Time Uncertainty Research Articles

Modelling a bi-level multi-objective post-disaster humanitarian relief logistics network design problem under uncertainty

Post-disaster humanitarian relief logistics focuses on managing the supply and shipment of relief items. Owing to the complexity arising from the hierarchical decision relationship and the uncertainties of demand and transportation, it is challenging to develop an effective humanitarian relief logistics network (HRLN). A globalized robust bi-level multi-objective HRLN model involving uncertainties in transportation time, transportation cost and demand is developed to address this problem, in which an upper-level decision-maker focuses on the allocation problem and a lower-level decision-maker considers the supply problem. For tractability, the goal programming approach is employed to trade off conflicting timeliness, satisfaction and operational cost objectives. The proposed globalized robust bi-level HRLN model is reformulated as its globalized robust counterpart formulations under two different perturbation structures. The primal–dual approach is adopted to transform the bi-level models into equivalent mixed-integer conic programs. A case study concerning an Iranian earthquake is conducted to prove the performance of the proposed model. Globalized robust models are more flexible and able to immunize against uncertainty by comparison with traditional robust optimization and deterministic solutions. The managerial insights of using the globalized robust bi-level optimization approach are reported for disaster response management. The design of an emergency aviation network to circumvent the effects of COVID-19 could be considered in future research.

Reliability assessment of distribution system using Petri net for enhancement of situational awareness

The sprawl of power system network paved a challenge to achieve quality and uninterrupted power to the consumers. Lack of situational awareness (SA) is a key concern, because it adversely affects the reliability of the system. This paper concerns about the co-ordination of protective equipment in distribution side of the power system network when there is a fault on any section of the network. Petri net (PN) based approach has been proposed for modeling the co-ordination between protective equipment for different fault scenarios to assess the operational reliability. The PN model encapsulates the dynamic behavior of the protective equipment together with their co-ordination. This assists in predicting the future disturbances and helps to take precautionary measures for the pre-warning situation and prevent cascaded failures, leading to enhanced SA. To analyze the effect of uncertainties in the operating time of protective equipment, fuzzy Petri net (FPN) approach has been explored. In addition, this research also incorporates a comparison between PN and fault tree, to analyze the impact of protection co-ordination modeling on power system reliability. Furthermore, event tree analysis (ETA) is used to evaluate the loss of probability of SA. The studies have been carried out on a part of distribution system and 14 bus ABB distribution system for implementation and validation of the proposed method.

Linear Clock Tree Topology for Dynamic Source Synchronous and Fully Synchronous 3-D Interfaces

Reliable timing verification is a primary challenge in three-dimensional (3-D) integrated circuits. Process, temperature, and voltage variations between tiers within an integrated system introduce timing uncertainty into the clock and data paths, resulting in reduced operating speed. A clock tree topology that supports synchronous and source synchronous timing schemes is proposed for 3-D interfaces. The interface operates in a source synchronous scheme for fast data transfer and transitions to a synchronous scheme for bidirectional data flow. Phase compensation for the synchronous scheme is achieved via a delay locked loop.

Offline Time-Independent Multiagent Path Planning

This study examines a novel planning problem for multiple agents that cannot share holding resources, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Offline Time-Independent Multiagent Path Planning (OTIMAPP)</i> . Given a graph and a set of start-goal pairs, the problem to be addressed is assigning a path to each agent, such that every agent eventually reaches its destination without blocking others, regardless of when each agent starts and finishes each own action. This motivation stems from timing uncertainties, including the reality gaps between planning and robot execution. In contrast to conventional solution, concepts of multirobot path planning that rely on timings, once OTIMAPP solutions are obtained, they can be executed without any synchronization between robot actions. Moreover, there is a theoretical guarantee that all robots eventually reach their destinations, provided they avoid interrobot collisions. This study attempts to establish OTIMAPP both theoretically and practically. Specifically, we present a formalization of the problem, solution conditions based on a categorization of deadlocks, computational complexities showing that OTIMAPP is computationally intractable, practical relaxation of the solution concept, two algorithms to solve OTIMAPP based on multiagent pathfinding algorithms, empirical results showing large OTIMAPP instances can be solved to some extent, as well as robot demonstrations of asynchronous OTIMAPP execution.

A Stochastic Approach to Reconstruct Gamma-Ray-burst Light Curves

Gamma-ray bursts (GRBs), as they are observed at high redshift (z = 9.4), are vital to cosmological studies and investigating Population III stars. To tackle these studies, we need correlations among relevant GRB variables with the requirement of small uncertainties on their variables. Thus, we must have good coverage of GRB light curves (LCs). However, gaps in the LC hinder the precise determination of GRB properties and are often unavoidable. Therefore, extensive categorization of GRB LCs remains a hurdle. We address LC gaps using a stochastic reconstruction, wherein we fit two preexisting models (the Willingale model; W07; and a broken power law; BPL) to the observed LC, then use the distribution of flux residuals from the original data to generate data to fill in the temporal gaps. We also demonstrate a model-independent LC reconstruction via Gaussian processes. At 10% noise, the uncertainty of the end time of the plateau, its correspondent flux, and the temporal decay index after the plateau decreases by 33.3%, 35.03%, and 43.32% on average for the W07, and by 33.3%, 30.78%, 43.9% for the BPL, respectively. The uncertainty of the slope of the plateau decreases by 14.76% in the BPL. After using the Gaussian process technique, we see similar trends of a decrease in uncertainty for all model parameters for both the W07 and BPL models. These improvements are essential for the application of GRBs as standard candles in cosmology, for the investigation of theoretical models, and for inferring the redshift of GRBs with future machine-learning analyses.

Improving Traveling Wave-Based Transmission Line Fault Location by Leveraging Classical Functions Available in Off-the-Shelf Devices

This letter presents a practical methodology to improve traveling wave (TW)-based fault location (TWFL) on transmission lines by leveraging classical single- and double-ended TWFL functions available in off-the-shelf relays and digital fault recorders. It eliminates negative effects of uncertainties in line propagation velocity and travel time, being free of time-consuming or complicated procedures. Real TW records are studied to demonstrate that the proposed solution can improve the accuracy and reliability of TWFL estimations when uncertainties about line propagation velocity or travel time exist.

Search algorithm for the reflected signal of a satellite radio altimeter

Satellite altimeters are installed on spacecraft designed to solve problems of Earth exploration, and in addition to altitude, they are able to measure the degree of sea surface roughness and some other parameters. A rough determination of the delay time of the probing signal of the altimeter is the signal search procedure, the quality of which in real conditions can be degraded by the presence of an unknown rate of change in the height of the spacecraft. The need for a search procedure arises every time the altimeter loses the reflected signal, for example, after the satellite crosses the border of the land with the water surface. The presence of the rate of change in the height of the apparatus during the search leads to a slip of the signal relative to the time scale of the altimeter, reducing the efficiency of the search. The decrease in the influence of this factor is facilitated by a reduction in the duration of coherent signal accumulation. The aim of the work is to develop an algorithm for searching for the echo signal of a space-based altimeter, less sensitive to possible changes in the height of the spacecraft while reducing the duration of the search. The paper analyzes and compares the characteristics of two echo signal search algorithms. The first algorithm is the usual one-step procedure that checks the time uncertainty zone step by step using a bank of correlators of a given size. As an alternative, a two-stage search is proposed, the first stage of which selects the points of the uncertainty zone in which the presence of a signal is suspected. At the second stage, the results of the first are retested in order to identify a single point that should be recognized as containing a signal, while the coherent accumulation time is determined by the longest stage. It is shown that the two-stage algorithm is less sensitive to changes in the altitude of the spacecraft, provides a reduction in the duration of the search with the same probability of its correct outcome, and can be recommended for use in a space radio altimeter.

Making opportunity sales in attended home delivery

Research on time window management in attended home delivery mainly focuses on influencing customers’ delivery time choices to reduce monetary and environmental costs. In this study, we adopt a different perspective and propose exploiting otherwise idle time and vehicle capacities to generate extra profits through opportunity sales for e-groceries. We consider nudging potential target customers (residing in locations that are ”easy” to insert into the delivery tours) with push notifications to generate new sales. These customers are incentivized for purchases by dropping the terms imposed on standard e-grocery sales such as service fees or minimum purchase quantities. Managing the delivery operations for this innovative business model requires concurrently choosing the target customers and planning the vehicle routes under the offer acceptance and response time uncertainties. To solve this challenging problem, we propose an integer linear programming model that enables a decomposition of the problem into a routing problem and several customer selection problems. For the solution to the customer selection problems, we propose mathematical models with varying risk-taking levels. We also investigate the benefits of dynamic policies to take advantage of the information revealed during the delivery operations in order to adjust customer selection and vehicle routing decisions. Our extensive numerical experiments show that these models, equipped with dynamic decision making, can compete with the risk-ignorant models for the total profit while generating more sales per offer, as well as ensuring timely execution of the delivery operations.

Design and Fabrication of a Test Rig to Determine Helium Permeability through Fabrics

The fabric of a Lighter-Than-Air (LTA) system experiences permeation of Helium over a period of time. Due to the small size of the molecules of Helium, it permeates through the intermolecular spaces of the fabric. This leakage increases with time due to the degradation of the fabric. LTA systems need to be deployed for long durations. The loss of Helium due to permeation reduces the relative lift of the LTA system. The unknown permeation rates thus lead to uncertainties in the deployment time of the system. This paper describes the design and fabrication of a test rig to determine the permeability of Helium through different fabrics. This method involves a permeation cell divided into upper and lower chambers arranged to hold a specimen fabric between them, with the provision of a continuous supply of Helium in the lower chamber. A Phoenix L300i Helium Leak Detector (HLD) is connected to the upper chamber to detect Helium and measure its concentration. The further objective of this research is to study the variation of helium permeability through the fabrics at constant temperature and input pressure with time.

Comparison of Corecorded Analog and Digital Systems for Characterization of Responses and Uncertainties

Abstract One of the most prominent challenges related to legacy seismic data is determining how these data can be appropriately used in modern research applications. The wide variety of instrumentation used in the analog era, the format of recording on paper wrapped around a helicorder drum, and limited metadata information introduces ambiguities that are not typical of modern digital data. Therefore, techniques must be developed to help characterize uncertainties in legacy data. This article presents an analysis that compares corecorded signals from two instruments—a Trillium Compact or Press-Ewing (PE) seismometer for sensing ground motion and two recording systems: a modern Q330 digitizer or heated-stylus system. Analyses of the recordings in both time and frequency domains indicate time uncertainty on the order of one second, identify a flat response in a 10–60 s band for the PE and drum recorder, and highlight how specific features of scans and paper seismograms (e.g., repeated portions of scans and line thickness) can cause timing jumps or reduced trace amplitude.

Adaptive FNN Backstepping Control for Nonlinear Bilateral Teleoperation With Asymmetric Time Delays and Uncertainties

Adaptive FNN Backstepping Control for Nonlinear Bilateral Teleoperation With Asymmetric Time Delays and Uncertainties

Insights on the combination of off-line and on-line measurement approaches for source apportionment studies

This paper presents a source apportionment study performed on a dataset collected at a trafficked site in Coimbra (Portugal) during the period December 2018–June 2019. The novelty of this work consists in the methodological approach used and the sensitivity study carried out to give hints to potential future applications. Indeed, a multi-time resolution and multi-parameter study was performed joining together aerosol data from 24-h chemically characterized samples and high-time resolution multi-wavelength absorption coefficients retrieved by an Aethalometer. A detailed sensitivity study on the most suitable combination of time resolution and uncertainties was carried out to obtain reliable physical and stable solutions over all analyses. In parallel, a regular EPA-PMF source apportionment study using chemical and optical variables averaged on 24 h is presented and discussed in comparison to the more complex multi-time and multi-parameter approach. Apart from results pertaining to the identification and relevance of different sources in Coimbra, the methodological results shown here can give guidance for readers who want to implement optical variables jointly with chemical ones in the same model run.

A Stochastic Modelling and Optimization for the Design of an LNG Refuelling System in the Piraeus Port Region

Port activity is assumed to be an integral part of maritime activity. Ships are supplied with water, food, fuel, electricity, spare parts and consumables when they are berthed in ports. Therefore, the purpose of the service system is to reduce the waiting cost, from the time the ship’s arrival until its service. Delays in the system, and particularly in the queuing system, could occur due to irregularities in the ships’ arrival time causing uncertainty in time of service. On the contrary, a complex waiting system would require an increased commitment of capital for the construction and maintenance of appropriate infrastructure. As such an optimum size and operation of the port service system must be identified. To address this problem, this study presents a method for maximizing port administrator’s profit, by modelling and optimizing the waiting system based on Monte Carlo simulation techniques. Port of Piraeus is used to test the method. The case study assumes increased bunkering port calls due to the addition of a liquified natural gas (LNG) refuelling station. The results showed that a range of 3 to 5 stations generate high profit while using too many service stations could lead to deteriorations of the profit.

Production planning in maintenance facilities under uncertain repair time

Production planning in maintenance facilities that rely on the availability of certified operators is a challenging task. This is primarily attributed to the need for the integration of workforce training with the operations schedule, in addition to the uncertain repair time of some components. We confine our attention to the repair and overhaul of advanced technological devices that are composed of several components. In this context, this study proposes a novel approach to embedding uncertain repair times of faulty components and develops a multi-stage stochastic programming model for integrated production and workforce planning in such facilities under independent random repair time. Our numerical experiments conducted on a range of problem instances expose the significant value of incorporating repair time uncertainty in this problem.

Data-driven integrated home service staffing and capacity planning: Stochastic optimization approaches

We address a home service staffing and capacity planning (HSCP) problem under uncertainty. Specifically, given sets of providers, service types, and days in the planning horizon, we aim to determine the number of providers to hire (staffing) and the allocation of hired providers to different types of services (capacity planning). Data from a collaborating home services provider in Beijing demonstrate significant variability in customer demand within and across service types. Service duration is also random. The objective is to minimize the total cost associated with staffing, capacity allocation, over-staffing, and under-staffing. We propose two-stage stochastic programming (SP) and data-driven distributionally robust optimization (DRO) approaches to address demand and service time uncertainty considering two types of decision-makers, namely an everything-in-advance decision-maker (EA) and a flexible adjustment decision-maker (FA). In the EA models, we determine the staffing and capacity allocation decisions in the first stage before observing the demand. In the FA models, we make staffing decisions in the first stage and then determine the allocations based on demand realizations in the second stage. We derive equivalent mixed-integer linear programming (MILP) reformulations of the proposed DRO models for the EA decision-maker that can be implemented and efficiently solved using off-the-shelf optimization software. We propose a computationally efficient column-and-constraint generation algorithm with valid inequalities to solve the proposed DRO models for the FA decision-maker. Numerical experiments based on data from a home services provider in Beijing are used to compare the proposed approaches and illustrate the potential for impact in practice.

Benefit of wind networking for aircraft arrival scheduling in terminal manoeuvring area

The effectiveness of air traffic management depends on the amount of obtained information and the accuracy of prediction, leading to a reduction in uncertainty disturbances, and an improvement in the robustness of decision-making results. Among all controlled areas, the Terminal Manoeuvring Area (TMA) is a complex and sophisticated airspace surrounding a coordinated airport in which there exists a high density of traffic, especially with arrivals converging from different directions. With the purpose of improving safety levels and operational efficiency, this paper addresses the aircraft arrival scheduling problem at the microscopic level, integrating the meteorological uncertainty based on wind networking into the optimization model. Wind uncertainty affects the flight speed, which in turn leads to uncertainty in the flight arrival time at one waypoint. Conflict detection and resolution are performed at waypoints and links in a predefined network, based on the predicted time information from the designated route. In order to optimize the time and speed for each aircraft entering TMA, this paper proposes a mathematical model that seeks to minimize the number of potential conflicts, flight delays and the number of flights deviating from the original plan under two assumptions. The first one considers regular weather forecasts for predicting the future positions of aircraft for conflict detection. The second one investigates the potential benefit for aircraft to share their local wind measure thanks to the ADSB-IN and ADSB-OUT channels. A resolution approach is proposed combining a simulated annealing algorithm and a simulation framework. Using data from a case study with 437 flights overflying the Paris Charles de Gaulle (CDG) airport TMA, we demonstrate the validity of the proposed scheduling strategy. A performance analysis is then conducted between standard forecast and wind networking.

Powrót do przeszłości V: pandemia. Nostalgia a świat postpandemiczny

One of the phenomena which scope expanded during the period of restrictions related to COVID-19 was a social turn towards the past, observed especially in social media, identified as a nostalgia trend. Understood as a sentimental longing for something that has already passed that causes sadness or pleasure by remembering something and wishing it could be experienced again, nostalgia was one of the tools for dealing with the hardships and uncertainty of the pandemic times. Individual users of social media willingly undertook communication focused around memories, expressing regret for times long gone that had been seen in their idealized form as easier and happier. This trend has been effectively used in marketing focused on nostalgia. It was only effective when it evokes positive memories and evokes good feelings in the recipients that encourages the more to reach for the product or service offered. This specific turn towards this period of time was used as a tool that allowed not having to face terrifying moments and often difficult present, and even more so the unknown future. Thus, uncertain of the future and terrified of the present, the pandemic consumer was all the more susceptible to peaceful and happy images from the past, which both algorithms and commercial entities gave him. In these circumstances, one can point to a specific management of pandemic nostalgia by entities that are essentially external to the individual user.

Understanding how the research question impacts trial design: Examples from discectomy trials

BackgroundFormulating a pertinent research question is of the utmost importance in clinical research. An ill-conceived question may lead to an erroneous trial design, which may adversely affect the care of patients and provide uninformative or even misleading results. MethodsWe review the research question of a randomized trial on the timing of lumbar discectomy. We compare the resulting design with other trials, real or hypothetical, that would have been more appropriate. ResultsThe RCT we examine randomly allocated patients to early or delayed surgery to answer a theoretical question of the effect of time on the efficacy of surgery. The trial was interpreted to have shown that early surgery was associated with better clinical and functional outcomes as compared to delayed surgery. This conclusion is clinically misleading. Valid comparisons between groups should be performed on intent-to-treat analyses and at the same time points after randomization (and not at a fixed follow-up period after surgery). The clinically pertinent comparison is not between the theoretical efficacy of surgery performed at various times, but between surgery and conservative management in patients presenting at various times. Better-designed trials on the clinical benefits of lumbar discectomy, including the treatment of chronic sciatica, have been published. ConclusionTheoretical research questions inspired from observational data can lead to erroneous trial design. Prospective randomized trials impact practice immediately: they are unique occasions to address clinical problems and optimize care under uncertainty in real time. However, they require the research question to be formulated with great care.

The Hot Strip Mill Scheduling Problem With Uncertainty: Robust Optimization Models and Solution Approaches.

In this article, we focus on a biobjective hot strip mill (HSM) scheduling problem arising in the steel industry. Besides the conventional objective regarding penalty costs, we have also considered minimizing the total starting times of rolling operations in order to reduce the energy consumption for slab reheating. The problem is complicated by the inevitable uncertainty in rolling processing times, which means deterministic scheduling models will be ineffective. To obtain robust production schedules with satisfactory performance under all possible conditions, we apply the robust optimization (RO) approach to model and solve the scheduling problem. First, an RO model and an equivalent mixed-integer linear programming model are constructed to describe the HSM scheduling problem with uncertainty. Then, we devise an improved Benders' decomposition algorithm to solve the RO model and obtain exactly optimal solutions. Next, for coping with large-sized instances, a multiobjective particle swarm optimization algorithm with an embedded local search strategy is proposed to handle the biobjective scheduling problem and find the set of Pareto-optimal solutions. Finally, we conduct extensive computational tests to verify the proposed algorithms. Results show that the exact algorithm is effective for relatively small instances and the metaheuristic algorithm can achieve satisfactory solution quality for both small- and large-sized instances of the problem.

A Robust Optimization Approach for the Resource Investment Problem of an Aircraft Final Assembly Line

In an aircraft final-assembly line (AFAL), test processes account for over 20% of all processes. And the processing times of test processes are usually uncertain owing to the fact that possible test failure brings about additional rounds of tests, which has a considerable impact on the resource investment of the AFAL. This paper studies a robust resource investment problem to minimize the resource investment cost for all realized processing times within budget uncertainty sets. A robust optimization model is formulated to determine the resource investment strategy. The robust model is reformulated and then solved by an exact solution approach based on an enhanced column and constraint generation algorithm featuring two novel acceleration techniques. Computational results demonstrate the effectiveness of our approach in solving realistic problem. Sensitivity analyses provide valuable managerial insights to real production. <i>Note to Practitioners</i>&#x2014;The uncertainty of processing time has a considerable impact on the resource investment of the aircraft final-assembly line, since longer processing time incurs more resource investment than expected and consequently delay the whole assembly process. In this article, we present a robust model to deal with the uncertainty by finding an optimal robust resource investment strategy. The model that captures the characteristics of the real assembly line is computationally tractable and solved by an enhanced algorithm that is very efficient for practical application. The proposed approach allows the shop floor managers to make a satisfactory tradeoff between the total investment cost and the completion time, the total investment cost and the levels of robustness and uncertainty by adjusting the values of related parameters. In future, we will explore more effective algorithms for solving larger instances and consider other uncertainties like resource disruption.