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The efficiency of material supply for workstations using Automatic Guided Vehicles (AGVs) is largely determined by the performance of the AGV dispatching scheme. This paper proposes a new solution approach for the AGV dispatching problem (AGVDP) for material replenishment in a general manufacturing workshop where workstations are in a matrix layout, and where uncertainty in replenishment time of workstations and stochastic unloading efficiencies of AGVs are dynamic contextual factors. We first extend the literature proposing a mixed integer optimisation model with a delivery satisfaction soft constraint of material orders and two objectives: transportation costs and delivery time deviation. We then develop a new knowledge-guided estimation of distribution algorithm with delivery satisfaction evaluation for solving the model. Our algorithm fuses three knowledge-guided strategies to enhance optimisation capabilities at its respective execution stages. Comprehensive numerical experiments with instances built from a real-world scenario validate the proposed model and algorithm. Results demonstrate that the new algorithm outperforms three popular multi-objective evolutionary algorithms, a discrete version of a recent multi-objective particle swarm optimisation, and a multi-objective estimation of distribution algorithm. Findings of this work provide major implications for workshop management and algorithm design. [ABSTRACT FROM AUTHOR]

Conceptual design is a pivotal stage of new product development. The function-behaviour-structure framework is adopted in this stage to help designers search design space and generate conceptual solutions iteratively. Computer-aided methods developed within this framework will yield significant insight into facilitating the cognitive activities of designers. In order to solve the mapping process from behaviours to structures which is a typical constraint satisfaction problem, a cerebellar operant conditioning-inspired constraint satisfaction approach is proposed in this paper. The design constraints-driven operant conditioning and its regulation mechanism by the cerebellum are analysed for the first time. Proposition logic is applied to transfer the constraint satisfaction problem into a propositional satisfiability problem while an undirected graph is utilised to model design space. Inspired by the modularised cerebellar structure, a modularised constraint satisfaction neural network is constructed to determine the satisfiability of design problems. Conceptual solutions can be generated by clustering the embedding of nodes in this network. The proposed approach imitates the design constraint-driven operant conditioning to narrow down design space without assigning specific values to design components. It reduces design iterations and avoids combinatorial explosions during conceptual design. [ABSTRACT FROM AUTHOR]

This paper studies the global attitude stabilization of a rigid body, a task that is subjected to topological obstacles. As a result of these obstructions, continuous feedbacks cannot globally stabilise the rigid body attitude. Therefore, this paper presents an observer-based hybrid controller to overcome these restrictions. Consequently, a new kind of synergistic potential function is designed which induces a gradient vector field to globally stabilise a given set. Moreover, the gradient of the proposed potential functions is utilised to derive a hybrid observer. Furthermore, this paper considers two types of constraints: angular velocity constraint and torque constraint. Afterward, these constraints are formulated in terms of the Linear Matrix Inequalities (LMI) optimisation problem to perform constraints satisfaction. Besides, this paper introduces a novel hybrid quantiser to deal with the problem of the low-price wireless network. Finally, a comparative study in simulations is provided to assess the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Distributed switched large-scale systems are composed by dynamically coupled subsystems, in which interactions among subsystems vary over time according a switching signal. This paper presents a distributed robust switched model-based predictive control (DSwMPC) to control such systems. The proposed method guarantees stabilising the origin of the whole closed-loop system and ensures the constraints satisfaction in the presence of an unknown switching signal. In the distributed model-based predictive control (DMPC) used in this work, by considering the interactions among subsystems as an additive disturbance, the effect of the switch is reflected on the dynamic equation, local, and consistency constraint sets of the nominal subsystems. To compensate the effect of switching signal which creates a time-varying network topology, a robust tube-based switched model-based predictive control (RSwMPC) with switch–robust control invariant set as the target set robust to unknown mode switching is used as local controller. The scheme performance is assessed using three typical examples. The simulation results show that the input and state constraints are satisfied by the proposed DSwMPC at all times. They also validate that the closed-loop system converges to the origin. Also, a comparison of the DSwMPC with a centralised SwMPC (CSwMPC) and a decentralised SwMPC (DeSwMPC) are performed. [ABSTRACT FROM AUTHOR]

Model predictive control is a popular choice for systems that must satisfy prescribed constraints on states and control inputs. Although much progress has been made in distributed model predictive control, existing algorithms tend to be computationally expensive. This limits their use in systems with fast dynamics. In this paper, we propose a new distributed model predictive control algorithm that we term as instant distributed model predictive control (iDMPC). The proposed algorithm employs a realisation of the primal-dual algorithm in the controller dynamics for fast computation. We show that the closed-loop system trajectories with the proposed iDMPC algorithm converge asymptotically to a desired reference. To ensure the satisfaction of the state constraints during the transient, we also include an explicit reference governor in the feedback loop. [ABSTRACT FROM AUTHOR]

In this paper we propose a robust output feedback control scheme for constant reference tracking in uncertain linear systems subject to state and control constraints. First, we establish conditions under which a polyhedral set is Output Feedback Controlled Invariant with respect to a linear system subject to polytopic uncertainties, i.e. a set guaranteeing robust constraint satisfaction via output feedback. Then, we build a dynamic output feedback controller for the uncertain model based on set-membership observers, allowing the reduction of the set of possible states consistent with the measurements. Finally, to reduce the tracking error, we propose a model update procedure that adjusts the nominal model used for tracking to the output measurements. Numerical experiments illustrate the ability of the proposed controller to achieve reference tracking with reduced offset for the uncertain systems under consideration. To the best of our knowledge, it is one of the first works that addresses constant reference tracking in uncertain linear systems under constraints with output feedback. [ABSTRACT FROM AUTHOR]

As a tribute to Ray Levitt's early computer science contributions, this paper presents the research methodology he and I in close collaboration with Barbara Hayes-Roth followed to develop the SightPlan system. This research methodology falls under the umbrella of what is known as "design science." Design science concerns itself with designing and making artefacts to fulfil a purpose, and then testing and validating that they indeed are fit-for-purpose. Design science does not belong in the category of physical science or of fundamental science; instead, it falls in the category of the sciences of the artificial. This paper describes the methodological steps pursued in design science and in parallel illustrates how these steps were instantiated and what artefacts were produced in the course of developing the SightPlan system, a blackboard expert system that lays out temporary facilities on construction sites. The aims of this paper are to recognise Ray's work as an advisor and researcher, reflect on and promote the use of design science in construction management and economics and thereby participate in the ongoing discussion on research methods in this journal, and illustrate to scholars in this field that research is an opportunistic endeavour. [ABSTRACT FROM AUTHOR]

This paper describes the basis of a Decision Support System (DSS) designed to schedule fertiliser production orders to be delivered within time windows, in plants made up of multiple heterogeneous parallel processors (production lines), considering that fertiliser production rates and nomenclatures depend on lines, that setup times depend on sequence and lines, and taking into account downtime constraints (preventive maintenance ...). A mixed linear programming model is encapsulated in the DSS which considers the schedule's impacts, immediately upstream and downstream of plants in the supply chain. These side-effects may make the proposed solution unfeasible and the DSS helps redefining the problem to avoid them. [ABSTRACT FROM AUTHOR]

In traditional practices, maintenance system and spare parts inventory control are usually considered in isolation, resulting in suboptimality. In a military system, the level of repair analysis (LORA) is often employed to help operate its repair networks. In this paper, we consider an integrated LORA and inventory control problem and formulate this problem as a mixed-integer nonlinear programming problem with chance constraints. Two second-order cone constraints are proposed to approximate the chance constraints. Furthermore, we propose an outer approximation (OA) algorithm based on the OA cuts. Extensive numerical results show that the OA algorithm significantly improves the computational efficiency under various types of components and network complexity. Next, we investigate the influence of service level and resource capacity, and propose the findings. Our results indicate that a higher service level leads to steeper costs, more resources, larger storage and heavier repair burdens at operating sites. Moreover, enhancements in resource capacity from the status quo lead to improvements in repairs and shrinkage in discards, bringing direct economic benefits. The insights extend to uncertain settings. It may be initially counterintuitive for many practitioners that demand uncertainty poses relatively subtle impacts. [ABSTRACT FROM AUTHOR]

Job shop scheduling, as one of the classical scheduling problems, has been widely studied in literatures, and proved to be mostly NP-hard. Although it is extremely difficult to solve job shop scheduling with no-wait constraint to optimality, the two-machine no-wait job shop scheduling to minimise makespan could be solvable in polynomial time when each job has exactly two equal length operations (proportionate job shop). In the present paper, an extension is attempted by considering a proportionate flexible two-stage no-wait job shop scheduling problem with minimum makespan, and a set-covering formulation is put forward which contains a master problem and a pricing problem. To solve this problem, a column generation (CG)-based approach is implemented. In comparison, a mixed integer programming model is constructed and optimised by Cplex. A series of randomly generated numerical instances are calculated. And the testing result shows that the mixed integer model handled by Cplex can only solve small scale cases, while the proposed CG-based method can conquer larger size problems in acceptable time. [ABSTRACT FROM AUTHOR]

During the architectural design phase, the decisions made have a crucial impact on developing complex systems. These decisions often limit the performance bounds and steer subsequent research directions. However, the multitude of architectural components and intricate interrelationships cause component combination explosion. Additionally, the diverse requirements of different stakeholders add complexity to determining the weight of indicators, posing challenges to complex system architecture generation and trade-off. This paper proposes a method for exploring the architectural design space in complex systems realisation, which aims to resolve issues related to the formal representation of architectures, selection within the architectural design space, and trade-offs. Firstly, the architectural design space is represented by formulating and mathematically describing a morphological matrix. Then, the construction of the Constraint Satisfaction Problem model aims to achieve a reduction in the scale of the design space. Moreover, through the evolutionary algorithm, the Pareto front is identified. Weight sensitivity analysis and the improved Technique for Order Preference by Similarity to the Ideal Solution method are combined to assist designers in finding a satisfactory architecture from the design space with many combinations. Finally, the proposed method is verified by the design of the launch vehicle's primary and secondary separation system. [ABSTRACT FROM AUTHOR]

This paper aims to design a controller to compensate the faults for a class of time-delay nonlinear systems. This controller is based on the back-stepping design approach. A barrier Lyapunov function has been employed to ensure the satisfaction of output constraints. This controller is proposed for nonlinear systems with parameter uncertainty, time-delay and uncertain faults in actuators while no bounds for parameters, delays and actuators faults are supposed. The boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighbourhood of the origin without violation of the constraint. In the end, the simulation results show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper investigates drivers of the effectiveness of risk assessments in risk workshops dominated by 'quantitative skepticism'. Moreover, it contrasts our findings with those of previous research that assumed the dominance of 'quantitative enthusiasm'. Quantitative skepticism is a calculative culture characterized by an attitude that regards risk assessments as learning tools supporting the holistic formation of judgments incorporating difficult-to-quantify information. It contrasts with quantitative enthusiasm, which is a calculative culture that considers risk assessments as fully descriptive of reality. Prior research primarily focused on understanding the effectiveness of risk assessments under a calculative culture of quantitative enthusiasm. To understand what drives the correctness of risk assessment and the time needed to assess risks in workshops under a calculative culture of quantitative skepticism, we use an agent-based model that simulates risk assessment with risk workshops and that models agents' cognitive processes using ECHO, a constraint satisfaction network (CSN). Our simulations show that, compared to risk workshops under conditions of quantitative enthusiasm, there are often lengthy periods of stagnation in individual and collective risk assessments and a strong path dependency on discussions. Prioritizing concerned participants improves the correct assessment of high risks at the expense of the correct assessment of low risks. Notwithstanding similarities in the drivers of the effectiveness of risk assessment across different calculative cultures, our results show that the predominant calculative culture matters when—to enhance their effectiveness—designing and implementing risk workshops. [ABSTRACT FROM AUTHOR]

This paper presents a new control scheme on trajectory tracking of wheeled mobile robot with nonholonomic constraints. Extended state observer is introduced to estimate unknown disturbances and velocity information. A robust tracking controller is designed to implement the accurate trajectory tracking and disturbance compensation. By theoretical, position and velocity tracking errors of wheeled mobile robot are proven uniformly ultimately asymptotically stable. Simulation results are given to illustrate the effectiveness of the developed technique. [ABSTRACT FROM PUBLISHER]

In this paper, a two-loop Model Predictive Controller (MPC) with an improved tube is proposed for industrial application with bounded uncertainties subject to input and state constraints. This scheme attempts to remove some existing obstacles against exploiting MPC in industrial applications, such as (i) risk and cost of a new controller replacement, (ii) difficulties of attaining a precise open-loop model of an industrial system and (iii) high computational burden of MPC methods. To this end, tube conservatism and calculation burden are reduced using the transient response of the error dynamics. Thus the feasible region of the MPC is enlarged and its computation time is reduced. To reduce modelling difficulties, the investigated approach does not require the open-loop model dynamics of the system and utilises the closed-loop model instead. On the other hand, it allows the existing inner-loop controller to remain unchanged without any manipulations, which results in eliminating a new controller replacement risk and cost. Additionally, for the proposed control method, robust stability and recursive feasibility are guaranteed without terminal gradients. Finally, an illustrative example is carried out in the simulation results to show the effectiveness of the proposed approach. [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]

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 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]

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]

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]

Linear controllers with gain scheduling have been successfully used in the control of nonlinear systems for the past several decades. This paper proposes the design of gain scheduled continuous-time model predictive controller with constraints. Using induction machine as an illustrative example, the paper will show the four steps involved in the design of a gain scheduled predictive controller: (i) linearisation of a nonlinear plant according to operating conditions; (ii) the design of linear predictive controllers for the family of linear models; (iii) gain scheduled predictive control law that will optimise a multiple model objective function with constraints, which will also ensure smooth transitions (i.e. bumpless transfer) between the predictive controllers; (iv) experimental validation of the gain scheduled predictive control system with constraints. [ABSTRACT FROM PUBLISHER]

In this paper, we study a constrained utility maximization problem following the convex duality approach. After formulating the primal and dual problems, we construct the necessary and sufficient conditions for both the primal and dual problems in terms of forward and backward stochastic differential equations (FBSDEs) plus some additional conditions. Such formulation then allows us to explicitly characterize the primal optimal control as a function of the adjoint process coming from the dual FBSDEs in a dynamic fashion and vice versa. We also find that the optimal wealth process coincides with the adjoint process of the dual problem and vice versa. Finally we solve three constrained utility maximization problems, which contrasts the simplicity of the duality approach we propose and the technical complexity of solving the primal problem directly. [ABSTRACT FROM AUTHOR]

The paper describes a smooth controller of an articulated mobile robot with switching constraints. The use of switching constraints associated with grounded/lifted wheels is an effective method of controlling various motions; e.g. the avoidance of a moving obstacle. A model of an articulated mobile robot that has active and passive wheels and active joints with switching constraints is derived. A controller that accomplishes the trajectory tracking of the robot’s head and subtasks using smooth joint input is proposed on the basis of the model. Simulations and experiments are presented to show the effectiveness of the proposed controller. [ABSTRACT FROM PUBLISHER]

In this paper, for finding a minimal efficient solution of nonconvex multiobjective programming, a constraint shifting combined homotopy is constructed and the global convergence is obtained under some mild conditions. The method requires that the initial point needs to be only in the shifted feasible set not necessarily in the original feasible set, and the normal cone condition need only be satisfied in the boundary of the shifted feasible set not the original constraint set. Some numerical tests are done and made comparison with a combined homotopy interior point method. The results show that our method is feasible and effective. [ABSTRACT FROM PUBLISHER]

This paper aims to investigate the problem of robust nonfragile guaranteed cost control for uncertain Takagi-Sugeno fuzzy systems with Markov jump parameters, time-varying delay and input constraint. A nonfragile mode-dependent fuzzy controller with mode-dependent average dwell time (MDADT) is designed with input constraint. A sufficient condition is developed to ensure that the resulting closed-loop system is robust almost surely asymptotically stable with guaranteed cost index not exceeding the specified upper bound. Subsequently, the controller gain and upper bound of the guaranteed cost index can be obtained by solving a set of linear matrix inequalities. Finally, numerical and practical examples are provided to demonstrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents the design of an adaptive fuzzy dynamic surface control for a class of stochastic MIMO discrete-time nonlinear pure-feedback systems with full state constraints using a set of noisy measurements. The design approach is described as follows. The nonlinear uncertainty is approximated by using the fuzzy logic system at the first stage, secondly the proposed adaptive fuzzy dynamic surface control is designed based on a new saturation function for full state constraints, thirdly the number of the adjustable parameters is reduced by using the simplified extended single input rule modules, and finally the simplified weighted least squares estimator is in a simplified structure designed to take the estimates for the un-measurable states and the adjustable parameters. The simulation provides that the proposed approach is effective for the improvement of the system performance. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time stabilisation for a class of switched nonlinear systems with state constraints. Some power orders of the system are allowed to be ratios of positive even integers over odd integers. A Barrier Lyapunov function is introduced to guarantee that the state constraint is not violated at any time. Using the convex combination method and a recursive design approach, a state-dependent switching law and state feedback controllers of individual subsystems are constructed such that the closed-loop system is finite-time stable without violation of the state constraint. Two examples are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Qualitative spatial reasoning on topological relations can extract hidden spatial knowledge from qualitatively described topological information, which is of significant importance for decision-making and query optimization in spatial analysis. Qualitative reasoning on spatial topological information based on semantic knowledge and reasoning rules is an efficient means of reducing both the known relations and the corresponding rules, which can result in enhanced reasoning performance. This paper proposes a qualitative reasoning method for spatial topological relations based on the semantic description of reasoning rules and constraint set. Combined with knowledge from the Semantic Web, the proposed method can easily extract potential spatial results consistent with both unique and non-unique rules. The Constraint-Satisfaction-based approach, describing constraint set with semantic expressions, is then used together with an improved path consistency algorithm to verify the consistency of the unique-rules-based and non-unique-rules-based reasoning results. The verification can eliminate certain reasoning results to ensure the reliability of the final results. Thus, the task of qualitative spatial reasoning on topological relations is completed. [ABSTRACT FROM AUTHOR]

This paper studies the problem of stabilisation of switched nonlinear systems with output and input constraints. We propose a recursive approach to solve this issue. None of the subsystems are assumed to be stablisable while the switched system is stabilised by dual design of controllers for subsystems and a switching law. When only dealing with bounded input, we provide nested switching controllers using an extended backstepping procedure. If both input and output constraints are taken into consideration, a Barrier Lyapunov Function is employed during operation to construct multiple Lyapunov functions for switched nonlinear system in the backstepping procedure. As a practical example, the control design of an equilibrium manifold expansion model of aero-engine is given to demonstrate the effectiveness of the proposed design method. [ABSTRACT FROM PUBLISHER]

This paper looks in depth at the territorial problems posed by the incorporation of Information and Communication Technologies (ICTs) into the knowledge bases of product fields in manufacturing industry, resulting in what has become known as Industry 4.0. Using a formal, logical approach we build up a reference taxonomy that identifies various territorial problems based on the cognitive composition of the technology solutions used by manufacturing companies. We assess those problems in terms of their effects in the form of proximity constraints on technological collaboration activities and geographical location for producers. Once the various territorial problems are formally set out, it becomes possible to identify each product field with the problem that corresponds to the cognitive composition of the technical solutions that it uses. [ABSTRACT FROM AUTHOR]

This paper proposes a baseline constrained PPP method to enhance the performance. The approach is based on constrained Kalman filter, and applied to BeiDou PPP in static, pseudo-kinematic, real kinematic modes. Both zero baseline and short baseline constrained PPP are verified in static mode, while a marine geodesy environment is designed to evaluate the performance in kinematic mode. The experiment results show that, compared to the PPP without constraint, the baseline constrained PPP can significantly improve the positional accuracy. Furthermore, the accuracy of baseline constrained static PPP can reach centimetre level, while the kinematic PPP can reach decimetre level. [ABSTRACT FROM AUTHOR]

Calibration approach is becoming more important in survey sampling. Calibration estimation helps in improving the estimates of population parameters by making use of auxiliary information. This paper proposes a new calibration estimator for estimating the population mean in the stratified random sampling with a set of new calibration constraints using known coefficient of variation of the auxiliary variable. The result has also been extended to the case of double sampling in stratified sampling. The proposed estimator has been compared with the estimator developed by Tracy et al. (2003) with the help of simulation study on real datasets. [ABSTRACT FROM AUTHOR]

In this paper, we study the generalized Hadamard well-posedness of infinite vector optimization problems (IVOP). Without the assumption of continuity with respect to the first variable, the upper semicontinuity and closedness of constraint set mappings are established. Under weaker assumptions, sufficient conditions of generalized Hadamard well-posedness for IVOP are obtained under perturbations of both the objective function and the constraint set. We apply our results to the semi-infinite vector optimization problem and the semi-infinite multi-objective optimization problem. [ABSTRACT FROM AUTHOR]

In this paper, a trajectory tracking control law is proposed for a class of marine surface vessels in the presence of full-state constraints and dynamics uncertainties. A barrier Lyapunov function (BLF) based control is employed to prevent states from violating the constraints. Neural networks are used to approximate the system uncertainties in the control design, and the control law is designed by using the Moore-Penrose inverse. The proposed control is able to compensate for the effects of full-state constraints. Meanwhile, the signals in the closed-loop system are guaranteed to be semiglobally uniformly bounded, with the asymptotic tracking being achieved. Finally, the performance of the proposed control has been tested and verified by simulation studies. [ABSTRACT FROM PUBLISHER]

This paper presents a numerical scheme for solving the continuous-time convex linear-quadratic (LQ) optimal control problem with mixed polyhedral state and control constraints. Unifying a discretization of this optimal control problem as often employed in model predictive control and that obtained through time-stepping methods based on the differential variational inequality reformulation, the scheme solves a sequence of finite-dimensional convex quadratic programs (QPs) whose optimal solutions are employed to construct a sequence of discrete-time trajectories dependent on the time step. Under certain technical primal–dual assumptions primarily to deal with the algebraic constraints involving the state variable, we prove that such a numerical trajectory converges to an optimal trajectory of the continuous-time control problem as the time step goes to zero, with both the limiting optimal state and costate trajectories being absolutely continuous. This provides a constructive proof of the existence of a solution to the optimal control problem with such regularity properties. Additional properties of the optimal solutions to the LQ problem are also established that are analogous to those of the finite-dimensional convex QP. Our results are applicable to problems with convex but not necessarily strictly convex objective functions and with possibly unbounded mixed state–control constraints. [ABSTRACT FROM PUBLISHER]

Constraint-reduction schemes have been proposed for the solution by means of interior-point methods of linear programs with many more inequality constraints than variables in the standard dual form. Such schemes have been shown to be provably convergent and highly efficient in practice. A critical requirement of these schemes is the availability of an initial dual-feasible point. In this paper, building on a general framework (which encompasses several previously proposed approaches) for dual-feasible constraint-reduced interior-point optimization, for which we prove convergence to a single point of the sequence of dual iterates, we propose a framework for ‘infeasible’ constraint-reduced interior-point optimization. Central to this framework is an exact (ℓ1 or ) penalty function scheme endowed with a mechanism for iterative adjustment of the penalty parameter, which aims at yielding, after a finite number of iterations, a value that guarantees feasibility (for the original problem) of the minimizers. Finiteness of the sequence of penalty parameter adjustments is proved under mild assumptions for all algorithms that fit within the framework, including ‘infeasible’ extensions of a ‘dual’ algorithm proposed in the early 1990s (Dantzig and Ye, A build-up interior-point method for linear programming: Affine scaling form, Working paper, Department of Management Science, University of Iowa, 1991) and of two recently proposed ‘primal–dual’ algorithms (Tits, Absil, and Woessner, Constraint reduction for linear programs with many inequality constraints, SIAM J. Optim. 17 (2006), pp. 119–146; Winternitz, Nicholls, Tits, and O’Leary, A constraint-reduced variant of Mehrotra’s predictor–corrector algorithm, Comput. Optim. Appl. (published on-line as of January 2011), DOI: 10.1007/s10589-010-9389-4). The last one, a constraint-reduced variant of Mehrotra’s Predictor–Corrector algorithm, is then more specifically considered: further convergence results are proved, and numerical results are reported that demonstrate that the approach is of practical interest. [ABSTRACT FROM PUBLISHER]

This paper deals with the possibility of using constraint propagation in function and value analyses. It proposes a method to automatically generate a constraint satisfaction problem (CSP) from the functional structure (function or component hierarchy) of a product. The proposed method, by itself, has the advantage of assisting designers in selecting a compatible (technical) and preferable (economical) configuration of subsystems. To this end, an extension to the Functional Analysis System Technique (FAST) model, called Constraint FAST (CFAST), is introduced, enabling functional relations to support diversity within product families. This paper shows how a CFAST diagram can be translated into a formal model and a set of arithmetic constraints. The resulting CSP is eventually solved with a constraint programming library. The process, from conception to completion, is illustrated with the design of a thermo-electric food cooler and heater. [ABSTRACT FROM AUTHOR]

Coupled tasks scheduling was originally introduced for modelling complex radar devices. It is still used for controlling such devices and applied in similar applications. This paper considers a problem of coupled tasks scheduling on one processor, under the assumptions that all processing times are equal to 1, the gap has a constant exact length and the precedence constraints are strict. Although it is proven that the problem stated above is NP-hard in the strong sense if the precedence constraints have a form of a general graph, it is possible to solve some of its relaxed versions in polynomial time. This paper contains a solution for the problem of coupled tasks scheduling with an assumption that the precedence constraints graph has a form of chains and it presents an algorithm that can solve the problem with such assumption in time O(n log n). [ABSTRACT FROM AUTHOR]

In this paper, a model-based prognosis method where the degradation cannot be directly measured but only detectable through the drift of a model parameter is considered. This parameter drift is viewed as an unknown input, whose reconstruction allows the estimation of the degradation state. Model-based prognosis is divided into a filtering step where the current degradation state is estimated, and an uncertainty propagation step where the future degradation state is predicted. During these two steps, model uncertainty and measurement uncertainty are taken into account within the set-membership framework using interval techniques. The filtering step is performed with an interval unknown input observer for linear time-invariant discrete systems. Then, based on a set-membership constraint satisfaction methodology, interval propagation is performed to estimate the bounds that include the future degradation state until some failure threshold is reached, allowing the deduction of the interval including the remaining useful life. In order to demonstrate the efficiency of the proposed model-based prognosis methodology, the degradation of a suspension system is studied. [ABSTRACT FROM AUTHOR]

In this paper, the problem of estimation of mean through calibration technique has been discussed. In the current investigation, we proposed new calibrated estimators of mean in simple random sampling, probability proportional to size sampling and stratified random sampling by considering non-linear constraints of an auxiliary variable. It has been shown through simulation study that the resultant estimators are more efficient than the combined regression as well as the combined ratio estimator in all three sampling designs. [ABSTRACT FROM AUTHOR]

The growing attention to environmental sustainability is not reflected in the effective implementation of ecodesign principles in the industrial context. The main barriers are related to the propensity of companies to optimise only specific aspects directly affected by legislation and to the higher priority given to other drivers such as performance, cost and aesthetics. The present paper faces this issue by proposing an approach to support designers to concurrently deal with environmental, economic and technical performance in all the key phases of the product development process (selection of drivers, evaluation of design alternatives, preservation, sharing and reuse of knowledge, etc.). The approach is grounded on a multi-criteria index, called Product Impact Index (PII), which allows weighting different drivers, verifying the satisfaction of the company's goals, and comparing different design solutions. The method has been successfully applied in two case studies to support the redesign of a cooker hood and a freestanding cooker. Results confirmed its usefulness and effectiveness in overcoming the common barriers to ecodesign implementation in the industrial context. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to multi-agent planning under temporal constraints and uncertainty of admissible operations. A planning problem in AI is usually specified as follows: given a description of the current state of some system, a set of actions that can be performed on the system and a description of a goal set of states for the system, find a sequence of actions that can be performed to transform the system into one of the goal states. The problem described in this paper is to find a way to reach a set of goals satisfying all specified constraints. By constraints, we mean temporal constraints and other constraints in the form of costs, number of agents, etc. Temporal constraints concern both qualitative and quantitative aspects of the system. We consider a set of agents, a set of goals and a description of the environment. The objective is, for one or more agents, to execute a set of tasks to satisfy all goals while respecting all the constraints. A planning procedure over AND-OR graphs is used to generate plans. The proposed multi-agent planning method is based on a centralized scheme in which all the coordination is done at a central location and the agents have no autonomy at the planning level. A specific feature of the task statement in this paper is that we aim at solving tasks of planning and scheduling, the complexity of which is conditioned by temporal and other constraints. [ABSTRACT FROM AUTHOR]

Distributed constraint satisfaction has drawn much attention in the past years, with a number of algorithms proposed to tackle the problem. Research in the area has followed two directions: distributed search techniques and distributed filtering techniques. This paper presents a new distributed filtering algorithm, named Distributed Singleton Arc Consistency ( Dis-SAC ), which is based on the singleton consistency algorithm. Dis-SAC is a parallel, coarse-grain filtering algorithm aimed at improving the performance of singleton consistency by distributing the work to be done to a number of agents. The current paper presents the basic idea behind the algorithm and two versions of it that employ different communication policies along with experimental results obtained on a set of random binary CSP problems. [ABSTRACT FROM AUTHOR]

The configurations of Reconfigurable Manufacturing Systems (RMS) evolve over time in order to provide the functionality and capacity needed, when it is needed. This paper provides a model for optimizing the capital cost of RMS configurations with multiple aspects using Genetic Algorithms (GAs). The optimized configurations can handle multiple parts and their structure is that of a flow line allowing paralleling of identical machines in each production stage. The various aspects of the RMS configurations being considered include arrangement of machines (number of stages and number of parallel machines per stage), equipment selection (machine type and corresponding machine configuration for each stage) and assignment of operations (operation clusters assigned to each stage corresponding to each part type). A novel procedure to overcome the complexity of the search space by mapping from the discrete domain of the decision variables to a continuous domain of variables that guarantees the generation of feasible alternatives is introduced. A case study is presented to demonstrate the use of the developed optimization model for which a toolbox was developed using MATLAB software. The results show that the developed procedure not only overcomes the challenge of constraint satisfaction of such a complicated problem but also generates economical configurations in a reasonable time. This methodology can support manufacturing systems configuration selection decisions both at the initial design and reconfiguration stages. [ABSTRACT FROM AUTHOR]

A product configurator has been an effective software tool in successful implementation of mass customization strategy. It enables manufacturers to automatically generate product configuration information tailored to individual customers' needs. However, current product configurator techniques are not adequate to solve an engineering product configuration problem, because the constraints in such a problem are often expressed by mathematical formulae and computable procedures. This type of constraint posts challenges on constraint modelling and solving within the constraint satisfaction paradigm. Constraints made up of mathematical formulae and computable procedures are not naturally supported with pre-defined constraint semantics in a constraint model. It is also difficult to achieve search efficiency for constraints over n-ary continuous variables. This paper presents an innovative approach to modelling and solving an engineering product configuration problem based on the constraint satisfaction paradigm. It aims at developing a methodology for a generic configurator that is able to solve an engineering product configuration problem with complex constraints. [ABSTRACT FROM AUTHOR]

Multistage stochastic programming with endogenous uncertainty is a new topic in which the timing of uncertainty realization is decision-dependent. In this case, the number of nonanticipativity constraints (NACs) increases very quickly with the number of scenarios, making the problem computationally intractable. Fortunately, a large number of NACs are typically redundant and their elimination leads to a considerable reduction in the problem size. Identifying redundant NACs has been addressed in the literature only in the special case where the scenario set is equal to the Cartesian product of all possible outcomes for endogenous parameters; however, this is a scarce condition in practice. In this paper, we consider the general case where the scenario set is an arbitrary set; and two approaches, able to identify all redundant NACs, are proposed. The first approach is by mixed integer programming formulation and the second one is an exact polynomial time algorithm. Proving the fact that the proposed algorithm is able to make the uppermost reduction in the number of NACs is another novelty of this paper. Computational results evaluate the efficiency of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper examines the effects of different types of complexity facing novice designers in constraint satisfaction tasks. The nature of the complexity in a design task was varied by manipulating different aspects of the extrinsic constraints, which refer to restrictions concerning how design components can be assembled. We investigated the effect of the number of constraints (Study 1) and the number of different types of constraint (Study 2) in a simulated office design task. Results indicated that tackling a design task with a greater number of constraints, or more types of constraint, resulted in decrements in performance. Study 3 examined the effect of reasoning about constraints that involved a fixed location in the office layout and those that did not. It was found that having a higher proportion of constraints that referenced a fixed location led to better design performance. The theoretical and practical aspects of these results are discussed. Practitioner summary:This paper identifies sources of constraint complexity facing the novice designer in an office design task. Three features of constraints proved problematic: the number of constraints, the number of types of constraint and whether the constraint involved a specific location. Training and decision support solutions are discussed. [ABSTRACT FROM AUTHOR]

In this paper, a systematic design with multiple hierarchical layers is adopted in the integrated chassis controller for full drive-by-wire vehicles. A reference model and the optimal preview acceleration driver model are utilised in the driver control layer to describe and realise the driver's anticipation of the vehicle's handling characteristics, respectively. Both the sliding mode control and terminal sliding mode control techniques are employed in the vehicle motion control (MC) layer to determine the MC efforts such that better tracking performance can be attained. In the tyre force allocation layer, a polygonal simplification method is proposed to deal with the constraints of the tyre adhesive limits efficiently and effectively, whereby the load transfer due to both roll and pitch is also taken into account which directly affects the constraints. By calculating the motor torque and steering angle of each wheel in the executive layer, the total workload of four wheels is minimised during normal driving, whereas the MC efforts are maximised in extreme handling conditions. The proposed controller is validated through simulation to improve vehicle stability and handling performance in both open- and closed-loop manoeuvres. [ABSTRACT FROM AUTHOR]

In this paper we investigate the problem of optimising the speed of a vehicle over a fixed path for minimum time traversal. We utilise a change of variables that has been known since the 1980s, although the resulting convexity of the problem was not noted until recently. The contributions of this paper are three fold. First, we extend the convexification of the problem to a more general framework. Second, we identify a wide range of vehicle models and constraints which can be included in this expanded framework. Third, we develop and implement an algorithm that allows these problems to be solved in real time, on embedded systems, with a high degree of accuracy. [ABSTRACT FROM AUTHOR]