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The constrained 0-1 quadratic programming problem (CBQP) is an important problem of integer programming, and many combinatorial optimization problems can be converted to CBQP problem. Because BQP is NP-hard problem, the solving time and accuracy of traditional optimization algorithm are very dependent on the size of the problem, and the local optimal solution obtained by the heuristic algorithm is unstable. Deep learning algorithm has great advantages in solving such problems. In this paper, for the CBQP problem with linear constraints, we creatively apply two algorithms and models to solve it: the graph pointer network model (GPN) trained by hierarchical reinforcement learning (HRL), and the multi-head attention-based pointer network model trained by Advantage Actor-Critic (A2C), which greatly improves the solving speed, accuracy and constraint satisfaction rate of CBQP problems of different scales. At the same time, the bidirectional mask mechanism is innovatively introduced into the network so that the constraint satisfaction rate of the solution is very high. For the two algorithms, this paper solved the 0-1 knapsack (BKP) problem and the quadratic knapsack (QKP) problem, which are equivalent to the CBQP problem, and compared the results of the CBQP problem with different data distribution and scales. The experiment shows that no matter the objective function of the CBQP problem is linear or nonlinear, different data set distribution, or the scale, the pointer network trained by reinforcement learning in this paper has better results than traditional optimization algorithms in solving time, accuracy, stability and constraint satisfaction rate, and with the increase in the size of the problem, this advantage becomes more obvious. [ABSTRACT FROM AUTHOR]

At present, with the intelligence that has been achieved in computer and communication technologies, vehicles can provide many convenient functions to users. However, it is difficult for a vehicle to deal with computationally intensive and latency-sensitive tasks occurring in the vehicle environment by itself. To this end, mobile edge computing (MEC) services have emerged. However, MEC servers (MECSs), which are fixed on the ground, cannot flexibly respond to temporal dynamics where tasks are temporarily increasing, such as commuting time. Therefore, research has examined the provision of edge services using additional unmanned aerial vehicles (UAV) with mobility. Since these UAVs have limited energy and computing power, it is more important to optimize energy efficiency through load balancing than it is for ground MEC servers (MECSs). Moreover, if only certain servers run out of energy, the service coverage of a MEC server (MECS) may be limited. Therefore, all UAV MEC servers (UAV MECSs) need to use energy evenly. Further, in a high-mobility vehicle environment, it is necessary to have effective task migration because the UAV MECS that provides services to the vehicle changes rapidly. Therefore, in this paper, a federated deep Q-network (DQN)-based task migration strategy that considers the load deviation and energy deviation among UAV MECSs is proposed. DQN is used to create a local model for migration optimization for each of the UAV MECSs, and federated learning creates a more effective global model based on the fact that it has common spatial features between adjacent regions. To evaluate the performance of the proposed strategy, the performance is analyzed in terms of delay constraint satisfaction, load deviation, and energy deviation. [ABSTRACT FROM AUTHOR]

This paper deals with the impulsive formation control of spacecraft in the presence of constraints on the position vector and time. Determining a set of path constraints can increase the safety and reliability in an impulsive relative motion of spacecraft. Specially, the feasibility problem of the position norm constraints is considered in this paper. Under assumptions, it is proved that if a position vector be reachable, then the reach time and the corresponding time of impulses are unique. The trajectory boundedness of the spacecraft between adjacent impulses are analyzed using the Gerschgorin and the Rayleigh–Ritz theorems as well as a finite form of the Jensen's inequality. Some boundaries are introduced regarding the Jordan–Brouwer separation theorem which are useful in checking the satisfaction of a constraint. Two numerical examples (approximate circular formation keeping and collision-free maneuver) are solved in order to show the applications and visualize the results. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach that facilitates the design of active controllers to mitigate seismically induced damage in structural systems. The proposed method is based on stochastic Modified Black Hole optimization algorithm. Two traditional controllers, namely Proportional-Integral-Derivative (PID) and Linear–Quadratic Gaussian (LQG) controllers were designed, and their performance was demonstrated on a benchmark 20-story steel-framed building. Evaluation criteria were defined to satisfy constraints on various response quantities, including drift, base shear, ductility, residual story drift, and control force. The constraint limits were defined in view of performance-based design requirements for the benchmark structure. The performance of the controllers was contrasted with that of traditional LQG, and significant reductions of all response quantities were achieved for design-level earthquakes. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for designing an event‐triggered predictive sliding mode control (SMC) for discrete‐time systems subject to constraints. The proposed controller is based on an existing reach‐law‐based SMC, and it formulates an optimization control problem (OCP) to generate predictive control actions that satisfy both state and input constraints. An event‐triggered strategy is introduced to reduce the frequency of OCP solving by taking into account the sliding mode controller and unmeasurable states between sampling instants. The paper also analyzes the recursive feasibility of the proposed controller, which ensures the feasibility of each OCP solution. Numerical simulations and comparison studies are performed to validate the theoretical results. The proposed approach offers an effective way to design event‐triggered predictive sliding mode controllers that reduce the computational burden of OCP solving while guaranteeing the satisfaction of system constraints. [ABSTRACT FROM AUTHOR]

The Constraint Satisfaction Problem (CSP) is a significant research area in artificial intelligence, and includes a large number of symmetric or asymmetric structures. A backtracking search combined with constraint propagation is considered to be the best CSP-solving algorithm, and the consistency algorithm is the main algorithm used in the process of constraint propagation, which is the key factor in constraint-solving efficiency. Max-restricted path consistency (maxRPC) is a well-known and efficient consistency algorithm, whereas the lmaxRPC3 r m algorithm is a classic lightweight algorithm for maxRPC. In this paper, we leverage the properties of symmetry to devise an improved pruning strategy aimed at efficiently diminishing the problem's search space, thus enhancing the overall solving efficiency. Firstly, we propose the maxRPC3 s i m algorithm, which abandons the two complex data structures used by lmaxRPC3 r m . We can render the algorithm to be more concise and competitive compared to the original algorithm while ensuring that it maintains the same average performance. Secondly, inspired by the RCP3 algorithm, we propose the maxRPC3 s i m R algorithm, which uses the idea of residual support to cut down the redundant operation of the lmaxRPC3 r m algorithm. Finally, combining the domain/weighted degree (dom/wdeg) heuristic with the activity-based search (ABS) heuristic, a new variable ordering heuristic, ADW, is proposed. Our heuristic prioritizes the selection of variables with symmetry for pruning, further enhancing the algorithm's pruning capabilities. Experiments were conducted on both random and structural problems separately. The results indicate that our two algorithms generally outperform other algorithms in terms of performance on both problem classes. Moreover, the new heuristic algorithm demonstrates enhanced robustness across different problem types when compared to various existing algorithms. [ABSTRACT FROM AUTHOR]

A smart home needs to be human-centric, where it tries to fulfill human needs given the devices it has. Various works are developed to provide homes with reasoning and planning capability to fulfill goals, but most do not support complex sequence of plans or require significant manual effort in devising subplans. This is further aggravated by the need to optimize conflicting personal goals. A solution is to solve the planning problem represented as constraint satisfaction problem (CSP). But CSP uses hard constraints and, thus, cannot handle optimization and partial goal fulfillment efficiently. This paper aims to extend this approach to weighted CSP. Knowledge representation to help in generating planning rules is also proposed, as well as methods to improve performances. Case studies show that the system can provide intelligent and complex plans from activities generated from semantic annotations of the devices, as well as optimization to maximize personal constraints’ fulfillment. Note to Practitioners—Smart home should maximize the fulfillment of personal goals that are often conflicting. For example, it should try to fulfill as much as possible the requests made by both the mother and daughter who wants to watch TV but both having different channel preferences. That said, every person has a set of goals or constraints that they hope the smart home can fulfill. Therefore, human-centric system that automates the loosely coupled devices of the smart home to optimize the goals or constraints of individuals in the home is developed. Automated planning is done using converted services extracted from devices, where conversion is done using existing tools and concepts from Web technologies. Weighted constraint satisfaction that provides the declarative approach to cover large problem domain to realize the automated planner with optimization capability is proposed. Details to speed up planning through search space reduction are also given. Real-time case studies are run in a prototype smart home to demonstrate its applicability and intelligence, where every planning is performed under a maximum of 10 s. The vision of this paper is to be able to implement such system in a community, where devices everywhere can cooperate to ensure the well-being of the community. [ABSTRACT FROM AUTHOR]

A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks’ delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks. Note to Practitioners—This paper investigates the profit maximization problem for a green data center (GDC) while meeting delay constraints for all admitted tasks. Previous task scheduling algorithms do not jointly investigate temporal variation in revenue, green energy, and price of grid. Thus, they fail to meet the delay constraints of all admitted tasks. In this paper, a new approach that overcomes drawbacks of existing algorithms is proposed. It is obtained by using a hybrid metaheuristic algorithm that solves a constrained nonlinear optimization problem. Simulation results show that compared with several existing algorithms, it increases both throughput and profit. It can be readily incorporated into real-life industrial GDCs. The future work needs to investigate the repair/failure effect of GDCs on the proposed time-aware task scheduling. [ABSTRACT FROM AUTHOR]

Mining frequent sequences (FS) with constraints in a sequence database (SDB) are a critical task in Data Mining, as it forms the basis for discovering meaningful patterns within sequential data. However, traditional algorithms tackling the direct mining of constrained FSs from the SDB often exhibit poor performance, especially when dealing with large SDBs and low support thresholds. Moreover, constraint-based sequence mining algorithms face additional challenges, such as increased runtime and memory usage, particularly when constraints change frequently. To address these issues, this paper introduces an efficient method for generating FSs that include a user-defined sub-sequence. Specifically, the discovered FSs must be super-sequences of the given sub-sequence. Rather than directly discovering these sequences from a sequence database (SDB) in the traditional manner, the proposed method quickly generates constrained FSs from frequent closed sequences (FCS) and frequent generator sequences (FGS). This process involves categorizing constrained FSs into equivalence classes; each represented by FCSs and FGSs. An efficient method is then adapted to swiftly generate constrained FSs within each class based on the representative elements, which are FCSs and FGSs. Additionally, a novel technique called Constraint Satisfaction Technique (CST) is introduced to circumvent computationally expensive checks for the inclusion relation among sequences during the generation process. Furthermore, a novel algorithm named MFS-SubSC is developed based on the proposed theoretical results to generate all constrained FSs efficiently. Experimental results demonstrate that the proposed algorithm surpasses state-of-the-art methods in terms of runtime, memory usage, and scalability. [ABSTRACT FROM AUTHOR]

In this paper, we greatly increase the number of impossible differentials for SIMON and SIMECK by eliminating the 1-bit constraint in input/output difference, which is the precondition to ameliorate the complexity of attacks. We propose an algorithm which can greatly reduce the searching complexity to find such trails efficiently since the search space exponentially expands to find impossible differentials with multiple active bits. There is another situation leading to the contradiction in impossible differentials except for miss-in-the-middle. We show how the contradiction happens and conclude the precondition of it defined as miss-from-the-middle. It makes our results more comprehensive by applying these two approach simultaneously. This paper gives for the first time impossible differential characteristics with multiple active bits for SIMON and SIMECK, leading to a great increase in the number. The results can be verified not only by covering the state-of-art, but also by the MILP model. [ABSTRACT FROM AUTHOR]

This paper addresses the fundamental theoretical development of a linear optimal noncausal control for wave energy converters (WECs) in a closed analytic form. It is well known that the WEC control is a noncausal control problem, i.e., the future wave information contributes to the present control action. This paper provides a reliable, efficient, and simple linear optimal controller with guaranteed stability for the WEC control problem. The proposed WEC linear optimal control consists of a causal linear state feedback part and an anticausal linear feedforward part to incorporate the influence of future incoming waves. The stability of the closed-loop WEC control system with the proposed linear optimal controller is proven. The contribution of the noncausal term using wave prediction information to the optimal control and the energy output is analyzed quantitatively. The proposed linear controller can be more preferred when constraint satisfaction becomes a less important issue for mild sea states and some well-designed WECs with an ample operation range. The proposed optimal control strategy can be extended for a generic class of energy maximization problems. Numerical simulations are presented to justify the efficacy of the proposed WEC optimal control. [ABSTRACT FROM AUTHOR]

This paper considers the task of finding the smallest circle into which one can pack a fixed number of non-overlapping unit squares that are free to rotate. Due to the rotation angles, the packing of unit squares into a container is considerably harder to solve than their circle packing counterparts. Therefore, optimal arrangements were so far proved to be optimal only for one or two unit squares. By a computer-assisted method based on interval arithmetic techniques, we solve the case of three squares and find rigorous enclosures for every optimal arrangement of this problem. We model the relation between the squares and the circle as a constraint satisfaction problem (CSP) and found every box that may contain a solution inside a given upper bound of the radius. Due to symmetries in the search domain, general purpose interval methods are far too slow to solve the CSP directly. To overcome this difficulty, we split the problem into a set of subproblems by systematically adding constraints to the center of each square. Our proof requires the solution of 6, 43 and 12 subproblems with 1, 2 and 3 unit squares respectively. In principle, the method proposed in this paper generalizes to any number of squares. [ABSTRACT FROM AUTHOR]

Constraints ubiquitously exist in many real-life applications for entity resolution. However, it is always challenging to effectively specify and use such constraints for performing ER tasks. In particular, not every constraint is equally robust. Adding weights to express the "confidence" on constraints thus becomes a natural choice. In this paper, the authors study entity resolution (ER), the problem of determining which records in one or more databases refer to the same entities, in the presence of weighted constraints. They propose a unified framework that allows us to associate a weight for each constraint, capturing the confidence for its robustness in an ER model. The authors develop an approach to learn weighted constraints based on domain knowledge, and investigate how effectively and efficiently weighted constraints can be used for generating an ER clustering and for determining a propagation order across multiple entity types. Their experimental study shows that using weighted constraints can lead to improved ER quality and scalability. [ABSTRACT FROM AUTHOR]

Planning, scheduling and constraint satisfaction are important areas in artificial intelligence (AI). Many real-world problems are known as AI planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. Therefore, solving these problems requires an adequate mixture of planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Constraint satisfaction plays also an important role to solve real-life problems, so that integrated techniques that manage planning and scheduling with constraint satisfaction remains necessary. This special issue on Planning, Scheduling and Constraint Satisfaction compiles a selection of papers of CAEPIA’2007 workshop on Planning, Scheduling and Constraint Satisfaction and COPLAS’2007: CP/ICAPS 2007 Joint Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems. This issue presents novel advances on planning, scheduling, constraint programming/constraint satisfaction problems (CSPs) and many other common areas that exist among them. On the whole, this issue mainly focus on managing complex problems where planning, scheduling, constraint satisfaction and search must be combined and/or interrelated, which entails an enormous potential for practical applications and future research. Furthermore, this issue also includes a complete survey about constraint satisfaction, planning, scheduling and integration among these areas. [ABSTRACT FROM AUTHOR]

Switched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system's operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model's computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model's accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values. [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]

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]

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]

Robust tracking of piece‐wise constant reference signals for constrained systems with parametric plant uncertainty and additive disturbances is addressed in this paper. The parametric uncertainty is decreased online by set‐membership estimation and a nominal model is updated for improving set‐point tracking. The online estimated parametric uncertainty is used for an online‐determined terminal set which enlarges the set of reachable references close to the system constraints when compared to an offline worst‐case consideration. An artificial target state is introduced which can deviate from the nominal target state. This new target state is used to ensure recursive feasibility for unreachable references and changes in the reference signal. Moreover, a novel "recovery mode" is specified which is deployed in case the new nominal model yields an infeasible control problem. Control algorithms are developed for time‐invariant systems and systems with arbitrarily fast changing plants but known relative bounds. Constraint satisfaction and l2$$ {l}_2 $$‐stability are guaranteed for the proposed algorithms. Controlling the engine load of a self‐propelled work machine is used as a practical example. [ABSTRACT FROM AUTHOR]

Dominance breaking is a powerful technique in improving the solving efficiency of Constraint Optimization Problems (COPs) by removing provably suboptimal solutions with additional constraints. While dominance breaking is effective in a range of practical problems, it is usually problem specific and requires human insights into problem structures to come up with correct dominance breaking constraints. Recently, a framework is proposed to generate nogood constraints automatically for dominance breaking, which formulates nogood generation as solving auxiliary Constraint Satisfaction Problems (CSPs). However, the framework uses a pattern matching approach to synthesize the auxiliary generation CSPs from the specific forms of objectives and constraints in target COPs, and is only applicable to a limited class of COPs. This paper proposes a novel rewriting system to derive constraints for the auxiliary generation CSPs automatically from COPs with nested function calls, significantly generalizing the original framework. In particular, the rewriting system exploits functional constraints flattened from nested functions in a high-level modeling language. To generate more effective dominance breaking nogoods and derive more relaxed constraints in generation CSPs, we further characterize how to extend the system with rewriting rules exploiting function properties, such as monotonicity, commutativity, and associativity, for specific functional constraints. Experimentation shows significant runtime speedup using the dominance breaking nogoods generated by our proposed method. Studying patterns of generated nogoods also demonstrates that our proposal can reveal dominance relations in the literature and discover new dominance relations on problems with ineffective or no known dominance breaking constraints. [ABSTRACT FROM AUTHOR]

An introduction to the special issue is presented in which the guest editor discusses various reports within the issue on topics including constraints used in a alphabet-based unique game, probabilistically checkable proofs (PCPs), and semi-definite programming.

The article discusses papers which appeared in the May 2012 issue of "Multiagent & Grid Systems," including one which considered the practice of applying keywords known as tags to user-created web content, and another which considers the problem of distributed constraint satisfaction.

Political redistricting is the redrawing of electoral district boundaries. It is normally undertaken to reflect population changes. The process can be abused, in what is called gerrymandering, to favor one party or interest group over another, resulting thereby in broadly undemocratic outcomes that misrepresent the views of the voters. Gerrymandering is especially vexing in the United States. This paper introduces an algorithm, with an implementation, for creating districting plans (whether for political redistricting or for other districting applications). The algorithm, Seed-Fill-Shift-Repair (SFSR), is demonstrated for Congressional redistricting in American states. SFSR is able to create thousands of valid redistricting plans, which may then be used as points of departure for public deliberation regarding how best to redistrict a given polity. The main objectives of this paper are: (i) to present SFSR in a broadly accessible form, including code that implements it and test data, so that it may be used for both civic deliberations by the public and for research purposes. (ii) to make the case for what SFSR essays to do, which is to approach redistricting, and districting generally, from a constraint satisfaction perspective and from the perspective of producing a plurality of feasible solutions that may then serve in subsequent deliberations. To further these goals, we make the code publicly available. The paper presents, for illustration purposes, a corpus of 11,206 valid redistricting plans for the Commonwealth of Pennsylvania (produced by SFSR), using the 2017 American Community Survey, along with descriptive statistics. Also, the paper presents 1,000 plans for each of the states of Arizona, Michigan, North Carolina, Pennsylvania, and Wisconsin, using the 2018 American Community Survey, along with descriptive statistics on these plans and the computations involved in their creation. [ABSTRACT FROM AUTHOR]

A hierarchical model predictive control (MPC) approach is developed for energy management of aircraft electro-thermal systems. High-power electrical systems on board modern and future aircraft perform a variety of mission- and flight-critical tasks, while thermal management systems actively cool these electronics to satisfy component-specific temperature constraints, ensuring safe and reliable operation. In this paper, coordination of these electrical and thermal systems is performed using a hierarchical control approach that decomposes the multi-energy domain, constrained optimization problem into smaller, more computationally efficient problems that can be solved in real-time. A hardware-in-the-loop (HIL) experimental testbed is used to evaluate the proposed hierarchical MPC in comparison to a baseline controller for a scaled, laboratory representation of an aircraft electro-thermal system. Experimental results demonstrate that the proposed approach outperforms the baseline controller across a range of electrical loading in terms of both efficient energy management and constraint satisfaction. [ABSTRACT FROM AUTHOR]

The Quantum Approximate Optimization Algorithm (QAOA) is an algorithmic framework for finding approximate solutions to combinatorial optimization problems. It consists of interleaved unitary transformations induced by two operators labelled the mixing and problem Hamiltonians. To fit this framework, one needs to transform the original problem into a suitable form and embed it into these two Hamiltonians. In this paper, for the well-known NP-hard Traveling Salesman Problem (TSP), we encode its constraints into the mixing Hamiltonian rather than the conventional approach of adding penalty terms to the problem Hamiltonian. Moreover, we map edges (routes) connecting each pair of cities to qubits, which decreases the search space significantly in comparison to other approaches. As a result, our method can achieve a higher probability for the shortest round-trip route with only half the number of qubits consumed compared to IBM Q's approach. We argue the formalization approach presented in this paper would lead to a generalized framework for finding, in the context of QAOA, high-quality approximate solutions to NP optimization problems. [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]

Transients are commonly triggered in urban water distribution networks (WDNs) due to daily system management and operation. While these transients are unlikely to cause catastrophic consequences immediately, frequent occurrences can result in prolonged deterioration of infrastructure safety and life cycles in the long term. To account for such impacts in the design of WDNs, a multi-objective optimization method coupled with the Non-dominated Sorting Genetic Algorithm III is proposed in this paper, where two transient-based objectives are incorporated into the WDN design process. Additionally, an engineering design constraint in the decision space is developed to ensure that the sizes of upstream pipes are not smaller than those downstream, thereby improving the engineering practicality of the optimal design solutions. Two WDN cases with transient conditions triggered by pump switching are applied to demonstrate the effectiveness of the proposed method. The results show that the widely used reliability metric based on steady-state conditions is unable to fully represent the transient impacts and that upsizing pipes can reduce transient impacts but at the expense of high economic costs. It is also found that optimally designed pipe diameters can be effective to mitigate transient impacts, in addition to the use of traditional protection devices. The proposed method represents the first step in investigating the underlying relationships between WDN design and unsteady flow effects and is a supplement to current WDN design criteria. [ABSTRACT FROM AUTHOR]

Purpose: Assembly is the last step in manufacturing processes. The two-sided assembly line balancing problem (TALBP) is a typical research focus in the field of combinatorial optimization. This paper aims to study a multi-constraint TALBP-I (MC-TALBP-I) that involves positional constraints, zoning constraints and synchronism constraints to make TALBP more in line with real production. For enhancing quality of assembly solution, an improved imperialist competitive algorithm (ICA) is designed for solving the problem. Design/methodology/approach: A mathematical model for minimizing the weighted sum of the number of mated-stations and stations is established. An improved ICA is designed based on a priority value encoding structure for solving MC-TALBP-I. Findings: The proposed ICA was tested by several benchmarks involving positional constraints, zoning constraints and synchronism constraints. This algorithm was compared with the late acceptance hill-climbing (LAHC) algorithm in several instances. The results demonstrated that the ICA provides much better performance than the LAHC algorithm. Practical implications: The best solution obtained by solving MC-TALBP-I is more feasible for determining the real assembly solution than the best solution obtained by solving based TALBP-I only. Originality/value: A novel ICA based on priority value encoding is proposed in this paper. Initial countries are generated by a heuristic method. An imperialist development strategy is designed to improve the qualities of countries. The effectiveness of the ICA is indicated through a set of benchmarks. [ABSTRACT FROM AUTHOR]

This paper studies a single beacon-based three-dimensional multirobot localization (MRL) problem. Unlike most of existing localization algorithms which use extended Kalman filter or maximum a posteriori, moving horizon estimation (MHE), and convex optimization are novelly designed to perform MRL with constraints and unknown initial poses. The main contribution of this paper is three-fold: 1) a constrained MHE-based localization algorithm, which can bound localization error, impose various constraints and compromise between computational complexity and estimator accuracy, is proposed to estimate robot poses; 2) constrained optimization is examined in the perspective of Fisher information matrix to analyze why and how multirobot information and constraints are able to reduce uncertainties; 3) a semidefinite programming-based initial pose estimation, which can efficiently converge to global optimum, is developed by using convex relaxation. Simulations and experiments are conducted to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Geomagnetically induced currents (GICs) introduced by geomagnetic disturbances (GMDs) can damage transformers, increase reactive power losses, and cause reliability issues in power systems. Finding an optimal strategy to place blocking devices (BDs) at transformer neutrals is essential to mitigating the negative impact of GICs. In this paper, we study and solve the problem of optimal BD placement (OBP), aiming at minimizing the damages of GMDs, subject to various practical constraints. In particular, our approach accounts for the potential impact of BD placement to neighboring interconnected systems; we also consider the time-varying nature of the geoelectric field in BD placement. To the best of our knowledge, neither has been considered in the existing works. Under these constraints, the combined complexity of solving the OBP problem on a large-scale system poses a big challenge. To address this, we developed a simulated annealing-based algorithm that can achieve near-optimal solutions for the OBP problem at a reduced computational complexity, while taking the above constraints into account. More importantly, this paper provides a general mathematical framework that can be used to solve various OBP problems, with different objective functions and constraints. We demonstrated the effectiveness and the efficiency of our method by using power systems of various sizes. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient energy management system (EMS) for industrial microgrids (MGs). Many industries deploy large pumps for their processes. Oftentimes, such pumps are operated during hours of peak electricity prices. A lot of industries use a mix of captive generation and imported utility electricity to meet their energy requirements. The MG considered in this paper includes diesel generators, battery energy storage systems, renewable energy sources, flexible loads, and interruptible loads. Pump loads found in shipyard dry docks are modelled as exemplar flexible industrial loads. The proposed EMS has a two-stage architecture. An optimal MG scheduling problem including pump scheduling and curtailment of interruptible loads (ILs) is formulated and solved in the first stage. An optimal power flow problem is solved in the second stage to verify the feasibility of the MG schedule with the network constraints. An iterative procedure is used to coordinate the two EMS stages. Multiple case studies are used to demonstrate the utility of the proposed EMS. The case studies highlight the efficacy of load management strategies such as pump scheduling and curtailment of ILs in reducing the total electricity cost of the MG. [ABSTRACT FROM AUTHOR]

Planning motions for two robot arms to move an object collaboratively is a difficult problem, mainly because of the closed-chain constraint, which arises whenever two robot hands simultaneously grasp a single rigid object. In this paper, we propose a manipulation planning algorithm to bring an object from an initial stable placement (position and orientation of the object on a support surface) toward a goal stable placement. The key specificity of our algorithm is that it is certified-complete: for a given object and a given environment, we provide a certificate that the algorithm will find a solution to any bimanual manipulation query in that environment whenever one exists. Moreover, the certificate is constructive: at run-time, it can be used to quickly find a solution to a given query. The algorithm is tested in software and hardware on a number of large pieces of furniture. Note to Practitioners—This paper presents an algorithm to solve a difficult class of bimanual manipulation planning problems where a movable object can be moved only when grasped by two robots. This problem arises naturally when manipulating a large and/or heavy object such as a piece of furniture and is therefore essential to industrial automation. The algorithm first precomputes a certificate, a set of robot motions to move the object between different placement classes that helps guarantee that the algorithm will find a solution to any planning query whenever one exists. This certificate is then used to quickly construct a solution trajectory to a planning query and can be reused under the same environment. The algorithm has been empirically verified through software and hardware experiments on a number of large pieces of furniture. An open-source implementation is provided. [ABSTRACT FROM AUTHOR]

Precoding is a beamforming technique that supports multi-stream transmission in which the RF chain plays a significant role as a digital precoding at the receiver for wireless communication. The traditional precoding contains only digital signal processing and each antenna connects to each RF chain, which provides high transmission efficiency but high cost and hardware complexity. Hybrid precoding is one of the most popular massive multiple input multiple output (MIMO) techniques that can save costs and avoid using complex hardware. At present, network services are currently in focus with a wide range of traffic volumes. In terms of the Quality of Service (QoS), it is critical that service providers pay a lot of attention to this parameter and its relationship to Quality of Experience (QoE) which is the measurement of the overall level of user satisfaction. Therefore, this paper proposes hybrid precoding of a partially structured system to improve transmission efficiency and allocate resources to provide network services to users for increasing the user satisfaction under power constraints that optimize the quality of baseband precoding and radio frequency (RF) precoding by minimizing alternating algorithms. We focus on the web browsing, video, and Voice over IP (VOIP) services. Also, a Mean Opinion Score (MOS) is employed to measure the level of user satisfaction. The results show that the partially structured system provides a good user satisfaction with the network's services. The partially structured system provides high energy efficiency up to 85%. Considering web service, the partially structured system for 10 users provides MOS at 3.21 which is higher than 1.75 of fully structured system. [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]

The paper presents a new Lyapunov-type predefined-time stabilization control algorithm for stochastic high-order nonlinear systems with asymmetric output constraints. In contrast to stochastic finite-time and fixed-time stabilization, the average value of the settling-time function for stochastic predefined-time stabilization control is independent of both the initial value and the control factors. To mitigate the significant uncertainties arising from the asymmetric output constraint, a tan-type barrier Lyapunov function is formulated. Furthermore, by harnessing the previously mentioned barrier Lyapunov function and integrating the power integrator technique, a controller design strategy is formulated based on the backstepping method. The rigorous analysis in this study proves that the designed controller ensures both the attainment of predefined-time convergence of the system states to the origin in probability and the satisfaction of the output constraint. Finally, an example of a roll angle subsystem for quadrotor UAVs and a numerical illustration are presented to corroborate the theoretical analysis. • A novel Lyapunov-type stochastic predefined-time stable control algorithm is presented in Theorem 1. • Controller design based on the backstepping method is formulated through the power integrator technique. • The tan-type barrier Lyapunov function extends the order range and absorbs the inherent properties of the nonlinear terms. [ABSTRACT FROM AUTHOR]

Chance constraints are a valuable tool for the design of safe decisions in uncertain environments; they are used to model satisfaction of a constraint with a target probability. However, because of possible non-convexity and non-smoothness, optimizing over a chance constrained set is challenging. In this paper, we consider chance constrained programs where the objective function and the constraints are convex with respect to the decision parameter. We establish an exact reformulation of such a problem as a bilevel problem with a convex lower-level. Then we leverage this bilevel formulation to propose a tractable penalty approach, in the setting of finitely supported random variables. The penalized objective is a difference-of-convex function that we minimize with a suitable bundle algorithm. We release an easy-to-use open-source python toolbox implementing the approach, with a special emphasis on fast computational subroutines. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach for constrained rendezvous trajectory optimization (CRTO) using stochastic optimal feedback control and unscented transform (UT) uncertainty quantification. The method overcomes limitations of traditional deterministic CRTO solutions that suffer from uncontrolled terminal state error growth and severely deteriorated path constraint satisfaction when initial state uncertainty, dynamics uncertainty, and navigation uncertainty are considered. The approach involves constructing a stochastic optimal feedback control (SOFC) problem with chance constraints and introducing linear feedback control to regulate both the mean and variance of terminal state errors. UT is employed to approximately quantify the state errors and their propagation, transforming the SOFC problem into an unconstrained deterministic optimization (UDO) problem. Differential dynamic programming (DDP) is then used to solve the UDO problem. The obtained stochastic optimal solution provides a robust rendezvous trajectory and a corresponding explicit closed-loop guidance law, improving terminal accuracy and path constraint satisfaction in the presence of various considered uncertainties. The influence of different term weights in the objective function on terminal accuracy and path constraint satisfaction is also studied. The effectiveness of the proposed approach is verified through Monte Carlo simulation, demonstrating the robustness of the closed-loop control strategy. The results highlight the potential of the method in enhancing terminal accuracy and path constraint satisfaction in uncertain rendezvous scenarios. • The uncertainty-resilient rendezvous trajectory optimization algorithm provides not only a nominal trajectory but an explicit optimal state-feedback control policy. • Rendezvous trajectory uncertainty caused by initial state errors, dynamics errors, and navigation errors is propagated using an unscented transformation method. • The proposed method considers common input constraints and path constraints in space rendezvous missions. [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]

Injecting prior knowledge into the learning process of a neural architecture is one of the main challenges currently faced by the artificial intelligence community, which also motivated the emergence of neural-symbolic models. One of the main advantages of these approaches is their capacity to learn competitive solutions with a significant reduction of the amount of supervised data. In this regard, a commonly adopted solution consists of representing the prior knowledge via first-order logic formulas, then relaxing the formulas into a set of differentiable constraints by using a t-norm fuzzy logic. This paper shows that this relaxation, together with the choice of the penalty terms enforcing the constraint satisfaction, can be unambiguously determined by the selection of a t-norm generator, providing numerical simplification properties and a tighter integration between the logic knowledge and the learning objective. When restricted to supervised learning, the presented theoretical framework provides a straight derivation of the popular cross-entropy loss, which has been shown to provide faster convergence and to reduce the vanishing gradient problem in very deep structures. However, the proposed learning formulation extends the advantages of the cross-entropy loss to the general knowledge that can be represented by neural-symbolic methods. In addition, the presented methodology allows the development of novel classes of loss functions, which are shown in the experimental results to lead to faster convergence rates than the approaches previously proposed in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a generalized three-dimensional (3D) cooperative guidance law with seeker's field-of-view (FOV) constraints is designed for multiple missiles to achieve salvo attack against a stationary target. The proposed generalized guidance law is composed of two parts: a proportional navigation guidance (PNG) component for target capture and a biased feedback component for simultaneous arrival. Two novel auxiliary functions are integrated into the biased feedback component to guarantee the satisfaction of FOV constraint. Furthermore, a time-varying lower bound that contains the impact time error is utilized to avoid the guidance command singularity. The proposed guidance law can be easily applied to various missile communication topologies, including centralized connected, distributed connected, and no-connected topologies. The convergence of impact time error, FOV constraint satisfaction, and command non-singularity of the proposed guidance law are theoretically analyzed and proved. Extensive numerical simulations with comparative studies are conducted to demonstrate the effectiveness and advantages of the proposed guidance law. [ABSTRACT FROM AUTHOR]

Constraint satisfaction problem is one of the most basic NP-complete problems in artificial intelligence. Over the years, with the in-depth study of constraint satisfaction problem, scholars at home and abroad have proposed a variety of case models. RB model is an example of growth domain constraint satisfaction problem with precise phase transition, which is very challenging to solve. In order to find a new efficient algorithm and promote the research of RB model algorithm for constraint satisfiability problem, firstly, this paper analyzed the model development and solving techniques of constraint satisfaction problem. Secondly, it sorted out various examples of RB model solving algorithms, and divided the solving algorithm literature into backtracking heuristic, information propagation and meta-heuristic related improved algorithms. This paper compared and summarized the algorithm principles, improved strategies, convergence and accuracy. Finally, it gave the research trend and development direction of solving RB model example algorithm. [ABSTRACT FROM AUTHOR]

Constraint satisfaction problems have a wide range of applications in areas such as basic computer theory research and artificial intelligence, and many major studies in industry are not solved directly, but converted into instances of satisfiability problems for solution. Therefore, the solution of the satisfiability problem is a central problem in many important areas in the future. A large number of solution algorithms for this problem are mainly based on completeness algorithms and heuristic algorithms. Intelligent optimization algorithms with heuristic policies run significantly more efficiently on large-scale instances compared to completeness algorithms. This paper compares the principles, implementation steps, and applications of several major intelligent optimization algorithms in satisfiability problems, analyzes the characteristics of these algorithms, and focuses on the performance in solving satisfiability problems under different constraints. In terms of algorithms, evolutionary algorithms and swarm intelligence algorithms are introduced; in terms of applications, the solution to the satisfiability problem is studied. At the same time, the performance of the listed intelligent optimization algorithms in applications is analyzed in detail in terms of the direction of improvement of the algorithms, advantages and disadvantages and comparison algorithms, respectively, and the future application of intelligent optimization algorithms in satisfiability problems is prospected. [ABSTRACT FROM AUTHOR]

This paper presents the multi-agent system fuzzy-constraints offer negotiation of Workflow Scheduling in Fog-Cloud environment, called (Fuzzy-Cone) approach, to solve the workflow scheduling problem with conflicting constraints in Fog-Cloud IT infrastructures. A client agent and a supplier agent are created to represent the client and supplier sides respectively, with a win–win strategy based on negotiation. The novelty of this approach is the design of a multi-agent system with agents supervised by a strategy based on a fuzzy inference system modeling all possible cases, thus facilitating decision-making. The workflow scheduling problem is treated as a set of fuzzy constraint satisfaction problems (FCSP). Each agent has an FCSP modeling a set of fuzzy constraints based on the negotiation with other agents by proposing offers or counter-offers. The proposed negotiation approach is implemented to respect all the imposed restrictions and represent the imprecise preferences of the approach entities by pre-defining the fuzzy constraints and optimizing the workflow scheduling solution in terms of time and cost. of compiling. The proposed approach has been tested with different experiments and compared with state-of-the-art algorithms. The experimental results show that the negotiation between the solutions, of mutually satisfactory scheduling, considerably improved the values of time and cost of compilation, while respecting the set of the imposed constraints. The proposed approach achieves a workflow scheduling scheme that reduces compilation time by 37% and increases cost by 6% compared to state-of-the-art algorithms. The different solutions we have proposed respect the constraints of time and budget by executing workflows of different sizes in reasonable time and cost. [ABSTRACT FROM AUTHOR]

This paper conducts research on obstacle and collision avoidance under kinematical constraints in unknown environments, while communication and simultaneous localization and mapping (SLAM) are unavailable to agents. Then a strategy based on mixed‐integer programming is proposed, in which velocity constraints are established with a modified Barrier function for obstacles that can be completely detected. As for obstacles that cannot be completely detected, a feasible set is built for the velocity programming based on the convex theory, and the contradicted constraints are addressed with the logic metric method. Besides, the actuator saturation is avoided by converting kinematical constraints into the restrictions on the magnitude, the restrictions on the direction, and the negative correlations between the components of the velocity respectively. Given that invalid nominal velocity leads to collisions, a virtual goal is estimated for nominal velocity improvement. In addition, the local extremum brought by empty programming space due to multiple constraints is repaired by fixing the velocity constraints. The feasibility of the proposed strategy is analyzed, and numerical simulations are provided to verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]