1. Introduction
Facility location problems constitute a fundamental research area in logistics and supply chain management, aiming to determine the optimal placement of facilities, such as warehouses and distribution centers [1]. These problems have been extensively studied in various domains, including commercial logistics, healthcare, and public services, whereby the placement of strategic facilities plays a crucial role in operational efficiency [2]. Traditional facility location models typically seek to minimize costs while improving service accessibility, considering factors such as transportation distances, demand distribution, and capacity constraints [1].
In recent years, there has been growing interest in temporary and modular facility location problems, particularly in scenarios requiring rapid infrastructure deployment or adaptation. Unlike permanent facilities, temporary locations can be established to address fluctuating demand, emergencies, or short-term projects [3]. This flexibility is especially valuable in disaster response operations, for which immediate action is required to provide critical aid and services [4].
The role of facility location in humanitarian logistics has become increasingly relevant due to the growing frequency and severity of natural disasters. In 2023 alone, a total of 399 disasters were reported, resulting in 86,473 deaths, affecting 93.1 million people, and causing economic losses of $202.7 billion [5]. Humanitarian logistics, defined as the process of planning, implementing, and controlling the flow and storage of goods and services to support people in crises [6], faces unique challenges that differentiate it from commercial logistics. These challenges include high uncertainty, limited resources, disrupted infrastructure, and the urgency of response efforts [7,8].
One of the key decisions in humanitarian logistics is the strategic placement of temporary and modular facilities to optimize aid distribution and minimize human suffering [9]. Unlike conventional supply chain networks, humanitarian operations require dynamic facility location strategies that account for rapidly changing conditions, uncertain demand patterns, and limited accessibility to affected regions [10]. Effective facility location planning can significantly improve disaster response by reducing transport times, optimizing resource allocation, and ensuring a fair distribution of relief supplies [4].
The deployment of temporary and modular facilities has been documented in various disaster response efforts, such as the 2015 Nepal earthquake and the 2016 Ecuador earthquake, when mobile medical units and emergency shelters played a crucial role in relief operations [11]. These facilities are often established in public spaces such as parks, stadiums, and churches [9], using tents, prefabricated structures, or repurposed buildings to shelter displaced populations and facilitate the distribution of aid [12]. The ability to rapidly set up and relocate these facilities is essential in post-disaster environments, in which conditions evolve quickly and relief efforts must remain highly adaptive.
Despite the critical role of temporary and modular facilities in humanitarian logistics, research on this topic remains fragmented. Building on this background, this article aims to address the following research question: What is the current understanding of location problems related to temporary or modular facilities in the field of humanitarian logistics?
To address this question, this study systematically examines the existing body of literature, identifying key trends, challenges, and research gaps related to temporary facility location problems in humanitarian logistics. The findings will contribute to a more comprehensive understanding of how facility location models can be adapted to improve disaster response efficiency. Furthermore, this study aligns with Sustainable Development Goal (SDG) 11.5, which seeks to mitigate disaster-related fatalities and vulnerabilities by enhancing preparedness and response mechanisms.
Research Objectives and Main Contributions
This study aims to conduct a comprehensive analysis of existing research on the location of temporary and modular facilities in humanitarian logistics, identifies key trends in the deployment of these facilities during disaster response, besides highlighting gaps in the literature and proposing future research directions to advance the field.
This research is necessary due to the increasing frequency and severity of disasters, which demand efficient and adaptable facility location strategies. Unlike permanent structures, temporary and modular facilities provide a flexible and rapid response to fluctuating demands and infrastructure disruptions. However, current research remains fragmented.
The main contributions of this study are:
It systematically reviews and consolidates existing research on temporary facility location problems in humanitarian logistics, providing a structured synthesis of key findings in the field.
It highlights current trends in the literature, offering insights into the predominant themes and focal areas of recent studies.
It identifies critical research gaps and suggests avenues for future research to enhance the effectiveness of temporary facility location strategies.
The remainder of this article is structured as follows: Section 2 outlines the methodology, detailing the systematic review process and data collection criteria. Section 3 presents the results, organized according to key research categories. Section 4 discusses the findings and their implications, while Section 5 concludes the study and suggests directions for future research.
2. Method
A systematic literature review involves the rigorous identification, critical evaluation, and synthesis of existing research within a specific field [13]. As described by [14], this process requires an in-depth analysis of academic publications centered on a particular theme or research question. In this study, we conducted a systematic literature review focusing on temporary facility location problems within the domain of humanitarian logistics.
The review methodology followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework [15,16], which consists of four key phases: identification, screening, data extraction, and reporting of relevant studies. The PRISMA checklist utilized in this review is provided in Supplementary Materials, while the registration details on the Open Science Framework (OSF) are included in the Data Availability Statement.
The inclusion criteria comprised peer-reviewed articles written in English and published up to 2024, explicitly addressing temporary facility location problems in humanitarian logistics. Exclusion criteria were applied to remove articles that:
Did not focus on location problems.
Did not focus on temporary or modular facilities.
Did not pertain to humanitarian logistics.
Primarily addressed disaster risk mapping, vulnerability assessment, or waste management.
The identification phase involved retrieving articles from two major academic databases: Scopus and Web of Science. These databases were selected due to their extensive coverage of indexed journals [17]. The search query, detailed in Table 1, was structured using specific keywords applied to relevant fields, such as Topic, Language, and Document Type. Keyword Group 5 was excluded using the logical operator AND NOT to filter out studies focusing on disaster risk mapping and assessment, which fall outside the scope of this research. The search was conducted on 15 January 2025.
Figure 1 illustrates the PRISMA flow diagram detailing the research process. An initial search identified 343 articles from Scopus and 352 from Web of Science, yielding a total of 678 records. After removing 261 duplicates, 434 unique articles proceeded to the screening phase.
During the screening phase, abstracts were reviewed to assess the relevance of each article. As a result, 332 articles were excluded for not aligning with the study scope. Retrieval efforts were made for the remaining articles; however, 16 remained inaccessible. A full-text review was conducted on the remaining 102 articles, leading to the exclusion of 23 additional studies that did not explicitly address temporary facility location problems. Additionally, two articles were included through citation searching. Ultimately, 65 articles were selected for in-depth analysis, forming the final sample of this systematic review. To minimize bias, each study was independently evaluated by two researchers.
The relatively small number of selected articles reflects the niche nature of this research field. Temporary facility location in humanitarian logistics is a specialized area with a limited number of publications compared to broader logistics reviews, such as [3], which consider about 125 articles. Within this context, the proportion of studies focused specifically on humanitarian logistics is significant.
For data extraction, we adopted a categorization framework inspired by the taxonomy proposed by [3] in the context of modular facility location problems. This framework was further refined to incorporate key aspects of humanitarian logistics, drawing on prior research by [18]. The categorization included the following dimensions:
General Information: Includes journal name, country of the case study, and year of publication.
Humanitarian logistic related information: Specifies the type of disaster and the type of temporary facility being located.
Customer Demand: Distinguishes between service-based and product-based demand [3]. It also considers whether the model follows a single-product or multi-product approach, as per [1].
Objective Function: Identifies whether the model is single-objective or multi-objective and categorizes the type of objective function used [3].
Capacity Constraints: Differentiates between capacitated and uncapacitated facility location models [1].
Planning Horizon: Classifies models as single-period, multi-period, or continuous-time [3].
Uncertainty Consideration: Determines whether the model explicitly accounts for uncertainty using probability distributions [3].
Mathematical Modeling Approach: Identifies whether the model employs linear or nonlinear programming, as well as methodologies, such as deterministic optimization, robust programming, stochastic programming, fuzzy programming, or goal programming [3].
Solution Approach: Specifies whether solutions were obtained using commercial solvers, metaheuristic algorithms, Lagrangian relaxation, or other techniques [3].
Location Problem Decisions: Examines decisions related to facility opening, operation, and closure [3].
Modular Capacity Planning Decisions: Includes decisions regarding facility configuration, relocation, expansion, reduction, and operation of modular units [3].
By employing this structured methodology, we ensured a rigorous and comprehensive review of the literature on temporary facility location problems in humanitarian logistics to find valuable insights into existing research trends, gaps, and potential directions for future studies.
3. Results
The results of the systematic literature review are organized into the following sections based on the selection of categories presented in Section 2, which include general information, disaster-related information, customer demand, objective function, capacity constraints, planning horizon, uncertainty, mathematical modeling and solution approach, location problem decisions and modular capacity decisions.
3.1. General Information
This section presents a comprehensive analysis of the data, covering key aspects, such as the most frequently used terms, distribution of publication years, journals in which the research was published, and the most prolific authors.
In terms of publication years, the final sample includes articles published between 2012 and 2024. From 2012 to 2015, only one or two articles were published annually. Starting in 2016, the number of publications increased to five per year. A similar trend continued in 2017 and 2018, with 4 and 6 articles published, respectively. The peak in publications occurred between 2019 and 2022, with 8 to 9 articles released each year. However, there was a slight decline in 2023 and 2024, with only 5 and 4 articles published, respectively. Finally, as of March 2025, two articles have been published. This trend is illustrated in Figure 2.
Regarding journals, the International Journal of Disaster Risk Reduction published the highest number of articles on the subject, with a total of seven. As a specialized journal in disaster research, this is expected. The second most frequent journal is Socio-Economic Planning Sciences, with six articles, followed by Transportation Research Part E: Logistics and Transportation Review with five articles and Computers and Industrial Engineering with four articles. Both the International Journal of Industrial Engineering and the Journal of Humanitarian Logistics and Supply Chain Management each published three articles. Several other journals have contributed one or two articles on this topic. A summary of these findings is presented in Table 2.
The literature analysis shows sustained growth in research on temporary facility location in humanitarian logistics; however, the overall number of studies per year remains limited, indicating that research in this field is still emerging. The concentration of studies in specialized journals in disaster risk management, operations research, and logistics highlights the topic relevance across disciplines. However, gaps remain in empirical validation and practical application of the models proposed, indicating the need for more integrated and implementation-focused approaches.
3.2. Humanitarian Logistics-Related Information
This section analyzes the humanitarian logistics aspects addressed in each article. It begins by providing an overview of the case studies used to generate numerical results, including the types of disasters examined and the countries where the applications were conducted. Following this, the section details the types of facilities considered for location in the context of each study.
Earthquakes are the most frequently studied type of disaster, with 39 articles focusing on this topic. Only article [19] presents a case study on a non-natural disaster, specifically addressing the refugee crisis. Additionally, 11 articles do not specify the type of disaster in their application cases.
Temporary facilities in humanitarian logistics vary significantly depending on the type of service required. The most commonly studied facilities include shelters, which provide temporary housing for displaced individuals and families (e.g., [9,20,21]). Another frequently analyzed category is storage centers, which store essential supplies such as food, water, and medical equipment before distribution (e.g., [22,23,24]). Additionally, distribution centers, which facilitate the organized allocation of relief goods to affected populations, are a key focus in the literature (e.g., [25,26,27]). Medical centers, which provide emergency treatment and healthcare services to disaster-affected individuals, represent another critical facility type frequently examined in the literature (e.g., [28,29,30]). Finally, hubs function as central points for coordinating relief efforts, including transportation, supply management, and communication, and are often incorporated into analyses (e.g., [11,31,32]).
Each of these facilities addresses distinct needs during disaster response. Table 3 summarizes the types of temporary facilities considered, along with the corresponding disaster types and countries of application case.
The analysis of humanitarian logistics applications indicates that earthquakes are the most studied disasters due to their sudden onset and unpredictability, making temporary facility strategies the most effective response. Other crises, such as floods, epidemics, and refugee situations, receive considerably less attention. Facility location decisions primarily focus on shelters, distribution centers, and medical centers, which are crucial for last-mile logistics and direct victim assistance. However, studies often analyze these facilities individually rather than as integrated networks, underscoring the need for future research on more comprehensive planning approaches to enhance disaster response effectiveness.
3.3. Customer Demand
This section provides an overview of customer demand, which is categorized into two main types: products (tangible) and services (intangible). It also discusses whether the models focus on a single product/service or multiple products/services.
Regarding the type of customer demand, Figure 3 presents the percentage of articles classified as considering only products, only services, or both. As shown, 42% of the articles define demand in terms of products. For services, approximately half of the articles also include products.
A service refers to an intangible offering that fulfills customer needs through performance. Most of the articles reviewed center on medical services. For example, ref. [40] develops a model to locate healthcare facilities that provide immediate and appropriate assistance to patients in disaster situations.
A product is a tangible item produced to satisfy consumer needs. Most of the articles reviewed focus on food products for disaster relief distribution. For example, ref. [24] presents a mathematical model to optimize the distribution of food items, such as rice and water.
Another type of product discussed in the studies is medical supplies. For example, ref. [54] proposes a model to identify the locations of temporary medical relief shelters and to allocate medical supplies efficiently. In these cases, not only are supplies provided, but also services such as medical care.
Some articles focus on the blood supply chain (e.g., [47,69,76,79]). These studies address decisions related to the optimal placement of blood collection centers, blood processing centers, and medical facilities for blood distribution. For example, ref. [69] presents a model to make location decisions for the blood supply chain during disaster situations. In these cases, a service is provided in the collection of blood, followed by the processing and delivery of the blood (product).
In Figure 4, several articles also address location problems involving multiple products, highlighting the complexity of humanitarian logistics. For example, ref. [41] develops a model to determine the locations of relief centers that distribute various relief commodities, including pharmaceutical and medical items. Similarly, ref. [67] includes food, blood, water, blankets, and tents as products to be distributed.
The analysis of customer demand in humanitarian logistics distinguishes between tangible products and intangible services, with a primary focus on medical and food-related needs. While some studies consider only products, many integrate services, particularly in healthcare and blood supply chains. Most models adopt a multi-product and multi-service approach, offering advantages such as greater flexibility, optimized resource allocation, and a more comprehensive response to diverse needs. However, this approach also increases logistical complexity and coordination challenges due to the multiple stakeholders involved [80].
3.4. Objective Function
This section provides an overview of objective functions in facility location modeling, beginning with a classification based on the number of objectives—distinguishing between single-objective and multi-objective models. It also discusses the types of objectives optimized, emphasizing the specific criteria the models aim to achieve.
The majority of the articles in the sample include multi-objective optimization, accounting for 66% of the total. This information is illustrated in Figure 5.
Some studies adopt a single-objective approach in their modeling. For example, ref. [49] develops a model addressing the temporary depot problem after a flood, focusing on minimizing unattended demand. Similarly, ref. [35] proposes a model that minimizes total costs, including setup costs for temporary medical centers and transportation costs for casualties. These single-objective models prioritize specific aspects of disaster response, such as maximizing service rates or minimizing costs—objectives that are often in conflict, particularly in post-disaster scenarios.
In contrast, multi-objective models offer a more comprehensive framework for addressing the trade-offs inherent in humanitarian operations, whereby resources are limited, and needs are urgent [81]. These models help decision-makers balance competing objectives, addressing efficiency, equity, and effectiveness in disaster response.
For example, ref. [67] develops a mathematical model for determining the location of temporary relief distribution centers, considering patient transfer and relief distribution. This model integrates multiple objectives, including minimizing the number of patients not transferred across periods, reducing transfer time, minimizing fixed and distribution costs, and decreasing dissatisfaction with relief services. Such approaches highlight the value of multi-objective modeling in improving the overall performance of humanitarian operations.
The most frequently used objectives in temporary facility location problems in humanitarian logistics are minimizing cost or maximizing profit, as identified in 42 articles. The second most common objective is maximizing coverage or minimizing uncovered demand, cited in 23 articles. This is followed by minimizing time, mentioned in 17 articles, and finally, minimizing the distance traveled, referenced in 13 articles. This information is illustrated in Figure 6.
One of the most common objectives in facility location problems is cost minimization. In the humanitarian context, it is essential to account for not only monetary costs but also social costs, which reflect the suffering of victims [82]. For example, ref. [76] develops an optimization model for distribution and evacuation planning that includes minimizing total supply chain costs. This objective accounts for fixed facility establishment costs, the cost of providing and transporting commodities, and social costs, such as penalties for delayed storage and for unaddressed injuries.
Only two articles, refs. [39,72], focus on profit maximization. In their models, the objective is to maximize profits by covering demand points while subtracting facility location costs.
Another frequently used objective is related to demand coverage, including maximizing covered demand or minimizing uncovered demand. For example, ref. [34] proposes a temporary logistics hub location-allocation model for relief supply distribution, in which one objective is to maximize total demand coverage. Conversely, ref. [24] incorporates unmet demand penalty costs to penalize the model for failing to satisfy demand.
Travel distance minimization is another common objective. For example, ref. [4] focuses on minimizing the transfer time for patients and evacuees to temporary hospitals and evacuation centers after an earthquake in Iran.
Other articles include additional objectives, such as minimizing the number of facilities opened (e.g., [74]), maximizing the fill rate (e.g., [23]), minimizing mortality or injury risk (e.g., [50,51]), minimizing shortages (e.g., [56]), and minimizing the use of resources (e.g., [75]), among others.
In conclusion, facility location modeling in humanitarian logistics predominantly employs multi-objective approaches, reflecting the complexity of decision-making and the need to balance conflicting priorities. Nevertheless, this approach increases model complexity and requires solution methods capable of identifying non-dominant solutions [81].
3.5. Capacity Constraints
This section provides information about facility capacity considerations. According to ref. [1], many facility location models assume that facilities have unlimited capacity. In contrast, other models impose explicit capacity limits on facilities to better reflect real-world constraints.
The sample revealed that the vast majority, 92%, of the articles include capacity constraints in their modeling. This information is illustrated in Figure 7.
Few articles assume that facility capacity is unlimited. For example, ref. [42] develops a model using Genetic Algorithm and Bees Algorithm for the location–allocation of earthquake relief centers without considering the capacity of the relief centers.
In contrast, the majority of the articles consider facility capacity. Some focus on homogeneous capacity, such as [62], which examines mobile storage units with the same volume capacity. Others, such as [35], consider heterogeneous capacity, taking into account different capacities for locating temporary accommodation centers in each period.
Finally, the analysis of capacity constraints in facility location models reveals a strong preference for incorporating realistic limitations, recognizing the resource constraints in humanitarian operations. While some models assume unlimited capacity, this oversimplification can reduce practical applicability. Among capacity-constrained models, some adopt a homogeneous approach with uniform facility sizes, while others account for heterogeneous capacities to better reflect variations in facility capabilities.
3.6. Planning Horizon
The planning horizon defines the temporal scope of facility location decisions. According to [1], facilities can be categorized based on their planning horizon as either single-period or multi-period models.
An analysis of the selected articles revealed that the majority (62%) employ a multi-period planning horizon in their modeling. This distribution is illustrated in Figure 8.
While the single-period approach is less common, it is utilized in certain studies. For example, ref. [70] developed a single-period model for shelter planning in earthquake response, focusing on short-term relief operations. Similarly, ref. [71] proposed a single-period model for locating temporary medical centers following an earthquake, emphasizing immediate post-disaster needs.
In contrast, most studies adopt a multi-period approach, which allows for the dynamic adaptation of facility capacity over time [65]. For example, ref. [72] employs a multi-period model that enables to allocate modular units within facilities, with flexibility to transfer them between periods, thereby improving resource utilization and responsiveness.
Moreover, facility location models incorporate different planning horizons, with multi-period approaches being more commonly used due to their ability to support adaptive decision-making in disaster response. While single-period models effectively address immediate post-disaster needs and simplify decision-making, they may not fully capture resource fluctuations and long-term demand changes. In contrast, multi-period models offer greater flexibility by allowing capacity adjustments over time, improving resource allocation and operational efficiency, but they also increase computational complexity.
3.7. Uncertainty
This section discusses how uncertainty is addressed in the reviewed models. Several studies incorporate uncertainty into their frameworks, often through random probability distributions [3]. In location problems over time, many inputs are inherently uncertain, making it essential to account for these variables in the models [1]. This section also highlights the specific areas in which uncertainty is integrated into the modeling process.
The sample showed that 57% of the articles incorporate uncertainty into their modeling. This is depicted in Figure 9.
Following a disaster, the exact number of victims is unknown, making demand estimation challenging [8]. Incorporating uncertainty into the model enhances its realism. It is one of the most included uncertainties (e.g., [9,23]). For example, ref. [26] proposes a facility location model for temporary disaster response facilities in Turkey, using five scenarios with associated probabilities to address demand uncertainty for relief supplies.
Another type of uncertainty involves the potential failure of facilities. For instance, ref. [64] models the likelihood of accommodation and care center failures using a probabilistic, scenario-based approach.
The magnitude of a disaster itself can also be uncertain. For example, ref. [53] focuses on earthquakes in Tehran, generating discrete scenarios to account for which fault line might be activated and the earthquake magnitude.
Uncertainty can also pertain to the reliability of transportation routes. In this case, ref. [41] evaluates the reliability of each route based on the percentage of failures, and updates this reliability according to the repair team’s schedule.
Additionally, some studies address uncertainty related to the health conditions of casualties. For example, ref. [75] incorporates this factor into their model, reflecting the variability in medical needs.
Furthermore, incorporating uncertainty into facility location models is essential for accurately representing the dynamic nature of disaster scenarios. While a little over half of the reviewed studies account for uncertainty, they do so through diverse approaches, addressing various sources of unpredictability in humanitarian logistics. However, while these models enhance realism and adaptability, they also add complexity, increasing computational demands and requiring more time to find exact solutions, which can hinder timely decision-making in urgent situations.
3.8. Mathematical Modeling
This section provides an overview of the mathematical modeling approaches commonly used in facility location problems. In the literature, facility location models are typically formulated using techniques, such as linear or nonlinear programming, robust programming, stochastic programming, fuzzy programming, and goal programming [3]. These methods offer distinct approaches for addressing the complexities involved in making facility location decisions.
Regarding the use of linear or nonlinear modeling, Figure 10 shows that 94% of the articles were found to use a linear approach. The remaining four articles [27,59,68,83] employ a nonlinear approach. In most cases, the nonlinear modeling is used to represent deprivation costs, which, according to [82], is an economic valuation of the human suffering associated with a lack of access to a good or service. However, nonlinear models increase computational complexity, making them more challenging to solve compared to linear models.
Figure 11 shows the articles categorized by the type of modeling used. Deterministic optimization refers to a type of optimization in which all parameters, variables, and conditions are known with certainty and are fixed. For example, ref. [49] develops a mixed-integer linear programming formulation to determine the optimal location of temporary depots in response to flood disasters, in which the parameters were previously known.
Robust optimization handles uncertainty by describing it as interval data that fluctuates around a nominal value, independently of probability distribution [29]. For example, ref. [10] develops a robust optimization model for a multi-objective, multi-period location-routing problem in epidemic logistics.
Stochastic programming is an optimization method that models decision-making under uncertainty by integrating random variables and probability distributions to find solutions that perform well across various possible scenarios [23]. An example is [67], which develops a Two-Stage Multi-Objective Stochastic Model for patient transfer and relief distribution in COVID-19 lockdown areas.
Fuzzy programming is an optimization technique that addresses uncertainty and imprecision by using fuzzy sets and membership functions to represent vague or incomplete information in decision-making. For instance, ref. [34] uses a fuzzy chance-constrained programming approach for temporary logistics hub location.
In conclusion, mathematical modeling is essential in facility location problems, with a predominant reliance on linear formulations. Nonlinear approaches, albeit less common, are useful for modeling deprivation costs, capturing the economic impact of limited access to essential goods and services. Deterministic models remain widely applied, offering solutions under fixed parameters but lacking adaptability to changing conditions. In response, there is a growing shift toward incorporating uncertainty through robust, stochastic, and fuzzy programming, enhancing model flexibility in dynamic disaster environments. However, while these advanced approaches improve realism, they also increase computational complexity and solution time, posing challenges to rapid decision-making.
3.9. Solution Approach
This section provides a detailed overview of the solution approaches used to address mathematical models in facility location problems. These approaches include the use of commercial solvers, metaheuristics, and relaxations [3].
The solution approaches used in the reviewed articles are varied, with a significant number employing different techniques. Among them, 39 articles utilize commercial solvers, making it the most commonly applied approach. Metaheuristics are also widely used, appearing in 15 articles, while Lagrangian relaxation is employed in 8 articles. Additionally, 4 articles adopt other solution methods not categorized under the main approaches. This distribution can be seen in Figure 12.
Commercial solvers for optimization models are software tools developed to solve complex mathematical optimization problems. Some of the commercial solvers used were CPLEX (e.g., [35]) and Gurobi (e.g., [20]). These solvers offer a key advantage due to their ease of use, as they do not require the development of additional solution methods, making them accessible to researchers and practitioners. However, in highly complex models, their application becomes less feasible due to long computational times and high memory demands, which can limit their efficiency in large-scale optimization problems.
Metaheuristics provide alternative approaches to finding effective solutions within a reasonable computational time. However, they are more time-consuming to develop compared to commercial solvers. For example, ref. [54] proposes a greedy algorithm for a medical relief shelter location problem; the numerical results show that the heuristic achieves good results, while LINGO may fail to find a solution. Similarly, ref. [60] uses a genetic algorithm to locate shelters in flood scenarios, and ref. [79] applies a Tabu search heuristic to determine the optimal number of temporary blood centers.
Other techniques include the Branch-and-Benders-cut algorithm [66], Lagrangian relaxation methods [40], Lagrangian decomposition heuristics [39], and decomposition-based heuristic algorithms [83].
To conclude, solution approaches in facility location problems vary widely, reflecting the complexity of optimization in humanitarian logistics. Commercial solvers are the most frequently used, providing exact solutions but often encountering computational limitations in large-scale problems. Metaheuristic methods, including genetic algorithms and Tabu search, offer efficient alternatives when exact methods become impractical. Additionally, techniques such as Lagrangian relaxation and decomposition-based heuristics further expand the range of available approaches, balancing computational efficiency and solution quality.
3.10. Location Problem Decisions
This section presents information about the decisions involved in facility location problems. According to [3], these decisions may include opening and closing facilities, and some variants, such as reopening or operating facilities.
Figure 13 presents information about the primary location decisions, considering both opening and closing decisions. Since all the models involve opening decisions, they were classified as either only opening decisions or both opening and closing decisions. Only 6% of the models consider both opening and closing decisions.
The opening decision involves determining the optimal location to open or install a facility [3]. For example, ref. [69] develops an integrated blood supply chain network under disaster conditions, whereby a decision is made to open temporary blood collection centers and field hospitals during specific periods. Another example is [19], which proposes using schools that are only in use during the morning to provide education services for refugee children in the afternoon by opening a new shift.
The closing decision is made when it becomes necessary to permanently shut down a previously constructed or installed facility [3]. For instance, ref. [52] develops robust models to quantify the impact of decentralization in post-disaster healthcare facility location decisions, evaluating the effects of closing facilities over short time horizons based on dynamic needs and resource levels.
Closing a facility helps reallocate resources and reduce maintenance costs [49], but premature closures can disrupt essential services if demand resurges. Reopening previously closed facilities often involves high additional costs, including re-installation of equipment, and logistical coordination. In disaster-prone areas, aftershocks or secondary crises may force unexpected reopening, further increasing expenses and complicating resource planning.
In summary, decision-making in facility location problems primarily focuses on selecting optimal sites for new facilities, while closure decisions receive less attention. Most models emphasize opening decisions, which are essential for establishing temporary infrastructure, such as blood collection centers and multipurpose educational facilities. However, closure decisions, albeit less frequently explored, are crucial for optimizing resource allocation over time, particularly in post-disaster recovery, whereby the efficient redistribution of resources can enhance long-term resilience and sustainability.
3.11. Modular Capacity Planning Decisions
This section presents information about the planning decisions related to modular facilities. According to [3], modularity enables the movement of modules, allowing for changes in the total capacity of a site. Decisions related to modularity can involve aspects such as the configuration, relocation, expansion, and operation of the modules.
One key advantage in this approach is its scalability, enabling facilities to adjust capacity without requiring permanent infrastructure [66]. This ensures efficient resource allocation and improves response times in dynamic disaster environments.
However, modular strategies also present challenges. Frequent reconfiguration can increase setup time, labor costs, and structural wear, affecting long-term efficiency. Relocation, while enhancing adaptability, introduces additional operational costs, logistical complexity, and potential service disruptions.
Figure 14 presents information based solely on articles that include a modular approach. Of the 65 articles, 12 address modular configuration, 9 focus on relocating decisions, 5 on expanding decisions, and 3 on reducing decisions.
Configuration decisions involve the assembly, disassembly, and reconfiguration of modular units [3]. For instance, ref. [62] consider mobile storage units assembled in storage areas.
However, modular strategies also present challenges. Frequent reconfiguration can increase setup time, labor costs, and structural wear, affecting long-term efficiency. Relocation, while enhancing adaptability, introduces higher operational costs, logistical complexity, and potential service disruptions.
Relocating decisions involve planning the movement of modular capacity from one location to another [3]. According to [72], transferability is an important specification of modularity design. In this case, ref. [66] develops a model that considers the relocation of temporary facilities, accounting for the additional costs associated with relocation, which involves closing the current facility at one location and reopening a new facility at another location.
Expanding decisions involve determining whether to increase total capacity by installing new modular units [3]. In this case, ref. [21] considers whether the candidate facility has the flexibility to scale up in the future, prioritizing facilities with this characteristic.
Reducing decisions involves decreasing the capacity at each facility by removing or deactivating modular units [3]. For example, ref. [72] examines the allocation of modules to selected facilities, whereby, after the modules complete their mission in one affected area during a given period, they can be moved to another area in the next period to provide services there. Since the facility consists of different modules, its capacity can be reduced if it is no longer needed.
In conclusion, modular capacity planning is crucial for enhancing the adaptability of humanitarian logistics, yet it remains relatively underexplored. While some studies address configuration, relocation, expansion, and reduction of modular units, these decisions are often considered in isolation rather than within a comprehensive planning framework. The ability to dynamically adjust facility capacity through modularity provides significant advantages in disaster response, improving resource allocation and operational efficiency. However, most models fail to fully leverage this flexibility, limiting their potential to optimize humanitarian operations in rapidly changing environments.
4. Discussion
Temporary facilities offer a cost-effective alternative to permanent structures in the aftermath of a disaster [66]. In scenarios where hospitals and other fixed facilities cannot accommodate a sudden influx of victims, temporary facilities serve as a crucial solution to expand humanitarian services, ensuring that more individuals receive timely attention [35]. These facilities can be rapidly deployed and scaled according to demand, making them a flexible and adaptive response mechanism in crises [49]. Additionally, they can be strategically placed in proximity to affected populations, reducing the burden on transportation networks and improving accessibility to urgent care [72]. However, despite their advantages, the effectiveness of temporary facilities is often constrained by the availability of essential equipment and personnel, which may be limited due to logistical challenges, supply chain disruptions, and the strain on humanitarian resources during large-scale disasters [22]. Another major drawback is the lack of medium and long-term sustainability of humanitarian operations, as temporary facilities often function as short-term solutions [75] rather than integrated elements of broader disaster recovery efforts.
The study of temporary facility location in humanitarian logistics is an emerging research area [84], exhibiting a steady growth trend despite a relatively low number of annual publications. This may be due to the specialized nature of the topic, which requires interdisciplinary knowledge spanning logistics, disaster response, and operational research [70]. Research on this subject is primarily published in specialized humanitarian logistics journals, such as the International Journal of Disaster Risk Reduction and the Journal of Humanitarian Logistics and Supply Chain Management, reflecting the niche focus of the field. Additionally, relevant studies appear in broader operations management and logistics-focused journals, including Transportation Research Part E: Logistics and Transportation Review and European Journal of Operational Research, which suggests that the field is gradually gaining recognition within the wider academic community. Despite its potential impact, further interdisciplinary collaboration and increased funding are necessary to accelerate research efforts and to translate theoretical findings into practical, scalable solutions for humanitarian operations.
Considering the insights from the results section, Table 4 outlines the main problems in humanitarian logistics related to the location of temporary facilities and the corresponding strategies currently used to address them.
Among the various disaster types, earthquakes are the most extensively studied in humanitarian logistics due to their sudden onset and unpredictability, and the critical need for rapid response—factors that result in extensive infrastructural damage and immediate demand for relief supplies [8]. Given that maintaining permanent emergency infrastructure exclusively for earthquakes is neither cost-effective nor feasible, temporary facilities such as mobile hospitals, emergency shelters, and logistics hubs play a crucial role in bridging the gap between urgent needs and limited permanent resources [66,75]. Most studies focus on case applications in developing Asian countries, particularly in Iran, China, and Turkey, where earthquake frequency is high (e.g., [11,21,42]). However, other earthquake-prone areas, such as the western regions of South America along the Pacific Ring of Fire, including Chile, Peru, and Ecuador, receive less attention despite their high seismic risk. These regions differ from Asia in terms of infrastructure, governance, and cultural factors, affecting disaster response strategies and highlighting the need for research tailored to their specific logistical and sociopolitical contexts.
Besides earthquakes, other disasters also require the deployment of temporary facilities but remain underexplored. Other sudden-onset disasters are relatively understudied, leading to an increased demand [8]. In this context, similar temporary facilities, as in earthquakes, are required to assist the affected population while considering the unique characteristics of each disaster. For example, hurricanes and cyclones are seasonal events that frequently occur in the Caribbean and Pacific regions, requiring strategically placed evacuation centers, emergency supply depots, and temporary bridges to restore connectivity in affected areas [9]. Other types of disasters, such as slow-onset crises such as epidemics that affect large populations, require the establishment of temporary intermediate storage centers. However, ref. [85] warns that this could be problematic in developing countries due to the need for an additional transportation step, as additionally, human-induced crises, such as armed conflicts and forced displacements in the Middle East and South America, require semi-permanent refugee camps, mobile medical centers, and educational facilities to support displaced populations over extended periods, considering the sociocultural context of each country [35]. Expanding research to incorporate these diverse disaster contexts and geographical settings is essential for optimizing temporary facility location strategies, ensuring effective humanitarian response across different crises.
Shelters, distribution centers, and medical facilities are the most extensively studied types of temporary facilities in humanitarian logistics, due to their critical role in housing displaced populations and ensuring the efficient delivery of aid [60]. Research has also explored the placement of collection centers, which serve as designated sites for receiving and temporarily storing donations—primarily food and medical supplies. These centers are strategically located to maximize accessibility for donors [56]. Similarly, processing centers play a crucial role in inspecting, sorting, and preparing aid for final distribution, ensuring that supplies meet quality and safety standards before reaching affected populations [86]. In long-term crises, the placement of humanitarian logistics hubs is also critical for maintaining continuous aid distribution by enabling efficient temporary storage, consolidation, and redistribution of essential supplies [32].
The primary focus of demand modeling in humanitarian logistics is to ensure the efficient delivery of essential items that guarantee survival and minimize the suffering of affected populations [82]. Among these, blood is a particularly critical commodity, as it must be collected, processed, and transported promptly to severely injured individuals to prevent fatalities [69]. Given the diverse needs of disaster-affected populations, a multi-product approach is essential to ensure a comprehensive response [41]. This involves optimizing the distribution of various life-saving resources, such as food, water, medicine, and medical equipment, while considering factors such as perishability, storage conditions, and transportation constraints [56].
Humanitarian logistics models often employ multi-objective optimization approaches to address the complexity of disaster response while balancing multiple priorities simultaneously [81]. A key focus is cost minimization, which enables assistance to reach a larger population efficiently [66], and service coverage, ensuring that a greater proportion of the affected population has access to nearby facilities [71]. Given the dynamic nature of post-disaster environments, multi-period planning is particularly relevant, allowing for adaptive responses to evolving conditions [8]. However, single-period models remain applicable to services with stabler, medium-term planning horizons, whereby dynamic adjustments are less critical. Additionally, capacity constraints play a crucial role in facility location problems, for accurately representing real-world operations [22]. Another fundamental challenge in facility location modeling is uncertainty, as disasters often lead to unpredictable demand, supply chain disruptions, and shifting logistical constraints [8]. To enhance model reliability and applicability, researchers have incorporated robust optimization, stochastic programming, and fuzzy programming to account for these uncertainties [24].
A variety of mathematical modeling techniques are used in humanitarian logistics, with most studies adopting linear approaches. However, ref. [8] argues that disaster response costs are often nonlinear, convex, and monotonic concerning the duration of deprivation. Thus, nonlinear modeling approaches could better capture the complexities of disaster response operations. Given the increasing computational complexity of these models [76], researchers have explored metaheuristic methods and relaxation techniques to generate high-quality solutions within practical time constraints.
Temporary facilities in disaster response contexts are infrastructures designed to operate for a limited period and adapt to changing conditions [52]. The ability to dynamically open, close, or reopen facilities plays a crucial role in optimizing resource allocation and responding to evolving needs [3]. Reopening previously closed facilities, in particular, could offer a cost-effective strategy in dynamic disaster scenarios by reducing setup costs and leveraging existing infrastructure [49].
Also, modularity plays a crucial role in enhancing adaptability, allowing facilities to dynamically adjust their capacity in response to fluctuating demand [49]. By allowing adjustments in the number and size of deployed modules, this approach enhances responsiveness to changing needs [39]. Modular structures, such as tents, have been widely employed in humanitarian operations [11] and can be mounted in public spaces such as parks and churches [9]. Despite their advantages, modular facilities remain underutilized in facility location problems in humanitarian logistics research. They also enable configuration, relocation strategies, and capacity adjustments through module expansion or reduction [3].
Finally, considering the points discussed, Table 5 presents the key trends and gaps in temporary facility location in humanitarian logistics.
5. Conclusions
Research on temporary facility location in humanitarian logistics has shown significant potential to enhance disaster response, particularly in sudden-onset emergencies. Compared to permanent structures, these facilities offer a flexible and cost-effective solution for rapidly expanding operational capacity when existing infrastructure is overwhelmed. Their modular design and ability to be relocated allow for dynamic adjustments to post-disaster conditions, facilitating a more agile and adaptive humanitarian response. Additionally, their scalability supports better management of secondary crises, strengthening the resilience of aid distribution systems.
Despite these advantages, temporary facilities also present several challenges. Their limited durability makes them less reliable for prolonged crises, while logistical costs tend to rise due to setup, relocation, and maintenance. Deployment delays and inefficiencies can hinder response efforts, and resource utilization issues may lead to underuse or excess capacity. Environmental concerns, such as waste disposal, and regulatory or community resistance further complicate operations.
The integration of modularity into facility location models remains underdeveloped, despite its clear benefits for scalability and flexibility. Greater emphasis is needed on optimizing the dynamic configuration, relocation, expansion, and reduction of facility modules to enhance responsiveness in evolving crises. Additionally, incorporating uncertainty—be it in demand fluctuations, supply chain disruptions, or infrastructure failures—into facility location models is critical for improving the robustness and practicality of disaster response strategies.
From a methodological perspective, most facility location models rely on deterministic and linear optimization approaches. While these methods provide valuable insights, they often fail to capture the nonlinear dynamics and uncertainties inherent in disaster scenarios. More advanced techniques, such as stochastic programming, robust optimization, and fuzzy logic, can improve decision-making in unpredictable environments. The increasing computational complexity of these problems also underscores the need for efficient solution techniques, including metaheuristics and decomposition algorithms, to generate high-quality solutions within practical time constraints.
Decision-making in temporary facility location has traditionally focused on the establishment of new sites, often overlooking strategies for closure, reopening, and reallocation. Integrating dynamic facility management approaches can enhance adaptability by allowing resources to be redistributed as needs evolve. Additionally, incorporating modularity into multi-period planning frameworks will enable more efficient capacity adjustments, ensuring that facilities can scale up or down in response to real-time demand.
5.1. Theoretical Considerations
From a theoretical perspective, temporary facility location problems in humanitarian logistics extend classical models by incorporating additional complexities, such as uncertainty, modularity, and dynamic adaptation. Unlike permanent facilities, temporary facilities must be rapidly deployed, relocated, and scaled in response to fluctuating disaster conditions. This requires the use of multi-period optimization models, which account for temporal variations in demand and resource availability.
These models must achieve a balance between cost minimization, equitable resource distribution, and response time efficiency. Given the complexity of these trade-offs, multi-objective optimization approaches are commonly employed to ensure effective decision-making under evolving disaster scenarios.
5.2. Limitations
This study is constrained by the search equation used, which may have inadvertently excluded relevant studies. Additionally, the articles reviewed primarily focus on temporary facility location within the classical humanitarian logistics supply chain, overlooking critical areas, such as needs assessment, mapping, and waste management. This gap may introduce bias by neglecting essential post-disaster operations, including road clearing, debris removal, and waste disposal, which are crucial for infrastructure restoration and effective disaster response.
Another limitation arises from the study focus on academic articles, which may overlook practical insights from practitioners. To address this, additional references were incorporated into the discussion. Furthermore, most of the studies reviewed examine earthquake response in Asia, often within the region specific sociopolitical context. This regional focus may limit the direct applicability of findings to other contexts, needing adjustments when implementing these strategies in different geographical areas.
5.3. Future Research Directions
Temporary facilities are widely used in sudden-onset disasters, particularly earthquakes, and play a crucial role in large-scale, slow-onset crises, as seen during the COVID-19 pandemic. Further research should explore their adaptability across different humanitarian scenarios to enable more efficient deployment. Key areas of focus include (1) the use of modular facility design in temporary facilities and (2) the placement of temporary facilities to improve first- and middle-mile humanitarian logistics. Additional research gaps are outlined in Table 5.
Modular facility design enhances adaptability in post-disaster situations by offering flexibility and scalability. However, optimizing its implementation remains challenging due to uncertainties in demand, transportation networks, and logistical constraints. Future research should focus on refining facility configurations, improving relocation strategies, and adjusting capacity to evolving disaster conditions. Multi-period planning is critical for accommodating shifting needs, particularly in the initial response phase, where modular facilities must rapidly scale in size and function to meet urgent demands before transitioning into a more stable recovery configuration.
First- and middle-mile large-scale humanitarian logistics face significant challenges due to supply chain bottlenecks and coordination issues. Deploying temporary donation collection and processing centers after a disaster, along with logistic hubs in large disaster-affected areas, can significantly improve intake efficiency. Specifically, processing centers ensure proper classification and quality control, preventing unsuitable shipments and enhancing overall supply chain effectiveness. Future research should focus on optimizing the placement and management of these facilities while incorporating insights from practitioners.
Improving these two key areas will make humanitarian logistics more adaptable and efficient, strengthening disaster response.
Conceptualization, M.F.C.Q. and H.T.Y.Y.; methodology, M.F.C.Q.; validation, M.F.C.Q., H.T.Y.Y. and I.d.B.J.; formal analysis, M.F.C.Q., L.D.C.M., H.T.Y.Y. and I.d.B.J.; investigation, M.F.C.Q. and L.D.C.M.; data curation, M.F.C.Q. and L.D.C.M.; writing—original draft preparation, M.F.C.Q.; writing—review and editing, M.F.C.Q., L.D.C.M., H.T.Y.Y. and I.d.B.J.; visualization, M.F.C.Q. and L.D.C.M.; supervision, H.T.Y.Y. and I.d.B.J.; project administration, M.F.C.Q. and H.T.Y.Y.; funding acquisition, H.T.Y.Y. and I.d.B.J. All authors have read and agreed to the published version of the manuscript.
The data presented in this study are available on request from the corresponding author. The data collected from the sample are available at
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Search Equation.
| Group | Logical Operator | Search Field | Keywords |
|---|---|---|---|
| 1 | AND | Topic | location |
| 2 | AND | Topic | tempor * OR modul * |
| 3 | AND | Topic | humanitarian OR relief OR disaster |
| 4 | AND | Topic | optimization OR model * |
| 5 | AND NOT | Topic | map * OR assess * |
| 6 | AND | Language | English |
| 7 | AND | Document Type | Article |
The asterisk (*) is a truncation operator that retrieves variations of a word.
Articles by journal.
| Quantity | Journal |
|---|---|
| 7 | International Journal of Disaster Risk Reduction |
| 6 | Socio-Economic Planning Sciences |
| 5 | Transportation Research Part E: Logistics and Transportation Review |
| 4 | Computers and Industrial Engineering |
| 3 | International Journal of Industrial Engineering, Journal of Humanitarian Logistics and Supply Chain Management |
| 2 | Applied Sciences, European Journal of Operational Research, International Journal of Systems Science: Operations and Logistics, Journal of Combinatorial Optimization, Mathematical Problems in Engineering, Production and Operations Management |
| 1 | Annals of Operation Research, Applied Mathematical Modelling, Applied Mathematics and Computation, Environmental Science and Pollution Research, IEEE Access, Information Sciences, International Journal of Environmental Research and Public Health, International Journal of Geographical Information Science, International Journal of Industrial Engineering and Production Research, International Journal of Operations Research and Information Systems, International Journal of Production Economics, Journal of Advanced Transportation, Journal of Industrial Engineering International, Journal of Modelling in Management, Journal of Multi-Criteria Decision Analysis, Journal of Simulation, Management Science Letters, Mathematics, Naval Research Logistics, Natural Hazards, Natural Hazards Review, Operations Research for Health Care, RAIRO - Operations Research, Simulation, Transportation Research Part B: Methodological |
Disaster-related information.
| Art. | Disaster | Country | Facility * | Art. | Disaster | Country | Facility * |
|---|---|---|---|---|---|---|---|
| [ | Hurricane | USA | SC | [ | Earthquake | Iran | CC |
| [ | Hurricane | USA | Shelter | [ | Earthquake | Nepal | Hub |
| [ | Earthquake | China | Shelter | [ | Earthquake | Turkiye | MC |
| [ | Earthquake | China | SC | [ | Earthquake | Turkey | Shelter |
| [ | - | USA | DC | [ | - | - | Shelter |
| [ | Earthquake | Turkiye | Shelter | [ | - | Brazil | SC, DC |
| [ | Earthquake | Turkiye | DC | [ | - | Japan | DC |
| [ | Earthquake | Taiwan | MC | [ | Earthquake | USA | DC |
| [ | Earthquake | Iran | DC | [ | Earthquake | China | MC |
| [ | Earthquake | Haiti | Hub | [ | Earthquake | China | Shelter |
| [ | - | USA | SC | [ | Earthquake | Pakistan | MC |
| [ | - | - | CC | [ | Epidemic | China | DC |
| [ | Earthquake | Iran | MC | [ | Flood | Thailand | SC |
| [ | Earthquake | USA | MC | [ | Earthquake | China | MC |
| [ | earthquake | Haiti | MC | [ | Earthquake | Iran | Shelter |
| [ | Tsunami | Korea | Shelter | [ | Earthquake | Mexico | DC |
| [ | Earthquake | Iran | CC | [ | Refugee | Turkiye | School |
| [ | Earthquake | Iran | Shelter | [ | Flood | Bangladesh | SC |
| [ | - | - | DC | [ | Earthquake | Iran | MC |
| [ | Earthquake | Nepal | Hub | [ | Epidemic | China | DC |
| [ | Flood | India | Shelter | [ | Earthquake | China | MC |
| [ | Earthquake | Turkiye | MC | [ | Earthquake | China | MC |
| [ | Earthquake | Nepal | SC | [ | Epidemic | Turkiye | MC |
| [ | Earthquake | Iran | MC, Shelter | [ | - | Iran | CC |
| [ | - | Syria | Shelter | [ | Epidemic | Iran | MC, DC |
| [ | Earthquake | China | MC | [ | Epidemic | China | DC |
| [ | Earthquake | Nepal | Hub | [ | Epidemic | China | DC |
| [ | Earthquake | - | SC | [ | - | - | CC |
| [ | Earthquake | Nepal | Shelter | [ | Earthquake | Iran | MC, PC |
| [ | - | Japan | DC | [ | Earthquake | Iran | MC |
| [ | Earthquake | Turkiye | DC | [ | Earthquake | Turkiye | MC |
| [ | Earthquake | Iran | MC, DC | [ | Earthquake | Iran | CC |
| [ | Earthquake | Turkiye | MC |
* MC: Medical center, DC: Distribution center, CC: Collection center, PC: Processing center.
Temporary facility location in humanitarian logistics: main problems and strategies.
| Main Problem | Strategy |
|---|---|
| Unexpected surge in demand exceeding the available capacity | - Establishment of temporary facilities in strategic locations to provide immediate assistance to affected populations. |
| - Implementation of temporary logistical hubs and storage centers to enhance coordination and streamline the supply chain. | |
| Dynamic and evolving demand in post-disaster scenarios | - Development of adaptive planning models incorporating multi-period planning horizon. |
| - Adoption of modular capacity strategies to allow for flexible scaling, ensuring expansion or reduction of capacity as needed. | |
| - Consideration of facility relocation strategies. | |
| Operational challenges in the immediate response phase | - Utilization of multi-objective optimization models to minimize response time, maximize coverage, and minimize cost simultaneously. |
| - Integration of uncertainty modeling techniques, including stochastic, robust, and fuzzy logic approaches, to enhance decision-making under unpredictable conditions. | |
| - Due to the complexity of mathematical models, consideration of more advanced solution approaches. | |
| Lack of consideration for the temporary nature of emergency facilities | - Strategic planning for the timely closure of temporary facilities to maximize efficiency and sustainability. |
| Need to collect, classify, sort, conduct quality control, and package donations | - Implementation of temporary collection and processing centers. |
Trends and gaps in temporary facility location in humanitarian logistics.
| Trends | Gaps |
|---|---|
| -Publication of articles in journals on humanitarian logistics | -Lack of integration in article publications among OR researchers, HL researchers, and HL practitioners |
| -Temporary facility location in earthquake immediate response in Asian countries | -Lack of studies on temporary facility location in sudden-onset disasters considering logistical and sociopolitical contexts |
| -Lack of studies on temporary facility location in human-induced or slow-onset disasters | |
| -Lack of studies on facility location during the reconstruction phase or integrating response and reconstruction phase | |
| -Temporary facility location in the last mile (shelters, medical centers, distribution centers) | -Lack of research on the location of collection (including sorting operations) and processing centers |
| -Limited research on the location of temporary logistic hubs for coordination and efficiency | |
| -Need for models integrating multi-purpose facilities | |
| -Use of a multi-product approach, primarily food and medical supplies | -Need to consider adapted facilities for specialized products, such as those related to blood. |
| -Use of deterministic models and commercial solvers | -Lack of consideration of stochastic, fuzzy, and robust approaches, as well as heuristic solutions and relaxations |
| -Mathematical models for temporary facility location problems considering a multi-objective linear approach and multi-period planning | -Need to incorporate modularity strategies and module relocation in multi-period planning |
| -Need to develop nonlinear models, considering that deprivation costs are inherently nonlinear. | |
| -Mathematical modeling considering capacity constraints | -Lack of analysis on the expansion and reduction capacity of temporary facilities based on demand fluctuations |
| -Only opening decisions in temporary facility location problems | -Lack of studies on both opening and closing decisions in temporary facility location problems |
| -Use of standard temporary facilities | -Need to use modular temporary facilities for better adaptability and flexibility |
Supplementary Materials
The following supporting information can be downloaded at:
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; Lucciana Débora Chambilla Mamani 1
; Hugo Tsugunobu Yoshida Yoshizaki 1
; Irineu de Brito Junior 2
1 Production Engineering Department, Polytechnic School, Universidade de São Paulo—USP, São Paulo 05508-010, Brazil;
2 Production Engineering Department, São Paulo State University, Bauru 17033-360, Brazil;