Relief Items Research Articles

Multistage stochastic programming for integrated network optimization in hurricane relief logistics and evacuation planning

AbstractIn this article, we study the integrated hurricane relief logistics and evacuation planning (IHRLEP) problem, integrating hurricane evacuation and relief item pre‐positioning operations that are typically treated separately. We propose a fully adaptive multistage stochastic programming (MSSP) model and solution approaches based on two‐stage stochastic programming (2SSP). Utilizing historical forecast errors modeled using the auto‐regressive model of order one, we generate hurricane scenarios and approximate the hurricane process as a Markov chain, and each Markovian state is characterized by the hurricane's location and intensity attributes. We conduct a comprehensive numerical experiment based on case studies motivated by Hurricane Florence and Hurricane Ian. Through the computational results, we demonstrate the value of fully adaptive policies given by the MSSP model over static ones given by the 2SSP model in terms of the out‐of‐sample performance. By conducting an extensive sensitivity analysis, we offer insights into how the value of fully adaptive policies varies in comparison to static ones with key problem parameters.

Genetic Algorithm-based Optimization Model for Emergency Shelters Location Allocation in Public Health Emergency Situations

Deploying emergency resources after a disaster is crucial for reducing the event’s impact. A multi-objective (MO) resource allocation (RA) method is developed to achieve efficient distribution of relief items and the best choice of transportation routes, taking into account the variable and persistent nature of rescue operations. The MO Cellular Genetical Approach (MOC-GA) addresses the concept by incorporating auxiliary populations and neighborhood architecture into the cellular automaton. The comparative experiment conclusively demonstrates that MOC-GA performs favorably compared to Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and Dragon Fly Optimisation (DFO) in several metrics such as the Pareto Front (PF), hypervolume, mean objective function value, and PF ratios. The findings demonstrate that MOC-GA effectively addresses the MO dynamical emergency RA approach, offering decision-makers a more comprehensive range of superior and diversified candidate rescue strategies than alternative methods. This research analyzes the existing rescue methods and proposes a theoretical rescue strategy to aid decision-makers in making scientifically informed decisions.

The planning of the distribution of relief items and road network repair through multiple heterogeneous crews and prioritizing damaged roads under a flexible and uncertain environment

This study introduces an integrated and flexible program to coordinate two critical operations in the response phase of disaster management, namely the distribution of relief items and the repair of damaged roads under different types of road damage. In this regard, considerations such as the classification of critical areas according to the severity of the incident, location-allocation, scheduling, and routing of heterogeneous multiple crews, prioritization of road repair, sequence of operations, and the simultaneity of various activities are considered. Therefore, a new multi-period and multi-objective mathematical model is presented, where the first objective minimizes the total operation cost, the second objective minimizes the total operation time, and the third objective maximizes the prioritization of repairing damaged roads. Also, owing to the uncertain essence of a disaster, containing the types of demand and service time in each operation, a robust optimization approach is used to tackle this concern. The proposed model is ultimately checked for validation in a real case study. The acquired computational outcomes and presented managerial insights show the efficiency and effectiveness of the proposed model.

Forecasting and stock control for fast-moving humanitarian aid supplies

In Indonesia, effective disaster management is imperative due to the region's susceptibility to a wide array of natural and man-made disasters. The establishment of the National Disaster Management Agency (BNPB) underscores the significance of this issue. This study investigates the improvement of disaster logistics, with a particular emphasis on the challenges associated with fast-moving humanitarian supplies. The primary obstacle in disaster logistics lies in the unpredictability of demand after the disaster occurred, coupled with the potential for supply disruptions stemming from inadequate inventory in warehouses. A critical focus of this research is placed on fast-moving humanitarian aid supplies, which are essential for immediate relief efforts, even when lead times are short. The study's primary objective is the development of a safety stock level policy for BNPB's national warehouse, adjusted through the analysis of demand forecasts and lead times. Notably, the research results for the year 2022 reveal minimal errors in demand forecasts. This outcome highlights the effectiveness of the safety stock policy in proactively addressing spikes in demand and mitigating uncertainties related to supply timelines. The research underscores the critical role of accurate inventory management, particularly in the case of fast-moving humanitarian supplies, within the context of disaster relief efforts. It emphasizes the importance of forward-thinking planning to meet urgent needs, ultimately contributing to more efficient disaster response efforts and ensuring the timely delivery of vital relief items to affected populations.

Relief item inventory planning under centralized and decentralized bilateral cooperation and uncertain transshipment quantities

Pre-positioning relief inventory ensures timely delivery of in-kind aid after a catastrophe. Tragic disasters like major earthquakes are rare and unpredictable; therefore, stockpiled items may not be used. To avoid over-stocking and reduce shortage risk, the cooperation of two humanitarian agencies in supporting each other in case of shortages is suggested in the literature. In this study, we utilize newsvendor-based quantitative models to optimize the pre-disaster stocking decisions of agencies under centralized and decentralized cooperation mechanisms. In the former, both agencies jointly determine their inventory levels to maximize their combined benefits of relief operations, whereas, in the latter, each agency establishes its stocking level in isolation via a game theoretic approach. In both systems, the two agencies agree to transship their excessive items to the other party if needed. In this regard, we investigate the situation where only a portion of the transshipped items, denoted as the reliability factor, can be received and effectively utilized at the destination due to the chaotic nature of the disaster. Considering a deterministic reliability factor, we obtain the singular optimal inventory levels in the centralized system and identify the unique Nash Equilibrium in the decentralized system. Subsequently, we formulate a two-stage stochastic program, considering a random reliability factor for both cooperation systems. The study concludes by offering a range of managerial insights. Our analyses quantify the sub-optimality resulting from decentralized decision-making across diverse parameter settings using the concept of the price of anarchy. The findings highlight that centralized cooperation becomes particularly advisable when the average demand within either agency is high, the transshipment process is secure (i.e., the reliability factor is high), and transshipment costs remain low.

A Mathematical Model for Integrated Disaster Relief Operations in Early-Stage Flood Scenarios

When a flood strikes, the two most critical tasks are evacuation and relief distribution. It is essential to integrate these tasks, particularly before the floodwater reaches the vulnerable area, to minimize loss and damage. This paper presents a mathematical model of vehicle routing problems to optimize an integrated disaster relief operation. The model addresses routing for both the evacuation and relief distribution tasks in the early stages of a flood, aiming to identify a minimal number of vehicles required with their corresponding routes to transport vulnerable individuals and simultaneously distribute emergency relief. The new model incorporates several features, including vehicle reuse, multi-trip and split delivery scenarios for evacuees and emergency relief items, uncertainty in evacuation demands, and closing time windows at evacuation points. Due to the complexity of vehicle routing problems, particularly in large-scale scenarios, the exact approach for obtaining optimal solutions is time-consuming. Therefore, we propose the use of a metaheuristic algorithm, specifically a modified genetic algorithm, to find an approximate solution for the proposed model. We apply the developed model and modified algorithm to various simulated flood scenarios and a real-life case study from Indonesia. The experimental results demonstrate that our approach requires fewer vehicles compared to standard models for similar scenarios. Moreover, while the exact approach fails to find optimal solutions within a reasonable timeframe for large-scale scenarios, our new approach provides near-optimal solutions in a much shorter time. In smaller simulated scenarios, the modified genetic algorithm obtains optimal or near-optimal solutions approximately 92.5% faster than the exact approach.

Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives

Every organization typically comprises various internal components, including regional branches, operations centers/field offices, major transportation hubs, and operational units, among others, housing a population susceptible to disaster impacts. Moreover, organizations often possess resources such as staff, various vehicles, and medical facilities, which can mitigate human casualties and address needs across affected areas. However, despite the importance of managing disasters within organizational networks, there remains a research gap in the development of mathematical models for such scenarios, specifically incorporating operations centers/field offices and external stakeholders as relief centers. Addressing this gap, this study examines an optimization model for both before and after disaster planning in a humanitarian supply chain and logistical framework within an organization. The affected areas are defined as regional branches, operational units, major transportation hubs, operations centers/field offices, external stakeholders, and medical facilities. A mixed-integer nonlinear model is formulated to minimize overall costs, considering factors such as penalty costs for untreated injuries and demand, delays in rescue and relief item distribution operations, and waiting costs for the injured in emergency medical vehicles and air ambulances. The model is implemented using GAMS software 47.1.0 for various test problems across different scales, with the Grasshopper Optimization Algorithm proposed for larger-scale scenarios. Numerical examples are provided to show the effectiveness and feasibility of the proposed model and to validate the metaheuristic approach. Sensitivity analysis is conducted to assess the model’s performance under different conditions, and key managerial insights and implications are discussed.

Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty

Flooding, a pervasive and severe natural disaster, significantly damages environments and infrastructure and endangers human lives. In affected regions, disruptions to transportation networks often lead to critical shortages of essential supplies, such as food and water. The swift and adaptable delivery of relief goods via vehicle is vital to sustain life and facilitate community recovery. This paper introduces a novel model, the Vehicle Routing Problem for Relief Item Distribution under Flood Uncertainty (VRP-RIDFU), which focuses on optimizing the speed of route generation and minimizing waiting times for aid delivery in flood conditions. The Genetic Algorithm (GA) is employed because it effectively handles the uncertainties typical of NP-Hard problems. This model features a dual-population strategy: random and enhanced populations, with the latter specifically designed to manage uncertainties through anticipated route performance evaluations, incorporating factors like waiting times and flood risks. The Population Sizing Module (PSM) is implemented to dynamically adjust the population size based on the dispersion of affected nodes, using standard deviation assessments. Introducing the Complete Subtour Order Crossover (CSOX) method improves solution quality and accelerates convergence. The model’s efficacy is validated through simulated flood scenarios that emulate various degrees of uncertainty in road conditions, affirming its practicality for real-life rescue operations. Focusing on prioritizing waiting times over travel times in routing decisions has proven effective. The model has been tested using standard CVRP problems with 20 distinct sets, each with varying node numbers and patterns, demonstrating superior performance and efficiency in generating vehicle routing plans compared to the shortest routes, which serve as the benchmark for optimal solutions. The results highlight the model’s capability to deliver high-quality solutions more rapidly across all tested scenarios.

A risk-averse distributionally robust optimisation approach for drone-supported relief facility location problem

Drone-supported last-mile humanitarian logistics applications play a crucial role in the rapid and efficient delivery of essential relief items, such as medicine, blood, and vaccines, during disaster and emergency situations. This paper explores a novel drone-supported relief facility location problem (DSRFLP) aimed at establishing an effective drone-supported last-mile humanitarian logistics system. The problem involves making joint decisions for both pre-disaster and post-disaster phases while considering the characteristics of drone-based delivery operations and uncertain demands. In the pre-disaster phase, we make the decisions regarding the locations of drone-supported relief facilities, inventory prepositioning of relief items, assignment of drones to the opened facilities, and allocation of drones to disaster demand sites. In the post-disaster phase, we make decisions related to delivery quantities. To tackle the challenge of incomplete demand distribution information in chaotic disaster environments, we establish a distributionally robust optimisation (DRO) model to handle the uncertainty of demands. This model adopts worst-case mean Conditional Value-at-Risk as the risk measurement, reflecting the risk-averse attitude of humanitarian organisers. In this paper, three ambiguity sets (box, ellipsoidal, and polyhedral) are considered to describe the ambiguity distributions of demands. To overcome the computational challenge, we reformulate the DRO model under three ambiguity sets into two mixed-integer linear programming models and one second-order cone programming model, which can be efficiently solved by off-the-shelf solvers. Furthermore, we validate our proposed DRO model through a small-scale example and a large-scale case study based on the Lushan earthquake in China. The computational outcomes underscore the superior performance of the proposed DRO model to mitigate the impact arising from incomplete probability distributions. We propose managerial implications and insights to support the decision-making of humanitarian organisations based on the experimental results. Finally, we propose two extended models to incorporate multiple relief items and equity constraints in priority settings and conduct numerical experiments to adapt to various real-world disaster scenarios.

Integrated relief pre-positioning and procurement planning considering non-governmental organizations support and perishable relief items in a humanitarian supply chain network

The escalating frequency and severity of disasters on a global scale have sparked inquiries into the efficacy of current disaster planning strategies in various scenarios. Despite the pivotal role of humanitarian supply chain planning in aiding impacted populations, much of the existing research is grounded in simplistic assumptions that limit their practicality. Addressing this gap, our proposed bi-objective model aligns response time and total cost, while also accommodating the collaboration between non-governmental organizations and governmental organizations to mirror real-world intricacies. This study comprehensively delves into various logistics aspects, encompassing pre- and post-disaster phases, including location, allocation, supplier selection, fleet size, supply contract, inventory, distribution, and transportation. This multifaceted approach enhances the model's suitability for managing genuine real-world emergencies. To mitigate disruption risks and unforeseen events, the model introduces pre-positioning, quantity flexibility contract, backup suppliers, and a multi-sourcing policy, thus enhancing the resilience and reliability of the logistics network. We present solutions for diverse scenarios through a scaled weighted sum method, while tackling uncertainty via a heuristic approach known as the backward scenario reduction method. Furthermore, to manage large-scale problems within an acceptable time frame, we propose an advanced hybrid algorithm. This algorithm synergizes a parallel differential evolution framework with reinforcement learning-enhanced local search mechanisms, aiming to improve both computational efficiency and solution accuracy. Finally, we validate the model's applicability through a real case study focusing on a flood scenario in Iran.

A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach

Annually, different regions of the world are affected by natural disasters such as floods and earthquakes, resulting in significant loss of lives and financial resources. These events necessitate rescue operations, including the provision and distribution of relief items like food and clothing. One of the most critical challenges in such crises is meeting the blood requirement, as an efficient and reliable blood supply chain is indispensable. The perishable nature of blood precludes the establishment of a reserve stock, making it essential to minimize shortages through effective approaches and designs. In this study, we develop a mathematical programming model to optimize supply chains in post-crisis scenarios using multiple objectives. Presented model allocates blood to various demand facilities based on their quantity and location, considering potential situations. We employ real data from a case study in Iran and a robust optimization approach to address the issue. The study identifies blood donation centers and medical facilities, as well as the number and locations of new facilities needed. We also conduct scenario analysis to enhance the realism of presented approach. Presented research demonstrates that with proper management, crises of this nature can be handled with minimal expense and deficiency.

A scenario-based game theory integrating with a location-allocation-routing problem in a pre- and post-disaster humanitarian logistics network under uncertainty

PurposePre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.Design/methodology/approachIn this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.FindingsManagers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.Originality/valueThe model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Toward more ethically oriented humanitarian logistics operations: An exploratory research

Ethical standards in humanitarian logistics have been frequently overlooked in the literature. This research aims to explore the interaction between logistics decisions and ethical standards implementation in the context of humanitarian relief operations. The paper adopts a data triangulation approach to collect and validate the primary and secondary data through a literature review, focus group, and interview. The results revealed that logistics decisions and processes affect the implementation of ethical standards. These decisions are physical distribution, facility location, procurement, outsourcing, deprivation cost analysis, visibility and information flow, supply chain collaboration, staff selection and training, types of relief items, staff security, and localization of logistics activities. This research can provide practitioners with insight into a complete set of ethical standards and inform their understanding of which factors affect their implementation. The novelty of this research comes from combining two disciplines, humanitarian logistics and social work, to compile a complementary list of ethical standards in humanitarian operations. It shows how logistics decisions can affect implementing ethical standards and hence achieving core values of social work.

Measuring the shortage cost through deprivation and envy in collaborating contract between the local authority and the enterprise

Collaboration between the local authority and the enterprise is important when facing more frequent disasters and pandemics, and contract design is a key step. However, most designed contracts in relevant studies fail to precisely measure the shortage cost of relief supplies as they ignore the perceptions of victims, i.e., deprivation due to lacking relief supplies and envy due to unequal allocation. To fill the research gap, in this paper, we use the sum of deprivation (measured by deprivation cost, Holguin-Veras et al., 2012) and envy (measured by negative inequity aversion, Fehr and Schmidt, 1999) to calculate the shortage cost in contract design between the local authority and the enterprise. We then analyze three different contexts (i.e., low-risk, high-risk, mixed) based on the background of Covid-19 lock-down in China and conduct a corresponding numerical experiment. The result shows that through a Stackelberg game the local authority and the enterprise can reach an agreement. Furthermore, deprivation and envy have the same effect on the contract design, based on which the local authority will decrease the relief items shortage to mitigate the suffering of victims, thus represents a ‘people-centered’ idea.

Use of a toll-free call center for COVID-19 response and continuity of essential services during the lockdown, Greater Kampala, Uganda, 2020: a descriptive study.

on March 21, 2020, the first case of COVID-19 was confirmed in Uganda. A total lockdown was initiated on March 30 which was gradually lifted May 5-June 30. On March 25, a toll-free call center was organized at the Kampala Capital City Authority to respond to public concerns about COVID-19 and the lockdown. We documented the set-up and use of the call center and analyzed key concerns raised by the public. two hotlines were established and disseminated through media platforms in Greater Kampala. The call center was open 24 hours a day and 7 days a week. We abstracted data on incoming calls from March 25 to June 30, 2020. We summarized call data into categories and conducted descriptive analyses of public concerns raised during the lockdown. among 10,167 calls, two-thirds (6,578; 64.7%) involved access to health services, 1,565 (15.4%) were about social services, and 1,375 (13.5%) involved COVID-19-related issues. Approximately one-third (2,152; 32.7%) of calls about access to health services were requests for ambulances for patients with non-COVID-19-related emergencies. About three-quarters of calls about social services were requests for food and relief items (1,184; 75.7%). Half of the calls about COVID-19 (730; 53.1%) sought disease-related information. the toll-free call center was used by the public during the COVID-19 lockdown in Kampala. Callers were more concerned about access to essential health services, non-related to COVID-19 disease. It is important to plan for continuity of essential services before a public health emergency-related lockdown.

Designing a humanitarian supply chain network in dynamic conditions for transferring of relief items under demand uncertainty

Designing a humanitarian supply chain network in dynamic conditions for transferring of relief items under demand uncertainty

Institutional arrangement for mitigating and adapting to climate change-related flood risk in Greater Accra Metropolitan Area (GAMA)

Episodic floods, attributable primarily to climate change, global warming, and sea level rise, have worsened and continue to be a significant threat to life. Building resilience and improving the adaptive capacities of communities under threat will require institutional and cross-agency collaboration. This paper assessed institutional arrangements for mitigating and adapting to climate change-related flood risk in GAMA, Ghana. Using a descriptive cross-sectional survey, the study collected and analysed data from 65 private businesses and governmental, community, and civil society organisations to assess how institutions collaborate in preparing and adapting to flood risks in the study area. The study finds that frequent flooding, destruction of property and lives, and regular community agitations have triggered high-level consciousness, institutional arrangements, and collaboration within and among communities to build capacity and resilience. In addition, organisations have been educating indigenes on flood preparedness, providing relief items to support flood victims, and conducting training and research. Organisations have also supported communities in developing sandbags, planning sea defence mechanisms, providing rescue and evacuation assistance, providing transportation and logistics, and relocating stranded flood victims. Despite these efforts, the lack of adequate funding and logistics, coupled with the absence of proper consultation and involvement of most organisations in making national-level decisions on climate change and flood-related issues, have rendered flood management efforts less effective. This paper makes the case that proactive and collaborative flood responses should replace reactive and individual approaches to improve flood control efficacy and minimise casualties and property losses.

Barriers to vehicle-sharing among NGOs during disaster relief operations: Findings from a developing country's perspective

NGOs (Non-governmental Organizations) are still lagging in adopting the benefits of vehicle-sharing during disaster relief operations. Therefore, the primary objective of this investigation is to scrutinize the impediments that impede the cooperative efforts among NGOs in the context of vehicular resource sharing within the framework of disaster relief operations. The idea is to enhance relief operations' transport capacity, reduce pollution and congestion, and benefit the total environment without increasing the number of vehicles. In order to study this little-known area of research, an exploratory study is conducted by interviewing experts (i.e., those responsible for administering relief distributions) from both local and international NGOs in a regularly disaster-troubled developing country, Bangladesh. Using the innovation resistance theory, this study examines and categorizes a list of potential important barriers. In this study, various types of barriers are identified based on the innovation resistance theory. These barriers are categorized into five groups, including risk (consisting of 10 barriers), usage (comprising of 7 barriers), tradition (including 4 barriers), value (comprised of 2 barriers), and image (consisting of 1 barrier). Accordingly, vehicle-sharing is linked with many potential side-effects or uncertainties, as the majority of the identified barriers are connected to risks. It is also worth noting that certain vehicle-sharing barriers, which may seem crucial, can be addressed for better outcomes. For example, a strategy could be to communicate potential legal barriers and predetermine accountability for possible accidents. The research outcomes also suggest that the implementation of a vehicle-sharing initiative within the realm of disaster relief operations is susceptible to a multitude of intricate and technically oriented usage challenges that are inherently unavoidable. Notable among these issues are the physical characteristics of the vehicles, the potential for complications arising from the commingling of similar relief items, instances of vehicular overloading, and encounters with roadblocks along the designated routes. The findings have implications for HOs (Humanitarian Organizations), logistics service providers, as well as disaster preparedness, response, and recovery professionals.

A flexible framework to coordinate debris clearance and relief distribution operations: A robust machine learning approach

This study aims to offer an integrated, flexible action plan to bridge two vital operations in the response phase of disaster management, namely debris clearance and relief items distribution. On this subject, a broad range of associated decisions, including location-allocation, inventory, scheduling, and routing, are reflected in a multi-period environment. Also, so as to provide a pliable decision chain, two substantial kinds of flexibility are considered, including network design and due dates. So, a new bi-objective mathematical model is proposed, where the first objective minimizes the total cost, and the second one minimizes the total operation time. Since travel time information accuracy has an undeniable influence on system performance, a new two-phase data-driven methodology, including a hybrid machine learning model and the distributionally robust optimization with φ- divergence, is offered to estimate reliable travel time and tackle its uncertainty. Additionally, the application and validation of the recommended model and solution methodology are investigated in a real case study. What is more, in order to explain the superiority of the proposed solution approach concerning robustness solution, various simulation experiments are executed. Ultimately, several numerical experiments are implemented, and captured results manifest that the proposed framework can enhance the system’s performance. The obtained results reveal that considering the flexibility in network design and delivery date reduces the total costs and operation time by 7 and 6 percent.

Expert oriented hybrid approach for modeling coordination motivators in humanitarian supply chain: Managerial implications to actors’ coordination enhancement

Humanitarian supply chain coordination (HSCC) has been identified as a critical factor in disaster relief success. Stakeholders must find different ways to communicate information and coordinate relief item flows during the relief processes. Many recent studies have advanced HSCC from multiple perspectives. However, the field still lacks a comprehensive analysis to summarize existing research, explicitly identify all the possible motivators for coordination, and investigate how the motivators can be carried out at the humanitarian supply chain interfaces. A conceptual model is developed for HSCC in this research. HSCC motivators are discovered through a review of the literature. Data is collected from experts using a structured questionnaire. The integrated best-worst method (BWM) and interpretive structural modeling (ISM) approaches are proposed to prioritize and explore the underlying relationships between the motivators. The driving/dependence power of motivators is investigated in this study. Based on the findings, “Commitment” is the most critical coordination motivator in HSC. The results will aid managers and policymakers in the relief and rescue sector to make better decisions for HSCC.