Look-ahead Horizon Research Articles

Surveying Business Uncertainty

We elicit subjective probability distributions from business executives about their own-firm outcomes at a one-year look-ahead horizon. In terms of question design, our key innovation is to let survey respondents freely select support points and probabilities in five-point distributions over future sales growth, employment, and investment. In terms of data collection, we develop and field a new monthly panel Survey of Business Uncertainty. The SBU began in 2014 and now covers about 1,750 firms drawn from all 50 states, every major nonfarm industry, and a range of firm sizes. We find three key results. First, firm-level growth expectations are highly predictive of realized growth rates. Second, subjective uncertainty is highly predictive of forecast errors and the magnitude of future forecast revisions. Third, subjective uncertainty rises with the firm’s absolute growth rate in the previous year and the extent of recent news about its growth prospects. We aggregate over firm-level forecast distributions to construct monthly indices of business expectations (first moment) and uncertainty (second moment) for the U.S. private sector.

Surveying Business Uncertainty

We develop a new monthly panel survey of business executives and a new question design that elicits subjective probability distributions over own-firm outcomes at a one-year lookahead horizon. Our Survey of Business Uncertainty (SBU) began in 2014 and now covers 1,500 firms drawn from all 50 states, every major industry in the nonfarm private sector, and a full range of firm sizes. We use SBU data to measure expected future outcomes for the growth of sales, employment, and investment for each firm and the uncertainty surrounding those expectations. Mean expectations are highly predictive of realized growth rates in the firm-level data, and subjective uncertainty is highly predictive of absolute forecast errors. We also use the SBU data to produce a Business Expectations Index (first moment) and a Business Uncertainty Index (second moment) for the U.S. economy. In Granger causality tests, the Business Expectations Index has statistically significant predictive power for a range of prominent business cycle indicators. The SBU also includes special questions that elicit additional information, including the perceived effects of specific government policy developments on the firm's decisions and outcomes.

Predictive velocity control in a hilly terrain over a long look-ahead horizon

This paper presents a computationally efficient velocity control of vehicles driving in a possibly hilly terrain and over long look-ahead horizons that may stretch to hundreds of kilometers. The controller decouples gear scheduling into an offline optimization problem, from the remaining optimization problem that governs two real-valued states. One of the states, the travel time, is adjoined to the objective by applying the necessary optimality conditions, which results into an online optimization problem that has kinetic energy as the single state. Finally an inner approximation is proposed for the online problem to obtain a quadratic program that can be solved efficiently. The efficiency of the proposed controller is shown for different horizon lengths.

Data Perturbation-Based Sensitivity Analysis of Real-Time Look-Ahead Economic Dispatch

In this paper, the sensitivity of look-ahead economic dispatch in real-time power markets with respect to data perturbation is studied. In the look-ahead dispatch optimization problem, a small change in the data that are used for setting the interspatial and temporal equality/inequality constraints and the objective function may negatively affect normal operations, such as the calculation of real-time wholesale electricity prices and operating costs. This could lead to more distorted prices and larger operating costs with the look-ahead dispatch than with a static dispatch that use data for a single future time. We perturb Karush–Kuhn–Tucker conditions of the look-ahead dispatch optimization formulation and then, using them, derive a linear sensitivity matrix that assesses the impact of data corruption on look-ahead dispatch. This matrix illustrates the changing optimal solution of look-ahead dispatch subject to potential corruption in various types of spatial and temporal data—generator's bidding cost coefficients, capacity limits for generators and transmission lines, ramp rates and the estimate of initial generation output with ramp constraints, and multiple-time series of the forecast load for the look-ahead horizon. The results of the simulation are illustrated with numerical examples in the IEEE 14-bus system.

Multi-aircraft Conflict Detection and Resolution Based on Probabilistic Reach Sets

In this brief, a novel scheme to multi-aircraft conflict detection and resolution is introduced. A key feature of the proposed scheme is that uncertainty affecting the aircraft future positions along some look-ahead prediction horizon is accounted for via a probabilistic reachability analysis approach. In particular, ellipsoidal probabilistic reach sets are determined by formulating a chance-constrained optimization problem and solving it via a simulation-based method called scenario approach . Conflict detection is then performed by verifying if the ellipsoidal reach sets of different aircraft intersect. If a conflict is detected, then the aircraft flight plans are redesigned by solving a second-order cone program resting on the approximation of the ellipsoidal reach sets with spheres with constant radius along the look-ahead horizon. A bisection procedure allows one to determine the minimum radius such that the ellipsoidal reach sets of different aircraft along the corresponding new flight plans do not intersect. Some numerical examples are presented to show the efficacy of the proposed scheme.

Discretization-Based and Look-Ahead Algorithms for the Dubins Traveling Salesperson Problem

A new class of discretization-based look-ahead algorithms (DLAAs) for the Dubins traveling salesperson problem (DTSP) is presented that compares favorably with the existing algorithms from the literature. The discretization level and the length of the look-ahead horizon are the two parameters that uniquely determine a DLAA, and depending on the application in hand, their values can be easily modified to strike a balance between the execution time and the length of the resulting admissible tour. The time complexity of a DLAA is the sum of two terms, one linear in the number of targets (cities) and one that corresponds to the specification of an initial order for the targets. For instances of the DTSP with densely distributed targets, an algorithm that relies on clustering and leads to shorter tours than the DLAA is also presented.

Obstacle Avoidance in Real Time With Nonlinear Model Predictive Control of Autonomous Vehicles

A Nonlinear model predictive control (NMPC) for trajectory tracking with the obstacle avoidance of autonomous road vehicles traveling at realistic speeds is presented in this paper, with a focus on the performance of those controllers with respect to the look-ahead horizon of the NMPC. Two different methods of obstacle avoidance are compared and then the NMPC is tested in several simulated but realistic tracking scenarios involving static obstacles on constrained roadways. In order to simplify the vehicle dynamics, a bicycle model is used for the prediction of future vehicle states in the NMPC framework. However, a high-fidelity, nonlinear CarSim vehicle model is used to evaluate the vehicle performance and test the controllers in the simulation results. The CPU time is also analyzed to evaluate these schemes for real-time applications. The results show that the NMPC controller provides satisfactory online tracking performance in a realistic scenario at normal road speeds while still satisfying the real-time constraints. In addition, it is shown that the longer prediction horizons allow for better responses of the controllers, which reduce the deviations while avoiding the obstacles, as compared with shorter horizons.

Scheduling and Pricing for Expected Ramp Capability in Real-Time Power Markets

Higher variable renewable generation penetrations are occurring throughout the world on different power systems. These resources increase the variability and uncertainty on the system which must be accommodated by an increase in the flexibility of the system resources in order to maintain reliability. Many scheduling strategies have been discussed and introduced to ensure that this flexibility is available at multiple timescales. To meet variability, that is, the expected changes in system conditions, two recent strategies have been introduced: time-coupled multi-period market clearing models and the incorporation of ramp capability constraints. To appropriately evaluate these methods, it is important to assess both efficiency and reliability. But it is also important to assess the incentive structure to ensure that resources asked to perform in different ways have the proper incentives to follow these directions, which is a step often ignored in simulation studies. We find that there are advantages and disadvantages to both approaches. We also find that look-ahead horizon length in multi-period market models can impact incentives. This paper proposes scheduling and pricing methods that ensure expected ramps are met reliably, efficiently, and with associated prices based on true marginal costs that incentivize resources to do as directed by the market. Case studies show improvements of the new method.

Flexible Operation of Batteries in Power System Scheduling With Renewable Energy

The fast growing expansion of renewable energy increases the complexities in balancing generation and demand in the power system. The energy-shifting and fast-ramping capability of energy storage has led to increasing interests in batteries to facilitate the integration of renewable resources. In this paper, we present a two-step framework to evaluate the potential value of energy storage in power systems with renewable generation. First, we formulate a stochastic unit commitment approach with wind power forecast uncertainty and energy storage. Second, the solution from the stochastic unit commitment is used to derive a flexible schedule for energy storage in economic dispatch where the look-ahead horizon is limited. Analysis is conducted on the IEEE 24-bus system to demonstrate the benefits of battery storage in systems with renewable resources and the effectiveness of the proposed battery operation strategy.

Airport Capacity Prediction with Explicit Consideration of Weather Forecast Uncertainty

This paper describes a stochastic analytical model for predicting airport capacity with a look-ahead horizon suitable for strategic traffic flow management. The model extends previous research on airport capacity estimation by explicitly integrating the impact of terminal weather and its uncertainty. Different types of weather forecast inputs are explored, including deterministic forecasts, deterministic forecasts with forecast error models, and ensemble forecasts, to produce distributions of predicted arrival and departure capacity for each runway configuration at an airport. The paper introduces a mathematical capacity prediction model and weather data sources supported by a proof-of-concept prototype implementation, including results of validation studies at Hartsfield–Jackson Atlanta International Airport. These results are compared with standard airport benchmark capacities and actual observed throughputs. Results show that our analytical airport capacity model accurately predicts maximum available airport capacity for Hartsfield–Jackson Atlanta International Airport for different weather conditions. The validation studies reveal a limited impact of forecast uncertainty representation on the accuracy of airport capacity predictions.

Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments.Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research.

Online preference learning for adaptive dispatching of AGVs in an automated container terminal

This paper proposes an online preference learning algorithm named OnPL that can dynamically adapt the policy for dispatching AGVs to changing situations in an automated container terminal. The policy is based on a pairwise preference function that can be repeatedly applied to multiple candidate jobs to sort out the best one. An adaptation of the policy is therefore made by updating this preference function. After every dispatching decision, each of all the candidate jobs considered for the decision is evaluated by running a simulation of a short look-ahead horizon. The best job is then paired with each of the remaining jobs to make training examples of positive preferences, and the inversions of these pairs are each used to generate examples of negative preferences. These new training examples, together with some additional recent examples in the reserve pool, are used to relearn the preference function implemented by an artificial neural network. The experimental results show that OnPL can relearn its policy in real time, and can thus adapt to changing situations seamlessly. In comparison to OnPL, other methods cannot adapt well enough or are not applicable in real time owing to the very long computation time required.

Optimization Approaches to the Single Airport Ground-Holding Problem

This paper surveys optimization approaches to the single airport ground-holding problem. The article provides a comprehensive summary of six approaches, highlights the merits and limitations of each, and compares the performance of four models over a large range of cost functions. The first sequential evaluation model for the single airport ground-holding problem is introduced. Motivated by current practice in Collaborative Decision Making planning under Collaborative Decision Making, the sequential model evaluates a method of choosing planned airport acceptance rates. The evaluator is used to compare the performance of a static model applied iteratively to traditional dynamic models and to evaluate the performance of a static model with a limited look-ahead horizon. The findings suggest that research on the single airport ground-holding problem has focused either on developing high-performance complex dynamic models whose outputs are not consistent with current practice or static models that perform less well in theory but can incorporate operational considerations. The experiments show that dynamic models outperform static models by a large margin in simulation but that the performance gap can be reduced by limiting the look-ahead horizon of a static model, which allows it to imitate the ability of a dynamic model to “wait and see.” The models were evaluated using real arrival and departure schedules from San Francisco International Airport.

Cooperative Path Planning for Target Tracking in Urban Environments Using Unmanned Air and Ground Vehicles

As the need for autonomous reconnaissance and surveillance missions in cluttered urban environments has been increasing, this paper describes a cooperative path planning algorithm for tracking a moving target in urban environments using both unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs). The novelty of the algorithm is that it takes into account vision occlusions due to obstacles in the environment. The algorithm uses a dynamic occupancy grid to model the target state, which is updated by sensor measurements using a Bayesian filter. Based on the current and predicted target behavior, the path planning algorithm for a single vehicle (UAV/UGV) is first designed to maximize the sum of the probability of detection over a finite look-ahead horizon. The algorithm is then extended to multiple vehicle collaboration scenarios, where a decentralized planning algorithm relying on an auction scheme is designed to plan finite look-ahead paths that maximize the sum of the joint probability of detection over all vehicles.

Impact of Stock Market Indices and Other Regional Exogenous Factors on Predictive Modeling of Border Traffic with Neural Network Models

This paper analyzes the impact of stock market indices, as indicators of political and economic stability, and other regional exogenous factors on the performance of predictive modeling of border traffic using neural network models. To prove the concept, the Saudi–Bahrain corridor through King Fahd causeway is selected as our area of study. These two countries have strong cultural ties and a wide variety of variables affects the incoming and outgoing traffic flows. Various models of artificial neural networks are constructed for different prediction horizons and look-back periods using a dataset prepared for the period from 2003 till 2013. In our study, stock market indices are proposed, for the first time, to be used in border traffic forecasting. These indices are added as a surrogate measure of the political and economic conditions of the countries which are under study. Their effects on models with varying ranges of time-series inputs and different prediction horizons are studied in detail. It is found that including stock market indices and other most relevant local factors has generally improved the prediction performance of the neural network models in all cases. Additional reduction in the prediction error is achieved by the proposed ensemble model trained with different time lags. Yet, the degree of improvement depends on the look-ahead horizon for prediction.

3D X-Y-T Space Path Planning for Autonomous Mobile Robots Considering Dynamic Constraints

An autonomous mobile robot in a human living space should be able to not only realize collision-free motion but also give way to humans depending on the situation. Although various reactive obstacle avoidance methods have been proposed, it is difficult to achieve such motion. On the other hand, 3D X-Y-T space path planning, which takes into account the motion of both the robot and the human in a look-ahead time horizon, is effective. This paper proposes a real-time obstacle avoidance method for an autonomous mobile robot that considers the robots dynamic constraints, the personal space, and human directional area based on grid-based 3D X-Y-T space path planning. The proposed method generates collision-free motion in which the robot can yield to humans. To verify the effectiveness of the proposed method, various experiments in which the humans position and velocity were estimated using laser range finders were carried out.

A reinforcement learning based approach for a multiple-load carrier scheduling problem

This paper studies the problem of scheduling a multiple-load carrier which is used to deliver parts to line-side buffers of a general assembly (GA) line. In order to maximize the reward of the GA line, both the throughput of the GA line and the material handling distance are considered as scheduling criteria. After formulating the scheduling problem as a reinforcement learning (RL) problem by defining state features, actions and the reward function, we develop a Q($$\lambda $$?) RL algorithm based scheduling approach. To improve performance, forecasted information such as quantities of parts required in a look-ahead horizon is used when we define state features and actions in formulation. Other than applying traditional material handling request generating policy, we use a look-ahead based request generating policy with which material handling requests are generated based not only on current buffer information but also on future part requirement information. Moreover, by utilizing a heuristic dispatching algorithm, the approach is able to handle future requests as well as existing ones. To evaluate the performance of the approach, we conduct simulation experiments to compare the proposed approach with other approaches. Numerical results demonstrate that the policies obtained by the RL approach outperform other approaches.

Crane scheduling for opportunistic remarshaling of containers in an automated stacking yard

Loading of containers onto a vessel can be delayed if the containers are inappropriately stacked at the yard. Remarshaling is the preparatory task of rearranging the containers ahead of loading time to minimize the delay at the time of loading. Previous works on remarshaling derive a remarshaling plan assuming that a certain amount of continuous period of time is available for the remarshaling jobs to be done in a batch; remarshaling jobs cannot be mixed together with the current main jobs in the crane schedule. In contrast, the method proposed in this paper allows remarshaling jobs to be scheduled together with the current jobs under the framework of iterative rescheduling, in which scheduling and execution of the jobs within a predetermined fixed-length look-ahead horizon are repeated in a regular interval. The remarshaling jobs selected for the mix are those that contribute the most to the reduction of future loading delay. The number of remarshaling jobs and their temporal positions within the mixed schedule for a look-ahead horizon are determined appropriately to best utilize the idle time of the automated cranes, which is enforced by an objective function to minimize the delay of the current main jobs as well as the makespan of all the jobs in the horizon.

Schedule-Driven Coordination for Real-Time Traffic Network Control

Real-time optimization of the dynamic flow of vehicle traffic through a network of signalized intersections is an important practical problem. In this paper, we take a decentralized, schedule-driven coordination approach to address the challenge of achieving scalable network-wide optimization. To be locally effective, each intersection is controlled independently by an on-line scheduling agent. At each decision point, an agent constructs a schedule that optimizes movement of the observable traffic through the intersection, and uses this schedule to determine the best control action to take over the current look-ahead horizon. Decentralized coordination mechanisms, limited to interaction among direct neighbors to ensure scalability, are then layered on top of these asynchronously operating scheduling agents to promote overall performance. As a basic protocol, each agent queries for newly planned output flows from its upstream neighbors to obtain an optimistic projection of future demand. This projection may incorporate non-local influence from indirect neighbors depending on horizon length. Two additional mechanisms are then introduced to dampen ``nervousness'' and dynamic instability in the network, by adjusting locally determined schedules to better align with those of neighbors. We present simulation results on two traffic networks of tightly-coupled intersections that demonstrate the ability of our approach to establish traffic flows with lower average vehicle wait times than both a simple isolated control strategy and other contemporary coordinated control strategies that use moving average forecast or traditional offset calculation.

Evolving small-board Go players using coevolutionary temporal difference learning with archives

Evolving small-board Go players using coevolutionary temporal difference learning with archivesWe apply Coevolutionary Temporal Difference Learning (CTDL) to learn small-board Go strategies represented as weighted piece counters. CTDL is a randomized learning technique which interweaves two search processes that operate in the intra-game and inter-game mode. Intra-game learning is driven by gradient-descent Temporal Difference Learning (TDL), a reinforcement learning method that updates the board evaluation function according to differences observed between its values for consecutively visited game states. For the inter-game learning component, we provide a coevolutionary algorithm that maintains a sample of strategies and uses the outcomes of games played between them to iteratively modify the probability distribution, according to which new strategies are generated and added to the sample. We analyze CTDL's sensitivity to all important parameters, including the trace decay constant that controls the lookahead horizon of TDL, and the relative intensity of intra-game and inter-game learning. We also investigate how the presence of memory (an archive) affects the search performance, and find out that the archived approach is superior to other techniques considered here and produces strategies that outperform a handcrafted weighted piece counter strategy and simple liberty-based heuristics. This encouraging result can be potentially generalized not only to other strategy representations used for small-board Go, but also to various games and a broader class of problems, because CTDL is generic and does not rely on any problem-specific knowledge.