Hierarchical Solution Research Articles

Short-Term Optimal Operation Method for Hydro–Wind–Thermal Systems Considering Wind Power Uncertainty

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas.

Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting

This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution’s effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck.

Alternative service network design for bus systems responding to time-varying road disruptions

In practice, road disruptions occur frequently, interrupting multiple bus routes at the same time and causing widespread passenger delays. Typically, these disrupted roads are repaired sequentially and then gradually put into service. In response to such time-varying road disruptions, this paper aims to assist bus operators in developing effective alternative service networks for passengers. The proposed approach involves the joint optimization of service-based route adjustments, bus timetables, and passenger assignment to minimize the total passenger cost and weighted bus operation time. Specifically, a novel service-based adjustment strategy is introduced to flexibly adapt each bus service to time-varying road disruptions. An integer programming model is built for the studied problem based on the set of passengers’ time-space itineraries. To efficiently generate these time-space itineraries and solve models for large-scale problems, this paper develops a hierarchical solution framework. The framework consists of three key parts: (1) a column generation procedure to iteratively explore passengers’ spatial paths; (2) a customized extension algorithm to extend these spatial paths to time-space itineraries; and (3) a tailored adaptive large neighbourhood search heuristic to solve the final itinerary-based model. After that, the overall methodology is tested with both an illustrative example and a real-world example in Beijing. Experimental results show that our methodology produces a high-performance solution with only 7.3% of unserved passengers. Besides, compared to the two benchmark adjustment strategies, our service-based adjustment strategy reduces the average itinerary cost for all passengers by 27.0% and 43.3%, respectively.

A hierarchical solution framework for dynamic and conflict-free AGV scheduling in an automated container terminal

Container terminals worldwide are experiencing their transitions into automated and intelligent terminals in the face of the ever increasing container handling demand and cost pressure. A key to cost-effective operations in automated container terminals is the efficient AGV scheduling algorithm that enables on-time fulfillment of container loading and discharging tasks. In this paper, we study an integrated task assignment and path planning problem for AGV scheduling in an automated container terminal. We propose a hierarchical solution framework to empower dynamic AGV scheduling, where the higher level employs a reinforcement learning algorithm for dynamic task assignment and the lower level makes use of a tailored path generation algorithm to generate low-cost and conflict-free paths for AGVs to serve the tasks. Additionally, we propose a container matching heuristic and a two-layer grid map to enhance the learning ability of the reinforcement learning algorithm. We compare the performance of the hierarchical solution framework against various benchmark methods on problem instances of practical scales. The results show that our approach is effective in reducing task delays and mitigating path conflicts, making the task assignment and path planning decisions more applicable for AGV scheduling in an automated container terminal.

Psychometric properties of the Buss-Perry Aggression Questionnaire-short form among law enforcement officers.

Repeatedly capturing national headlines, excessive law enforcement officer (LEO) use of force in critical incident encounters is one of the most divisive human rights issues in the United States. Valid and reliable measures of potential precursors to LEO excessive use of force, such as aggression, are needed. The Buss-Perry Aggression Questionnaire-short form (BPAQ-SF) is a validated measure of aggression across various populations; however, evaluation of this easily administered measure in high-stress, frontline populations such as LEOs is limited. The primary goal of this study was to evaluate the validity, reliability, and sensitivity to change the BPAQ-SF in a sample of LEOs. A confirmatory factor analysis suggested that the hierarchical solution provides a mixed fit to the data:SBχ²(25.84) = 62.50, p = .0001; comparative fit index = .94, non-normed fit index = .92, root mean square error of approximation = .19 (90% confidence interval = .17-.21), standardized root mean squared residual = .08. The BPAQ-SF demonstrated good internal consistency (α = .84) and test-retest reliability (r = .86), correlations in the expected direction with predictors of and buffers against aggression, and sensitivity to change among LEOs who participated in an intervention targeting aggression. Results support and extend previous findings suggesting that the BPAQ-SF is a valid and reliable measure of aggression among LEOs.

Optimal hydrogen infrastructure planning for heat decarbonisation

Energy decarbonisation is essential to achieve Net-Zero emissions goal by 2050. Consequently, investments in alternative low-carbon pathways and energy carriers for the heat sector are required. In this study, we propose an optimisation framework for the transition of heat sector in Great Britain focusing on hydrogen infrastructure decisions. A spatially-explicit mixed-integer linear programming (MILP) evolution model is developed to minimise the total system’s cost considering investment and operational decisions. The optimisation framework incorporates both long-term planning horizon of 5-year steps from 2035 to 2050 and typical days with hourly resolution. Aiming to alleviate the computational effort of such multiscale model, two hierarchical solution approaches are suggested that result in computational time reduction. From the optimisation results, it is shown that the installation of gas reforming hydrogen production technologies with CCS and biomass gasification with CCS can provide a cost-effective strategy achieving decarbonisation goal. What-if analysis is conducted to demonstrate further insights for future hydrogen infrastructure investments. Results indicate that, as cost is highly dependent on natural gas price, Water Electrolysis capacity increases significantly when gas price rises. Moreover, the introduction of carbon tax policy can lead to lower CO2 net emissions.

Mixed Integer Optimization of the Flow Pattern for Stochastic Feeder Reconfiguration

Mixed-integer conic programming provides an approach to solving the Optimal Feeder Reconfiguration (OFR) problem with guarantees on the quality of the solution. Integrating renewable generation into the distribution network and its associated variability renders stochastic OFR, which simultaneously considers several snapshots of the network, a more viable approach. While the established mixed-integer conic program for deterministic OFR can be extended to the stochastic case, the resulting optimization problem that still uses the bus injection relaxation is challenging to solve. This paper proposes a new mixed integer conic optimization of the flow pattern for solving the stochastic OFR. The new optimization framework exploits the perspective reformulation to obtain a tighter relaxation for stochastic OFR, which improves computational performance, and a feasibility pump heuristic to give a feasible solution. The mixed integer optimization of the flow pattern can also provide an initial solution to the mixed integer conic program employing the bus injection relaxation, giving rise to a hierarchical solution approach. Numerical results on stochastic OFR show that the hierarchical approach provides much-improved system performance compared to solutions considering a single snapshot. In addition, the proposed feasibility pump heuristic gives rise to network configurations close to global optimality in several test instances.

제도분석틀을 활용한 협력적 거버넌스에 관한 연구

Governance has been highlighted as an alternative mechanism to the traditional hierarchical solution method to solve problems in modern society. It has been attracting attention in various fields, by adding a descriptive word “collaborative,” despite the fact that “collaboration” is a key element in the meaning of governance. This raises the need for governance that complements the limitations of the “collaborative governance” led by the administrative authorities. In this study, we aimed to explore the necessity and possibility of “citizen-led collaborative governance” centered on citizen initiative to replace the administration-led collaborative governance. As an example of citizen-driven collaborative governance, we utilized Ostrom's Institutional Analysis and Development Framework to identify factors of citizen initiative in the creation of the Banghakcheon Culture and Arts Street in Dobong-gu, Seoul. This is a case where collaborative governance was established and operational because there was a clear common purpose to solve the inconvenience of the residents due to the existence of the densely concentrated harmful businesses for decades and the position of the city government, which was promoting a regional development strategy centered around culture. Through collaborative governance, all 31 harmful businesses were voluntarily closed, 17 of the vacant spaces were transformed into workshops and public spaces, and the surrounding streets were successfully regenerated with cafes and bookstores. As a result of the analysis through the institutional analysis framework, we concluded that the factors that enabled the citizen-led operation were the capacity of civil society accumulated over the years, the consensus of public-private partnership spread through participatory projects such as village communities in the administration, and the institutional devices and promotion systems such as ordinances that embodied citizen initiative.

A stochastic programming approach to perform hospital capacity assessments.

This article introduces a bespoke risk averse stochastic programming approach for performing a strategic level assessment of hospital capacity (QAHC). We include stochastic treatment durations and length of stay in the analysis for the first time. To the best of our knowledge this is a new capability, not yet provided in the literature. Our stochastic programming approach identifies the maximum caseload that can be treated over a specified duration of time subject to a specified risk threshold in relation to temporary exceedances of capacity. Sample averaging techniques are applied to handle probabilistic constraints, but due to the size and complexity of the resultant mixed integer programming model, a novel two-stage hierarchical solution approach is needed. Our two-stage hierarchical solution approach is novel as it combines the application of a meta-heuristic with a binary search. It is also computationally fast. A case study of a large public hospital has been considered and extensive numerical tests have been undertaken to highlight the nuances and intricacies of the analysis. We conclude that the proposed approach is effective and can provide extra clarity and insights around hospital outputs. It provides a way to better calibrate hospitals and other health care infrastructure to future demands and challenges, like those created by the COVID pandemic.

Upgrade of D+ software for hierarchical modeling of X-ray scattering data from complex structures in solution, fibers and single orientations.

This article presents an upgrade of the D+ software [Ginsburg et al. (2019 ▸). J. Appl. Cryst. 52, 219-242], expanding its hierarchical solution X-ray scattering modeling capabilities for fiber diffraction and single crystallographic orientations. This upgrade was carried out using the reciprocal grid algorithm [Ginsburg et al. (2016 ▸). J. Chem. Inf. Model. 56, 1518-1527], providing D+ its computational strength. Furthermore, the extensive modifications made to the Python API of D+ are described, broadening the X-ray analysis performed with D+ to account for the effects of the instrument-resolution function and polydispersity. In addition, structure-factor and radial-distribution-function modules were added, taking into account the effects of thermal fluctuations and intermolecular interactions. Finally, numerical examples demonstrate the usage and potential of the added features.

Hierarchical collision-free trajectory planning for autonomous vehicles based on improved artificial potential field method

To enable autonomous vehicles to generate smooth and collision-free trajectories and improve their driving performance on structured roads, this paper proposes a hierarchical trajectory planning algorithm based on an improved artificial potential field method. To improve the applicability of the algorithm to complex scenarios, the Frenet coordinate system was established to address these limitations. First, the safety distance model is applied to the risk assessment of the improved artificial potential field method. Then, the hierarchical solution is carried out, and the road solvable convex space and the rough path solution are solved by combining the artificial potential field method. On this basis, the potential field term and the smoothing term cost function are established, and the sequential quadratic programming (SQP) algorithm is used to solve the exact path that meets the requirements of safety and smoothness. Hierarchical planning shortens the solution time by quickly determining the bounds of the convex space. Finally, in the speed planning, in order to take into account the comfort and safety of the occupants, the speed curve is solved by considering the dynamic constraints of the vehicle. The obstacle avoidance effects of the algorithm on static and dynamic obstacles are tested in different simulation scenarios. The results of the simulation experiment show that the proposed algorithm can successfully achieve obstacle avoidance on complex structured roads.

Robust Hierarchical Scheduling Approach to Coordinate Wind Power Penetrated Transmission System and LNG Railway Transportation

Rail transport of liquefied natural gas (LNG), as an essential means of transmitting energy across regions, has been widely implemented and promoted. The significant growth of gas-fired power and power-to-gas units has intensified the interaction and interdependency between wind power penetrated transmission system and LNG transported railway network. Hence, a robust and hierarchical scheduling approach to coordinate the transmission system and LNG railway transportation is innovatively proposed. First, an advanced time-space network (TSN) model is established with additional security constraints to exactly simulate the railway traffic. Then, a two-stage robust scheduling model is formulated with an uncertainty set incorporating random components failures of the transmission system and the railway network as well as variability of wind power and loads. Subsequently, the two-stage robust scheduling problem is decomposed into two interactive levels according to the analytical target cascading (ATC) technique, thus ensuring the information privacy and decision independency of the power and railway systems. Finally, an ATC-based hierarchical solution framework embedded with the column-and-constraint generation (C&CG) algorithm is presented to solve the proposed model. Case studies demonstrate the effectiveness and benefit of collaboratively managing the power and railway systems.

Enabling hierarchical control of coherent pluggable transceivers in SONiC packet–optical nodes

Traditional metro networks are composed of packet switching nodes (i.e., routers) interconnected by optical transport links. In this scenario, the packet and optical domains are clearly separated, using dedicated controllers. Standalone muxponders/transponders will be replaced in optical metro and transport networks by the utilization of hybrid packet–optical nodes equipped with coherent pluggable transceivers. Thus, the traditional packet control plane is unable to manage and fully support the specific optical parameters required to configure such pluggable modules. Moreover, the coordination between the optical and packet layers within this hybrid node has not been standardized yet and requires careful design to enable effective management of connectivity services. This paper proposes two software-defined networking (SDN)-based hierarchical solutions to coordinate and control coherent pluggable transceivers in a multi-layer network exploiting hybrid packet–optical nodes. The two solutions have been designed and validated focusing on the pluggable module configuration and the communication within the SDN hierarchical architecture. An experimental testbed including two packet–optical nodes, running an extended version of the open-source Software for Open Networking in the Cloud (SONiC) operating system, is deployed to show the effectiveness of the two solutions, with a deep analysis of the time required to set up end-to-end connections spanning the packet and optical domains.

Self-Sustained Programmable Hygroelectronic Interfaces for Humidity-Regulated Hierarchical Information Encryption and Display.

The emerging moisture-driven energy generation (MEG) technology opens up new possibilities for humidity-responsive materials, devices, and interdisciplinary opportunities in fields like information security. However, such potential remains untapped. Here, an original MEG structure with a hygroionic energy-conversion route by selective coating of ionic hygroscopic hydrogels on a carbon black surface is reported. The hygroionic route features a process in which the scavenged energy is stored in the electrical double layers formed at the interfaces between the ionic hydrogel and the carbon nanoparticles. The resultant electrical field developed across the hydrogel-coated wet carbon and the rest of the dry carbon area is thus durably lasted. Based on this unique structure, hygroelectronic information interfaces (HEII) for humidity-regulated information encryption and displayare put forward by devising hydrogel patterns on a carbon platform. Further by tuning the hygroscopicity of the ionic hydrogels and incorporating encoding methods (e.g., Morse code), it is demonstrated that the HEII platform is programmable to carry different information in certain humidity ranges. Unlike those conventional anti-counterfeiting methods that optically reveal the hidden information once the required stimulus is provided, the new HEII serves as a hierarchical solution for high-security encryption and display.

Recommending-and-Grabbing: A Crowdsourcing-Based Order Allocation Pattern for On-Demand Food Delivery

With the development of intelligent transportation technologies, on-demand food delivery has become a prevalent logistics service that brings convenience to our daily life. However, problems such as inappropriate assignment and delayed delivery still exist in current food delivery platforms. To address these issues, this paper presents a Crowdsourced Recommending-and-Grabbing (CRG) system for order allocation in on-demand food delivery service. Unlike other traditional logistics systems, the CRG system considers the preferences of crowdsourced riders and allocates orders by recommending them to suitable crowdsourced riders. Riders can grab their favorite orders from personalized order lists. To optimize the experience of both customers and crowdsourced riders, a hierarchical solution framework composed of prediction and optimization is proposed to generate satisfactory order allocation schemes. First at the prediction part, we predict the preferences of crowdsourced riders by establishing a predictive model using eXtreme Gradient Boosting (XGBoost). Then at the optimization phase, an allocating-and-sequencing algorithm is designed to appropriately expose orders to crowdsourced riders and sequence the order list for each rider. Extensive experiments are conducted on real-world datasets and the comparative results demonstrate the effectiveness of the proposed methods, suggesting that the presented CRG system is promising to facilitate the on-demand food delivery effectively and efficiently.

Study of Long-Term Energy Storage System Capacity Configuration Based on Improved Grey Forecasting Model

Distributed generation equipment improves renewable energy utilization and economic benefits through an energy storage system (ESS). However, dominated by short-term data, the configuration of long-period ESS capacity is absent based on the dynamic change of load, which leads to a large deviation from the expected return. Considering the system characteristics of lack of data and less information, after introducing the grey theory, we propose a new long-term capacity configuration method for ESS and establish the long-term grey forecasting model (GFM) of user load, improving the basic forecasting model to improve the accuracy of the long-term forecasting model. Then, the scheduling model is established with the maximum economic and social benefits as the optimization objective. Based on the forecast data of the improved grey forecasting model (IGFM), the hierarchical solution method is used to solve the scheduling model. Finally, the parameters are configured based on the service life of the equipment and the expected rate of return. The simulation results show that higher accuracy is realized in the improved prediction model, and the improved algorithm gets higher convergence speed and precision. Apart from that, the nonlinear correlation trend of the EES return rate between the capacity and life cycle is revealed. Compared with the ESS configuration in a short period, this study provides more comprehensive and accurate data support for the capacity configuration of the ESS, reducing the error between the actual return and the expected return significantly.

Small-Signal Angle Stability-Oriented False Data Injection Cyber-Attacks on Power Systems

The small-signal angle stability (SSAS) of a power system is determined by the property of operation points. The widely applied false data injection (FDI) cyber-attack, however, is able to stealthily mislead the optimal power flow (OPF) and thus compromise operation points, leading to damages to the SSAS margin. To provide insights for cyber defenders, this paper proposes and investigates a stealthy SSAS-oriented FDI cyber-attack focusing on two attacking purposes, i.e., the SSAS margin and operation cost, with higher priority on the former one. First, this paper establishes a novel bi-level model with an implicit SSAS constraint based on a structure preserving model to compromise operation points. Then, for the SSAS interarea mode in a typical two-area system, this paper formulates closed-form expressions of how the SSAS margin and operation cost behave with respect to stealthy injections. By comparison, for the SSAS local mode in general power systems, this paper proposes a moving target cyber-attack-based hierarchical solution algorithm. Simulation results on a two-area system, a Kundur 11 bus system, and a modified IEEE 14 bus system demonstrate the significant damaging effects of the proposed SSAS-oriented FDI cyber-attack and the conflict between the two attacking purposes.

VNF Orchestration and Power-Disjoint Traffic Flow Routing for Optimal Communication Robustness in Smart Grid With Cyber-Physical Interdependence

We explore the use of software-defined networking (SDN) technology in building a communication network for smart grid. With cyber-physical interdependence, such communication network may suffer from cross-network cascading failures. To prevent the failures, we perform virtual network function (VNF) orchestration jointly with power-disjoint routing. Our work is novel in proposing an efficient scheme to find power-disjoint routes at the same time of performing VNF orchestration. We formulate an optimization to maximize the ratio of power-disjoint route count to VNF orchestration cost. The optimization has a non-linear non-convex objective function. We propose a two-level hierarchical solution approach. At higher level, the scheme converts the problem into a fractional maximum flow circulation, which can be solved using simplex method to find the maximum number of power-disjoint routes. Given a higher level solution, the lower level aims to minimize the VNF orchestration cost while satisfying VNF chaining and placement requirements. This lower level hierarchy uses the Dijkstra’s algorithm in building a sequence of minimum spanning trees, each roots at the current VNF hosting node in a VNF chain. Extensive simulation results confirm that the proposed scheme can find the maximum number of power-disjoint routes and minimize the cost within a second, for a system with 120 communication nodes. The results show that the number of power-disjoint routes can be increased by increasing either the number of nodes or node degree, but only the node degree can keep the cost flat. Therefore, one should build a robust software-defined smart grid communication network by enhancing node connectivity.

Human Performance Modeling and Rendering via Neural Animated Mesh

We have recently seen tremendous progress in the neural advances for photo-real human modeling and rendering. However, it's still challenging to integrate them into an existing mesh-based pipeline for downstream applications. In this paper, we present a comprehensive neural approach for high-quality reconstruction, compression, and rendering of human performances from dense multi-view videos. Our core intuition is to bridge the traditional animated mesh workflow with a new class of highly efficient neural techniques. We first introduce a neural surface reconstructor for high-quality surface generation in minutes. It marries the implicit volumetric rendering of the truncated signed distance field (TSDF) with multi-resolution hash encoding. We further propose a hybrid neural tracker to generate animated meshes, which combines explicit non-rigid tracking with implicit dynamic deformation in a self-supervised framework. The former provides the coarse warping back into the canonical space, while the latter implicit one further predicts the displacements using the 4D hash encoding as in our reconstructor. Then, we discuss the rendering schemes using the obtained animated meshes, ranging from dynamic texturing to lumigraph rendering under various bandwidth settings. To strike an intricate balance between quality and bandwidth, we propose a hierarchical solution by first rendering 6 virtual views covering the performer and then conducting occlusion-aware neural texture blending. We demonstrate the efficacy of our approach in a variety of mesh-based applications and photo-realistic free-view experiences on various platforms, i.e., inserting virtual human performances into real environments through mobile AR or immersively watching talent shows with VR headsets.

A new defense of Tarski's solution to the liar paradox

Tarski's hierarchical solution to the Liar paradox is widely viewed as ad hoc. In this paper I show that, on the contrary, Tarski's solution is justified by a sound philosophical principle that concerns the inner structure of truth. This principle provides a common philosophical basis to a number of solutions to the Liar paradox, including Tarski's and Kripke's. Tarski himself may not have been aware of this principle, but by providing a philosophical basis to his hierarchical solution to the paradox, it undermines the ad-hocness objection to this solution. Indeed, it contributes to the defense of Tarski's theory against other objections as well.