Set Of Constraints Research Articles

An appraisal of the LQI as an approach to setting target reliabilities in ISO 2394:2015

The idea of coupling economics and safety into one optimization exercise, and hence deriving target reliabilities explicitly from socio-economic consequences of limit state violation, appeared in the JCSS Probabilistic Model Code around the turn of the century; the effort culminated in the form of a Life Quality Index (LQI)-based marginal lifesaving costs (MLSC) methodology in ISO 2394:2015 for determining maximum acceptable failure probabilities, pT, in life safety limit states for civil engineering structures across all nations. Unfortunately, while the methodology does yield adequate levels of safety when applied to structures in the developed countries, the recommended values of pT turn out to be one to two orders of magnitude higher, and unacceptable to every known standard in the world including its own earlier edition (ISO 2394:1998) when applied to structures in the developing world (exemplified by India) with identical functions and expected fatalities. This arises from two shortcomings: (1) an MLSC approach is generally unable to provide an independent constraint on monetary optimization, and (2) the LQI-based measure of MLSC constraint is strongly dependent on a country’s per capita GDP (g) and the resultant pT is effectively governed by the reciprocal of g. This paper recommends continuing with the absolute − not marginal − consequences of violating life safety limit states, and setting constraints on monetary optimization that are consistent with fatality risks from engineering activities more universally accepted to be safe.

Adaptive penalty-based neurodynamic approach for nonsmooth interval-valued optimization problem

The complex and diverse practical background drives this paper to explore a new neurodynamic approach (NA) to solve nonsmooth interval-valued optimization problems (IVOPs) constrained by interval partial order and more general sets. On the one hand, to deal with the uncertainty of interval-valued information, the LU-optimality condition of IVOPs is established through a deterministic form. On the other hand, according to the penalty method and adaptive controller, the interval partial order constraint and set constraint are punished by one adaptive parameter, which is a key enabler for the feasibility of states while having a lower solution space dimension and avoiding estimating exact penalty parameters. Through nonsmooth analysis and Lyapunov theory, the proposed adaptive penalty-based neurodynamic approach (APNA) is proven to converge to an LU-solution of the considered IVOPs. Finally, the feasibility of the proposed APNA is illustrated by numerical simulations and an investment decision-making problem.

Chaotic Harris Hawks Optimization Algorithm for Electric Vehicles Charge Scheduling

Electric Vehicle (EV) technology and migration are hindered by battery sizing, short driving ranges, and optimal operations. This article focuses on developing a strategy for scheduling EV charging in a specific region, addressing waiting time, charging time, and uneven scheduling due to unevenly distributed charging stations (CS). The proposed approach optimizes CS using separate queues for different levels, reducing waiting time and costs during peak hours. Which considers trade-offs between time-aware fairness and overall waiting time, and factors like reachability, battery state of charge, depth of discharge limits, and charging rate constraints. A bi-objective formulation and online scheduling algorithm based on dynamic schedulable time, energy demand fluctuation and user’s prioritization are proposed. The aim is to allocate a charging station to each EV by considering travel needs and battery specifics, with the objective of minimizing travel time, queue time, recharging time, and energy costs. To achieve this, the scheduling system utilizes the Chaotic Harris Hawks Optimization (CHHO), an enhanced iteration of the previously discussed metaheuristic, the Harris Hawk Optimization. Validation of the system is conducted through Vehicular Ad-hoc Network (VANET) simulation and comparison with alternative algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer and Random allocation. The outcomes demonstrate noteworthy decreases in travel time, queue time, recharging time, and energy costs, all while adhering to set constraints.

Higher-order KKT optimality conditions through contingent derivatives for constrained nonsmooth vector equilibrium problems

In this paper, we deal with some higher-order optimality conditions for local strict efficient solutions to a nonsmooth vector equilibrium problem with set, cone and equality constraints. For this aim, the concept of m−stable and m−steady functions (m≥2 and integer) for single-valued functions and some constraint qualifications of higher order in terms of contingent derivatives are proposed accordingly. We analyze the sum calculus rule of mth-order adjacent set, mth-order interior set, asymptotic mth-order tangent cone and asymptotic mth-order adjacent cone. Subsequently, we employ the obtained calculus rules to treat KKT necessary and sufficient optimality conditions of higher order in terms of contingent derivatives for the mth-order (local) strict efficient solutions to such problem. Simultaneously, we employ these rules to study KKT higher-order optimality conditions for such efficient solutions for a nonsmooth vector optimization problem with constraints. Some illustrative examples are provided to demonstrate the main results of the literature.

Distributed Discrete-Time Convex Optimization With Closed Convex Set Constraints: Linearly Convergent Algorithm Design.

The convergence rate and applicability to directed graphs with interaction topologies are two important features for practical applications of distributed optimization algorithms. In this article, a new kind of fast distributed discrete-time algorithms is developed for solving convex optimization problems with closed convex set constraints over directed interaction networks. Under the gradient tracking framework, two distributed algorithms are, respectively, designed over balanced and unbalanced graphs, where momentum terms and two time-scales are involved. Furthermore, it is demonstrated that the designed distributed algorithms attain linear speedup convergence rates provided that the momentum coefficients and the step size are appropriately selected. Finally, numerical simulations verify the effectiveness and the global accelerated effect of the designed algorithms.

Identification of Real and Complex Solution Varieties and Their Singularities Defined by Loop Constraints of Linkages Using the Kinematic Tangent Cone

Abstract The configuration space (c-space) of a mechanism is the real-solution variety of a set of loop closure constraints, which is therefore the chief object in kinematic analysis. Singularities of this variety (referred to as c-space singularities) are singular configurations of the mechanism. In addition, a mechanism may exhibit other kinematic singularities that are not visible from the differential geometry of the c-space (referred to as hidden singularities). Such situations were analyzed by investigating the local geometry of the c-space and its corank stratification. It has been shown recently that hidden singularities and shakiness can be attributed to the fact that complex solution branches intersect with the c-space, i.e., with real-solution branches. This paper employs the kinematic tangent cone to identify local solution branches. While the kinematic tangent cone is an established generally applicable concept, which gives rise to a computational (numeric and symbolic) algorithm, it has yet only been applied to analyzing the real-solution set. Application of the method is shown for several examples. Further, the algebraic aspects are briefly elaborated.

A model-based approach for self-adaptive security in CPS: Application to smart grids

Security risk assessment is an important challenge in the design of Cyber Physical Systems (CPS). Even more importantly, the intrinsically dynamical nature of these systems, due to changes in their environment, as well as evolutions in their infrastructures, makes them self-adaptive systems, where security aspects have to be considered in terms of management of detections and reactions for self-protection. In this work, we propose an approach to autonomously mitigate the threats in each reconfiguration at application or infrastructure levels of CPS. We propose and implement a framework for self-adaptive security: software architecture, design method, and integration with model-based decision. We use Attack-Defense Trees for modeling threats, and our approach involves security risk assessment, taking into account its balancing and coordination with quality-of-service aspects. We formulate and formalize the on-line decision problem to be solved at each cycle of the self-adaptation control loop in terms of Constraint Programming (CP) modeling and resolution. The CP model implements a set of constraints that allow to specify secure configurations, evaluated regarding their impact on system performance to pinpoint the most relevant one portraying a good balance between the security and quality of service. We perform validation of our approach with its application to Smart Grids, more particularly to an industrial case study from RTE (the French Energy Transmission company).

Predictive fault-tolerant control for nonlinear batch processes with small time delays via iterative learning control: A Lyapunov–Razumikhin approach

For batch processes with small time delays and actuator partial fault, the existing methods based on iterative learning control still have some limitations, including the conservative and computationally burdensome of stability conditions and the limited fault-tolerant control capabilities. For this background, an iterative learning robust predictive fault-tolerant control method is developed, which integrates the Lyapunov–Razumikhin function method and derives stability conditions based on robust positive definite invariant set and terminal constraint set. With small time delays, the stability conditions of the system deduced using the Lyapunov–Razumikhin function are solved at a lower computational cost, which is due to the fact that the dimensionality of the stabilization condition is directly related to the size of the time delay, and thus the small time delay implies a lower dimensionality. Especially, the computational effort for solving the stability conditions online is reduced, allowing real-time control law gains to be obtained and combined with historical batches of control inputs, reducing the learning cycles of the system, and realizing stable tracking of the setpoints within shorter operating batches. Moreover, the robust positive invariant set and the set of terminal constraints are able to constrain the state of the system within a safe range for all possible uncertainties, bounded disturbances, and faults. This makes the proposed methods based on them more robust and fault tolerant. Finally, a nonlinear batch reactor is used as an example to demonstrate the effectiveness and feasibility of the developed method.

Optimal Consolidation of Polling Locations

Problem definition: Many logistical and financial challenges of facilitating an election lead election officials to consolidate polling locations. However, determining when it is appropriate to consolidate polling locations and how to consolidate polling locations, if necessary, is a difficult and high-stakes decision that influences voter participation. Methodology/results: We formalize the set of constraints and criteria that election officials should follow as the polling location consolidation problem (PLCP), which is formulated as an integer programming model. The PLCP simultaneously selects which polling locations will be used in the upcoming election, reassigns voter precincts to polling locations, and allocates critical resources to the selected polling locations. The PLCP minimizes the increased distance that voters must travel to their updated polling location. Because empirical research also demonstrates the importance of reducing the voters’ wait times, we require that most voters do not wait longer than a prespecified limit, such as 30 minutes, using a chance constraint. We prove that identifying a feasible solution to PLCP is NP-complete, which demonstrates the difficulty in making consolidation decisions, as well as the importance of optimization for this problem. Managerial implications: This paper introduces a structured and transparent approach to support election officials in making informed, data-driven decisions regarding how and when to consolidate polling locations that minimally impact voters and encourage voter participation. The approach could be used to develop preapproved contingency plans that could be employed if there are major disruptions to an election. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0497 .

Co-optimization of power and water distribution systems for simultaneous hydropower generation and water pressure regulation

Water Distribution Systems (WDSs) are gravitational systems supplying water, mainly. Little research has been conducted on harnessing the great amount of energy that is currently dissipated during water pressure regulation, usually performed with “passive” devices like Pressure Reduction Valves (PRVs). This paper develops a co-optimization framework between Water and Power Distribution Systems (PDS) and introduces the concept of Integrated Power-Water Distribution System (IPWDS). Under this framework, PRVs are replaced by hydro-turbines, which form the interconnecting links between the two systems. They regulate pressure at the WDS while injecting the power resulting from their action to PDS. The complete WDS and PDS mathematical models are adopted and the detailed turbine design and performance model that accounts for turbines feasible operational range is considered. The suggested method optimizes the power generation of the hydro-turbines subject to the hydraulic restrictions and power flow constraints imposed by each network. Optimal Power Flow (OPF) has been extended in order to accommodate turbine’s model and constraints imposed by the varying daily flows of the WDS and PDS. Results taken under the suggested framework from a typical WDS, demonstrated that more than 393 MWh (approximately $40 K) of clean power could be produced per year. However, the violation of the unified set of constraints from the IPWDS may reduce the amount of renewable power generation by a significant 7%. This paper focuses mainly on the technical aspect of such an approach. Future work will focus on the economic assessment and viability of such joint projects, resulting from co-optimization and on social-technical aspects that aim on maximizing the penetration of turbines within existing PDS.

Dual‐mode resilient model predictive control for cyber‐physical systems against DoS attacks

AbstractIn this article, a resilient model predictive control algorithm incorporating a dual‐mode control strategy is proposed for a class of cyber‐physical systems subject to state and input constraints, additive disturbances and denial‐of‐service (DoS) attacks. An adversary aims to disrupt the controller‐to‐actuator channel by deploying adversarial jamming signals. By exploring the prediction uncertainty between a nominal model and an actual model and utilizing a constraint tightening technique, a novel set of constraints is developed, which guarantees robust constraint satisfaction in open‐loop mode caused by DoS attacks. A robust positive invariant set is designed, which allows the control mode to switch from rolling optimization to a state‐feedback control law when the state enters its interior, thus saving computational resources. We explicitly determine a critical duration for DoS attacks and, through theoretical analysis, establish that when the duration of DoS attacks falls below this critical value, the proposed algorithm ensures guaranteed recursive feasibility and robust asymptotic stability. Finally, some comparisons are provided to illustrate the effectiveness of the proposed approach.

Review on structural optimization techniques for additively manufactured implantable medical devices

Recent developments in additive manufacturing (AM) have led to significant opportunities in the design and fabrication of implantable medical devices due to the advantages that AM offers compared to conventional manufacturing, such as high customizability, the ability to fabricate highly complex shapes, good dimensional accuracy, a clean build environment, and reduced material usage. The study of structural design optimization (SDO) involves techniques such as Topology Optimization (TO), Shape Optimization (SHO), and Size Optimization (SO) that determine specific parameters to achieve the best measurable performance in a defined design space under a given set of loads and constraints. Integration of SDO techniques with AM leads to utmost benefits in designing and fabricating optimized implantable medical devices with enhanced functional performance. Research and development of various lattice structures represents a powerful method for unleashing the full potential of additive manufacturing (AM) technologies in creating medical implants with improved surface roughness, biocompatibility, and mechanical properties. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) in structural optimization has expanded opportunities to improve device performance, adaptability, and durability. The review is meticulously divided into two main sections, reflecting the predictability of the implant’s internal structure: (a) unpredictable interior topology, which explores topology-based optimization techniques, and (b) predictable inner topology, concentrating on lattice structures. The analysis of the reviewed literature highlights a common focus on addressing issues such as stress shielding, osseointegration enhancement, customization to individual needs, programmable functionalities, and weight reduction in implant designs. It emphasizes significant advances in reducing stress shielding effects, promoting osseointegration, and facilitating personalized implant creation. The review provides a detailed classification of optimization methods, with each approach scrutinized for its unique contribution to overcoming specific challenges in medical implant design, thus leading to more advanced, effective, and patient-oriented implantable devices.

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

Integrated location and inventory planning in service parts logistics with last-mile delivery outsourcing

Outsourcing last-mile delivery to express companies provides an innovative strategy for capital goods manufacturers to improve service parts logistics. Given delivery dilemma due to geographically dispersed customers and low-frequency uncertain demands, we develop an integrated location and inventory problem with last-mile delivery outsourcing in a two-echelon service parts logistics. The proposed non-linear mixed integer programming model captures important decisions involving (1) location of local warehouses, (2) customer allocation to opened local warehouses, (3) inventory levels at these local warehouses, (4) outsourcing or self-operating last-mile delivery. We then replace service level constraints that cause nonlinearity with two new sets of constraints, and construct a linear mixed integer formulation that could be solved by GUROBI solver for some small-scale problems. It is still a challenging problem for large scale case, for which we propose an adaptive large neighbourhood search (ALNS) algorithm. Our extensive computational experiments verify the effectiveness of our model and algorithm. Computational experiments’ results indicate that outsourcing last-mile delivery amounting to around 12 % total cost savings on average, compared to self-operating in a real-world large instance from our research partner. Furthermore, we test some key parameters with computational experiments, and provide some managerial implications.

Tracking control for nonlinear high-order fully actuated system with state constraints: an explicit reference governor approach

This paper studies the tracking control problem of nonlinear high-order fully actuated system subject to state constraints. In order to guarantee the stability of the closed-loop system, a generalised nonlinear proportional differential controller is designed to configure the system into a desired linear constant system. Then, an explicit reference governor for high-order system is introduced to modify the reference signal such that the system state and the state derivatives of certain orders always remain within a prescribed constraint set. Furthermore, we prove that the modified reference will converge to the original reference as much as possible. Therefore, the system state can finally track to the original reference by tracking the modified reference. Two numerical examples demonstrate the validity of the proposed method in this paper.

Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants

The paper presents a technique of motion planning for autonomous vehicles (AV) based on simultaneous trajectory and speed optimization. The method includes representing the trajectory by a finite element (FE), determining trajectory parameters in Frenet coordinates, composing a model of vehicle kinematics, defining optimization criteria and a cost function, forming a set of constraints, and adapting the Gaussian N-point scheme for quadrature numerical integration. The study also defines a set of minimum optimization parameters sufficient for making motion predictions with smooth functions of the trajectory and speed. For this, piecewise functions with three degrees of freedom (DOF) in FE’s nodes are implemented. Therefore, the high differentiability of the trajectory and speed functions is ensured to obtain motion criteria such as linear and angular speeds, acceleration, and jerks used in the cost function and constraints. To form the AV roadway position, the Frenet coordinate system and two variable parameters are used: the reference path length and the lateral displacement perpendicular to reference line’s tangent. The trajectory shape, then, depends only on the final position of the AV’s mass center and the final reference’s curvature. The method uses geometric, kinematic, dynamic, and physical constraints, some of which are related to hard restrictions and some to soft restrictions. The planning technique involves parallel forecasting for several variants of the AV maneuver followed by selecting the one corresponding to a specified criterion. The sequential quadratic programming (SQP) technique is used to find the optimal solution. Graphs of trajectories, speeds, accelerations, jerks, and other parameters are presented based on the simulation results. Finally, the efficiency, rapidity, and prognosis quality are evaluated.

Shaping the laser control landscape of a hydrogen transfer reaction by vibrational strong coupling. A direct optimal control approach.

Controlling molecular reactivity by shaped laser pulses is a long-standing goal in chemistry. Here, we suggest a direct optimal control approach that combines external pulse optimization with other control parameters arising in the upcoming field of vibro-polaritonic chemistry for enhanced controllability. The direct optimal control approach is characterized by a simultaneous simulation and optimization paradigm, meaning that the equations of motion are discretized and converted into a set of holonomic constraints for a nonlinear optimization problem given by the control functional. Compared with indirect optimal control, this procedure offers great flexibility, such as final time or Hamiltonian parameter optimization. A simultaneous direct optimal control theory will be applied to a model system describing H-atom transfer in a lossy Fabry-Pérot cavity under vibrational strong coupling conditions. Specifically, optimization of the cavity coupling strength and, thus, of the control landscape will be demonstrated.

Formal analytics for stealthy attacks against Contingency Analysis in power grids

Contingency Analysis (CA) is a critical part of the Energy Management System of a power grid, ensuring the optimal operating set-points without violating constraints, even in the face of contingency events. CA relies on State Estimation (SE) to perform its analysis, paving the way for Security Constrained Optimal Power Flow (SCOPF) that ensures optimal economic generator dispatches, even in scenarios like n−1 contingencies, such as a single line failure. This integrated approach enhances the grid’s resilience and efficiency under diverse conditions. However, it is shown that an adversary can alter specific power measurements to corrupt the SE without being detected. Such a corrupted estimation will provide CA with a fake model to deal with, which can further lead the grid to an even more vulnerable state. This research formally models and analyzes these attacks on CA, particularly on SCOPF, which are routed from SE. We formally model these complex attacks as a set of constraints and solve them using Satisfiability Modulo Theories (SMT) to synthesize potential attack vectors. Each of these vectors specifies a set of measurements that an adversary can alter to cause one or more transmission lines to be overloaded when some specific contingencies happen. We apply various sensitivity factors to make the solution to this complex synthesis problem tractable. We demonstrate our formal analytics on IEEE bus systems and verify the results using OPAL-RT, a standard virtual power grid testbed. We finally evaluate the tool with respect to various attack and grid characteristics.

Promoting Exploration During Learning: Effect of Imposed and Self-Controlled Practice Schedules on Learners’ Behavioral Flexibility

ABSTRACT Purpose: Enriching learners’ motor repertoires in a complex pluri-articular task, such as climbing, could help learners’ adaptation to various sets of task constraints. Promoting exploration with variable practice conditions is one solution recurrently proposed. However, recent studies have shown that a too elevated exploration-exploitation ratio during practice could impair learning. A proposed solution is to give learners some control over their practice schedule, which appeared to better respect the individual learning dynamic in comparison to the usual experimenter-imposed practice schedule. Thus, the aim of this study is to investigate whether giving learners the possibility of controlling when to confront to new climbing routes would result in greater flexibility in their motor repertoire compared to giving them an imposed schedule of climbing routes or a constant practice condition. Method: Participants were assigned to either a constant practice group (CPG), an imposed-variability group (IVG) or a self-controlled variability group (SVG) to carry out a climbing task. To assess participants’ behavioral flexibility, a scanning procedure was conceived by manipulating the route design and the instructions. Results: Participants showed an initial lack of flexibility as they strongly relied on a single coordination pattern. At posttest and retention, the three groups more frequently used a new hand coordination pattern and more often showed coordination patterns associated with high climbing fluency. Results suggest that the individualized rate of exploration in the self-controlled practice condition may have helped the learners improve their flexibility, whereas forcing exploration did not seem more beneficial than constant practice in this complex pluri-articular task.

Risk management in software development in the field of information protection

In the context of increasing digitalization of sea and river transport, the immersion of the entire transport infrastructure in the digital environment, the volume of information flows serving the transport process is increasing, and the requirements for information protection are increasing in conditions of an aggressive external information environment. In the digital environment, a software product must not only serve the document flow of transport chains, but also ensure its confidentiality. Automation of transport facilities (ships, platforms, berths, warehouses) management increases their potential vulnerability from unauthorized access to control systems; the latter must be taken into account in servicing programs, increasing their size (for example, there is a need to fragment coherent information blocks and their alternative routing). This, in turn, increases the risk of errors in the software products themselves and significantly complicates their structure. Risks of failures (including confidentiality violations) during the software operation that implements information exchange can entail significant material and reputational losses for the developer. If the developer is legally and functionally involved in the industry, such losses can and should be considered among the general range of risks in water transport. An important aspect of risk management, which until recently was practically not considered in a quantitative aspect within the framework of mathematical models, is the joint consideration of losses when a risk situation occurs and the developer’s costs associated with reducing the likelihood of such occurrences; the latter may entail a reduction in the total expected losses, formalized as the corresponding mathematical expectation. As a result, risk management can be formulated in terms of mathematical programming problems with different (discrete or continuous) sets of constraints and with different properties of objective functions.