Disruption Recovery Research Articles

Enhancing resilience in transnational E-commerce supply chains: Critical factors, perspectives and strategic action plan

This study develops a hybrid model to investigate the factors affecting transnational e-commerce supply chain resilience (TNSCRE) by integrating the Entropy Weight Method (EWM), Simple Additive Weighting (SAW), and Interpretive Structural Modeling (ISM). The study identifies 36 critical factors categorized under supply chain adaptability, supply chain efficiency, and supply chain evolution, and five criteria are used to rank these factors. The EWM is used to calculate the relative weights of the criteria, and the SAW method is used to rank the factors based on their weighted scores. The ISM is then used to evaluate the interrelationships among the key factors. The research highlights the significance of several factors, such as the speed of supply chain disruption recovery, interactive collaboration, and response time to supply chain disruption. Sensitivity analysis was performed to assess the robustness of the findings. Finally, a SWOT analysis is conducted to develop a strategic action plan for addressing these significant factors. The study provides a comprehensive understanding of the factors that impact TNSCRE from the perspective of multiple stakeholders. The findings can help e-commerce business owners improve their existing supply chain resilience and achieve sustainable growth in the context of globalization.

Chance-constrained UAM traffic flow optimization with fast disruption recovery under uncertain waypoint occupancy time

Trajectory-based operations offer a promising solution for effective urban air mobility (UAM) traffic management with conflict-free four-dimensional trajectories. However, these trajectories generated in strategic phases by existing methods could be significantly disrupted due to uncertainties such as flight delays and trajectory deviations. This paper develops a chance-constrained UAM traffic flow management (UTFM) optimization model with fast disruption recovery to solve observed disrupted trajectories and improve their resilience. Our model introduces a novel concept of waypoint occupancy time to cope with the probabilistic separation constraints induced by variables, notably urban wind field. We then convert the probabilistic constraint into a deterministic one, incorporating risk-bounded separation guarantees derived from flight experiment data. Furthermore, we develop a hierarchical stochastic search algorithm for solving the redefined deterministic optimization problem. Our comprehensive numerical studies demonstrate the model's effectiveness in restoring disrupted flights and resolving conflicts within seconds. Additionally, our reliability testing showcases the resilience of the model in managing disruptions, even at levels as high as 50%, and its adaptability in addressing varying levels of uncertainty risk. We further demonstrate the ability of the UTFM model to capture tail risks across Gaussian and non-Gaussian uncertainty distributions. Lastly, our scalability analysis highlights the potential capacity of the model to support up to 3,000 flights per hour in Singapore's urban airspace below 400 ft. This study introduces an adaptable framework to facilitate the modelling of robust traffic flow management and performance-based separation for a variety of eVTOL types under uncertainties.

A sustainable demand disruption recovery model considering non-zero lead time and advance payment policy during any emergency like COVID-19 pandemic

A sustainable demand disruption recovery model considering non-zero lead time and advance payment policy during any emergency like COVID-19 pandemic

Supply chain disruption recovery strategies for measuring profitability and resilience in supply and demand disruption scenarios

This paper examines the recovery of a three-level manufacturing supply chain under supply and demand disruptions. The paper proposes new combined recovery strategies, which aim to cope with interruption by adjusting the supply chain structure and material flows. This study integrates both supply chain performance and supply chain capability dimensions. We develop a bi-criteria mixed integer linear programming model with profit and resilience maximization as the objective. The model combines supply-side supply expansion, manufacturer capacity impairment, and demand regulation on the demand side. In a numerical example, we find that a “reciprocal disruption overlay” occurs when supply and demand disruptions, but the supply chain still loses some profit. The results suggest that the combined recovery strategies reduce profit loss and increase supply chain resilience. Furthermore, the strategies are also the optimal recovery strategies under unilateral disruptions. This model facilitates the coordination of a disrupted supply chain and can help managers decide on the best recovery plan.

Schedule Disruption Recovery in Liner Shipping Service Based on a Reinforcement Learning-Enabled Adaptive Genetic Algorithm

Schedule Disruption Recovery in Liner Shipping Service Based on a Reinforcement Learning-Enabled Adaptive Genetic Algorithm

Towards efficient airline disruption recovery with reinforcement learning

Disruptions to airline schedules precipitate flight delays/cancellations and significant losses for airline operations. The goal of the integrated airline recovery problem is to develop an operational tool that provides the airline with an instant and cost-effective solution concerning aircraft, crew members and passengers in face of the emerging disruptions. In this paper, we formulate a decision recommendation framework which incorporates various recovery decisions including aircraft and crew rerouting, passenger reaccommodation, departure holding, flight cancellation and cruise speed control. Given the computational hardness of solving the mixed-integer nonlinear programming (MINP) model by the commercial solver (e.g., CPLEX), we establish a novel solution framework by incorporating Deep Reinforcement Learning (DRL) to the Variable Neighborhood Search (VNS) algorithm with well-designed neighborhood structures and state evaluator. We utilize Proximal Policy Optimization (PPO) to train the stochastic policy exploited to select neighborhood operations given the current state throughout the Markov Decision Process (MDP). Experimental results show that the objective value generated by our approach is within a 1.5% gap with respect to the optimal/close-to-optimal objective of the CPLEX solver for the small-scale instances, with significant improvement regarding runtime. The pre-trained DRL agent can leverage features/weights obtained from the training process to accelerate the arrival of objective convergence and further improve solution quality, which exhibits the potential of achieving Transfer Learning (TL). Given the inherent intractability of the problem on practical size instances, we propose a method to control the size of the DRL agent’s action space to allow for efficient training process. We believe our study contributes to the efforts of airlines in seeking efficient and cost-effective recovery solutions.

Optimization for Green Container Shipping: A Review and Future Research Directions

Maritime freight transportation is one of the least emissions-producing transportation alternatives in terms of transported tonnage per distance. However, it produces a high amount of emissions as around 80% of international freight transportation is conducted through seas and 20% of maritime transportation is conducted through container shipping. This makes it crucial to reduce emissions in container shipping. In this regard, this study reviewed previous studies on the environmental optimization of container shipping and identified various future research directions. The results showed that in the sea segment of environmental optimization of container shipping, decisions which require further attention include resource allocation, emission reduction technology choice, disruption recovery, freight rate optimization, and shipment scheduling. The decisions that require future research in the port segment are related to internal transportation and handing operations in container terminals (i.e., yard crane deployment, yard truck deployment, yard truck scheduling, yard container stack allocation, yard container retrieval), renewable energy source installation, and emission reduction technology choice. Vessel scheduling and speed optimization decisions are the most frequently studied decisions in the sea segment, but they are rarely considered for inland shipping of containers. In the sea-port combined segment of container shipping, future studies are required in quay crane scheduling, vessel scheduling, container route allocation, ship route allocation vessel deployment, and emission reduction technology choice. The least studied decision in the door-to-door segment of container shipping includes hub location-allocation, empty container relocation, ship route allocation, vessel deployment, environmental taxation and subsidy scheme, emissions reduction technology choice, and speed optimization. It was also demonstrated that modeling of future studies should more frequently consider uncertainties and social sustainability parameters.

Resilient Supply Chain Optimization Considering Alternative Supplier Selection and Temporary Distribution Center Location

The global supply chain is facing huge uncertainties due to potential emergencies, and the disruption of any link may threaten the security of the supply chain. This paper considers a disruption scenario in which supply disruption and distribution center failure occur simultaneously from the point of view of the manufacturer. A resilient supply chain optimization model is developed based on a combination of proactive and reactive defense strategies, including manufacturer’s raw material mitigation inventory, preference for temporary distribution center locations, and product design changes, with the objective of obtaining maximum expected profit. The proposed stochastic planning model with demand uncertainty is approximated as a mixed integer linear programming model using Latin hypercube sampling (LHS), sample average approximation (SAA), and scenario reduction (SR) methods. In addition, an improved genetic algorithm (GA) is also developed to determine the approximate optimal solution. The algorithm ensures the feasibility of the solution and improves the solving efficiency through specific heuristic repair strategies. Numerical experiments are conducted to verify the application and advantages of the proposed disruption recovery model and approach. The experimental results show that the proposed resilient supply chain optimization model can effectively reduce the recovery cost of manufacturers after disruption, and the proposed approach performs well in dealing with related problems.

Managing omni-channel reverse logistics risk during supply chain disruption recovery in the South African fashion industry

Background: Omni-channel retailing is blurring the lines between online and physical stores for consumers as it provides consumers with more choices, convenience and a seamless shopping experience. An integral aspect of implementing an omni-channel retail strategy is having an efficient reverse logistics process. However, retailers seem reluctant to implement omni-channel reverse logistics because of the various risk types that affect the economic wellbeing of a firm, especially during supply chain disruption recovery (SCDR) when the firm is in distress. Prior research primarily examines the risks associated with reverse logistics in a single channel. While the existing findings are promising, there is a lack of understanding regarding the specific risks involved in omni-channel reverse logistics and how to mitigate these risks in the context of SCDR.Objectives: The purpose of this study was to explore omni-channel reverse logistics risks and mitigation strategies during SCDR in the South African fashion retail industry.Method: The study employed a generic qualitative design using purposive sampling methods. Fourteen semi-structured interviews were conducted to collect data. The data were analysed using a thematic analysis approach.Results: The study identified specific types of omni-channel reverse logistics risks. The findings indicate that omni-channel reverse logistics risk during SCDR is managed through proactive and reactive strategies such as technology implementation, collaborative relationships, quality insurance inspections, customised policy changes and disruption-specific reverse logistics teams. The findings show that mitigating omni-channel reverse logistics risk can help create a competitive advantage because of increasing customer loyalty, value recovery and profits.Conclusion: The findings provide valuable insight on how to manage omni-channel reverse logistics risk during SCDR and, if mitigated correctly, can contribute to a competitive advantage.Contribution: This study expands on the current literature by identifying multiple types of omni-channel reverse logistics risks and strategies used to manage omni-channel reverse logistics risk in a SCDR context.

Internal supply chain integration during disruption recovery: A case study in the South African liquor industry

Internal supply chain integration during disruption recovery: A case study in the South African liquor industry

How liner shipping heals schedule disruption: A data-driven framework to uncover the strategic behavior of port-skipping

Service disruption hurts the reliability of liner shipping schedules and the lack of high-quality data impedes research on schedule recovery in liner shipping. With the support of the Automatic Identification System (AIS), a satellite-based tracking system acquiring real-time records of vessels’ navigation trajectories worldwide, this study develops a novel data-driven framework to uncover the vessel schedule disruption recovery behavior (i.e., port-skipping). The framework consists of a series of independent yet closely related algorithms, which are in turn tasked with screening port calls, estimating closed-loop routes, measuring similarity between different routes, and identifying port-skipping behavior, respectively. Compared with partial proprietary data from the shipping industry and port authorities, the identification results can provide transparent datasets with wide applications and much easier access publicly. The developed data-driven framework is implemented with vessel trajectory information of around 2,000 container vessels worldwide from January 2016 to December 2020, and the results prove its validity and practical value for liner shipping scheduling management.

Disruption recovery for the pickup and delivery problem with time windows—A scenario-based approach for online food delivery

Recently, the pickup and delivery problem has attracted increasing attention due to its applications in online food delivery services. At the same time, unforeseen events, such as vehicle breakdowns, traffic jam, and service time changes, often lead to the delay of delivery services. This paper defines the disruption recovery problem for the pickup and delivery problem with time windows (DR-PDPTW) and presents a scenario-based approach to solve the problem in a computationally efficient way. The approach starts with pre-disruption preparation that generates disruption scenarios and corresponding candidate recovery solutions. Once a disruption occurs, the approach will first check if recovery efforts are necessary based on the magnitude of delays caused by the disruption. If such delays are not acceptable, the approach will move to the post-disruption response stage to match the disruption with an already generated disruption scenario. Then corresponding candidate recovery solutions will be adjusted based on the actual disruption and executed. Computation experiments demonstrate the quality and efficiency of the proposed approach. This study can help to provide real-time decision support for disruption management in online food delivery services.

A problem-specific parallel pareto local search for the reactive decision support of a special RCPSP extension

The disaster information collection mission should be executed after the disaster occurs to provide details for the decision-makers. During the execution of the information collection mission, some disruptions may occur and prevent the resource used for information collection from completing the mission as planned. It is difficult for decision-makers to make reactive resource scheduling plan that optimize the mission’s execution time, quality, and cost at the same time under such circumstances. This article focuses on designing the reactive decision support algorithm for the disaster information collection resource scheduling, which aims to provide multi high-quality scheduling plans for decision-makers to choose. The problem studied in this article is modeled as an extension of Resource-Constrained Project Scheduling Problem (RCPSP). First, the basic problem formulation for a normal schedule and two disruption recovery models are presented. Second, a novel framework of a parallel pareto local search based on decomposition is designed to repair the schedule within the time limit. Third, two solution acceptance criteria based on constraint handling and negative correlation are specially designed to maintain high-quality population with diversity. The experiments show that the proposed method outperforms the other competitors with respect to Inverted Generational Distance, Spacing, and Hypervolume, which means that the proposed method can help decision-makers to make better decisions.

Supply chain disruption recovery in the evolving crisis—Evidence from the early COVID-19 outbreak in China

The speed of recovery from supply chain disruption has been identified as the predominant factor in building a resilient supply chain. However, COVID-19 as an example of an evolving crisis may challenge this assumption. Infection risk concerns may influence production resumption decision-making because any incidents of infection may lead to further shutdowns of production lines and undermine firms’ long-term cash flows. Sampling 244 production resumption announcements by Chinese manufacturers in the early COVID-19 crisis (February–March 2020), our analysis shows that, generally, investors react positively to production resumptions. However, investors perceived the earlier production resumptions were higher risk (indicated by declined stock price). Such concerns were exacerbated by more locally confirmed cases of COVID-19 but were less salient for manufacturers with high debts (liquidity pressure). This study calls for a reassessment of the current disruption management mindset in response to new evolving crises (e.g., COVID-19) and provides theoretical, practical, and policy implications for building resilient supply chains.

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.

Research on COVID-19 Vaccine Supply Chain Disruption Recovery Strategy from Resilience Perspective

The COVID-19 pandemic has greatly exacerbated the uncertainty of the external environment, meanwhile, the COVID-19 vaccine supply chain is highly complex and fragile, so its probability of disruption is higher and it is more difficult to recover, resulting in delays and shortages of vaccine supply. In order to develop the disruption recovery strategy of the COVID-19 vaccine supply chain and optimize the disruption recovery process, a multi-objective model was established and solved. Taking the shortest disruption recovery time, the largest reliability of the supply chain system, and the lowest recovery cost as the objective function, and taking the four dimensions of resilience, namely redundancy, robustness, resourcefulness and agility, as the decision variables, the model uses the nondominated ranking genetic algorithm with elite strategy (NSGA-II) to solve the problem, and uses the Pareto frontier analysis method to optimize the solution set, and the sensitivity analysis of the key parameters in the model is analyzed. Finally, the redundancy, robustness, resourcefulness and agility of the COVID-19 vaccine supply chain disruption recovery combination strategy under different decision-making preferences are given.

Recovery from plant-level supply chain disruptions: supply chain complexity and business continuity management

PurposeThe purpose of our study is to investigate how a manufacturing plant’s internal operations along with its network of connections (upstream and downstream) can have an impact on its recovery time from a disruption. The authors also examine the inverse-U impact of complexity. Finally, the authors test the moderating role that business continuity management plans (BCP) at the plant level have on recovery time.Design/methodology/approachTo test our hypotheses, the authors partnered with Resilinc Corporation, a Silicon Valley-based provider of supply chain risk management solutions to identify focal firms’ suppliers, customers and plant-level data including information on parts, manufacturing activities, bill of materials, alternate sites and formal business continuity plans. The authors employed censored data regression technique (Tobit).FindingsSeveral important findings reveal that the plant’s internal operations and network connections impact recovery time. Specifically, the number of parts manufactured at the plant as well as the number of internal plant processes significantly increase disruption recovery time. In addition, the number of supply chains (upstream and downstream) involving the plant as well as the echelon distance of the plant from its original equipment manufacturer significantly increase recovery time. The authors also find that there exists an inverted-U relationship between complexity and recovery time. Finally, the authors find partial support that BCP will have a negative moderating effect between complexity and recovery time.Originality/valueThis research highlights gaps in the literature related to supply chain disruption and recovery. There is a need for more accurate methods to measure recovery time, more research on recovery at the supply chain site level and further analysis of the impact of supply chain complexity on recovery time.

Supply Chain Management during a Public Health Emergency of International Concern: A Bibliometric and Content Analysis

A public health emergency of international concern, such as a pandemic, disrupts the normal operation of the global supply chain, which necessitates in-depth research on supply chain management. In this paper, we used bibliometric and content analysis to provide a systematic analysis of the supply chain industry from this background. The descriptive analysis provides insights into the publication growth trajectory, in terms of the contributing authors, countries, and subject categories, which presents an intuitive display of previous research. In addition, the existing research mainly covers three dimensions of supply chain disruption, strategies, and sustainability, which can be clustered into supply chain disruption, disruption recovery, reconfiguration, digital intelligence, optimization, and sustainability. By revisiting the supply chain industry, we explored the transformation of its characteristics in the pandemic, covering themes ranging from expansion to contraction, from traditional to digital intelligence, and from fragile to sustainability, which suggests potential research directions for future studies. This contributes to the further research of supply chain management during the pandemic and provides supply chain managers with a practical approach for dealing with supply chain disruption risks and improving supply chain sustainability in this context.

Vessel schedule recovery strategy in liner shipping considering expected disruption

How to deal with disruptions in liner shipping service is a common operation problem faced by shipping companies. In particular, the spread of COVID-19 brings more uncertainty, and frequent disruptions have a great impact on global maritime trade. In this paper, a new mixed integer programming mathematical model is proposed to solve the Vessel Schedule Recovery Problem (VSRP) in liner shipping service, and a variety of recovery strategies are considered, including adjusting sailing speed, adjusting port productivity, port skipping and port swapping. In order to cope with the different scenarios in reality, we studied the disruption events that can obtain information in advance. A series of numerical studies were conducted to verify the effectiveness of the proposed model. The results show that adjusting sailing speed and port productivity are always adopted, regardless of the scenario. In addition, port skipping is more used for disruption recovery, and port swapping is more used for expected disruption. A comparative analysis shows obtaining disruption information in advance can help vessel save up to 17.33%. The research results not only expand the theoretical research of the VSRP, but also provide scientific decision support for the operation of shipping companies.

Sequencing interdependent disruption recovery projects: Exact solution via network flow reformulation

Sequencing interdependent disruption recovery projects: Exact solution via network flow reformulation