Disruption Propagation Research Articles

Deep imperative mutations have less impact

Information theory and entropy loss predict deeper more hierarchical software will be more robust. Suggesting silent errors and equivalent mutations will be more common in deeper code, highly structured code will be hard to test, so explaining best practise preference for unit testing of small methods rather than system wide analysis. Using the genetic improvement (GI) tool MAGPIE, we measure the impact of source code mutations and how this varies with execution depth in two diverse multi-level nested software. gem5 is a million line single threaded state-of-the-art C++ discrete time VLSI circuit simulator, whilst PARSEC VIPS is a non-deterministic parallel computing multi-threaded image processing benchmark written in C. More than 28–53% of mutants compile and generate identical results to the original program. We observe 12% and 16% Failed Disruption Propagation (FDP). Excluding internal errors, exceptions and asserts, here most faults below about 30 nested function levels which are Executed and Infect data or divert control flow are not Propagated to the output, i.e. these deep PIE changes have no visible external effect. Suggesting automatic software engineering on highly structured code will be hard.

Tracing the propagation of disruptions in supply chain scenarios: A case study of photovoltaics diversification

Diversifying supply chains through reshoring and friendshoring is increasingly proposed as a key strategy for supply security and resilience. Quantitative analyses characterizing to what extend diversification shield countries from supply disruptions remain however scarce. In this paper, we present a methodology to assess the supply risk exposure of countries in different supply diversification scenarios – business-as-usual, reshoring, friendshoring. For each scenario, the propagation of three types of upstream disruptions – supply shortage, export restriction, bilateral trade conflict – is simulated. A fragility ratio metric is introduced to quantify the potential downstream shortages caused by these disruptions. A novel friendshoring modelling approach is also proposed. It consists in determining risk-optimized trade relations based on criteria such as supply concentration and UN voting similarity.The Python-based model is tested on the case of diversified photovoltaics supply chains, e.g., if the US, EU, and India increase domestic production from polysilicon to module. Beyond building up manufacturing capacities, choosing between vertical integration and trade is highly determinant in risk exposure. Each diversification scenario shows pros and cons depending on the country and process considered.Overall, this paper underlines the need for supply risk research to nuance diversification recommendations. It would be particularly helpful to improve indicators accounting for a region's technical and economic ability to supply a given product, and to realistically model the challenges of reshoring.

Optimisation of recovery policies in the era of supply chain disruptions: a system dynamics and reinforcement learning approach

Incidents like the COVID-19 pandemic or military conflicts disrupted global supply chains, causing long-lasting shortages in multiple sectors. This so-called ripple effect denotes the propagation of disruptions to further elements of the supply chain. Due to the severity of the impact that the ripple effect has on revenues, service levels, and reputation among supply chain entities, it is essential to understand the related implications. Given the unpredictable nature of disrupting events, this study emphasises the value of a reactive development of effective recovery policies on an operational level. In this article, a system dynamics model for a supply chain is used as framework to investigate the ripple effect. Based on this model, recovery policies are generated using reinforcement learning (RL), which represents a novel approach in this context. As main findings, the experimental results demonstrate the applicability of the proposed approach in mitigating the ripple effect based on secondary data from a major aerospace and defence supply chain and furthermore, the results indicate a broad applicability of the approach without the need for complete information about the disruption characteristics and supply chain entities. With further refinement and real-world implementation, the presented approach provides the potential to enhance supply chain resilience in practice.

Research on recovery strategies of supply chain network under disruption propagation using memetic algorithm

Research on recovery strategies of supply chain network under disruption propagation using memetic algorithm

Vulnerability to geopolitical disruptions of the global electric vehicle lithium-ion battery supply chain network

In the rapidly expanding global electric vehicle lithium-ion battery supply chain network (EV LIB SCN), intricate intercontinental and interrelated connections render it susceptible to geopolitical disturbances. The complex supplier–buyer dynamics within this network facilitate the propagation of disruptions, complicating the identification and evaluation of the consequences of geopolitical disruptions. Existing recommendations, grounded in macro-level material flow data, offer limited guidance for EV LIB-associated enterprises in managing such disruptions. To address this gap, this study constructs a meso-level EV LIB SCN incorporating realistic supplier–buyer relationship data among relevant firms. A supply chain network vulnerability (SCNV) index has been designed to quantify the disruption impact. Furthermore, a geopolitical disruption diffusion (GDD) model has been developed to simulate the disruption propagation in two distinct scenarios, i.e., single-region blockade and inter-region blockade. This study broadens the scope of EV LIB SCN investigations from macro- to meso-level analyses, and the evaluation of both direct and indirect impacts of geopolitical disruptions enhances comprehension of geopolitical disruption risks in global supply chain networks.

A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system

A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system

Open to Evolve Embodied Intelligence

For the goal of automatically evolving Embodied Intelligence (EI), we investigate an open software architecture inspired by the high surface area to volume ratio of animal lungs, which aims to avoid information theoretic limits on long term evolution experiments (LTEE) encountered with monolithic genetic programming trees. Instead individuals are teams composed of 1023 trees whose inputs and outputs are linked by a low entropy loss branching data (air) pathway. Most trees are shallow and software engineering’s failed disruption propagation (FDP) is observed in the small fraction of deep trees. After initial search, most improvements are at intermediate depths and performance is still rising even after 100 000 generations.Despite the use of double precision for the bifurcating data interconnect, some information loss is seen, particularly in early generations. The static optimisation benchmark, appears to encourage early convergence, which locks the population into possibly sub-optimal phenotypes. Later thousands of small improvements, sometimes in large bloated ensemble members, appear to compensate for early overfitting.Using tournament fitness selection and subtree crossover, we target pure nested side-effect free floating point functions, which are known to have low FDP, and high fidelity data paths, in the hope of generating code which is not too robust so as to prevent on going improvement. However, we again find genetic changes deep within trees are silent. For single precision, we find a maximum evolvability sweet spot with trees of depth 10 to 100. Accordingly, we suggest to evolve very large very complex programs needed for Embodied Intelligence, an open structure with a high surface area permitting most mutation sites to be within 10-100 levels of the organism’s environment, and many better placed test oracles to monitor the impact of mutated code, will be needed.

Modelling and analysing supply chain disruption: a case of online grocery retailer

Supply Chains (SCs) are becoming more vulnerable to disruption risks because of globalisation, competitiveness, and uncertainties. This study is motivated by an online grocery retailer in the UK that experienced multiple disruption risks, such as demand and supply shocks, facility closures, and disruption propagation simultaneously in 2020. The main purpose of this study is to model and perform quantitative analyses of a range of SC disruption risks affecting the UK online retailer. We have attempted to study how UK retailers responded to the first and second waves of the pandemic and the effect on multiple products. Six scenarios are developed based on SC disruption risks and their impacts on SC performance are analysed. The quantitative analysis of two strategies used by grocery retailers during the pandemic, namely vulnerable priority delivery slots and rationing of products, illustrates that rationing of products had a greater SC impact than the use of priority delivery slots. The effects of two resilience strategies, backup supplier and ramping up distribution centre capacity, are also quantified and discussed. Novel managerial insights and theoretical implications are discussed to make online grocery SC more resilient and robust during future disruptions.

Evaluating supply chain network resilience considering disruption propagation

Amidst the current global disruptions on supply chains, having a comprehensive framework for evaluating supply chain resilience is of utmost importance. However, existing techniques suffer from limitations such as a lack of comprehensiveness and failure to account for the ripple effect, leading to biased assessments. To address these challenges, this study proposes a comprehensive evaluation scheme that quantitatively describes the state of supply chain resilience under disruption propagation. The proposed approach introduces six metrics to assess the absorptive, adaptive, and restorative capacities of resilience. By considering these metrics, a disruption/recovery simulation procedure is employed to evaluate the resilience of three network structures widely found in real-world supply chain problems. The results indicate that networks exhibiting scale-free structure offer enhanced reliability, responsiveness, persistence, and quicker recovery time. To validate the applicability of the proposed approach in assessing supply chain resilience, it is implemented on two real-world supply chain networks. Additionally, this study uncovers the limitations of single-measure methodologies, which overlook the multi-faceted nature of resilience. It highlights the shortcomings of approaches that fail to accurately reflect resilience behaviour in the presence of ripple effects. These findings emphasize the significance of adopting a more comprehensive evaluation scheme, as proposed in this study, to achieve accurate results.

Bi-objective optimization for supply chain ripple effect management under disruption risks with supplier actions

In practice, supplier actions are often taken to reduce the impact of disruption propagation in the supply chain and ensure continuity of material flows. However, these actions can be very costly. The selection of appropriate supplier actions to reduce the disruption risk is of great interest to both academics and practitioners. However, there is no study on optimally selecting supplier actions to find the best balance between the cost of these actions and the disruption risk. This work investigates a new bi-objective supply chain ripple effect management problem, considering supplier actions. The two objectives are to minimize the manufacturer’s disruption risk and the expected total action cost. To efficiently address the problem, an integrated approach that combines Markov decision process (MDP), dynamic Bayesian network (DBN), and bi-objective nonconvex mixed-integer programming model, along with optimization techniques, is designed. From this study, the following managerial insights can be drawn: (i) for different desired risk reductions, cost-effective supplier actions are different and can be identified by the proposed approach to support decision-making; (ii) the risk decreases with the increase of the total action cost before the risk threshold is achieved, and the disruption risk cannot be smaller than the risk threshold, even if more costly actions are taken; (iii) the costs of supplier actions have no impact on the risk threshold, while the state probability distributions of suppliers and the manufacturer affect the risk threshold.

Collaborative emergency adaptation for ripple effect mitigation in intertwined supply networks

AbstractFor the first time, the ripple effect is examined in the setting of an intertwined supply network. Through simulations, we model the disruption propagation in supply chains having common suppliers. We explore conditions under which a collaborative coordination of re-purposed capacities and shared stocks can help mitigate the ripple effect and improve recovery performance. As a result, we conceptualize the notion of collaborative emergency adaptation contributing to development of “network-of networks” and viability perspective in supply chain resilience management. We illustrate our approach with anyLogistix simulations and deduce some generalized theoretical and managerial insights on how and when a collaborative emergency adaptation can be implemented and help improve supply chain resilience and viability.

A social network analysis approach to estimate export disruption spread in the US during the Covid-19 pandemic: how policy response and industry ties relate

We examine how the Covid-19 pandemic led to the propagation of export disruptions on a state-by-state basis using a social network analysis model. We measure the impact of import disruptions, Covid-related hospitalizations, subsequent policy responses, and structural network effects on economic outcomes. In addition to examining contemporaneous effects, we include lagged policy response variables to determine their effect on disruption recovery trends. Findings suggest that disruptions cluster along shared industry connections. The results are consistent with previous work that shows that non-pharmaceutical policy interventions had limited contemporaneous and medium-term effects on trade flows.

Employee associations with R&D investment, firm performance, disruption risk, and supply chain performance during the COVID-19 pandemic: A multiple mediational model

Purpose: To timely manage supply chain disruptions, experts have focused their attention on the impact of COVID-19 on industries worldwide. Epidemic outbursts are a specific supply chain risk with long disruption propagation, disruption persistence, and high uncertainty. This study aimed to investigate the role of R&D investment and firm performance in mediating the relationship between disruption risk and supply chain performance in Pakistani manufacturing industries and supply chain employees during the recovery phase of the COVID-19 pandemic via the application of the dynamic capability theory.Methodology: From 21 July 2020 to 23 August 2020, 318 employees from supply chains of manufacturing industries in Rawalpindi and Islamabad, Pakistan, participated in this cross-sectional online web-based survey. The four standard research scales were used to examine the research and development, disruption risk, firm, and supply chain performance. The response link was distributed to respondents via Facebook, WhatsApp, and email. The study analyzed the data using structural equation modeling and a partial least squares technique.Results: The study’s findings suggest that disruption risk, research and development investment, and firm performance all improve supply chain performance, but the mediation effect is unsupported by the data. These measures help plan a better supply chain in the face of disruption risk. They provide one of the timely empirical conclusions on the role of R&D investment in mitigating risk disruptions and improving supply chain performance.

Disruption propagation and repair response in interdependent system: Network model and simulation approach

With the development of modern systems, many infrastructure networks are coupled together and, therefore, having interdependent topology. The fundamental property of interdependent networks is that a node failure in one part can lead to a recursive failure of a dependent node. The cascading failure, disruption propagation, can happen from a fraction of node to complete network system causing catastrophic damage. Therefore, the disruption must be timely and adequately contained lest irreversible propagation occurs. Here we develop a Network Disruption and Response (NDR) model that contains disruption propagation and interacting response to maintain the network robustness. Five response policies and four performance measures have been designed to compare different parametric settings. The realistic case study with the military information network is designed based on Naval Surface Warfare Center Crane data. The discrete-event simulation is adapted to NDR and conducts a comparative and quantitative analysis to evaluate the effects of different parametric settings on a system. Furthermore, simulation-based optimization algorithms are developed to provide insights into the system and guide the management to eliminate disruption propagation.

Robust goal programming approach for healthcare network management for perishable products under disruption

In this paper, a robust goal programming model for a healthcare network design problem under disruption is developed. The objective of the proposed multi-period, multi-echelon model is to simultaneously optimize the total deviation from the considered three goals, including minimizing network cost, maximizing network coverage, and maximizing network reliability. Distribution uncertainty of healthcare demand and facility capacity is captured. The perishability of healthcare products is regarded with limited shelf-life. Using an option contract ensures appropriate coordination with suppliers during pre and post-disaster periods. Via handling the tractable robust budget-based counterpart model, suitable strategies are proposed to present the designed network's efficiency and effectiveness. Risk mitigation strategies deal with disrupted demand and supply in the healthcare network with specific impacts on coverage, cost, and reliability of the services. The proposed cooperate coverage mechanism provides appropriate service coverage under disruption propagation. Finally, the option contract has achieved cost-efficient coordination under demand uncertainty. Furthermore, practical managerial insights are demonstrated by investigating a real-life case study and conducting extensive sensitivity analyses.

Supply Chain Network Resilience by Considering Disruption Propagation: Topological and Operational Perspectives

In recent years, some extreme natural disasters and public health events have led to supply chain disruptions, and scholars have become increasingly concerned about this issue. Some studies have analyzed the impact of different network structures on resilience in the supply chain network from both topological and performance perspectives. However, these are mainly concerned with the impact of vastly different network structures on resilience, such as comparing scale-free networks and random networks, and pay less attention to the impact of slight differences in structure on supply chain network resilience. At the same time, the relationships between different resilience metrics have received less attention after cascading failure. In this article, we construct a supply chain network structure-disruption model from both topological and operational perspectives. This model can analyze how disruptions propagate in a supply chain network with any structure and evaluate the resilience of networks under different types of disruptions (node or link failures). Finally, we conduct a numerical simulation and evaluate the impact of different structures on the resilience of agri-food supply chain networks under eight disruption scenarios (random and targeted disruptions). This research can help managers make topological adjustments during various disruption scenarios and protect the most vulnerable nodes/links.

Detecting metro service disruptions via large-scale vehicle location data

Urban metro systems are often affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. The crucial prerequisite of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. To pursue this goal, we detect the abnormal deviations in trains’ headway relative to their regular services by using Gaussian mixture models. Our method is a unique contribution in the sense that it proposes a novel, probabilistic, unsupervised clustering framework and it can effectively detect any type of service interruptions, including minor delays of just a few minutes. In contrast to traditional manual inspections and other detection methods based on social media data or smart card data, which suffer from human errors, limited monitoring coverage, and potential bias, our approach uses information on train trajectories derived from automated vehicle location (train movement) data. As an important research output, this paper delivers innovative analyses of the propagation progress of disruptions along metro lines, which enables us to distinguish primary and secondary disruptions as well as effective recovery interventions performed by operators.

Supply chain disruption propagation: A study of South African fast-moving consumer goods food and beverage manufacturers

Orientation: Supply chain disruptions have been the subject of substantial theoretical exploration, resulting from the organisational need to enable understanding and appropriate responses to increasingly frequent and severe disruptive events.Research purpose: The purpose of this study was to explore the factors that influence disruption propagation in the South African fast-moving consumer goods (FMCG) food and beverage manufacturing sector.Motivation for the study: Whilst disruption propagation can cause notable damage to a supply chain, little research exists on the underlying factors that cause disruptions to spread.Research design, approach and method: The study employed a generic qualitative design. Seventeen semi-structured interviews were conducted to collect data from senior managers working in the South African FMCG food and beverage manufacturing sector. A thematic analysis approach was followed to analyse the collected data.Main findings: This study identified silo thinking, counterparty risk and staffing as factors that cause disruption propagation. Furthermore, the findings indicate that collaboration allows for a quicker response to disruptions because of the increased visibility that decreases disruption propagation, whilst high levels of dependency contribute to disruption propagation. However, this study found that in certain situations, a high level of dependency can be beneficial and decrease disruption propagation.Practical/managerial implications: The study offers managers insight into the nature of supply chain disruption propagation. Managers should revise and adapt disruption mitigation strategies to prevent disruption from spreading by understanding the nature of collaboration and the implications of high levels of relationship dependency.Contribution/value-add: The study identified the factors and circumstances causing supply chain disruption propagation in the South African FMCG food and beverage sector.

SCHEDULING REPETITIVE CONSTRUCTION PROJECTS: STRUCTURED LITERATURE REVIEW

Construction projects that involve repetitive operations are often referred to as repetitive construction projects. Scheduling them proves a task more demanding than in the case of projects in other industries. Typical objectives of optimization, a characteristic set of constraints, as well as the schedule’s susceptibility to the propagation of disruptions caused by materializing risks, call for specific scheduling methods. The authors review the literature to summarize the existing repetitive scheduling methods and put forward their classification to identify the method’s aspects needing refinement. This is done to point to directions of further research. The authors hope that this study will contribute to better identification of existing problems in planning repetitive construction projects and faster development of decision support systems, eagerly anticipated by the construction practitioners. Though the focus is on applications to construction projects, the repetitive scheduling methods that account for volatile operating conditions may be of interest to researchers who develop planning techniques for other industries.

An optimization approach for multi-echelon supply chain viability with disruption risk minimization

The outbreak of extraordinary disruptive events, e.g., the COVID-19 pandemic, has greatly impacted the orderly operation in global supply chains (SCs), and may lead to the SC breakdown. Regulatory actions, such as government interventions during the pandemic, can greatly mitigate the disruption propagation (i.e., the ripple effect) and improve SC viability. However, existing works that focus on the disruption propagation management have not considered the possibility of such interventions. Motivated by the fact, in this study, we investigate a new disruption propagation management problem in a multi-echelon SC with limited intervention budget. The aim is to minimize disruption risk measured by the disrupted probability of target participants in the SC. For the problem, a novel approach, combining the Causal Bayesian Network (CBN), the do-calculus and the mathematical programming, is developed. Specially, two mixed-integer non-linear programming models are constructed to determine appropriate interventions. To enhance the proposed mathematical models, two valid inequalities are proposed. Then, a problem-specific genetic algorithm (GA) is developed for handling large-scale problem instances. Numerical experiments on a case study and randomly generated instances are conducted to evaluate the efficiency of the proposed models, the valid inequalities and the GA. Based on experiment analysis, managerial insights are drawn.