Random Disruptions Research Articles

A general metro timetable rescheduling approach for the minimisation of the capacity loss after random line disruption

This study proposes a generic metro timetable rescheduling method for the minimization of the capacity loss that integrates spatial and temporal information on random line disruptions and time-varying characteristics of passenger flows. The proposed emergency operating rules can be immediately deployed after a random disruption using one crossover track. The spatiotemporal information of a disruption and the current state of the line are integrated into a metro disruption management (MDM) model, which considers deviated from the original schedule and the number of stranded passengers as optimization objectives. An iterative meta-heuristic for the general metro rescheduling (IMH-GMR) algorithm is developed to flexibly classify an accident and determine rescheduling solutions for the MDM model within an effective running time (e.g., 15-60 sec). Test results show that the line capacity loss is significantly reduced (94.95%) compared with the total loss caused by the accident disposal in the test scenario.

A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet

The resilience and sustainability of supply chains are facing new challenges due to the increasing complexity of supply chains. Compared to traditional supply chain networks, the Physical Internet (PI) has the potential to address sustainable development challenges and build resilient supply chain networks by providing interconnected and open logistics services. However, the interplay between resilience and sustainability has yet to be thoroughly explored in the PI-enabled supply chain literature. This paper aims to investigate the relationship between resilience and sustainability in PI-enabled supply chains. An innovative hybrid approach that combines the resilient-sustainable performance scoring method with mixed-possibilistic programming is proposed to trade-off and analyze the relationship between the two. Applying the concepts of resilience and sustainability to PI-enabled supply chains involves optimizing costs, sustainable performance, and resilience levels simultaneously. This enables coping with unforeseen disruptions from suppliers, plants, and PI hubs. The results of computational experiments have demonstrated that the PI-enabled supply chain exhibits stronger sustainable and resilience performance in dealing with random disruptions compared to traditional supply chains. The fruitful research yielded important management insights and practical implications, thereby contributing to the reinforcement of the literature on PI.

A novel supply chain network evolving model under random and targeted disruptions

Due to the fact that there is a lack of comprehensive understanding of how the dynamic nature of supply chain networks (SCNs) interrelates with network structures, particularly network topologies under disruptions. This research employs a novel evolving model of a supply chain network (SCNE model) by modifying the Barabási and Albert (BA) model to capture the phenomenon of regional economy and the factor of firms’ attractiveness, considering the degree, the locality preference, and the heterogeneity of SCN members simultaneously. We then analyze the SCNE model via the mean-field theory and conduct simulation study to identify the scale-free characteristic of the proposed supply chain network model. Additionally, we leverage node and edge removal to emulate random and targeted disruptions. We measure and compare the robustness of four network models, i.e., the SCNE model, the Erdos and Rényi (ER) model, the BA model, and the Watts and Strogatz (WS) model using two essential metrics, i.e., the size of the largest connected component and the network efficiency. We find that the robustness of the SCNE model is better than the BA model and the WS model on the whole in the presence of disruptions. Also, from the node level, the SCNE model maintains resilience, behaving similarly to the ER model against random disruptions while it shows vulnerability under targeted disruptions, responding in line with the BA model and the WS model. From the edge level, the network efficiency of the SCNE model changes slowly, and the topological structure of the SCNE model slightly changes initially but decreases rapidly at some value, as well as the BA model, the WS model, and the ER model. Based on the results, we summarize key points of the implications for research and practice in supply chain management.

A reactive scheduling approach for the resource-constrained project scheduling problem with dynamic resource disruption

PurposeThis paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.Design/methodology/approachThis paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.FindingsThe results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.Originality/valueThe paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.

Dynamic modeling and analysis of multi-product flexible production line

ABSTRACT Mass customization has been a major challenge in the manufacturing sector. For this purpose, Flexible Manufacturing Systems (FMS) are usually designed to accommodate variations and manufacture different types of products in batches, but the products follow a single rigid path using a conveyor and in case of a failure on a single machine, the whole production line is affected. However, a multi-product dynamic production system can operate under an elongated failure on a single machine. A novel dynamic modeling method for a multi-product production line is developed to investigate dynamic properties of the system under random disruptions such as machine failures and market demand changes. Permanent production loss (PPL) and demand dissatisfaction for multi-production lines are formally defined as real-time performance metrics. A real-time analysis method is developed to evaluate the PPL and demand dissatisfaction. Numerical case studies are presented to validate the fidelity of the real-time multi-product production line model and the effectiveness of the real-time analysis method.

Impact of random and targeted disruptions on information diffusion during outbreaks

Outbreaks are complex multi-scale processes that are impacted not only by cellular dynamics and the ability of pathogens to effectively reproduce and spread, but also by population-level dynamics and the effectiveness of mitigation measures. A timely exchange of information related to the spread of novel pathogens, stay-at-home orders, and other measures can be effective at containing an infectious disease, particularly during the early stages when testing infrastructure, vaccines, and other medical interventions may not be available at scale. Using a multiplex epidemic model that consists of an information layer (modeling information exchange between individuals) and a spatially embedded epidemic layer (representing a human contact network), we study how random and targeted disruptions in the information layer (e.g., errors and intentional attacks on communication infrastructure) impact the total proportion of infections, peak prevalence (i.e., the maximum proportion of infections), and the time to reach peak prevalence. We calibrate our model to the early outbreak stages of the SARS-CoV-2 pandemic in 2020. Mitigation campaigns can still be effective under random disruptions, such as failure of information channels between a few individuals. However, targeted disruptions or sabotage of hub nodes that exchange information with a large number of individuals can abruptly change outbreak characteristics, such as the time to reach the peak of infection. Our results emphasize the importance of the availability of a robust communication infrastructure during an outbreak that can withstand both random and targeted disruptions.

A two-stage stochastic model for intermodal terminal location and freight distribution under facility disruptions

ABSTRACT A two-stage stochastic model is developed for intermodal facility location and freight distribution under random disruptions at shipper facilities and/or intermodal terminals (IMTs). The magnitude of the disruption and the impacted locations are uncertain parameters. A two-stage stochastic programming model is used to address supply uncertainty at shippers and throughput capacity uncertainty at IMTs. A level-method based decomposition approach and the L-shaped method are used to solve the model. The state of South Carolina in the U.S.A. is used as a case study with the goal of determining the set of IMT locations that minimise the total long-run network costs due to hurricane disruptions. A methodology is developed to generate realistic scenarios. The Freight Analysis Framework Version 4.5 data set is used to generate demands and supply, and k-means clustering is used with the Hurricane database (HURDAT2) to generate hurricane disruption scenarios. Sensitivity analyses are performed by varying the disruption probabilities, disruption duration, and direct shipping cost parameters. The results indicate that as disruptions increase, less disrupted intermodal facilities are opened. Also, as direct shipping costs increase, the long-term savings increase non-linearly for all magnitudes of disruptions.

A two-stage stochastic collaborative intertwined supply network design problem under multiple disruptions

An intertwined supply network (ISN) is a set of interconnected supply chains with dynamic structures that efficiently provide products and services to numerous customers. ISNs inherently involve collaboration among several enterprises that have their own supply chain. Such collaboration is pivotal in their long-term resiliency under major disruptions. This paper studies a two-stage stochastic network design problem that incorporates three different resilience strategies to design ISNs under a fair collaboration. We consider capacity expansion, capacity sharing, and rerouting as resilience strategies. We consider disruptions to be stochastic and to incorporate two sources of uncertainty: transportation costs and available capacity at facilities. We develop a Monte-Carlo simulation-based algorithm that uses the sample average approximation scheme to generate high quality solutions for our problem. We analyze the robustness and efficiency of the proposed model on a set of instances with up to 117 nodes and six collaborating enterprises. The value of collaboration on the ISN performance is assessed by a thorough sensitivity analysis of different metrics. In particular, we evaluate the impact of different levels and scales of random disruptions and flexibility on the network performance, which provides insights into the balance between cost and resilience.

A Study of Licklider Transmission Protocol in Deep-Space Communications in Presence of Link Disruptions

Delay/disruption-tolerant networking (DTN) is widely recognized as a key internetworking technology for implementing a space system of systems (SoS). Aimed at being the main data transport protocol of DTN, Licklider transmission protocol (LTP) is expected to provide reliable data transmission service in a challenging space SoS networking environment regardless of random link disruptions. In this paper, a study of LTP in deep-space communication networks in presence of link disruptions is presented. An analytical framework is developed for the effect of multiple random link disruptions on LTP's reliable data delivery in deep-space networks. The study indicates that the models accurately predict the transmission performance of LTP over a deep-space communication channel in presence of multiple link disruptions. A threshold interval value, obtained from the analytical modeling results, is proposed as a means of determining the reducibility of two successive link disruptions to a single disruption; that threshold value is verified by the experiments.

The role of RBC antigen transgene integration sites on RBC biology in mice.

Transgenic mice expressing RBC specific antigens are widely used in mechanistic studies of RBC alloimmunization. Existing RBC donor strains have random transgene integration, potentially disrupting host elements that can confound biological interpretation. Integration site and genomic alterations were characterized by both targeted locus amplification and congenic backcrossing in the five most commonly used RBC alloantigen donor strains (KEL-K2hi , KEL-K2med , and KEL-K2lo , and KEL-K1). A targeted transgenic approach was developed to allow RBC specific transgene expression from a safe harbor locus (ROSA26). Alloimmune responses were assessed by transfusing alloantigen expressing RBCs into wild-type recipients and measuring alloantibodies by flow cytometry. Four of the five analyzed strains had at least one gene disrupted by the transgene integration but none of the disrupted genes are known to be involved in RBC biology. The integration of KEL-K2med potentially altered the immunological properties of RBCs, although the biological significance of the observed changes is unclear. The ROSA26 targeted approach resulted in a single copy of the transgene that maintains RBC specific expression without random disruption of genomic elements. These findings provide a detailed characterization of genomic disruption by transgene integration found in commonly used RBC donor strains that is relevant to numerous previous publications as well as future studies. With the possible exception of KEL-K2med , transgene integration is not predicted to affect RBC biology in existing models, and new models can avoid this concern using the described targeted transgenic approach.

Friends in need: How chaperonins recognize and remodel proteins that require folding assistance.

Chaperonins are biological nanomachines that help newly translated proteins to fold by rescuing them from kinetically trapped misfolded states. Protein folding assistance by the chaperonin machinery is obligatory in vivo for a subset of proteins in the bacterial proteome. Chaperonins are large oligomeric complexes, with unusual seven fold symmetry (group I) or eight/nine fold symmetry (group II), that form double-ring constructs, enclosing a central cavity that serves as the folding chamber. Dramatic large-scale conformational changes, that take place during ATP-driven cycles, allow chaperonins to bind misfolded proteins, encapsulate them into the expanded cavity and release them back into the cellular environment, regardless of whether they are folded or not. The theory associated with the iterative annealing mechanism, which incorporated the conformational free energy landscape description of protein folding, quantitatively explains most, if not all, the available data. Misfolded conformations are associated with low energy minima in a rugged energy landscape. Random disruptions of these low energy conformations result in higher free energy, less folded, conformations that can stochastically partition into the native state. Two distinct mechanisms of annealing action have been described. Group I chaperonins (GroEL homologues in eubacteria and endosymbiotic organelles), recognize a large number of misfolded proteins non-specifically and operate through highly coordinated cooperative motions. By contrast, the less well understood group II chaperonins (CCT in Eukarya and thermosome/TF55 in Archaea), assist a selected set of substrate proteins. Sequential conformational changes within a CCT ring are observed, perhaps promoting domain-by-domain substrate folding. Chaperonins are implicated in bacterial infection, autoimmune disease, as well as protein aggregation and degradation diseases. Understanding the chaperonin mechanism and the specific proteins they rescue during the cell cycle is important not only for the fundamental aspect of protein folding in the cellular environment, but also for effective therapeutic strategies.

Sourcing decisions with uncertain time-dependent supply from an unreliable supplier

Recently, an increasing number of companies have encountered random production disruptions due to the COVID-19 pandemic. In this study, we investigate a two-stage supply chain in which a retailer can order products from a low-price (“cheap”) unreliable supplier (who may be subject to an uncertain production disruption and partially deliver the order) and an “expensive” reliable supplier at Stage 1 and a more “expensive” backup supplier at Stage 2. If the disruption happens, only the products that were produced before the disruption time can be obtained from the unreliable supplier. It is found that in the case with imperfect demand information updating, the unreliable supplier is always used while the reliable supplier can be abandoned. The time-dependent supply property of the unreliable supplier reduces the retailer's willingness of adopting the dual sourcing strategy at Stage 1, compared with the scenario with all-or-nothing supply. Different from the case with imperfect demand information updating, either the reliable or unreliable supplier can be abandoned in the case with perfect demand information updating. We derive the optimal ordering decisions and the conditions where single sourcing or dual sourcing is adopted at Stage 1. We conduct numerical experiments motivated by the sourcing problem of 3M Company in the US during the COVID-19 and observe that the unreliable supplier is more preferable when the demand uncertainty before or after the emergency order is higher. Interestingly, the retailer tends to order more from the unreliable supplier when the production disruption probability is larger in some cases.

Analysis of a maritime transport chain with information asymmetry and disruption risk

Maritime transport chain is facing huge information asymmetry after the outbreak of major emergencies, such as COVID-19 epidemic. The previous literature has proved that information investing and information sharing are two effective tactics to relieve information asymmetry between supply chain nodes, and help them improve the performance of the supply chain. This paper assumes random demand disruption is the main cause of the information asymmetry in a maritime transportation chain. To explore how the random demand disruption and channel competition jointly impact operational decisions in a dual-channel maritime transport chain composed of one port, two carriers and shippers, we construct a game-theoretical basic model, and proposed two strategies, i.e., information investing and information sharing. Several significant managerial insights are derived. First, we find that inaccurate disruption information leads to inaccurate decisions and huge losses; Second, investing in precise information benefits the port only if the chain members are optimistic about the market, and improves the revenue of the carrier who invested in information if the investment cost is reasonable; Third, accepting information sharing benefits the port only when the precise disruption and the distortion of information are relatively large, as well as the misappropriate rate is relatively small; and only when the port is pessimistic about the market or the channel competition is weak, sharing information may hurt the carrier who invested in information. Finally, the strength of the channel competition will enhance the impact of information inaccuracy on the maritime transport chain.

Chiral Open-[60]Fullerene Ligands with Giant Dissymmetry Factors.

The optical resolution of open-[60]fullerenes has been limited to only one example since 1998, while the recent advances revealed the excellence of fullerenes as revisited chiral functional materials. Different from conventional chiral induction on [60]fullerene by a multiple-functionalization, a random disruption of the spherical π-conjugation is avoidable for open-[60]fullerenes. Moreover, the macrocyclic orifices enable a metal coordination which endows modulated electronic structures on chiral chromophores. Herein, we showcase Li+-coordination behavior and optical resolution of three chiral open-[60]fullerene ligands, showing a giant dissymmetry factor up to 0.20 owing to a congenital topology of the spherical π-conjugation.

Interdependent effects of critical infrastructure systems under different types of disruptions

Modern critical infrastructure systems (CISs) are becoming increasingly dependent on each other for proper operation, which also exposes the systems to higher risks of cascading failures. Prior research has attempted to quantify the interdependent effect (IE) of CISs under random or intentional disruptions. However, those efforts are limited with respect to both the methodological approaches for simulating intentional disruptions and CISs failure propagation, and the granularity and quantification method of the simulated disruption impacts. To address these limitations, this study proposes a new framework for analyzing the IE of CISs. The framework first models interdependent CISs with a high level architecture (HLA)-based simulation approach that integrates existing knowledge, data, and models specific to each infrastructure domain. It then co-simulates the failure propagation through interdependent CISs under three different types of disruptions, namely random, topology-based and flow-based disruptions. Based on the simulation outputs, the framework quantifies the IE by comparing the disruption-induced system performance losses, which are measured with three different performance metrics, under both interdependent and independent conditions. A case study of two interdependent water and power supply systems was conducted to demonstrate the efficacy of the proposed framework. The case study results revealed a few new insights into the vulnerability of interdependent CISs under different disruption scenarios, which have important implications for the risk assessment and resilience management of interdependent CISs in practice.

Planning and scheduling of a parallel-machine production system subject to disruptions and physical distancing

Abstract This paper aims to quantify the effects of production disruptions (PDs) and physical distancing constraints due to the pandemic in a parallel-machine production environment. The machines are non-identical and are utilized for producing a finite set of jobs (parts) in a plastic injection moulding production. The production process is subjected to random production downtime disruptions. A mixed-integer linear programming (MILP) model is developed for optimizing the joint production plan and schedule, which maximizes the production’s total benefit. The model is utilized to plan and schedule a set of 17 machines in a Canadian manufacturing company. To explore the effects of physical distancing and PDs on the production’s total net profit, four different scenarios for normal operation and production during the pandemic, with and without production downtimes, are considered. A genetic algorithm is utilized to solve the model. The results show that considering machines’ random breakdowns and physical distancing individually reduces the total profit of the production by 71.58 and 57.98%, respectively; while their joint effect results in a 88.54% reduction in the annual net profit.

Resilient Urban Public Transportation Infrastructure: A Comparison of Five Flow-Weighted Metro Networks in Terms of the Resilience Cycle Framework

Real-world infrastructures modeled as complex networks have been popular topics in recent decades. As these infrastructures develop, becoming critical to support millions of people, their resilience in the face of disasters and attacks becomes an increasing concern to the engineers and stakeholders. Using a graph theory and complex network approach, we conducted an empirical study on the resilience of five metro, or subway systems selected globally. A multi-phase resilience framework was implemented from the resilience cycle concept, including preparedness, robustness, recovery, and adaptation. It also incorporated the historical flow data into the resilience analysis to account for user behavior impacts. The paper demonstrates a systematic comparison of five metro networks and explores trends in each resilience phase. Generally, diverse performances were observed among the five metro networks, with the Singapore metro showing the best resilience. It is robust to both random disruption and targeted attacks. Analysis suggests its advantage may come from both its moderate heterogeneous topology and the flow pattern, exhibiting the smallest average travel distance. Besides, the result highlights the significance of commuters’ relocation behavior between neighboring stations in the recovery stage. For the adaptation from a long-term perspective, we developed a screening tool to investigate the impact of the network expansion on the existing part of network by mapping its relationship with the characteristics of the newly connected nodes. In general, the observation of the networks’ different behaviors in flow-weighted analysis symbolizes that flow is necessary for consideration in the resilience design of real-world transportation systems.

Information technology-driven operational decisions in a supply chain with random demand disruption and reference effect

Frequent occurrence of emergencies results in the persistence of unexpected demand disruption, which adds complexity to the supply chain and leads to inaccurate decisions easily. Precise information can generally help decision-makers to make accurate decisions, and sharing information between supply chain nodes is powerful in improving the performance of the supply chain. This paper examines how the random demand disruption and the consumers’ reference effect impact operational decisions, including pricing, information collection and sharing, in a dual-channel supply chain composed of one supplier and one platform. Three progressive game-theoretical models are proposed. We find that inaccurate disruption information brings substantial losses to the supply chain, and the reference effect will aggravate the loss for the platform but mitigate the loss for the supplier. When the degree of information distortion is high, the platform invests in new information technologies to collect precise information will benefit him, but will hurt the supplier. At this moment, if the supplier is willing to accept an information sharing contract with the platform, the platform will always choose a revenue sharing scheme, while the supplier wants the platform to choose a fixed fee scheme, especially when the reference effect is weak. Oppositely, when the degree of information distortion is small, the supplier should not accept the information sharing contract.

Blockchained smart contract pyramid-driven multi-agent autonomous process control for resilient individualised manufacturing towards Industry 5.0

The production control for the mass individualisation paradigm of R&D-stage products is challenging due to the mix-flow and frequently-disturbed environment. With the convergence of the sustainable development goals and the increasing individualised demands in products, resilient manufacturing is envisioned in Industry 5.0 proposition. Concerning that conventional centralised production control methods suffer from low stability and inefficiency of decisions under frequent disruptions, this paper establishes a blockchained smart contract pyramid-driven multi-agent autonomous process control (BSCP-MAAPC) approach for improving the timeliness and adaptability of control towards resilient individualised manufacturing. Firstly, a blockchain-based multi-agent system architecture is designed based on agent encapsulation of manufacturing units. Blockchained smart contracts are used as the enabler of the multi-agent system for peer-to-peer negotiation and coordination of tasks. Secondly, a quad-play blockchained smart contract pyramid together with a series of decentralised control patterns are designed to enable the initial task dispatching of various individualised demands, as well as rapid dynamic adjustment of schedule in response to internal random disruptions. Finally, a blockchained smart contract pyramid-driven multi-agent autonomous process control system prototype is built in the ManuChain system, and experiments are conducted to analyze the proposed BSCP-MAAPC approach in different environments. HIGHLIGHTS A blockchained smart contract pyramid-driven multi-agent autonomous process control (BSCP-MAAPC) approach. A quad-play blockchained smart contract pyramid together with a series of decentralized control patterns. Blockchained smart contracts as the enabler of the multi-agent system for peer-to-peer negotiation and coordination of tasks.

Key graph properties affecting transport efficiency of flip-flop Grover percolated quantum walks

Quantum walks exhibit properties without classical analogues. One of those is the phenomenon of asymptotic trapping---there can be nonzero probability of the quantum walker being localized in a finite part of the underlying graph indefinitely even though locally all directions of movement are assigned nonzero amplitudes at each step. We study quantum walks with the flip-flop shift operator and the Grover coin, where this effect has been identified previously. For the version of the walk further modified by a random dynamical disruption of the graph (percolated quantum walks) we provide a recipe for the construction of a complete basis of the subspace of trapped states allowing to determine the asymptotic probability of trapping for arbitrary finite connected simple graphs, thus significantly generalizing the previously known result restricted to planar 3-regular graphs. We show how the position of the source and sink together with the graph geometry and its modifications affect the excitation transport. This gives us a deep insight into processes where elongation or addition of dead-end subgraphs may surprisingly result in enhanced transport and we design graphs exhibiting this pronounced behavior. In some cases this even provides closed-form formulas for the asymptotic transport probability in dependence on some structure parameters of the graphs.