Random Disruptions Research Articles

Distributed Resilient Estimator Design for Positive Systems Under Topological Attacks

This article is concerned with the distributed resilient estimation of a positive system over a sensor network. First, a heterogeneous sensor interaction framework, where each sensor is capable of sharing its local information of measurement as well as state estimate with its underlying neighbors via distinct interaction topologies, is proposed to account for different sensor communication capacities. During the information exchanges among the sensors, topological attacks are suitably modeled in such a way to incorporate the random and intermittent disruption of the heterogeneous sensor interaction topologies. Second, two sets of distributed resilient estimators are delicately constructed to cope with the resulting random denial of information exchanges within the specific repaired periods and compromised periods caused by the topological attacks. Third, the resilience performance analysis with a prescribed l1 -gain attenuation level is carried out, and a linear programming approach is then developed to achieve the design of the desired distributed estimators. Finally, the effectiveness of the proposed design method is verified through a vehicle formation monitoring system.

Dynamic and robust timetable rescheduling for uncertain railway disruptions

Unexpected disruptions occur frequently in the railways, during which many train services cannot run as scheduled. This paper deals with timetable rescheduling during such disruptions, particularly in the case where all tracks between two stations are blocked for hours. In practice, a disruption may become shorter or longer than predicted. To take the uncertainty of the disruption duration into account, this paper formulates the timetable rescheduling as a rolling horizon two-stage stochastic programming problem in deterministic equivalent form. The random disruption duration is assumed to have a finite number of possible realizations, called scenarios, with given probabilities. Every time a prediction about the range of the disruption end time is updated, new scenarios are defined, and a two-stage stochastic model computes the optimal rescheduling solution to all these scenarios. The stochastic method was tested on a part of the Dutch railways, and compared to a deterministic rolling-horizon method. The results showed that compared to the deterministic method, the stochastic method is more likely to generate better rescheduling solutions for uncertain disruptions by less train cancellations and/or delays, while the solution robustness can be affected by the predicted range regarding the disruption end time.

Reliable Hub Location Model for Air Transportation Networks Under Random Disruptions

Problem definition: In this paper, we study a reliable hub location model, the objective of which is to minimize the associated costs plus the penalty for unserved demands. The model assigns to eac...

A stochastic programming model with endogenous and exogenous uncertainty for reliable network design under random disruption

Designing and maintaining a reliable and efficient transportation network is an important industrial problem. Integrating infrastructure protection with the network design model is efficient as these models provide strategic decisions to make a transportation network simultaneously efficient and reliable. We studied a combined network design and infrastructure protection problem subject to random disruptions where the protection is imperfect and multi-level and the effect of disruption is imperfect. In this research, we modeled a resource-constrained decision maker seeking to optimally allocate protection resources to the facilities, and construct links in the network to minimize the expected post-disruption transportation cost (PDTC). We modeled the problem as a two-stage stochastic program with both endogenous and exogenous uncertainty: a facility’s post-disruption capacity depends probabilistically on the protection decision, making the uncertainty endogenous, while the link construction decision directly affects the transportation decision. We implemented an accelerated L-shaped algorithm to solve the model and predictive modeling techniques to estimate the probability of a facility’s post-disruption capacity for a given protection and disruption intensity. Numerical results show that solution quality is sensitive to the number of protection levels modeled; average reduction in the expected PDTC is 18.7% as the number of protection levels increases from 2 to 5. Results demonstrate that the mean value model performs very poorly as the uncertainty increases. Results also indicate that the stochastic programming model is sensitive to the estimation error of the predictive modeling techniques; on average the expected PDTC becomes 6.38% higher for using the least accurate prediction model.

A coordinated location-inventory problem with supply disruptions: A two-phase queuing theory–optimization model approach

Despite their prominent role in real-life, supply disruptions and inherent variability in parameters have been virtually overlooked in the area of the location-inventory problem. To push forward the relevant literature, addressing the aforementioned issues is of paramount importance. In this sense, this paper deals with a coordinated location-inventory problem in a stochastic supply chain system where facilities are subject to random supply disruptions. The demand and replenishment lead-times are also considered to be hemmed in by uncertainties. To tackle the location-inventory problem in the concerned supply chain, a two-phase approach based on the queuing theory and optimization model techniques is devised. Applying the queuing theory, the first phase tackles the uncertainty issues and gains some performance measures of the system. The attained results are later incorporated into the optimization model. In the second phase, the optimization model determines the strategic and tactical decisions across the supply chain in an integrated manner. Because the presented model is complicated to solve through exact methods, a tailored hybrid genetic algorithm embedded with direct search method is exploited, which is capable of finding quality solutions in an efficient way. Eventually, various sensitivity analyses are conducted from which interesting insights are derived.

The evolving topology of the Lightning Network: Centralization, efficiency, robustness, synchronization, and anonymity.

The Lightning Network (LN) was released on Bitcoin’s mainnet in January 2018 as a solution to favor scalability. This work analyses the evolution of the LN during its first year of existence in order to assess its impact over some of the core fundamentals of Bitcoin, such as: node centralization, resilience against attacks and disruptions, anonymity of users, autonomous coordination of its members. Using a network theory approach, we find that the LN represents a centralized configuration with few highly active nodes playing as hubs in that system. We show that the removal of these central nodes is likely to generate a remarkable drop in the LN’s efficiency, while the network appears robust to random disruptions. In addition, we observe that improvements in efficiency during the sample period are primarily due to the increase in the capacity installed on the channels, while nodes’ synchronization does not emerge as a distinctive feature of the LN. Finally, the analysis of the structure of the network suggests a good preservation of nodes’ identity against attackers with prior knowledge about topological characteristics of their targets, but also that LN is probably weak against attackers that are within the system.

Origin and Evolution of Cometary Nuclei

One of the key goals of the Rosetta mission was to understand how, where and when comets formed in our solar system. There are two major hypotheses for the origin of comets, both pre-Rosetta: (1) hierarchical accretion of dust and ice grains in the Solar Nebula and (2) the growth of pebbles, which are then brought together by streaming instabilities in the Solar Nebula to form larger bodies. Rosetta provided a wealth of new information on comet nuclei and confirmed many past ideas on comets, e.g., high volatile content, lack of aqueous alteration of grains, and the low bulk density of the nucleus. Rosetta also provided new data on the nature of cometary activity, the active geology on the nucleus surface and the interior structure and bulk density of the nucleus. Supporters of the above-mentioned origin hypotheses each find confirmation of their ideas in the Rosetta results. But the question of which hypothesis is preferred, or if there are other, better hypotheses that could be invoked, could not be answered. Theoretical studies suggest that comet nuclei were collisionally processed in the Primordial Disk though it is not clear that the nuclei we see today display the effects of that process. Both theoretical and observational studies suggest that the major end-states for cometary nuclei are dynamical ejection, random disruption and disintegration, and/or evolution of nuclei to inactive, asteroidal-appearing objects. Rosetta has provided us with many new insights that will help to guide future cometary missions, observations, experiments and theoretical investigations that will lead to answers to the fundamental questions with regard to cometary origin.

A Markovian production-inventory system with consideration of random quality disruption

A Markovian production-inventory system with consideration of random quality disruption

Supply Chain Design Under Disruptions Considering Risk Mitigation Strategies for Robustness and Resiliency

Disruptions have become more commonplace in the globalised business environment; thus, many firms have been attempting to find risk mitigation strategies (RMS) to create robust and resilient supply chains. To this extent, this study evaluates the performance of various supply chains utilising different RMS to cope with disruptions using optimisation and simulation. In the optimisation phase, mixed integer programming is used to create a three-echelon supply chain design. Then, the initial supply chain is further configured to incorporate two RMS for robustness (i.e., redundancy and multi-sourcing). In the simulation phase, the aforementioned supply chains, with and without RMS for robustness, are exposed to random disruptions of various severity levels, and the effectiveness of four RMS for resiliency (e.g., visibility, flexibility) are tested using key performance indicators such as order fulfilment rates and profits. The results indicate that the RMS for robustness alone is better suited under mild to moderate disruptions while a combination of RMS for robustness and resiliency is essential to handle severe disruptions.

Managing supply and transportation disruptions: a case of Indian fair price shops

Purpose India has the largest public distribution system (PDS) in the world, working through over five million fair price shops (FPS) to distribute food grains among its beneficiaries at a subsidized rate. In this paper, the authors study the inventory system of Indian FPS. The system involves a distributor, who is solely responsible for the replenishment of the FPS. In a real-world scenario, the distributor is subjected to random supply and transportation disruptions. The purpose of this paper is to investigate and minimize the impacts of such disruptions. Design/methodology/approach In this paper, the authors adopt a simulation-based technique to explore the impacts of various traits of disruptions like frequency and duration on the FPS inventory system. A simulation model for the Indian FPS is developed and the impacts of disruptions are investigated by a case study. Findings The authors use a simulation-based optimization technique to suggest a simple managerial change that can lead to a minimization of inventory shortage up to 60 per cent and system cost up to 21 per cent over the existing practice. Originality/value The present study addresses the FPS inventory system of Indian PDS, which is by its nature unique and has not been considered by any other previous literature. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.

Designing a resilient skip-stop schedule in rapid rail transit using a simulation-based optimization methodology

In recent years, rapid rail transit systems have played a unique role in transportation systems due to the demand increase in accommodating passengers. This study proposes a simulation–optimization method to improve the resiliency of the train timetable in rapid transit rail lines under uncertainty associated with the passenger flow and train running times. The aim is to evaluate the resiliency of the train timetable through a discrete-event simulation (DES) model and to provide an optimized schedule with the maximum degree of resiliency against random disruptions caused by passenger flow fluctuations. The problem is first formulated as a mixed-integer nonlinear programming model. The validity of the DES model is justified using convergence test analysis of the response variable, i.e., average passenger wait time, during the simulation run. Due to the complexity of the problem, a variable neighborhood search (VNS) and a genetic algorithm (GA) are proposed to solve large instances of the problem. A self-adaptive tuning approach is proposed to adjust the GA parameters. The benefit of the simulation–optimization approach is verified through numerical experiments based on real cases adopted from Line No. 1 of the Tehran underground metro system. The results indicate that the simulation-based optimization method could improve the resiliency of train services by almost 16.7%, on average, as against the all-stop service operation. The average improvement of using VNS as against the GA is about 47%. Also, VNS method provides better-quality solutions by average optimality gap of about 14% in all test instances when compared to an exact solution method, i.e., branch-and-reduce algorithm.

Energy-economic resilience with multi-region input–output linear programming models

In this work we propose an input–output linear programming model to study the energy resilience of a multi-region economy that accounts for the inter-sectoral and inter-regional dependencies in the economic structure. The model computes the impacts of random energy production disruptions on the entire multi-region input–output system in the form of final demand deficits. We then propose a formulation to evaluate the energy resilience of the system based on computing the largest set of disruptions, or what is termed as resilience guarantee levels, that can be sustained by the system within given demand deficit budgets. A computational study is then performed using a 3-region-42-sector model based on China's 2012 multi-regional input–output table. The results show that regional trade barriers, coal-to-gas switch policies and technological efficiency of selected energy-intensive industries can influence China's energy resilience significantly. This can provide the policy-makers important guidance on improving energy resilience in a systematic manner.

A multi-objective robust supply chain design considering reliability

ABSTRACTThis paper designed a four-echelon supply chain network consist of suppliers, manufacturers, distributors and customers. Here, the allocation of raw materials and products is subject to random disruptions based on weather conditions, natural events, and ownership changes. This is an unprecedented research to simultaneously consider the parameters uncertainty and reliability of the components in a dynamic chain with multiple products. The model is a multi-objective mixed integer linear programming into which the uncertainty is incorporated by using the robust optimization. The model seeks to minimize the network’s costs while the both manufacturer and customer satisfaction are maximized. To solve the model, three multi-objective decision-making methods are investigated and the best method is selected according to its performance in several examples. Also, suppliers and distributors with the highest reliability are obtained which, it enables managers to make the proper decision on both strategic and operational levels of their firms’ policies.

A production inventory supply chain model with partial backordering and disruption under triangular linguistic dense fuzzy lock set approach

This paper focuses on a three-level distribution process in a supply chain (SC) modeling where the raw materials are received batchwise with imperfect quality. Defective batches are rejected instantly under “all or none” policy. Allowing partial backlogging and random disruption in supply, we develop an expected average cost function of the production inventory SC model first. Then, considering the several cost components of the model as linguistic triangular dense fuzzy lock set, the cost function itself has been fuzzified according to the needs of the problem defined at case study. Utilizing the proper application (growth) of key vectors, the objective function has been solved under crisp, general fuzzy, dense fuzzy, dense fuzzy lock of single and double keys environment, respectively. For managerial importance, numerical results and graphical illustrations are made to justify the novelty.

An EPQ model for three-layer supply chain with partial backordering and disruption: Triangular dense fuzzy lock set approach

This article deals with three-echelon supply chain (SC) network involving flow of raw materials with imperfect quality, the manufacturer and multiple retailers under the effect of learning experiences in fuzzy decision-making process. Existing literature explores the SC model under full backordering and disruption. Thus, in this study we first develop a production-inventory control problem accompanied with partial backlogging and random disruptions. Any batch received from the supplier is inspected by the manufacturer and if any of them are found to be flawed then all the goods in the inspected batch are rejected. However, we present a case study for problem definition and to comprehend the model into practical applicability. To minimize the aggregate cost of the SC we have utilized the Triangular dense fuzzy lock set for controlling the cost vector of the proposed objective function of the model. Utilizing new defuzzification method and applying the proper keys, chosen by the decision maker, it is possible to minimize the average system cost exclusively. Finally, graphical illustrations and sensitivity analysis are made to justify the model.

Robust Fuzzy chance constraint programming for multi-item EOQ model with random disruption and partial backordering under uncertainty

ABSTRACTIn this paper, a new mathematical model for multi-item Economic Order Quantity model is proposed considering defective supply batches and partial backordering. The proposed model assumes that the supply batches may be defective and can be rejected. The aim is to minimize total inventory costs by determining optimal values of the decision variables including Time interval between successive perfect supply deliveries. Since in real-world situations the main parameters are uncertain, two mathematical programming approaches called: Basic Chance Constraint Programming and Robust Fuzzy Chance Constraint programming are used to handle uncertain parameters. The performance of the mathematical programming models is investigated in a numerical example. The results show that the RFCCP model is able to provide risk-averse solutions comparing to the BCCP model. In the end, sensitivity analyses are performed to determine the effect of any change in the main parameters on objective function value to determine the most critical parameters

An optimization approach to planning rail hazmat shipments in the presence of random disruptions

Hazardous materials (hazmat) shipments are integral to the development of any industrial society, and railroad is one the primary transportation modes in North America. However, the current academic literature has not studied the impact of disruption on rail hazmat shipments. This study proposes a bi-objective two-stage stochastic program that makes use a of new measure of risk to plan rail shipments in the presence of random disruptions. Complexity of the resulting mathematical model motivated the development of an augmented ɛ-constraint solution technique, which was used to study the railroad infrastructure in Midwest United States. The resulting analyses throw light on the trade-off between hazmat risk versus cost, and suggest that deploying contingency plans for disruptions can significantly mitigate hazmat risks with only a slight increase in total cost. We also find that with less than 1% increase in the total cost, our model reduces the variability in risk by as much as 99% in our case study.

A latent-class adaptive routing choice model in stochastic time-dependent networks

Transportation networks are inherently uncertain due to random disruptions; meanwhile, real-time information potentially helps travelers adapt to realized traffic conditions and make better route choices under such disruptions. Modeling adaptive route choice behavior is essential in evaluating real-time traveler information systems and related policies. This research contributes to the state of the art by developing a latent-class routing policy choice model in a stochastic time-dependent network with revealed preference data. A routing policy is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions on the link to take next. It represents a traveler’s ability to look ahead in order to incorporate real-time information not yet available at the time of decision.A case study is conducted in Stockholm, Sweden and data for the stochastic time-dependent network are generated from hired taxi Global Positioning System (GPS) readings. A latent-class Policy Size Logit model is specified, with routing policy users who follow routing policies and path users who follow fixed paths. Two additional layers of latency in the measurement equation are accounted for: 1) the choice of a routing policy is latent and only its realized path on a given day can be observed; and 2) when GPS readings have relatively long gaps, the realized path cannot be uniquely identified, and the likelihood of observing vehicle traces with non-consecutive links is instead maximized.Routing policy choice set generation is based on the generalization of path choice set generation methods. The generated choice sets achieve 95% coverage for 100% overlap threshold after correcting GPS mistakes and breaking up trips with intermediate stops, and further achieve 100% coverage for 90% overlap threshold.Estimation results show that the routing policy user class probability increases with trip length, and the latent-class routing policy choice model fits the data better than a single-class path choice or routing policy choice model. This suggests that travelers are heterogeneous in terms of their ability and/or willingness to plan ahead and utilize real-time information, and an appropriate route choice model for uncertain networks should take into account the underlying stochastic travel times and structured traveler heterogeneity in terms of real-time information utilization.

Study of Multidrug Efflux System Protein Degradation in E. coli Using Transposons Library

AcrB is a trimeric multidrug efflux pump in E. coli belonging to the Resistance‐Nodulation‐Division (RND) super family. We have previously found that the introduction of a small peptide tag, the ssrA tag, to the C‐terminus of AcrB lead to efficient degradation of the protein. Soluble proteases ClpP and ClpX have been found to be involved in the degradation. However, a significant reduction in the amount of AcrB‐ssrA could still be detected in knockout strains lacking functional ClpXP. We speculate that the reduction of AcrB‐ssrA level is due to another protease/proteases. This study focuses on the identification of other proteins capable of degrading AcrB‐ssrA. Transposons libraries containing random gene disruption were generated and screened for clones displaying increased expression of AcrB‐ssrA. Our hypothesis is that the increased activity of AcrB‐ssrA protein is due to a mutation or inactivation of a gene that is involved in the degradation of AcrB‐ssrA. The elevated level of AcrB‐ssrA expression was then confirmed using anti‐AcrB antibody and minimum inhibitory concentration assays. Several candidate genes have been identified, which we are currently characterizing for their potential role in AcrB‐ssrA degradation.Support or Funding InformationNSF grant CHE‐1709381This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Analysis of Licklider Transmission Protocol for Reliable File Delivery in Space Vehicle Communications With Random Link Interruptions

Delay/disruption tolerant networking (DTN) was proposed as a networking architecture for reliable data delivery in space vehicle communications despite long propagation delays and/or lengthy link disruptions. As one of the main data transport protocols of DTN, Licklider transmission protocol (LTP) was proposed by the NASA for reliable data delivery in challenging space environments in which the existing transport protocols of DTN, TCP, and UDP do not perform effectively. Pair-wise comparisons among LTP, TCP, and UDP running in the DTN stack have previously been done using statistical methods; evaluation results and performance analysis are available in the existing LTP research literature. However, there is as yet no solid analytical model of LTP performance in the presence of link disruptions. In this paper, we present an analytical method to predict transmission performance of LTP for reliable file delivery in space vehicle communications with focus on the expected file delivery delay (or latency) over space channels characterized by random link disruptions. Realistic file-transmission experiments using a PC-based infrastructure are presented to validate the analytical models. The analysis and study results presented in this paper are expected to be useful in understanding the impact of transmission conditions and configurations, on the efficiency of LTP when deployed for reliable data delivery in space missions using communication links that are subject to frequent and random interruptions.