecting the spatial distribution ofπ-containers. The proposed approach uses smartπ-containers equipped with wireless sensor nodes. The relative position ofπ-containers can be determined by the neighbourhood relationships between the sensor nodes. From this information, a constraint set is formulated in a Constraint Satisfaction Problem where each solution is a potential loading pattern. The methodology is developed and computational experiments are proposed, as a proof-of-concept. The results show that our proposed method yields quick and satisfactory results. [ABSTRACT FROM PUBLISHER]

In this paper, we give an overview of some recent work on applying tools from category theory in finite model theory, descriptive complexity, constraint satisfaction, and combinatorics. The motivations for this work come from Computer Science, but there may also be something of interest for model theorists and other logicians. The basic setting involves studying the category of relational structures via a resource-indexed family of adjunctions with some process category - which unfolds relational structures into tree-like forms, allowing natural resource parameters to be assigned to these unfoldings. One basic instance of this scheme allows us to recover, in a purely structural, syntax-free way: the Ehrenfeucht-Fraïssé game; the quantifier rank fragments of first-order logic; the equivalences on structures induced by (i) the quantifier rank fragments, (ii) the restriction of this fragment to the existential positive part, and (iii) the extension with counting quantifiers; and the combinatorial parameter of tree-depth (Nesetril and Ossona de Mendez). Another instance recovers the k-pebble game, the finite-variable fragments, the corresponding equivalences, and the combinatorial parameter of treewidth. Other instances cover modal, guarded and hybrid fragments, generalized quantifiers, and a wide range of combinatorial parameters. This whole scheme has been axiomatized in a very general setting, of arboreal categories and arboreal covers. Beyond this basic level, a landscape is beginning to emerge, in which structural features of the resource categories, adjunctions and comonads are reflected in degrees of logical and computational tractability of the corresponding languages. Examples include semantic characterisation and preservation theorems, and Lovász-type results on counting homomorphisms. [ABSTRACT FROM AUTHOR]

Currently used decision support systems allow decision-makers to evaluate the product performance, including a net present value analysis, in order to enable them to make a decision regarding whether or not to carry out a new product development project. However, these solutions are inadequate to provide simulations for verifying a possibility of reducing the total product cost through changes in the product design phase. The proposed approach provides a framework for identifying possible variants of changes in product design that can reduce the cost related to the production and after-sales phase. This paper is concerned with using business analytics to cost estimation and simulation regarding changes in product design. The cost of a new product is estimated using analogical and parametric models that base on artificial neural networks. Relationships identified by computational intelligence are used to prepare cost estimation and simulations. A model of product development, production process, and admissible resources is described in terms of a constraint satisfaction problem that is effectively solved using constraint programming techniques. The proposed method enables the selection of a more appropriate technique to cost estimation, the identification of a set of possible changes in product design towards reducing the total product cost, and it is the framework for developing a decision support system. In this aspect, it outperforms current methods dedicated for evaluating the potential of a new product. [ABSTRACT FROM AUTHOR]

Counting CSPd is the counting constraint satisfaction problem (# CSP in short) restricted to the instances where every variable occurs a multiple of d times. This paper revisits tractable structures in # CSP and gives a complexity classification theorem for # CSPd with algebraic complex weights. The result unifies affine functions (stabilizer states in quantum information theory) and related variants such as the local affine functions, the discovery of which leads to all the recent progress on the complexity of Holant problems. The Holant is a framework that generalizes counting CSP. In the literature on Holant problems, weighted constraints are often expressed as tensors (vectors) such that projections and linear transformations help analyze the structure. This paper gives an example showing that different classes of constraints distinguished by these algebraic operations may share the same closure property. [ABSTRACT FROM AUTHOR]

We prove a complexity classification for Holant problems defined by an arbitrary set of complex-valued symmetric constraint functions on Boolean variables. This is to specifically answer the question: Is the Fisher-Kasteleyn-Temperley (FKT) algorithm under a holographic transformation (Valiant, SIAM J. Comput. 37(5), 1565–1594 2008) a universal strategy to obtain polynomial-time algorithms for problems over planar graphs that are intractable on general graphs? There are problems that are #P-hard on general graphs but polynomial-time solvable on planar graphs. For spin systems (Kowalczyk 2010) and counting constraint satisfaction problems (#CSP) (Guo and Williams, J. Comput. Syst. Sci. 107, 1–27 2020), a recurring theme has emerged that a holographic reduction to FKT precisely captures these problems. Surprisingly, for Holant, we discover new planar tractable problems that are not expressible by a holographic reduction to FKT. In particular, a straightforward formulation of a dichotomy for planar Holant problems along the above recurring theme is false. A dichotomy theorem for #CSPd, which denotes #CSP where every variable appears a multiple of d times, has been an important tool in previous work. However the proof for the #CSPd dichotomy violates planarity, and it does not generalize to the planar case easily. In fact, due to our newly discovered tractable problems, the putative form of a planar #CSPd dichotomy is false when d ≥ 5. Nevertheless, we prove a dichotomy for planar #CSP2. In this case, the putative form of the dichotomy is true. (This is presented in Part II of the paper.) We manage to prove the planar Holant dichotomy relying only on this planar #CSP2 dichotomy, without resorting to a more general planar #CSPd dichotomy for d ≥ 3. A special case of the new polynomial-time computable problems is counting perfect matchings (#PM) over k-uniform hypergraphs when the incidence graph is planar and k ≥ 5. The same problem is #P-hard when k = 3 or k = 4, which is also a consequence of our dichotomy. When k = 2, it becomes #PM over planar graphs and is tractable again. More generally, over hypergraphs with specified hyperedge sizes and the same planarity assumption, #PM is polynomial-time computable if the greatest common divisor (gcd) of all hyperedge sizes is at least 5. It is worth noting that it is the gcd, and not a bound on hyperedge sizes, that is the criterion for tractability. [ABSTRACT FROM AUTHOR]

In this paper, we study optimal control and communication schedule co‐design for multi‐agent networked control systems, with assuming shared parallel communication channels and uncertain constrained linear time‐invariant discrete‐time systems. To that end, we specify the communication demand for each system using an associated robust control invariant set and reachability analysis. We use these communication demands and invariant sets to formulate tube‐based model predictive control and offline/online communication schedule co‐design problems. Since the scheduling part includes an infinite dimension integer problem, we propose heuristics to find suboptimal solutions that guarantee robust constraints satisfaction and recursive feasibility. The effectiveness of our approach is illustrated through numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, massive multiple‐input‐multiple‐output (MIMO) wireless communication systems are considered to investigate joint transceiver beamforming. A base station (BS) equipped with a uniform planar array (UPA) serves several multi‐antennas users in a single cell. Based on the channel state information (CSI), the low complexity design of transceiver beamforming to minimize the transmit power subject to some quality of service (QoS) constraints is investigated. As the upper bound of the transmit power performance, the existing iteration‐based algorithms are leveraged as a reference. A general deep learning (DL)‐based framework and deep neural network (DNN) structure are proposed to reduce the complexity of the existing algorithms, where the properly trained DNN structure can learn directly from CSI. Consider the complexity of the DNN structure itself, a heuristic algorithm is proposed to replace the DNN structure, which takes the max‐eigenvalue‐eigenvector of the CSI as the direction of receive beamforming directly. The DNN structure is trained in the offline stage, therefore, only the complexity in the online stage is taken into consideration. Based on the numerical simulation, the complexity of the proposed DL‐based framework and the transceiver beamforming algorithms is reduced significantly while maintaining nearly the optimal performance compared with the existing iterative algorithms. [ABSTRACT FROM AUTHOR]

After substantial progress over the last 15 years, the \algebraic CSP-dichotomy conjecture" reduces to the following: every local constraint satisfaction problem (CSP) associated with a finite idempotent algebra is tractable if and only if the algebra has a Taylor term operation. Despite the tremendous achievements in this area (including recently announce proofs of the general conjecture), there remain examples of small algebras with just a single binary operation whose CSP resists direct classification as either tractable or NP-complete using known methods. In this paper we present some new methods for approaching such problems, with particular focus on those techniques that help us attack the class of finite algebras known as \commutative idempotent binars" (CIBs). We demonstrate the utility of these methods by using them to prove that every CIB of cardinality at most 4 yields a tractable CSP. [ABSTRACT FROM AUTHOR]