Original Constraints Research Articles

Coupling the ILS optimisation algorithm and a simulation process to solve the travelling quay-crane worker assignment and balancing problem

In container terminals (CTs), the performance of quay cranes (QCs) is extremely impacted by their operators’ productivity, which makes the QC worker assignment a relevant element in the QC scheduling. Nevertheless, in the literature, this problem was tackled without considering the objectives of balancing operators’ workload and minimising the distance required to move between QCs, even though practitioners underline their importance in maintaining an efficient working environment. Accordingly, and based on a real-case study raised by a CT partner, this paper addresses a novel problem referred to as the travelling QC worker assignment and balancing problem. First, we propose a novel mathematical model with original constraints faced by the company to assign operators to QCs on a daily basis. Second, to find approximate solutions in a reasonable time, a constructive heuristic and an iterative local search (ILS) are proposed and tested on real datasets. We also propose a simulation-optimisation mechanism to evaluate the robustness of the solutions under real-time probabilistic perturbations. The proposed algorithms are integrated into a decision support tool, a network-linked application allowing users in a real CT to generate automatic assignments. The proposed simulation-optimisation procedure helps obtain balanced planning, avoid workload conflicts, and increase productivity.

Multidisciplinary design for structural integrity using a collaborative optimization method based on adaptive surrogate modelling

As one of the multidisciplinary design optimization methods, the collaborative optimization (CO) can enjoy a high degree of discipline autonomy. It has received extensive attention and been used in the modern engineering design process. However, the original CO is low optimization accuracy and efficiency for the design of high-dimensional nonlinearity systems because of the involved compatibility constraints. To solve this problem, an enhanced CO method based on the adaptive surrogate model is proposed in this study. The strategy of the proposed method includes the following steps. Firstly, the traditional surrogate models of system objective and performance function are constructed by the Latin hypercube sampling and the Kriging method. Then, the expected improvement function and the self-cycling optimization strategy are utilized to modify the traditional surrogate models into the corresponding adaptive surrogate models. Finally, the original objective and constraints at the system level and disciplinary level are replaced by their adaptive surrogate models, respectively. Consequently, the formulation of CO based on the adaptive surrogate model can be obtained. In the proposed method, the accuracy of the non-linear region of the response surface can be enhanced efficiently. An engineering structure design problem is introduced to show the effectiveness of the proposed method.

Problems of Ideological and Political Education of College Students in the Era of Big Data

The ever-changing rules and regulations, teaching systems and teaching methods of world education ensure great changes in the education system.In view of the practical problems existing in the construction of the rule of law culture in colleges and universities, it is necessary to realize the mufti-disciplinary, dimensional and multi-word collaborative innovation of integrating the rule of law culture into the ideological and political education of college students, break the original boundaries and constraints, truly achieve coordinated operation of departmental functions, integrated development of mufti-disciplines and effective participation of social resources, and comprehensively promote the new development of ideological and political education of college students. Under the guidance of collaborative innovation, we should grasp the concrete working logic in the practical construction, and make it clear that the construction of legal culture in colleges and universities is to adhere to the fundamental educational idea of “cultivating people by virtue”, integrate the concept of legal culture into ideological and political education, pursue the value orientation of legal culture, take socialist core values as the logical starting point, realize the main position of education from theoretical level to practical level, and make every effort to build a new pattern of ideological and political education for college students.The results show that: the number of students returning to the dormitory is the highest when the entrance guard is 20:50-21:10, and the lowest when the entrance guard is 17:50-18:20. The percentage of the students returning to the dormitory during the period of 13:40-13:55 is higher than that of the students in the period of 11:50-12:20.

Adopting a Conversion Design Approach to Maximize the Energy Density of Battery Packs in Electric Vehicles

Innovative vehicle concepts have been developed in the past years in the automotive sector, including alternative drive systems such as hybrid and battery electric vehicles, so as to meet the environmental targets and cope with the increasingly stringent emissions regulations. The preferred hybridizing technology is lithium-ion battery, thanks to its high energy density. The optimal integration of battery packs in the vehicle is a challenging task when designing e-mobility concepts. Therefore, this work proposes a conceptual design procedure aimed at optimizing the sizing of hybrid and battery electric vehicles. Particularly, the influence of the cell type, physical disposition and arrangement of the electrical devices is accounted for within a conversion design framework. The optimization is focused on the trade-off between the battery pack capacity and weight. After introducing the main features of electric traction systems and their challenges compared to conventional ones, the relevant design properties of electric vehicles are analyzed. A detailed strategy, encompassing the selection of battery format and technology, battery pack design and final assessment of the proposed set-up, is presented and implemented in an exemplary application, assuming an existing commercial vehicle as the reference starting layout. Prismatic, cylindrical and pouch cells are configured aiming at achieving installed battery energy as close as possible to the reference one, while meeting the original installation space constraint. The best resulting configuration, which also guarantees similar peak power performance of the reference battery-pack, allows reducing the mass of the storage system down to 70% of its starting value.

Robust Optimization for Models with Uncertain Second-Order Cone and Semidefinite Programming Constraints

In this paper, we consider uncertain second-order cone (SOC) and semidefinite programming (SDP) constraints with polyhedral uncertainty, which are in general computationally intractable. We propose to reformulate an uncertain SOC or SDP constraint as a set of adjustable robust linear optimization constraints with an ellipsoidal or semidefinite representable uncertainty set, respectively. The resulting adjustable problem can then (approximately) be solved by using adjustable robust linear optimization techniques. For example, we show that if linear decision rules are used, then the final robust counterpart consists of SOC or SDP constraints, respectively, which have the same computational complexity as the nominal version of the original constraints. We propose an efficient method to obtain good lower bounds. Moreover, we extend our approach to other classes of robust optimization problems, such as nonlinear problems that contain wait-and-see variables, linear problems that contain bilinear uncertainty, and general conic constraints. Numerically, we apply our approach to reformulate the problem on finding the minimum volume circumscribing ellipsoid of a polytope and solve the resulting reformulation with linear and quadratic decision rules as well as Fourier-Motzkin elimination. We demonstrate the effectiveness and efficiency of the proposed approach by comparing it with the state-of-the-art copositive approach. Moreover, we apply the proposed approach to a robust regression problem and a robust sensor network problem and use linear decision rules to solve the resulting adjustable robust linear optimization problems, which solve the problem to (near) optimality. Summary of Contribution: Computing robust solutions for nonlinear optimization problems with uncertain second-order cone and semidefinite programming constraints are of much interest in real-life applications, yet they are in general computationally intractable. This paper proposes a computationally tractable approximation for such problems. Extensive computational experiments on (i) computing the minimum volume circumscribing ellipsoid of a polytope, (ii) robust regressions, and (iii) robust sensor networks are conducted to demonstrate the effectiveness and efficiency of the proposed approach.

Loosening Neuro-Optic Structures Dosimetric Constraints Provides High 5-Year LocalRecurrence-FreeSurvival With Acceptable Toxicity in T4 Nasopharyngeal Carcinoma Patients Treated With Intensity-Modulated Radiotherapy.

ObjectiveWhether the original dosimetric constraints of neuro-optic structures (NOS) are appropriate for patients with nasopharyngeal carcinoma (NPC) undergoing intensity-modulated radiotherapy (IMRT) remains controversial. The present study compared the survival rates and radiation-induced optic neuropathy (RION) occurrence between T4 NPC patients whose NOS were irradiated with a near maximum dose received by 2% of the volume (D2%) >55 Gy and ≤55 Gy. Moreover, the NOS dosimetric parameters and their correlation with RION occurrence were also evaluated.MethodsIn this retrospective study, 256 T4 NPC patients treated with IMRT between May 2009 and December 2013 were included. Patient characteristics, survival rates, dosimetric parameters, and RION incidence were compared between the D2% ≤55 Gy and D2% >55 Gy groups.ResultsThe median follow-up durations were 87 and 83 months for patients in the D2% >55 Gy and D2% ≤55 Gy groups, respectively. The 5-year local recurrence-free survival rates were 92.0 and 84.0% in the D2% >55 Gy and D2% ≤55 Gy groups (P = 0.043), respectively. There was no significant difference in the 5-year overall survival (OS) between both groups (D2% >55 Gy, 81.6%; D2% ≤55 Gy, 79.4%; P = 0.586). No patients developed severe RION (Grades 3–5), and there was no significant difference (P = 0.958) in the incidence of RION between the two groups. The maximum dose of NOS significantly affected the RION incidence, with a cutoff point of 70.77 Gy.ConclusionAppropriately loosening NOS dosimetric constraints in order to ensure a more sufficient dose to the target volume can provide a better 5-year local recurrence-free survival and acceptable neuro-optic toxicity in T4 NPC patients undergoing IMRT.

Control systems described by a class of fractional semilinear evolution hemivariational inequalities and their relaxation property

ABSTRACT We are concerned with control systems described by a class of semilinear fractional evolution hemivariational inequalities in separable reflexive Banach spaces. The constraint on the control is a nonconvex valued multivalued function which is lower semicontinuous with respect to the variable states. Along with the original system we also consider the system in which the constraint on the control is the upper semicontinuous convex valued regularization of the original constraint. We obtain the existence results of the control systems and the relaxation property among the solution sets of these systems. In the end, an example is provided to illustrate the application of the obtained theory.

The safety region-based model predictive control for discrete-time systems under deception attacks

In this paper, a resilient control problem is investigated for the discrete-time linear system subjected to both input constraint and additive disturbances. In addition, deception attacks are also taken into consideration which could compromise the communication channels. To deal with the mentioned complexities, the so-called safety region-based defense strategy (SRDS) is designed in the context of the robust model predictive control (RMPC) method. The original constraints of the system are used to establish the safety region. Sufficient conditions are established for the proposed SRDS such that the recursive feasibility and stability of the overall closed-system are guaranteed. Finally, two numerical examples are conducted to verify the effectiveness of the proposed methodology.

Design of A Parallel Decoding Method for LDPC Code Generated via Primitive Polynomial

An effective way of improving decoding performance of an LDPC code is to extend the single-decoder decoding method to a parallel decoding method with multiple sub-decoders. To this end, this paper proposes a parallel decoding method for the LDPC codes constructed by m-sequence. In this method, the sub-decoders have two types. The first one contains only one decoding module using the original parity-check constraints to implement a belief propagation (BP) algorithm. The second one consists of a pre-decode module and a decoding module. The parity-check matrices for pre-decode modules are generated by the parity-check constraints of the sub-sequences sampled from an m-sequence. Then, the number of iterations of the BP process in each pre-decode module is set as half of the girth of the parity-check matrix, resulting in the elimination of the impact of short cycles. Using maximum a posterior (MAP), the least metric selector (LMS) finally picks out a codeword from the outputs of sub-decoders. Our simulation results show that the performance gain of the proposed parallel decoding method with five sub-decoders is about 0.4 dB, compared to the single-decoder decoding method at the bit error rate (BER) of 10−5.

Excitation Retrieval for Phased Arrays With Magnitude-Only Fields Measured at a Fixed Location

An excitation retrieval method designed for phased arrays using magnitude-only field data is proposed. The reconstruction of complex excitations is formulated as a phase retrieval problem. After converting the original quadratic constraint to a linear one, a semidefinite convex optimization method is applied to calculate the solution. By controlling the excitation phases of array elements via phase shifters, independent and phaseless field data can be provided. Required conditions of the optimization method are satisfied in a proper way at the same time. Owing to this control strategy, it requires the position-fixed receiving probe only. The measuring process can be further simplified and probe shifting errors can be avoided.

Time-Varying Tube-Based Output Feedback MPC for Constrained Linear Systems with Intermittently Delayed Data

Abstract This paper proposes a time-varying tube-based output feedback model predictive control (MPC) design for constrained linear systems in the presence of intermittently delayed observations, where the delayed/missing data patterns for each period satisfy a finite-length language. The design consists of a dynamic state estimator whose estimation errors satisfy equalized recovery (a weaker form of invariance with time-varying finite bounds), as well as an output feedback control law that extends existing tube-based output feedback MPC approaches to allow time-varying tubes for tightening the original state and input constraints. The resulting time-varying tube-based output feedback MPC design is robust to time-varying disturbances and errors, including when the observations are intermittently delayed. Further, we provide sufficient conditions for recursive feasibility and robust exponential stability of the proposed design. Simulation results demonstrate that the proposed approach is able to robustly stabilize and control a constrained linear system despite disturbances, noise and missing/delayed data.

Tissue-specific deformable image registration using a spatial-contextual filter.

Intensity-based deformable registration with spatial-invariant regularization generally fails when distinct motion exists across different types of tissues. The purpose of this work was to develop and validate a new regularization approach for deformable image registration that is tissue-specific and able to handle motion discontinuities. Our approach was built upon a Demons registration framework, and used the image context supplementing the original spatial constraint to regularize displacement vector fields in iterative image registration process. The new regularization was implemented as a spatial-contextual filter, which favors the motion vectors within the same tissue type but penalizes the motion vectors from different tissues. This approach was validated using five public lung cancer patients, each with 300 landmark pairs identified by a thoracic radiation oncologist. The mean and standard deviation of the landmark registration errors were 1.3 ± 0.8 mm, compared with those of 2.3 ± 2.9 mm using the original Demons algorithm. Particularly, for the case with the largest initial landmark displacement of 15 ± 9 mm, the modified Demons algorithm had a registration error of 1.3 ± 1.1 mm, while the original Demons algorithm had a registration error of 3.6 ± 5.9 mm. We also qualitatively evaluated the modified Demons algorithm using two difficult cases in our routine clinic: one lung case with large sliding motion and one head and neck case with large anatomical changes in air cavity. Visual evaluation on the deformed image created by the deformable image registration showed that the modified Demons algorithm achieved reasonable registration accuracy, but the original Demons algorithm produced distinct registration errors.

GW190814: Impact of a 2.6 solar mass neutron star on the nucleonic equations of state

Is the secondary component of GW190814 the lightest black hole or the heaviest neutron star ever discovered in a double compact-object system [R. Abbott et al., ApJ Lett., 896, L44 (2020)]? This is the central question animating this letter. Covariant density functional theory provides a unique framework to investigate both the properties of finite nuclei and neutron stars, while enforcing causality at all densities. By tuning existing energy density functionals we were able to: (a) account for a 2.6 Msun neutron star, (b) satisfy the original constraint on the tidal deformability of a 1.4 Msun neutron star, and (c) reproduce ground-state properties of finite nuclei. Yet, for the class of models explored in this work, we find that the stiffening of the equation of state required to support super-massive neutron stars is inconsistent with either constraints obtained from energetic heavy-ion collisions or from the low deformability of medium-mass stars. Thus, we speculate that the maximum neutron star mass can not be significantly higher than the existing observational limit and that the 2.6 Msun compact object is likely to be the lightest black hole ever discovered.

A new proof of the dual optimization problem and its application to the optimal material distribution of SiC/graphene composite

This paper presents a simple and direct proof of the dual optimization problem. The stationary conditions of the original and the dual problems are exactly equivalent, and the duality gap can be completely eliminated in the dual problem, where the maximal or minimal value is solved together with the stationary conditions of the dual problem and the original constraints. As an illustration, optimization of SiC/graphene composite is addressed with an objective of maximizing certain material properties under the constraint of a given strength.

Analytic Deep Learning-Based Surrogate Model for Operational Planning With Dynamic TTC Constraints

The increased penetration of wind power introduces more operational changes of critical corridors and the traditional time-consuming transient stability constrained total transfer capability (TTC) operational planning is unable to meet the real-time monitoring need. This paper develops a more computationally efficient approach to address that challenge via the analytical deep learning-based surrogate model. The key idea is to resort to deep learning for developing a computationally cheap surrogate model to replace the original time-consuming differential-algebraic constraints related to TTC. However, the deep learning-based surrogate model introduces implicit rules that are difficult to handle in the optimization process. To this end, we derive the Jacobian and Hessian matrices of the implicit surrogate models and finally transfer them into an analytical formulation that can be easily solved by the interior point method. Surrogate modeling and problem reformulation allow us to achieve significantly improved computational efficiency and the yielded solutions can be used for operational planning. Numerical results carried out on the modified IEEE 39-bus and 68-bus systems demonstrate the effectiveness of the proposed method in dealing with complicated TTC constraints while balancing the computational efficiency and accuracy.

SINR-Outage Minimization of Robust Beamforming for the Non-Orthogonal Wireless Downlink

A probabilistically robust transmit beamforming problem is referred, when the wireless downlink (DL) communication is supported by a robust non-orthogonal transmission (NOT)-aided design. Realistic imperfect channel state information (CSI) is considered in the face of rapidly fluctuating vehicular wireless channels, when the road side unit (RSU) communicates with multiple vehicles. Our design objective is to keep the probability of each vehicle's signal-to-interference-plus-noise ratio (SINR) outage below a given threshold. Minimizing the outage probability presents a significant analytical and computational challenge, since it does not lend itself to tractable closed-form expressions. Assuming a Gaussian CSI uncertainty distribution, we provide an approximation method by resorting to the semidefinite relaxation (SDR) and then apply a convex restriction to the original SINR outage constraints. Furthermore, the infinite constraints are reformulated into linear matrix inequalities (LMIs) by exploiting the popular S-procedure. As a benefit, the reformulated program can be solved efficiently using off-the-shelf solvers. Computer simulations are performed for benchmarking our convex method both against the non-robust non-orthogonal as well as the classical orthogonal designs. The results show that our robust beamforming design offers excellent high-mobility performance.

Method for solving chance constrained optimal control problems using biased kernel density estimators

SummaryA method is developed to numerically solve chance constrained optimal control problems. The chance constraints are reformulated as nonlinear constraints that retain the probability properties of the original constraint. The reformulation transforms the chance constrained optimal control problem into a deterministic optimal control problem that can be solved numerically. The new method developed in this paper approximates the chance constraints using Markov Chain Monte Carlo sampling and kernel density estimators whose kernels have integral functions that bound the indicator function. The nonlinear constraints resulting from the application of kernel density estimators are designed with bounds that do not violate the bounds of the original chance constraint. The method is tested on a nontrivial chance constrained modification of a soft lunar landing optimal control problem and the results are compared with results obtained using a conservative deterministic formulation of the optimal control problem. Additionally, the method is tested on a complex chance constrained unmanned aerial vehicle problem. The results show that this new method can be used to reliably solve chance constrained optimal control problems.

Research on anchor chain visualization for a ship anchoring simulation training system.

In the development of ship anchorage training systems, the problems of low efficiency and poor fidelity exist in the simulation of flexible anchor chains, and a position-based dynamics (PBD) method is proposed to express the chain movement. To satisfy the requirements of simulating anchoring manipulation, the PBD method modifies the position of anchor chain particles by controlling constraints. Using the original distance constraint and bending constraint of the PBD approach, two novel constraints, namely, the long-range attachment (LRA) constraint and pin constraint, are developed to simulate the bending and stretching of the anchor chain. Simulation of ordinary ropes can be achieved using distance and bending constraints. The developed LRA constraint is capable of preventing anchor chain particles from being overstretched. Adoption of the pin constraint is proposed to integrate two particles into one to be calculated as an attempt to simulate the connection between the chain and the anchor. The continuous collision detection (CCD) constraint method considering friction and viscosity is used to detect collisions in the ship anchoring training system. Collision detection covers chain collisions with other objects and chains. Finally, the PBD method is more efficient and robust than the Newton method. Since it has sufficient visual plausibility and can realize real-time visualization, the simulation system developed by the PBD method effective for training crew members.

Integrated data‐driven framework for fast SCUC calculation

Security-constrained unit commitment (SCUC) is one of the most important optimisation problems for operation planning of power systems. Indeed, with the fast expansion of power grids and the increasing growth of heterogeneous market participants in electricity markets, the day-ahead SCUC continuously encounters significant performance challenges. To improve the computational performance of SCUC and achieve solutions of good quality, an integrated framework which combines a data-driven approach and a variable-aggregation method is presented in this study. The variable-aggregation method effectively approximates the original network security constraints with a reduced number of variables. Moreover, as aggregated constraints could reduce the feasible region of the original SCUC and potentially degrade solution quality for certain SCUC instances, it is preferable to only apply such an aggregation approach towards difficult SCUC instances. Therefore, a data-driven classification method, by adopting relevant SCUC input data as features, is integrated to first predict whether a SCUC instance is ‘easy’ or ‘hard’. To this end, the integrated framework could improve the overall performance of SCUC instances, by and large, in terms of computational efficiency and solution quality. Numerical results illustrate the effectiveness of the proposed integrated framework.

Decision Diagram Decomposition for Quadratically Constrained Binary Optimization

In recent years the use of decision diagrams within the context of discrete optimization has proliferated. This paper continues this expansion by proposing the use of decision diagrams for modeling and solving binary optimization problems with quadratic constraints. The model proposes the use of multiple decision diagrams to decompose a quadratic matrix so that each individual diagram has provably limited size. The decision diagrams are then linked through channeling constraints to ensure that the solution represented is consistent across the decision diagrams and that the original quadratic constraints are satisfied. The resulting family of decision diagrams are optimized over by a dedicated cutting-plane algorithm akin to Benders decomposition. The approach is general, in that commercial integer programming solvers can readily apply the technique. A thorough experimental evaluation on both benchmark and synthetic instances exhibits that the proposed decision diagram reformulation provides significant improvements over current methods for quadratic constraints in state-of-the-art solvers.