Duration Constraints Research Articles

Optimizing Drone Delivery Paths from Shared Bases: A Location-Routing Problem with Realistic Energy Constraints

Recently, Amazon patented fulfillment centers for drones on a large scale in densely populated areas. A network of such shared centers can be used for landing and launching drones as an alternative to the traditional private bases in near future. This paper studies how a user of such a shared delivery infrastructure can optimally determine the required bases among the existing bases to perform her delivery tasks. To this end, a location-routing problem is studied where the problem determines the drone launching centers and their routes to deliver parcels. A realistic energy function is used that incorporates the effect of load weight for calculating energy consumption in all flight phases including take-off, level flight, hovering, and landing. Although the energy consumption is nonlinear, the problem is formulated as a mixed-integer linear programming model and strengthened by valid inequalities and a pre-processing algorithm that enables us to solve instances with up to 100 customers using off-the-shelf optimization solvers. Moreover, a heuristic method is presented to solve instances with up to 200 customers. Results indicate the importance of incorporating the load-dependent energy formula in contrast to using fixed flight duration constraints for drones. The value of allowing multiple visits on each trip versus only simple back-and-forth trips is also assessed. The advantage of using an integrated location-routing approach is shown over the sequential approach in which the locations of bases are decided first and the routes are constructed next. Moreover, to manage future demand uncertainty, the model is extended for the case that a number of demand scenarios are given.

Optimal allocation and route design for station-based drone inspection of large-scale facilities

The utilization of drones to conduct inspections on industrial electricity facilities, including large-sized wind turbines and power transmission towers, has recently received significant attention, mainly due to its potential to enhance inspection efficiency and save maintenance costs. Motivated by the advantages of drones for facility inspection, we present a novel station-based drone inspection problem (SDIP) for large-scale facilities. The objective of SDIP is to determine the locations of multiple homogeneous automatic battery swap stations (ABSSs) equipped with drones, assign facility inspection tasks to the ABSSs with operation duration constraints, and design drone inspection routes with battery capacity constraints, such that minimize the sum of fixed ABSS costs and drone travel costs. The SDIP can be regarded as a variant of the location-routing problem, which is NP-hard and difficult to solve optimally. To obtain the optimal solution of SDIP efficiently, we firstly formulate this problem into an arc based formulation and a route based formulation, and then develop a logic-based Benders decomposition (LBBD) algorithm to solve it. The SDIP is decomposed into a master problem (MP) and a set of subproblems (SPs). The MP is solved by a branch-and-cut (BC) procedure. Once a feasible integer solution is found, the linear relaxation of SPs are solved by a stabilized column generation to generate Benders cuts. If the cost of all the SPs’ optimal LP solutions plus the cost of the MP’s solution is less that current best cost, the SPs are exactly solved by a Branch-and-Price (BP) algorithm to generate the logic cuts. The numerical results on five scales of randomly generated instances validate the effectiveness of the LBBD algorithm. Specifically, the LBBD can solve all small- and middle-sized instances, and seven out of ten large-sized instances in 1000 s. Furthermore, we conduct a sensitivity analysis by varying the attributes of ABSSs and drones, and provide valuable managerial insights for large-scale facility inspection.

A branch-and-cut algorithm for a skip pick-up and delivery problem

In this study we present a branch-and-cut algorithm for a skip pick-up and delivery problem. The study is motivated by a real-life problem in which full skips are transported from waste drop-off stations to treatment facilities where they are emptied, and then brought back to the original drop-off station. The transportation of the skips is done by trucks with the capacity of carrying two containers at a time. The planning problem is to design the routes of the trucks that perform the collection to satisfy a number of requests in a planning period. A truck route starts at the first pick-up, the truck then performs a sequence of pick-ups, treatments, and deliveries, and the route ends at the last delivery. From the truck perspective, the three actions of pick-up, treatment, and delivery can be performed in any order that respects the vehicle capacity of two and the route duration constraint, but for the single request, the three actions must be performed in the stated order. The problem is formulated as a mixed integer linear problem and several classes of valid inequalities are proposed and integrated into a branch-and-cut algorithm.

Hybrid event-triggered control for networked switched Takagi–Sugeno fuzzy systems with aperiodic DoS attacks

This article investigates the event-triggered control problem for networked switched Takagi–Sugeno fuzzy systems (NSTSFSs) with denial of service (DoS) attacks. First, an attack-instant-constrained hybrid event-triggered mechanism (ETM) is proposed, which uses the acknowledgment character (ACK) detection scheme. It can describe the triggering schemes under different attack intervals and modify the triggering frequency based on the performance of the system. Second, a new segmented Lyapunov function is designed that takes into account the asynchronous behavior of system and controller modes, as well as DoS frequency and duration constraints. Then, sufficient conditions are provided to ensure the exponential stability of the system and have an H∞ performance. Furthermore, a collaborative design scheme is proposed to obtain controller gains, the switching law, and event-triggered parameters. Finally, the accuracy and effectiveness of this method are verified through two simulation examples.

A New Vehicle Routing Problem: When Duration Constraints and Strategic Support Deposit Coexist

A New Vehicle Routing Problem: When Duration Constraints and Strategic Support Deposit Coexist

Variational multi-harmonic duality mode pursuit method for extracting repetitive transient components from vibration signals

The repetitive transient component (RTC) in vibration signals is a typical symptom reflecting the localised defects of rotating machinery, which can be mathematically defined by feature parameters, such as the inter-arrival interval, maximum amplitude, resonance frequency, and attenuation ratio. In particular, the inter-arrival interval and maximum amplitude reveal the location and severity of failure and provide critical information for condition monitoring and fault diagnosis. However, there are some shortcomings of applicability, anti-noise, and fidelity, which are unfavourable for pursuing fault-induced RTC and enabling reliable recognition of key feature parameters. Hence, a novel method called variational multi-harmonic duality mode pursuit (VMHDMP) is proposed to extract the RTC accurately. A multi-harmonic duality mode function (MHDMF) that comprehensively depicts the critical feature parameters of the RTC was first defined in the frequency domain. Based on the defined MHDMF, the pursuit of RTC is formulated as a frequency-domain variational optimisation problem with a local compact time duration constraint, which optimally searches for the mode with an explicit MHDMF from the complex-valued frequency signal. A strategy involving the mutual conversion of real- and complex-valued signals was incorporated into the solution algorithm to satisfy the decomposability of complex-valued frequency signals. Finally, an inverse Fourier transform is performed on the analytical form of the extracted MHDMF to obtain a faithful RTC. Essentially, VMHDMP provides an optimal ‘transient-pass’ inverse filter bank with compact passage only near the occurrence moment of each transient in RTC. Thus, the vital parameters closely related to the faults are fully retained, whereas the frequency-overlap noise and large-amplitude interference occurring at different moments can be effectively eliminated. Simulated and experimental examples were used to test the effectiveness of VMHDMP.

Waveform Selection Based on Discrete Prolate Spheroidal Sequences for Near-Optimal SNRs for Photoacoustic Applications

Waveform engineering is an important topic in imaging and detection systems. Waveform design for the optimal Signal-to-Noise Ratio (SNR) under energy and duration constraints can be modelled as an eigenproblem of a Fredholm integral equation of the second kind. SNR gains can be achieved using this approach. However, calculating the waveform for optimal SNR requires precise knowledge of the functional form of the absorber, as well as solving a Fredholm integral eigenproblem which can be difficult. In this paper, we address both those difficulties by proposing a Fourier series expansion method to convert the integral eigenproblem to a small matrix eigenproblem which is both easy to compute and gives a heuristic view of the effects of different absorber kernels on the eigenproblem. Another important result of this paper is to provide an alternate waveform, the Discrete Prolate Spheroidal Sequences (DPSS), as the input waveform to obtain near optimal SNR that does not require the exact form of the absorber to be known apriori.

Solving a real-life multi-period trailer-truck waste collection problem with time windows

By introducing local recovery networks, regional and local environmental authorities can play an important role in facilitating the circular economy. This paper studies a network design problem that encompasses the recovery of separately collected household waste streams, which are collected in containers at civic amenity sites. We formulate a generic tactical-operational container collection problem that will be solved using a mixed integer linear programming approach. This paper makes both a theoretical and practical contribution. We are the first to study a two container vehicle capacity restriction combined with collection site inventory capacities, time windows, shift break time, and shift duration constraints. The model is applied to a number of real-life test instances and scenarios. The results provide insight in how different combinations of scenarios exactly affect the fleet requirements. This not only includes the number of trucks or information under which circumstances an additional truck would (not) be needed, but also the 12-day collection schedule for the collection crews.

Quantized control for networked switched systems under denial-of-service attacks via a barrier event-triggered mechanism

This paper studies the quantized control problem for networked switched systems (NSSs) under denial-of-service (DoS) attacks. The quantized state information, together with the switching signal, is transmitted to the controller through a network. In order to reduce communication consumption and controller update frequency, a barrier event-triggered mechanism is utilized to monitor the state at discrete time. Because of the event-triggered mechanism and the DoS attacks on the network, the mismatch between the system mode and the controller mode is thus frequently encountered, which may lead to quantization saturation and system instability. To solve the problem, an update rule is presented for the dynamic quantizer by switching between zooming in and zooming out of the zooming variable, and a feedback controller is proposed with a jointly designed event-triggered mechanism and a dynamic quantizer. Sufficient conditions on the constraints of DoS frequency and duration are obtained to ensure the exponential stability of the switched system. The effectiveness of the obtained results is illustrated by simulation examples and comparative studies.

A Scalable Neuristor Based on a Half-Wave Memristor Emulator

The neuristor based on memristors can be used to mimic synapse and neurons of biological neural systems, and it is the key unit of spiking neural networks. However, the resistance states of realistic memristors are nonvolatile, which is not conducive to mimicking the forgetting function of the brain. Given that the resistance states of memristor emulators are volatile after power down, this paper exhibits a scalable neuristor built with a half-wave memristor emulator. The proposed neuristor demonstrates four critical features for action-potential-based computing: the all-or-nothing spiking of an action potential, threshold-driven spiking, diverse periodic spiking and symmetric anti-Hebbian learning rule of spike-timing-dependent plasticity. Particularly, there are no complex shape and duration constraints on pre- and post-spikes for implementing the symmetric anti-Hebbian learning rule.

Local Optima Network Analysis of Multi-Attribute Vehicle Routing Problems

Multi-Attribute Vehicle Routing Problems (MAVRP) are variants of Vehicle Routing Problems (VRP) in which, besides the original constraint on vehicle capacity present in Capacitated Vehicle Routing Problem (CVRP), other attributes that model diverse real-life system characteristics are present. Among the most common attributes studied in the literature are vehicle capacity and route duration constraints. The influence of these restrictions on the overall structure of the problem and the performance of local search algorithms used to solve it has yet to be well known. This paper aims to explain the impact of constraints present in different variants of VRP through the alterations of the structure of the underlying search space that they cause. We focus on Local Optima Network Analysis (LONA) for multiple Traveling Salesman Problem (m-TSP) and VRP with vehicle capacity (CVRP), route duration (DVRP), and both (DCVRP) constraints. We present results that indicate that measures obtained for a sample of local optima provide valuable information on the behavior of the landscape under modifications in the problem’s constraints. Additionally, we use the LONA measures to explain the difficulty of VRP instances for solving by local search algorithms.

An Optimized Plane-Change Solution for Microsatellite Formation Flying

The cross-track baseline requirement in a satellite formation flying mission requires an orbital plane-change maneuver during the acquisition phase. While the optimization of orbital plane-change maneuver has been well studied, the constraints of the propulsion system, such as the power limitation, battery charging cycle, and finite thrust durations, are often neglected. In addition, the self-pressurized chemical propulsion system used in this satellite mission experiences a performance degradation effect over prolonged use in terms of specific impulse and thrust force. To overcome these constraints on the orbital plane-change maneuver process, this article proposes an optimized algorithm by minimizing Edelbaum’s equation using a selective constrained ensemble Kalman filter that includes propulsion’s performance variation and firing duration constraints. Monte Carlo simulations have been conducted to benchmark the proposed method against the theoretical impulsive thrusts in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta V$ </tex-math></inline-formula> , and the commonly adopted natural precession-based plane-change method in terms of the total time taken. Results from the simulation have shown that the required <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta V$ </tex-math></inline-formula> of this proposed method is similar to the theoretical impulsive burn method. In addition, it requires a 75% shorter time than the natural precession method in achieving the desired plane change and is 36% more fuel efficient than the direct firing method. Furthermore, this study has verified that this algorithm can be directly integrated with the existing formation control algorithm.

Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) Assisted UAV Communications

A novel air-to-ground communication paradigm is conceived, where an unmanned aerial vehicle (UAV)-mounted base station (BS) equipped with multiple antennas sends information to multiple ground users (GUs) with the aid of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In contrast to the conventional RIS whose main function is to reflect incident signals, the STAR-RIS is capable of both transmitting and reflecting the impinging signals from either side of the surface, thereby leading to full-space 360 degree coverage. However, the transmissive and reflective capabilities of the STAR-RIS require more complex transmission/reflection coefficient design. Therefore, in this work, a sum-rate maximization problem is formulated for the joint optimization of the UAV’s trajectory, the active beamforming at the UAV, and the passive transmission/reflection beamforming at the STAR-RIS. This cutting-edge optimization problem is also subject to the UAV’s flight safety, to the maximum flight duration constraint, as well as to the GUs’ minimum data rate requirements. Given the unknown locations of obstacles prior to the UAV’s flight, we provide an online decision making framework employing reinforcement learning (RL) to simultaneously adjust both the UAV’s trajectory as well as the active and passive beamformer. To enhance the system’s robustness against the associated uncertainties caused by limited sampling of the environment, a novel “distributionally-robust” RL (DRRL) algorithm is proposed for offering an adequate worst-case performance guarantee. Our numerical results unveil that: 1) the STAR-RIS assisted UAV communications benefit from significant sum-rate gain over the conventional reflecting-only RIS; and 2) the proposed DRRL algorithm achieves both more stable and more robust performance than the state-of-the-art RL algorithms.

Crowdshipping: An open VRP variant with stochastic destinations

E-commerce continues to grow throughout the world due to people’s preference to stay at home rather than going to a brick-and-mortar retail store. COVID-19 has exacerbated this trend. Concurrently, crowd-shipping has been gaining in popularity due to both the increase in e-commerce and the current pressures due to COVID-19. We consider a setting where a crowd-shipping platform can fulfill heterogeneous delivery requests from a central depot with a fleet of professionally driven vehicles and a pool of capacitated occasional drivers. We divide delivery requests into sectors to represent different neighborhoods in a city. Occasional drivers have unknown destinations that can be anywhere inside the sectors. Route duration constraints are modeled to motivate participation and increase the probability of route-acceptance by keeping routes short. We assume that occasional drivers will choose routes that are better compensated and that the probability of route-acceptance is dependent on other routes being offered. We propose a two-stage stochastic model to formulate the problem. We use a branch-and-price algorithm capable of solving 50-customer instances, and develop a heuristic that can solve larger 100-customer instances quickly. An upper bound for the total number of occasional drivers is used to reduce the number of constraints in the master problem and reduce the complexity of the pricing problems. We show that occasional drivers with destinations far from the depot reduce the cost by over 30%, while occasional drivers with destinations that are near the depot reduce the cost by 20%. We show that route duration constraints and capacity constraints can restrict the occasional driver routes and both need to simultaneously increase in order to have cost reductions. This setting of crowd-shipping is a viable option for last-mile deliveries.

Value-at-risk in the presence of asset price bubbles

ABSTRACT In this study, we respond to the criticism that the value-at-risk (VaR) measure fails during financial crises and is only applicable during periods without asset price bubbles. We propose a new dating mechanism that is based on the work of Phillips (2015) to date-stamp the origination and termination of the asset price bubbles. Our method relaxed the minimum bubble duration constraint in the original model, and the empirical application statistically identified the bubbles periods in nine stock markets (Australia, Canada, China, Germany, Spain, Hong Kong, Japan, the United Kingdom, and the United States). We choose the two most widely adopted VaR models (RiskMetrics and RiskMetrics 2006) to test the performance. Our results show that the RiskMetrics model fails in most periods, whereas the RiskMetrics 2006 performs efficiently in the periods with asset price bubbles. These results prove the criticism that all the VaR models fail during crises as invalid.

Optimal selective maintenance scheduling for series–parallel systems based on energy efficiency optimization

With the industry sustainable development and increasing awareness of energy conservation, many manufacturing enterprises prefer to develop the operation and maintenance (O&M) with high production throughput and low energy consumption. In reality, many manufacturing systems are required to conduct a sequence of predefined missions with finite breaks between any two consecutive missions. To successfully complete the next mission production, maintenance actions are arranged and performed on machines during each scheduled break. In this paper, an energy-oriented selective maintenance policy (ESMP) for series–parallel systems is investigated. At each break, multiple maintenance actions with different impacts on machine degradation are available under limited maintenance resources. To obtain the throughput-and-energy based maintenance scheme, we first model the system energy efficiency based on system throughput and energy consumption. Then, we integer the energy efficiency modeling, production throughout analysis, and selective maintenance scheduling into an optimization model. And the model objective is to find the appropriate maintenance action for each machine at each break subject to cost and duration constraints. Numerical examples have been addressed to demonstrate the performance and adaptability of our proposed ESMP in long-term selective maintenance scheduling. Finally, a comparative analysis with traditional reliability-oriented policy shows the significant improvement of energy efficiency.

An Empirical Analysis of the Benefits of Corporate Bond Portfolio Optimization in the Presence of Duration Constraints

This article analyzes the out-of-sample performance of portfolio optimization models in the US corporate bond universe. In our empirical study, we measure the benefits of naive diversification and find that it eventually reaches a limit as the number of bonds increases. Also, we observe substantial improvements in the risk-adjusted performance of scientific portfolio constructions when compared to simple barbell strategies for the same given duration. When duration constraints are relaxed, we find that both naively and scientifically diversified portfolios outperform cap-weighted benchmarks in terms of Sharpe ratio.

The Magmatic Architecture of Continental Flood Basalts: 2. A New Conceptual Model

AbstractContinental flood basalts intruded and erupted millions of km3of magma over ∼1–5 Ma. Previous work proposed the presence of large (–106 km3) crustal magma reservoirs to feed these eruptions. However, in Paper I, we illustrated that this model is inconsistent with observations, by combining eruptive rate constraints with geochemical and geophysical observations from the Deccan Traps and other Continental flood basalt provinces (CFBs). Here, we use a new mechanical magma reservoir model to calculate the variation of eruptive fluxes (km3/year) and volumes for different magmatic architectures. We find that a single magma reservoir cannot explain the eruptive rate and duration constraints for CFBs. Using a 1D thermal model and characteristic timescales for magma reservoirs, we conclude that CFB eruptions were likely fed by a number of interconnected small‐medium (∼102–103 km3) magma reservoirs. It is unlikely that each individual magma reservoir participated in every eruption, thus permitting the occasional formation of large xenocrysts (e.g., megacrystic plagioclase). This magmatic architecture permits (a) large volume eruptive episodes with 10–100s of years duration, and (b) relatively short time‐periods separating eruptive episodes (1000s of years) since multiple mechanisms can trigger eruptions (via magma recharge or volatile exsolution, as opposed to long term (105–106 year) accumulation of buoyancy overpressure), and (c) lack of large upper‐crustal intrusive bodies in various geophysical datasets. Our new proposed magmatic architecture has significant implications for the tempo of CFB volatile release (CO2and SO2), potentially helping explain the pre‐K‐Pg warming associated with Deccan Traps.

Pulse duration constraint of whistler waves in magnetized dense plasma.

Interactions between large-amplitude laser light and strongly magnetized dense plasma have been investigated by one- and two-dimensional electromagnetic particle-in-cell simulations. Since whistler waves have no critical density, they can propagate through plasmas beyond the critical density in principle. However, we have found the propagation of whistler waves is restricted significantly by the stimulated Brillouin scattering. It is confirmed that the period during which the whistler wave can propagate in overcritical plasmas is proportional to the growth time of the ion-acoustic wave via the Brillouin instability. The allowable pulse duration of the whistler wave has a power-law dependence on the amplitude of the whistler wave and the external magnetic field.

Trajectory Design for Throughput Maximization in UAV-Assisted Communication System

In this paper, we study an unmanned aerial vehicle (UAV) assisted communication system to provide service to the ground users. We address the problem of UAV deployment in three-dimensional (3D) space to provide on-demand coverage to the users such that their sum rate is maximized. First, we find the optimal UAV location in 3D space where the sum rate is maximized for all ground users. Thereafter an optimal trajectory is designed for the UAV to travel from the initial to the optimal location, such that the overall average sum rate during the flight is maximized while meeting the on-board energy availability and flight duration constraints. The problem formulated is non-convex. To obtain the optimal location, we approximate the rate expression to obtain the concave regions and apply alternating optimization. An iterative scheme is proposed to obtain the optimal UAV trajectory, which computes the optimal location in each time slot sequentially, followed by a greedy approach to reach the final location. Simulation results provide useful insights into the optimal location and the UAV trajectory problem and show on an average 16.5% improvement over the benchmark schemes.