Humanitarian Logistics Operations Research Articles

A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations

AbstractProviding first aid and other supplies (e.g., epi‐pens, medical supplies, dry food, water) during and after a disaster is always challenging. The complexity of these operations increases when the transportation, power, and communications networks fail, leaving people stranded and unable to communicate their locations and needs. The advent of emerging technologies like uncrewed autonomous vehicles can help humanitarian logistics providers reach otherwise stranded populations after transportation network failures. However, due to the failures in telecommunication infrastructure, demand for emergency aid can become uncertain. To address the challenges of delivering emergency aid to trapped populations with failing infrastructure networks, we propose a novel robust computational framework for a two‐echelon vehicle routing problem that uses uncrewed autonomous vehicles (UAVs), or drones, for the deliveries. We formulate the problem as a two‐stage robust optimization model to handle demand uncertainty. Then, we propose a column‐and‐constraint generation approach for worst‐case demand scenario generation for a given set of truck and UAV routes. Moreover, we develop a decomposition scheme inspired by the column generation approach to generate UAV routes for a set of demand scenarios heuristically. Finally, we combine the decomposition scheme within the column‐and‐constraint generation approach to determine robust routes for both trucks (first echelon vehicles) and UAVs (second echelon vehicles), the time that affected communities are served, and the quantities of aid materials delivered. To validate our proposed algorithms, we use a simulated dataset that aims to recreate emergency aid requests in different areas of Puerto Rico after Hurricane Maria in 2017.

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.

Computational approaches for solving two-echelon vehicle and UAV routing problems for post-disaster humanitarian operations

During and after Hurricane Maria in 2017, the island of Puerto Rico faced an impossible situation: the transportation, power, and communication networks were all damaged, leading to numerous people being trapped. Humanitarian logistics service providers have the responsibility to reach as many affected populations as possible within short periods of time. Their extremely difficult operations are further hindered by these failures in our built environment. Failures in transportation mean that some routes cannot be used by conventional ground vehicles; failures in communication mean that demands are uncertain, and the locations of said demands may be difficult to pinpoint. In this work, we propose a novel two-echelon vehicle routing framework for performing many of the humanitarian logistics operations using aerial uncrewed autonomous vehicles (drones) to address the issues associated with these infrastructure failures. In our proposed framework, we assume that ground vehicles cannot always reach the trapped population directly, but they can transport drones from a depot to some intermediate satellite locations. The drones launched from these locations can serve to both identify demands for medical and other aids (e.g., epi-pens, medical supplies, dry food, water) and make deliveries to satisfy them. Specifically, we present an expert decision support system, in which the resulting optimization problem is formulated as a two-echelon vehicle routing problem with trucks as the first echelon vehicles and drones as the second echelon vehicles. Our framework utilizes two types of drones. Hotspot or communication providing drones have the capability of providing cell phone and internet reception, and hence, are used to capture demands and their locations. Delivery drones are subsequently employed to satisfy these obtained demands. To handle demand uncertainty, we decompose the decision problem into two stages: providing telecommunications capabilities in the first stage, thereby capturing demands precisely, and satisfying the resulting demands in the second stage. To solve the resulting models, we propose a matheuristic computational approach in the form of a decomposition algorithm inspired by column generation to identify optimal drone routes. To showcase the application of our proposed framework, we present results from numerical experiments on datasets created to simulate the demand for medical aid in Puerto Rico after Hurricane Maria.

Humanitarian Relief Distribution Problem: An Adjustable Robust Optimization Approach

Management of humanitarian logistics operations is one of the most critical planning problems to be addressed immediately after a disaster. The response phase covers the first 12 hours after the disaster and is prone to uncertainties because of debris and gridlock traffic influencing the dispatching operations of relief logistics teams in the areas affected. Moreover, the teams have limited time and resources, and they must provide equitable distribution of supplies to affected people. This paper proposes an adjustable robust optimization approach for the associated humanitarian logistics problem. The approach creates routes for relief logistics teams and decides the service times of the visited sites to distribute relief supplies by taking the uncertainty in travel times into account. The associated model allows relief logistics teams to adjust their service decisions according to the revealed information during the process. Hence, our solutions are robust for the worst-case realization of travel times, but still more flexible and less conservative than those of static robust optimization. We propose novel reformulation techniques to model these adjustable decisions. The resulting models are computationally challenging optimization problems to be solved by exact methods, and, hence, we propose heuristic algorithms. The state-of-the-art heuristic, which is based on clustering and a dedicated decision-rule algorithm, yields near-optimal results for medium-sized instances and is scalable even for large-sized instances. We have also shown the effectiveness of our approach in a case study using a data set obtained from an earthquake that hit the Van province of Turkey in 2011. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1204 .

A probabilistic fuzzy goal programming model for managing the supply of emergency relief materials.

The post-disaster humanitarian logistic operations deal with the supply of emergency relief materials to mitigate damages in the affected areas. Immediately after the disaster, it is challenging to estimate the demand for emergency relief materials. As a result, the demand for such materials at the point of demand and the corresponding transportation costs for the entire supply chain network becomes uncertain. This paper proposes a new probabilistic fuzzy goal programming model for making decisions to manage the post-disaster supply of emergency relief materials. A suggested procedure converts the proposed model to its deterministic equivalent when the demands for the relief materials follow uniform distributions. We implement the differential evolution, a metaheuristic technique, for analyzing demand satisfaction for relief materials under various scenarios. A case example based on the Nepal Earthquake in 2015 demonstrates the usefulness of the proposed approach. The solution of the model will help the Disaster Management Agency coordinate with the humanitarian organizations and foreign countries to provide the required emergency relief materials so that an adequate level of supply can be assured to the affected areas with the least possible transportation cost.

A stochastic look-ahead approach for hurricane relief logistics operations planning under uncertainty

In the aftermath of a hurricane, humanitarian logistics plays a critical role in delivering relief items to the affected areas in a timely fashion. This paper proposes a novel stochastic look-ahead framework that implements a two-stage stochastic programming model in a rolling horizon approach to address the evolving uncertain logistics system state during the post-hurricane humanitarian logistics operations. The two-stage stochastic programming model that executes in this rolling horizon approach is formulated as a mixed-integer programming problem. The model aims to minimize the total cost incurred in the logistics operations, which consist of transportation cost and social cost. The social cost is measured as a function of deprivation for unsatisfied demand. Our extensive numerical results and sensitivity analysis demonstrate the effectiveness of the proposed approach in reducing the total cost incurred during the post-hurricane relief logistics operations compared to the two-stage stochastic programming model implemented in a static fashion.

An entropy-based approach for disaster risk assessment and humanitarian logistics operations planning in Colombia

PurposeThis paper aims to design a vulnerability assessment model considering the multidimensional and systematic approach to disaster risk and vulnerability. This model serves to both risk mitigation and disaster preparedness phases of humanitarian logistics.Design/methodology/approachA survey of 27,218 households in Pueblo Rico and Dosquebradas was conducted to obtain information about disaster risk for landslides, floods and collapses. We adopted a cross entropy-based approach for the measure of disaster vulnerability (Kullback–Leibler divergence), and a maximum-entropy estimation for the reconstruction of risk a priori categorization (logistic regression). The capabilities approach of Sen supported theoretically our multidimensional assessment of disaster vulnerability.FindingsDisaster vulnerability is shaped by economic, such as physical attributes of households, and health indicators, which are in specific morbidity indicators that seem to affect vulnerability outputs. Vulnerability is heterogeneous between communities/districts according to formal comparisons of Kullback–Leibler divergence. Nor social dimension, neither chronic illness indicators seem to shape vulnerability, at least for Pueblo Rico and Dosquebradas.Research limitations/implicationsThe results need a qualitative or case study validation at the community/district level.Practical implicationsWe discuss how risk mitigation policies and disaster preparedness strategies can be driven by empirical results. For example, the type of stock to preposition can vary according to the disaster or the kind of alternative policies that can be formulated on the basis of the strong relationship between morbidity and disaster risk.Originality/valueEntropy-based metrics are not widely used in humanitarian logistics literature, as well as empirical data-driven techniques.

Insights from vulnerability-driven optimisation for humanitarian logistics

Natural disasters and the vulnerability of a population go hand in hand. We cannot understand the level of a disaster without grasping the extent of people�s vulnerability. But how can we ensure that humanitarian assistance is driven by people�s vulnerability when the lack of resources makes it impossible to support all those that need it? This study thus contributes to this line of research by enhancing our understanding of how we can �put the reality of the most vulnerable people first� (cf. Chambers 1995). For this purpose, we examine two experiences that have proposed to incorporate vulnerability concerns into the planning and optimisation of humanitarian logistics operations. The first experience relies on a very popular composite indicator called Social Vulnerability Index (SoVI) to build enhanced response capacity in more vulnerable areas. The second experience is built upon a poverty measure called Foster-Greer-Thorbecke (FGT) to identify the groups that potentially need the most relief aid supply and to help devising allocation plans in compliance with people�s income. These two experiences reveal that in most cases targeting more vulnerable areas increases their level of access to relief aid goods without greatly compromising the relief service levels of less vulnerable areas.

Emergence of Emergency Logistics Centre (ELC): Humanitarian Logistics Operations at the Straits of Malacca

Despite the apprehension of certain parties, the demand for maritime emergency logistics has emerged following the occurrence of maritime accidents around the world while trading activities are performed. The requirement of Emergency Logistics Centre (ELC) is crucial as part of the maritime disaster preparedness at the Straits of Malacca (SOM). The ELC prevents massive losses, should any disaster occur in the rough ocean. Based on unforeseen situations at sea, this paper explores the contributing factors of the ELC in proposing and improving strategies for Kuala Linggi seaport as an ELC. The Exploratory Factor Analysis (EFA) has been employed to achieve the objectives of this paper. The outcome of this paper indicates that resource availability, risk management, and geographical factors are the three key attributes that are substantially required to transform Kuala Linggi seaport into an ELC. Furthermore, disaster preparedness, ELC supply-chain management system, and safety procedures are the crucial components to enhance the operational efficacy at this seaport as an ELC. The findings of this research may contribute to the safety of the maritime route and disaster preparedness at the SOM.

Humanitarian Relief Sustainability: A Framework of Humanitarian Logistics Digital Business Ecosystem

Humanitarian logistics constraints in response to natural and human-made disasters are subjects of much scrutiny and concern by global humanitarian actors, government, scientist and the public. Disasters impact continues to make headlines annually as lives and properties are lost. The humanitarian community, through their logistics operations, attends to such disasters when it occurs. Despite the assistance of humanitarian relief operations support, the relief community is faced challenges such as huge logistics costs, fewer donor supports, which affects the delivery of relief to affected people. The high cost of humanitarian logistics operations on disaster finances continue to pose a hurdle for operators as donor support is not worthwhile. The study uses a systematic literature review coupled with an axiological philosophical lens approach to developing a Humanitarian Logistics Digital Business Ecosystem (HLDBE) framework as an alternative way to sustain the humanitarian logistics operations and reliefs through hybrid humanitarian- business logistics sector. Seven elements derived from the framework are collaborative blocks for establishing a sustainable HLDBE for relief logistics operations in creating of the financial reserve, capacity and empowerment building for domestic and international humanitarian logistics operations under a win-win opportunity for stakeholders. Implementation of this ecosystem will have a positive impact on affected economies. Future research direction is also proposed to further the research direction.

A review of literature on types, stages of recovery and humanitarian logistics operations in the tsunami and earthquake disaster in Indonesia

Recovery after a disaster determines the survival of the population in the affected area. However, there is still little lecturer to discuss recovery compared to other processes. This literature review aims to develop findings from previous literature reviews with a focus on the area of Indonesia and the earthquake and tsunami disaster. The conclusions that can be drawn are the majority of journals discussing earthquakes and researching on physical recovery. Economy recovery being the least discussed them, and the most widely researched recovery phase is a long term recovery. In 2016-2017 there is now a majority of themed journals of recovery management, and 2010-2012 the most widely publicized long term reconstruction journal. This article also presents 5 gaps in the recovery themed literature.

Agility and resource dependency in ramp-up process of humanitarian organizations

PurposeThe ramp-up in humanitarian logistics operations is a stage when the demand surges, often at the start of an emergency. In response, agility is a key strategy used by the humanitarian organizations (HOs). However, the HOs are constrained by their existing resources and have to respond in the ramp-up process under their resource dependency. The purpose of this paper is to develop a framework on agility-building strategies used by HOs for the ramp-up.Design/methodology/approachThis study applies both the dynamic capabilities perspective and resource dependence theory to humanitarian relief operations, and develops four testable propositions to explore the agility-building strategies of the HOs for the ramp-up process. A multiple-case study is conducted on six international HOs operating in Indonesia to verify them, in addition to an extensive literature search.FindingsThe case study shows that the human resource management, pre-positioning, standardization and supplier management of the HOs are all related to their resources and environment in the ramp-up process. The authors highlight the practical differences between the few super large, resource rich and centralized HOs with the second-tier HOs.Research limitations/implicationsGiven the small sample size and single country as the site of study, some findings may not be applicable to the other HOs or in other regions.Practical implicationsThe propositions could be applicable to other HOs operating under similar environments, and potentially to the commercial enterprises operating in a highly volatile environment with severe resource scarcity.Originality/valueThis study provides new insights into ramp-up operations and into how HOs build their agility and reduce their resource dependencies. Theoretically, the paper applies two established theories in the strategic management literature to a new field.

Customs and Humanitarian Logistics

Motivated by concerns about the impact of trade and customs procedures upon humanitarian logistic operations, this article seeks to open a new chapter in the relevant literature. It builds on three case studies, each of which is a household name within the humanitarian space and has been developed with the support of key informants in those organizations. The analysis of the case studies shows how customs considerations impact upon the entire relief chain. A review of relevant literature and documents provides the necessary context. A list of recommendations, which have been identified in consultation with research participants and are based on established ideas in contemporary trade facilitation policy, is offered. Further dialogue between customs and humanitarian logistics practitioners is keenly encouraged.

Deep uncertainty in humanitarian logistics operations: decision-making challenges in responding to large-scale natural disasters

Humanitarian logistics operations perform challenging tasks while responding to large-scale natural disasters. Decision makers at different stages of humanitarian operations exploit numerous problem-specific decision-making models or tools. When synchronising the outputs (decisions) from models into a unified solution, the situation becomes critical because of the lack of consensus on objectives and the availability of model alternatives with uncertainty in the models' key parameters and evaluation of the models' alternative outcomes. Thus, the operational environment becomes complex to respond urgently to humanitarian needs and makes the situation deeply uncertain. In this paper, we inspect humanitarian logistics problems and available deep uncertainty approaches to identify the adapting needs in the latter to be applicable to the former. Our research findings indicate that deep uncertainty approaches should incorporate the concept of short-term planning by considering time constraints, bounded process iteration, data transformation technique, handling process failure, and ways of identifying model assumptions.

Deep uncertainty in humanitarian logistics operations: decision-making challenges in responding to large-scale natural disasters

Humanitarian logistics operations perform challenging tasks while responding to large-scale natural disasters. Decision makers at different stages of humanitarian operations exploit numerous problem-specific decision-making models or tools. When synchronising the outputs (decisions) from models into a unified solution, the situation becomes critical because of the lack of consensus on objectives and the availability of model alternatives with uncertainty in the models' key parameters and evaluation of the models' alternative outcomes. Thus, the operational environment becomes complex to respond urgently to humanitarian needs and makes the situation deeply uncertain. In this paper, we inspect humanitarian logistics problems and available deep uncertainty approaches to identify the adapting needs in the latter to be applicable to the former. Our research findings indicate that deep uncertainty approaches should incorporate the concept of short-term planning by considering time constraints, bounded process iteration, data transformation technique, handling process failure, and ways of identifying model assumptions.

An empirical investigation of swift trust in humanitarian logistics operations

PurposeTrust is essential for any team working together. In humanitarian logistics operations, relief organizations often have to work collaboratively in hastily formed networks. Trust in such a context, termed as “swift trust” in the literature, is an important but less explored topic. The purpose of this paper is to empirically explore the antecedents of swift trust as well as its impact on the coordination among the humanitarian workers.Design/methodology/approachThe authors choose the humanitarian workers in Southeast Asia as the research sample. An exploratory survey study is conducted in three Southeast Asian countries, namely, Singapore, Indonesia, and the Philippines, with 89 usable responses.FindingsThe empirical results have shown support for most hypotheses. Third-party certification, competency, similarity in procedure standards and organizational values, can all generate swift trust. Moreover, swift trust can lead to greater openness in information sharing for coordination, though not to active assistance. Both coordination activities would lead to greater coordination effectiveness.Research limitations/implicationsFuture studies could examine the four antecedent conditions for swift trust with better proxies. The connection between swift trust and coordination effectiveness can be explored in depth.Practical implicationsNGOs and governments could use these means effectively to build swift trust among the humanitarian players. For example, organizing field-oriented training activities would be beneficial for humanitarian workers in both network building and enhancing personal competency.Originality/valueThe findings point to the importance of swift trust in humanitarian operations and identify several means to enhance this trust. It has filled a research gap on the empirical investigation of the antecedents and impact of swift trust on inter-organizational coordination in humanitarian logistics operations.

Multicriteria optimization approach to deploy humanitarian logistic operations integrally during floods

AbstractThis paper addresses frequent and foreseeable floods in the short‐term preparedness of an imminent event using a multicriteria optimization model integrated with a geographical information system to simulate flood levels, determine the best strategies, and update information. The proposed model takes into account the four main relief operations: location of emergency facilities (i.e., distribution centers, shelters, and meeting points), prepositioning of humanitarian aid, evacuation, and distribution of humanitarian aid. Three criteria are considered in the formulation to minimize: the maximum evacuation flow‐time, the maximum distribution flow‐time, and total cost of relief operations. The approximation to the efficient frontier is built using multiobjective programming through the use of commercial software. The usefulness and robustness of the model are verified using data from one of the worst Mexican floods considering various flood levels created from three key elements in humanitarian logistics. The strategies provided by the proposed methodology are compared with those implemented by the Mexican authorities during the studied disaster.

Embedded analytics: improving decision support for humanitarian logistics operations

Analytical techniques continue to advance in efficacy, as well as complexity. However, it is sometimes unrealistic to employ complex analyses during time-constrained humanitarian disaster operations. We propose that simple, embedded analytics tools can provide an effective and practical means toward managing humanitarian operations. In this paper, we demonstrate a real-world application of our technique in a patient evacuation context. This paper contributes to literature and practice by showing how simple analytic methods and open-source imagery tools can offer significant value to the humanitarian operations literature. The application also highlights some challenges to drawing a clear picture from disparate data sources in the humanitarian operations domain.

Long-endurance remotely piloted aircraft systems (LE-RPAS) support for humanitarian logistic operations

PurposeWhile the use of long-endurance remotely piloted aircraft systems (LE-RPAS) is frequently associated with military operations, their core capabilities of long-range, low-cost and high-quality optics and communications systems have considerable potential benefit in supporting the work of humanitarian logisticians. The purpose of this paper is, therefore, to demonstrate how LE-RPAS could be used to improve the logistic response to a rapid onset disaster.Design/methodology/approachUsing the response to the Cyclone Pam that struck Vanuatu in March 2015 as an example, this paper provides an overview of how LE-RPAS could be used to support the post-disaster needs assessment and subsequent response processes. In addition, it provides a high-level route map to develop the people, process and technology requirements that would support the operational deployment of the LE-RPAS capabilities.FindingsOn the basis of the analysis of the published literature and the resultant assessment of the benefits of LE-RPAS to support humanitarian logistic (HL) operations, it is concluded that a formal “proof of concept” trial should be undertaken, and the results be made available to the humanitarian community.Research limitations/implicationsThis paper is conceptual in nature, but has been developed through an analysis of the literature relating to remotely piloted aircraft systems (RPAS) and HLs. A route map through which the paper’s conclusions can be validated is also offered.Practical implicationsLE-RPAS have great potential to provide a swifter understanding of the impact of a disaster, particularly those where the location is remote from the main centres of population. This would allow the affected country’s National Disaster Management Organisation, together with those of supporting countries, to react more efficiently and effectively. In particular, it would allow a swifter transition from a “guess-based” push approach to one that more accurately reflects the disaster’s impact – i.e. a pull-based logistic response.Social implicationsGiven the military genesis of RPAS, it will be important to ensure that those engaged in their operation are sensitive to the implications of this. In particular, it will be essential to ensure that any humanitarian operations involving RPAS are undertaken in an ethical way that respects, for example, the privacy and safety of the affected population.Originality/valueWhile there is some emerging discussion on the humanitarian-related use of RPAS in the literature, this generally reflects the operation of small aircraft with limited range and payload capabilities. Useful though such RPAS unquestionably are, this paper expands the discussion of how such systems can support the humanitarian logistician by considering the benefits and challenges of operating long-endurance aircraft.

Multi objective optimization for humanitarian logistics operations through the use of mobile technologies

Purpose The purpose of this paper is to present the development and implementation of a multiobjective optimization model and information system based on mobile technology, to support decision making in humanitarian logistics operations. Design/methodology/approach The trade-off between economic and social (deprivation) costs faced by governmental and nongovernmental organizations (NGOs) involved in humanitarian logistics operations is modeled through a Pareto frontier analysis, which is obtained from a multiobjective optimization model. Such analysis is supported on an information system based on mobile technology. Findings Results show useful managerial insights for decision-makers by considering both economic and social costs associated to humanitarian logistics operations. Such insights include the importance of timely and accurate information shared through mobile technology. Research limitations/implications This research presents a multiobjective approach that considers social costs, which are modeled through deprivation functions. The authors suggest that a future nonlinear approach be also considered, since there will be instances where the deprivation cost is a nonlinear function throughout time. Also, the model and information system developed may not be suitable for other humanitarian aid instances, considering the specific characteristics of the events considered on this research. Practical implications The inclusion of several types of goods, vehicles, collecting points off the ground, distributions points on the ground, available roads after a disaster took place, as well as volume and weight constraints faced under these scenarios, are considered. Social implications Deprivation costs faced by affected population after a disaster took place are considered, which supports decision making in governmental and NGOs involved in humanitarian logistics operations toward welfare of such affected population in developing countries. Originality/value A numerical illustration in the Latin American context is presented, the model and information system developed can be used in other developing countries or regions that face similar challenges toward humanitarian logistics operations.