Disruption Scenarios Research Articles

Resilient shuttle vehicle plan under route disruption scenarios and ticketing policies: a case study of the Qiandaohu archipelago scenic area, China

ABSTRACT Scenic areas in an archipelago context are often accessed with shuttle vehicles to transport tourists between different scenic spots. Vehicle routing and schedule planning could be challenging when route disruptions are considered frequent and fragile areas are protected and conserved. Transport provisions also affect the number, sequence and patterns of visited spots for each tourist, which in turn may not only impact the revenue of the scenic area but also on visitor satisfaction. This paper develops a mixed-integer programming model for this optimisation problem at scenic areas by considering two common ticketing policies: ‘all-inclusive policy’ and the ‘two-part tariff policy’. Experimental results based on the case study of Qiandaohu scenic area in China demonstrate that our method can obtain high-quality solutions for real-sized problems where some routes are disrupted. It is found that ticketing policies may have different impacts on shuttle vehicle planning. Under the all-inclusive policy, the shuttle vehicle planning maximises the total profit. Under the two-part tariff policy, however, the plan needs to maximise the profit to strike a balance between the operational cost and the number of spots visited by tourists. Our study may shed light on the effectiveness of shuttle vehicle planning and ticketing policy selection.

Impact of various fairness-based distribution models on supply chain networks from the perspective of customer satisfaction

AbstractThis study investigates the effectiveness of two fairness-based distribution approaches—type I and type II—in enhancing the resilience of supply chain networks (SCNs) during disruptions. The research contributes to the growing body of knowledge on supply chain management by offering insights into how fairness principles can be applied to improve the restoration of disrupted networks. A mixed-integer programming model was developed to simulate these fairness-based distribution strategies, focusing on a water supply chain network of a privet company in Saudi Arabia. The SCN consists of a single supplier and ten demand nodes, each requiring multiple commodities. The model was tested under 100 random disruption scenarios, each reducing the capacity of randomly selected network segments. The performance of each fairness-based distribution approach was evaluated based on how quickly and effectively the SCN returned to its required service levels (SLs) across all demand nodes. Results indicate that Fairness Distribution Type I, which aims to minimize unmet demand across the entire network, generally outperformed Type II in terms of speed and efficiency. Type I was more effective at restoring SLs quickly at most nodes, while Type II showed localized advantages, particularly in restoring SLs for specific commodities at select nodes. The study concludes that while Type I is more suited for overall supply chain recovery, Type II may be beneficial in scenarios requiring focused recovery at specific demand nodes. These findings provide actionable insights for supply chain managers seeking to enhance network resilience through fairness-based distribution strategies, and suggest avenues for future research on hybrid and context-specific approaches.

Designing a resilient supply chain network: A multi-objective data-driven distributionally robust optimization method

Uncertainty of supply chain disruption poses a significant challenge to decision-making in many real-life problems. In this paper, a new multi-objective data-driven distributionally robust optimization (MDDRO) model for the resilient supply chain network (RSCN) design problem under disruption scenario is developed, which includes minimizing the total cost, carbon emissions, and delivery time. In the context of supply chain disruption, it is important self-evidently that products can be supplied normally and the practical demand can be met. In handling the partial probability distribution information of the considered uncertain demand, this paper uses a data-driven Wasserstein-Moment ambiguity set (WMAS), which incorporate the Wasserstein metric and Moment information, and a robust counterpart to transform the developed model into a tractable approximation form. The finite-sample performance guarantee of the optimal solution of MDDRO model with Wasserstein-Moment ambiguous chance constraint is given. An accelerated Branch and cut algorithm (BCA) is constructed to solve the MDDRO model. Finally, through the investigation of a real-life case and sensitivity analysis of key parameters, some managerial insights are put forward.

Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs.

Sensitivity of development goals to water scarcity of Iraq and transboundary regions

Iraq and its transboundary regions have significant challenges from water scarcity in combination with other social and environmental factors. There are short- and long-term implications for vulnerable demographics, such as youth. With Iraq's dependence on upstream water management, there is a need to address several critical factors of transboundary watersheds such as the Haditha, Mosul, Dokan, and Euphrates-Tigris basin. This paper develops the use of particular social, hydrological, and other environmental factors in a risk register of basins and vulnerable populations, where societal priorities vary across scenarios of hydrology and water. Social data (i.e., gridded youth population data) and hydrological observations (i.e., precipitation, temperature, root zone soil moisture, and Normalized Difference Vegetation Index (NDVI)) are obtained from publicly available satellite-based Earth observations and global models. Risk is defined and quantified as the disruption of basin order and spatial plots are provided to improve understanding of the spatial distribution of water scarcity challenges in the region with regard to vulnerable populations. The results feature identification of the most and least disruptive scenarios including: 1) population density is lowest in basins exposed to the highest air temperatures, 2) an urban-to-rural migration pattern (such as prompted by a public health crisis) would significantly disrupt basin order, and 3) populations with greatest exposure to extreme hydrological conditions of water scarcity are found in the southern basins of Iraq in or near the Al-Muthanna governorate. The impacts of this work are to steer future investments that mitigate risk of disrupted system orders and to increase system resilience of vulnerable populations to water scarcity.

Results of a Survey of UK Farmers on Food System Vulnerability over the Short and Long Term

We report the results of a survey of farmers and landowners to identify the most likely potential food system disruption scenarios for the UK and compare these with a previous expert elicitation with a much wider set of food system stakeholders. We found that 60% of farmers think a Societal Event in which 1 in 2000 people are injured in the UK is at least 20% likely to occur over the coming decade. Over a timeframe of 50 years, this increased to almost 90% of farmers. These results show that farmers and landowners are considerably more concerned about the vulnerability of the food system in the UK than the wider group of food system experts are. Farmers agreed with experts on the majority of potential causes of such vulnerability, which are climate change, trade policies (import and export), competition for land and ecological collapse (over 50 years). However, they also highlighted the importance of the power structure within the food system, with large corporations supplying to, or buying from, farmers creating lower revenue, making farming an unsustainable business. We conclude that an urgent systematic review of potential interventions that would improve resilience be conducted by the UK Government, in partnership with farmers.

Impacts of large-scale deployment of vertical bifacial photovoltaics on European electricity market dynamics

Self-sufficiency, climate change and increasing geopolitical risks have driven energy policies to make renewable energy sources dominant in the power production portfolios. The initial boom in the mid-2000s of global photovoltaic installations demonstrated the feasibility of the ambitious renewable energy targets. However, this rapid scale-up has introduced challenges, including price volatility and system integration issues. This communication calls the attention to these emerging challenges and offers quantitative insights on how rapid adoption of a more diversified photovoltaics deployment strategies can mitigate price volatilities, reduce fossil fuel dependence and steer Europe towards a forward-thinking sustainable energy pathway. The analysis reveals that as innovative bifacial photovoltaic systems are incorporated on a large-scale disruptive scenario, four main patterns emerge: economic value of solar production increases, base-load electricity price decreases, sun-rich countries expand their solar contributions, whereas nations with ample grid interconnections enhance their energy imports from neighbouring countries. It also underscores the importance of maintaining photovoltaics an attractive option for energy investors and traders in the future. Establishing this groundwork is critical since a successful integration of large-scale solar systems contributing to decrease price volatilities in Europe and US will carry significant repercussions for global energy policy formulation.

Navigating Discontinuity: Applying the Disruptive Scenario Approach in Engineering Consultancy

Navigating Discontinuity: Applying the Disruptive Scenario Approach in Engineering Consultancy

Scaling up solar and wind electricity: empirical modelling and a disruptive scenario for their deployments in Aotearoa New Zealand

ABSTRACT Deployment of wind and solar electricity technologies is crucial for the energy transition, yet anticipated deployment rates differ widely often underestimating actual deployments. This issue is significant for countries like Aotearoa New Zealand, leading the way in major energy transitions. This study addresses two main issues: the historical underestimation of deployment rates and the need to synthesise various future scenarios to assess their potential impacts on the energy system. To address these problems, an empirical modelling approach based on logistic growth is adopted to create and analyse scenarios for the deployment of solar and wind technologies in New Zealand. We propose two scenarios: a reference growth scenario, which represents prevailing estimations for the deployment of solar and wind from previous forecasts; and a disruptive scenario, which anticipates a more rapid deployment pace. Both scenarios envision phasing out fossil fuels by 2030 but differ in the remaining electricity supply. In the disruptive scenario, the installed electricity generation broadens, in which solar and wind generation could represent 72% of the electricity generation capacity by 2050. Our findings emphasise the transformative potential of rapid wind and solar technologies deployment and highlight the importance of preparing for a disruptive future that challenges conventional expectations.

Selecting Resilient Strategies for Cost Optimization in Prefabricated Building Supply Chains Based on the Non-Dominated Sorting Genetic Algorithm-Ⅱ: Facing Diverse Disruption Scenarios

As a new sustainable building production mode, prefabricated building supply chains can realize energy saving, environmental protection and full cycle value maximization of building products. Prefabricated building supply chains often experience disruptions due to supply instability, transportation delay and force majeure, resulting in project delays and cost escalations and posing challenges to the sustainable development objectives of enterprises. Therefore, it is important and essential to study the strategy of enhancing the resiliency of prefabricated building supply chains, which has not been comprehensively explored in previous papers. This paper constructs decision-making models for supply chain cost resilience strategies under varying scenarios of supply disruptions, incorporating both redundant inventory and back-up supplier strategy. It considers the total cost and resilience of the supply chain as dual objective functions. Parameter-tuned non-dominated sorting genetic algorithm-П (NSGA-П) algorithms were used innovatively to solve the project case, and the impacts of the redundant inventory coefficient and back-up supplier supply price coefficient on the model result were analyzed. The results indicate that the supply chain with resilience construction has a superior capability to cope with disruption. The results show that when there is a mild supply disruption, the general contractor uses the capacity within the supply chain and chooses a redundant inventory strategy to restore resilience. In the event of moderate disruption, both the easy inventory strategy and back-up supplier strategy are selected to maintain supply chain stability. In the event of a severe disruption, only the back-up supplier strategy is selected to cover the losses and maintain the project schedule. In addition, the choice of resilience strategy is impacted by the inventory levels and component prices of back-up suppliers. It further verifies the effectiveness of the model and the impacts of uncertain parameters in the model on the results. This study contributes to enhancing the resilience management of the prefabricated building supply chain by the general contractor, thereby elevating the overall efficiency and competitiveness of the supply chain and furthering the sustainable development of prefabricated buildings.

A methodology for ranking of critical links in transportation networks based on criticality score distributions

Criticality of transportation networks links is calculated through utilizing criticality metric(s) for various disruption scenarios that generally include one link removal/degradation at a time or simultaneous removal/degradation of multiple links. As also shown in this paper, single link removals do not reveal the actual criticality of link(s) due to network dependencies. Multiple simultaneous link removal scenarios can capture the network interactions; however, the results relate to the criticality ranking of scenarios than the links, e.g., the links in scenario-1 are more critical than the links in scenario-2. This paper addressed the need for a methodology that provides individual link criticality rankings based on failure scenarios. The proposed approach utilizes the distribution of the criticality scores for each link based on running traffic assignment for each failure scenario, e.g., the distribution for link #X is composed of the criticality scores of all link failure combinations that include link #X. The criticality ranking for each link is identified based on its criticality distribution's mean, coefficient of variation and skewness. The use of the proposed approach is illustrated on test networks to show that it provides robust link criticality rankings that accounts for network interactions across varying scales of link failure scenarios.

Risk and Systems Analysis for Renewable Power Generation with Environmental and Other Stressors

AbstractThe effects of climate change and water scarcity threaten the stability of critical infrastructure systems in developing regions. In particular, the interconnectedness of energy systems, natural resources, economic growth, and social welfare requires a systems‐level framework to identify scenarios which most impact these systems. This paper evaluates and quantifies infrastructure system risk, defined as the influence of scenarios on system priorities. A scenario‐based multi‐criteria preferences model assesses system component priorities for a baseline scenario as well as climate and related scenarios. The shift in priorities between the baseline and other future scenarios define scenario disruptiveness, or level of risk. The methods are demonstrated for the case of the emerging renewable energy sector of Iraq. Twenty‐five renewable energy system assets are prioritized by an assessment of system success criteria, which include economic, social, political, and climate considerations. The system prioritization is reevaluated in the case of seven disruptive scenarios relating to water scarcity, climate volatility, and social and economic shifts. This paper advances methods of the Systems Engineering Body of Knowledge (SEBoK) Part 3: Engineering and Management, by defining system risk and proposing methods for risk identification and risk analysis.

A novel heuristic algorithm for disruption mitigation in a global food supply chain

The Global Food Supply Chain (GFSC) often encounters challenges in maintaining the continuous flow of essential food products such as rice and wheat. In this paper, we consider possible disruptions in supply and transportation in GFSC. Disruptions may cause severe food shortages in some parts of the world. Moreover, a disruption is not necessarily a single independent event, as multiple disruptions may occur sequentially or simultaneously. After any interruption, it is essential to reoptimize the remaining flow activities in a short period under the changed conditions. Although both the initial flow plan and post-disruption plan can be generated by formulating them as Mixed Integer Linear Programming (MILP) models, such a mitigation plan is challenging because of time constraints and when multiple disruptions are considered. To address this issue, we proposed a novel heuristic algorithm that revises the ideal plan in the event of a disruption. The developed heuristic can deal with different types of disruptions, as well as a series of disruptions. The performance of the heuristic was judged by solving 300 problem instances and comparing the results with those obtained from the exact method. To demonstrate the applicability of the proposed algorithm in practice, we have solved three real-world disruption Scenarios. The analysis of results uncovered crucial managerial insights, recommending strategies for disruption mitigation, and proved to be an effective method for post-disruption planning.

Designing a resilient cloud network fulfilled by reinforcement learning

Cloud manufacturing networks operate in stochastic environments, making them susceptible to various disruptions. Designing a resilient cloud network that can withstand and recover from disruptions while maintaining an efficient service composition is crucial. This paper emphasizes the importance of developing resilient cloud manufacturing networks, particularly in the context of unprecedented challenges. It explores the complexities of service composition using reinforcement learning algorithms, specifically Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). SAC has emerged as a standout performer, demonstrating superior capabilities in enhancing network resilience. The model’s verification is conducted by investigating and comparing the convergence of the results. This research not only examines these reinforcement learning methodologies but also broadens its scope to provide ongoing development and evaluation of a robust network. The model is applied to a COVID-19 case study to validate the results. Beyond theoretical frameworks, this research highlights the need for adaptive and resilient manufacturing systems, offering insights into real-world challenges. The results emphasize the significance of using RL algorithms, particularly when customer satisfaction is the top priority. Moreover, when logistic costs and costs of changing servers are lower than customer dissatisfaction, the use of RL algorithms becomes essential for addressing disruption scenarios. The findings highlight the critical role of collaboration in intertwined supply networks in helping a disrupted network withstand and recover from a disruption.

Model for Intercity Railway Schedule Optimization Incorporating Metrorail Transfers

The promotion of multimodal coordination is of great significance for the joint development of diverse urban transportation systems. Focusing on a comprehensive urban passenger transport hub that converges both intercity modes and multiple inner-city modes, our study aims to incorporate intercity railway and Metrorail coordination into train scheduling optimization to enhance transfer efficiency and provide a seamless service for passengers in urban railway networks. By considering the variety of passenger demands in different transport modes (e.g., rail transit and traditional mainline railway), train loading capacity, and potential disruption scenarios, we specifically developed a mixed-integer nonlinear programming (MINLP) model to simultaneously generate the coordination strategies, arrival/departure times of trains at each station, and the number of carriages in service in the network. To enhance computational efficiency, we proposed several improvement strategies involving the model linearization to reformulate the MINLP model into a mixed-integer linear programming (MILP) model and the determination of appropriate big- M values. Finally, numerical experiments based on historical passenger data from a realistic urban railway network in China were implemented to verify the effectiveness of our approach. The results demonstrate that our intercity railway and Metrorail coordination strategies could, in practice, significantly improve passenger transfer efficiency by over 35% compared with the train schedule without coordination.

The local supply chain during disruption: Establishing resilient networks for the future

This paper explains the evolving role of the local supply chain across different disruption scenarios. A systematic literature review of 91 papers from 33 journals supports definition of the local supply chain and explication of benefits before, during and after COVID-19 disruption. Resilience emerges as the prevalent benefit at each stage, but to varying scales. In the pre-COVID-19 era, where the local supply chain serves as a back-up approach to the more dominant global supply chain, localisation's capacity to absorb change promotes resilience to mitigate low-magnitude disruption. At initial outbreak of COVID-19 disruption the local supply chain was necessitated as an emergency response to the untenability of global operations, demonstrating transformational resilience to survive. As disruption continued, the local supply chain upscaled and adapted to recover. In the post-COVID-19 era, resilience is expected to remain a strategic priority, promoting continued investment in local operations to thrive with embedded resilience. As a result, the COVID-19 pandemic welcomed a point of transition for the local supply chain as capacities and benefits are revaluated and localisation recognised as critical in developing resilient supply networks for the future. This study consolidates this evolution to offer a propositional framework showcasing the local supply chain across different disruption scenarios. This offers long overdue definition and explanation of the local supply chain and its relationship with resilience, addressing an existing lack of academic attention and encouraging alignment of local-versus-global decisions with changing strategic priorities.

Fluid and kinetic studies of tokamak disruptions using Bayesian optimization

When simulating runaway electron dynamics in tokamak disruptions, fluid models with lower numerical cost are often preferred to more accurate kinetic models. The aim of this work is to compare fluid and kinetic simulations of a large variety of different disruption scenarios in ITER. We consider both non-activated and activated scenarios; for the latter, we derive and implement kinetic sources for the Compton scattering and tritium beta decay runaway electron generation mechanisms in our simulation tool Dream (Hoppe et al., Comput. Phys. Commun., vol. 268, 2021, 108098). To achieve a diverse set of disruption scenarios, Bayesian optimization is used to explore a range of massive material injection densities for deuterium and neon. The cost function is designed to distinguish between successful and unsuccessful disruption mitigation based on the runaway current, current quench time and transported fraction of the heat loss. In the non-activated scenarios, we find that fluid and kinetic disruption simulations can have significantly different runaway electron dynamics, due to an overestimation of the runaway seed by the fluid model. The primary cause of this is that the fluid hot-tail generation model neglects superthermal electron transport losses during the thermal quench. In the activated scenarios, the fluid and kinetic models give similar predictions, which can be explained by the significant influence of the activated sources on the runaway dynamics and the seed.

Integration of risk, flexibility, and resilience in the optimization of water–energy nexus

AbstractCo‐location of power plants and desalination systems allows for a reduction in operational expense through energy integration. Furthermore, augmenting fossil‐based power plants with solar energy provides a means of reducing the carbon footprint of electricity generation. It is also critical to protect the combined energy–water system against internal and external risk factors to maintain a reliable supply of both electricity and water. Therefore, a systematic approach for assessing and mitigating risks is needed. Because of the complex water–energy interactions, a superstructure representation is created and a quantitative risk assessment is conducted to show potential risk factors that target specific sub‐systems. A surrogate model of the flexibility index analysis is built in order to optimize the superstructure for both cost and flexibility objectives. Finally, the generated design is simulated against disruption scenarios to obtain its resilience against various risk factors. This approach is applied to a case study on the Kuwait water–energy plant to show how the developed approach can help decision‐makers create operational strategies to protect against risk in a cost‐effective manner.

Resilience through appropriate response: a simulation study of disruptions and response strategies – case COVID-19 and the grocery supply chain

In the wake of disruptions such as the COVID-19 pandemic, supply chains (SCs) face unprecedented levels of uncertainty, underscoring the critical need for resilience capabilities. We study how the operational dynamics of SCs influence the efficacy of key resilience capabilities, specifically flexibility and visibility. Utilizing a system dynamics simulation model, we analyze the contingency effects within Finnish grocery SCs, examining how the operational tempo—characterized by lead times, inventory buffers, information updating—affects the efficiency of resilience strategies in response to diverse disruption scenarios. Our model, informed by real-world disruption events and responses, reveals distinct preferences for resilience capabilities based on SC operational dynamics. For fast-moving SCs, flexibility in ramping up capacity emerges as paramount, whereas for slower SCs, enhanced visibility becomes crucial. We contribute to the contingent Resource-Based View (RBV) on SC resilience by proposing that responsiveness is important moderator of flexibility and visibility as resilience capabilities. We highlight the importance of prevention of phantom ordering through improved visibility and caution against destocking following sudden demand declines. By aligning operational dynamics with tailored resilience strategies, our research offers actionable insights for SC managers aiming to navigate future disruptions more effectively.

Liner shipping network vulnerability to component disruptions: A China-Europe container flow analysis

This study conducts a vulnerability analysis of the China-Europe liner shipping network, including all relevant liner shipping services and China Railway Express services. The standard multicommodity network flow model is extended to assess network vulnerability and the relative importance of ports and services by simulating container flow redistribution in various disruption scenarios. The results show the network is highly resilient to both individual port disruptions and individual service disruptions. Additionally, the distinct shipping network structures of the three shipping alliances result in varying degrees of vulnerability, highlighting the significance of diversified market suppliers in enhancing the overall network resilience. Specifically, the Ocean Alliance is vital in facilitating China-Europe trade, especially along the Mediterranean route, and is more resilient to individual port disruptions.