Robust-Fuzzy-Probabilistic Programming for Humanitarian Aid Logistics Under Uncertainty: A Facility Location and Flow Allocation Study with Deprivation Cost Analysis

Khalili-Fard A., Hashemi M., Bakhshi A., Yazdani M., Jolai F., Aghsami A.

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

Faiz T.I., Vogiatzis C., Liu J., Noor‐E‐Alam M.

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.

Iraj M., Chobar A.P., Peivandizadeh A., Abolghasemian M.

The supply chain (SC) as the most significant factor influencing competitive advantage has a great impact on the organization's life and progress. Considering the expiration date of products in supply chain modeling is essential for ensuring operational efficiency, reducing waste, maintaining product quality, and ultimately driving business success. Therefore, this paper provides a two-level supply chain including manufacturer and distributor then chain modeling with considered conditions and applying the concept of Markowitz's theory. Finally, drawing the optimal cost curve of producer and distributor as an efficient frontier. For this purpose, the main contribution of the paper is determining the optimal cost of each level of the chain if the cost of another level is known and increasing the power of analysis. The proposed model applied in Darupakhsh Manufacturing Company and Mahya Daroo Distribution Company, a two-echelon supply chain design, including a manufacturer and a distributor is developed. At first, this chain is modeled, and then, implementing the concept of Markowitz theory, the optimal cost curve of producer and distributor is drawn as an efficient frontier. Findings show it is found that the optimal cost of the two echelons of the chain is inversely correlated and decreases with the increase of another one. Furthermore, it is found that the producer warehouse limit is redundant and has no effect on the final response. Also, with the increase in the cost of the manufacturer, the number of deliveries decreases, and instead, the number of products in each delivery increases, which results in a reduction in the costs of the distributor.

Aghsami A., Sharififar S., Moghaddam N.M., Hazrati E., Jolai F.

Military systems have many components, such as garrisons, border guards, and industries with a large population that may be affected by disasters. Garrisons, hospitals, clinics, and other facilities in military systems are needed to reduce human casualties. However, these facilities may not have enough resources to handle the disruption. There is no mathematical model to manage disasters with military systems including garrisons and border guards as relief forces interacting with non-military medical centers. This study develops a model for coordinating humanitarian supply chains and logistics within the military system. The affected military sites (AMSs) are military hospitals, clinics, industries, airports, military organizational settlements, garrisons, and border guards. A bi-objective mixed integer non-linear programming model is developed to minimize total costs during pre- and post-disaster planning, a weighted average of injury time in military vehicles, and total delay in relief items (RIs) distribution and rescue operations. For small and medium test problems, GAMS software is used, and for large test problems, the Grasshopper Optimization Algorithm (GOA) is proposed. We conducted some numerical examples to demonstrate the proposed model's efficiency and feasibility and evaluate the metaheuristic. Sensitivity analyses are conducted to evaluate model behavior under various conditions.

Taouktsis X., Zikopoulos C.

The distribution of humanitarian aid is a vital issue for humanity's future. In recent years, the management of humanitarian crises has become more crucial than it was a decade ago. Due to the volatility and urgency that characterize such situations, one of the most important challenges globally is the optimization of decisions regarding the timely distribution of aid during humanitarian operations. Our main goal is to develop an innovative decision-making tool, essential for non-profit organizations and governments that aims at the prompt selection of the location of the distribution center of humanitarian aid, in cases of natural or human-made disasters. The proposed tool is based on network science principles and can be used for selecting a suitable node for the installation of a distribution center during the beginning of a humanitarian crisis, considering that networks have a volatile nature and require quick decisions. For the configuration of the proposed tool we use a combination of a classical heuristic algorithm and predictive models based on a binary classification problem with the support of a supervised deep neural network. It is developed using the R programming language with the contribution of the “Shiny” package (web application framework for R) along with other packages for network analysis, data manipulation and visualization.

Delshad M.M., Chobar A.P., Ghasemi P., Jafari D.

Background: A logistics network plan could be a major key issue due to its effect on supply chain effectiveness and responsiveness. This study aims to investigate the inventory location in the humanitarian logistics response stage using a three-level logistics network to integrate location–allocation problems such as warehouse location and shelter allocation to each facility, and then determine the inventory level in each warehouse. Methods: In this research, the center and its distribution, as well as the reduction in service-level costs due to inventory deficit, have been considered to increase the level of shelter services. In order to investigate the network, in this study, bi-objective mixed-integer linear programming (BOMILP) is presented. Results: The first objective is to reduce location costs and inventory costs that take into account probable demand, consumption factors, and transportation costs, and the second objective is to raise the level of services offered to victims in the model. The software programs GAMS win32, 25.1.2 and MATLAB have been utilized with numerical examples in various dimensions. Conclusions: To maximize the efficiency and quality of the service, first, the model was numerically solved, and then the location where the most commodities could be transported at the lowest possible cost was identified.

Lotfi R., Hazrati R., Aghakhani S., Afshar M., Amra M., Ali S.S.

The research proposes a new method for Viable Supply Chain Network Design (VSCND) that incorporates Open Innovation (OI) and Blockchain Technology (BCT). A robust stochastic optimization and Conditional Value at Risk (CVaR) in the cost function is utilized to develop the model. This model's objective function incorporates the expected value, maximum cost, and CVaR cost. The OI and BCT platforms are suggested for an antifragile policy against disruption. In addition, CO2 emissions and energy consumption are proposed as sustainability requirements. Eventually, minimum demand satisfaction and resilience facilities by incorporating capacity based on specific scenarios are added to the model for an agile strategy. This research entails several parties, including vendors supplying the primary components and manufacturers creating products based on customer preferences. OI and BCT platforms aim to receive demand based on customer specifications and facilitate rapid transactions between components. Risk-averse decision-makers utilize a polyhedral Data-Driven Robust Optimization (DDRO) approach to manage uncertainty and flexible sets. Incorporating OI and BCT as antifragility instruments resulted in a 0.2% cost reduction for VSCNDOIBCT compared to when OI and BCT were not considered. This research suggests that integrating OI and BCT positively affects SC performance overall. The decreasing rate, DDRO coefficient, agility rate, demand variation, and problem size were subjected to a sensitivity analysis.

Bhavani G.D., Mahapatra G.S., Kumar A.

Noxious effect on environmental due to carbon emissions is being addressed worldwide by governments through carbon pricing instruments. Two of prevalent instruments adopted by governments are simple carbon tax and cap-and-trade policy. Effectiveness of carbon pricing instruments towards achieving reduction in carbon emissions is a matter of study on one hand. Whereas, the planning of supply chain operations under imposition of such financial instruments is a challenge for small and medium scale business enterprises. Addressing this situation, the present study develops a supply chain model for coordinated planning of production and inventory replenishment schedules along with decision on economic amounts of expenditure for green resources. This decision model is formulated separately under the enforcement of each of two carbon pricing policies. The study focuses a manufacturer-retailer duo which works by adopting certain sustainability and conservation practices. Manufacturer reworks on rectifiable proportion of defective units, while retailer launches discounts-based sales of partially damaged units. The decision-making model developed in this study incorporates such activities. Furthermore, practical aspects like a slowdown in production due to unforeseeable disruptions and the effect of the quality of product and advertisement campaigns on demand rates are included in the proposed model. Under the purview of each carbon pricing policy, decision-making model is formulated as a nonlinear constrained optimization problem with objective towards profit maximization. A novel conception of fuzziness has been suggested for tackling imprecision in the assessment of certain parameters involved in the model. A numerical study on an appropriate case of a manufacturer-retailer duo system is presented. Empirical results of numerical study evince that a substantial reduction in carbon emission is achieved, even with an escalation in the profit through appropriate green expenditure. This trend is observed under the imposition of each of the carbon pricing policies, thereby substantiating an encouragement to supply chain partners for making expenditures on green resources. Thereby, the hypothesis of getting desired response on curbing emissions by incentivization through carbon pricing is satisfied in the studied case. Furthermore, a sensitivity analysis concludes the stability of formulated model against most of the parameters involved.

Yin Y., Wang J., Chu F., Wang D.

Ali Modarresi S., Reza Maleki M.

The design of an efficient humanitarian relief supply chain (HRSC) for disaster management depends on the integration of pre- and post-disaster decisions, which reduces the suffering of the victims and saves lives. The use of strategies such as quantity flexibility contracts in the pre-disaster phase and evacuation management in the post-disaster phase significantly increase the efficiency of HRSCs for disaster management. Therefore, in this paper, for the first time, a comprehensive two-stage stochastic mixed-integer linear programming model is presented for integrating the pre- and post-disaster activities for possible disasters such as floods and earthquakes by considering quantity flexibility contract and equitable relief goods distribution. In addition, the proposed model determines the location of warehouses and inventory levels in these warehouses in the pre-disaster phase. It also deals with public donations and budget planning, planning vehicles and helicopters, locating hospitals and shelters, and optimizing the inventory flow between echelons in the post-disaster phase. In the proposed model, the quantity flexibility contract is applied to decrease the inventory level in the pre-disaster phase, and reduce the supply risk in the post-disaster phase. The efficiency and performance of the proposed model are evaluated using the data related to a possible earthquake in Karaj (one of the metropolises of Iran).

Pouraliakbari-Mamaghani M., Saif A., Kamal N.

The preparedness of humanitarian relief networks can be enhanced by pre-positioning resources in strategic locations and using them when disasters strike, a strategy that gives rise to a two-stage planning problem. This paper presents a novel two-stage stochastic-robust optimization approach for integrated planning of pre- and post-disaster positioning and allocation of relief resources, while taking into consideration the uncertainty about demand for relief services and disruptions in the relief facilities and the transportation network. The proposed approach enables planners to effectively use limited historical data and imperfect experts’ opinions to obtain robust solutions while avoiding the over-conservatism of classical robust optimization methods. The objective sought is to minimize the expected total time victims need to receive assistance, including both access time to facilities and waiting/service time in them. Congestion in relief facilities is accounted for by modeling them as queuing systems and penalizing waiting time. A decomposition method based on column-and-constraint generation is implemented to solve the problem, whereas the nonlinear terms corresponding to queuing in the second-stage problem are handled using a direct search procedure. Applicability of the proposed approach is demonstrated through a real case study and the numerical results are analyzed to draw managerial insights.

Shakibaei H., Farhadi-Ramin M.R., Alipour-Vaezi M., Aghsami A., Rabbani M.

PurposeEvery day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.Design/methodology/approachA new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.FindingsFor this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.Originality/valueObtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

Jahangiri S., Abolghasemian M., Ghasemi P., Chobar A.P.

Ehsani B., Karimi H., Bakhshi A., Aghsami A., Rabbani M.

Along with the destructive effects of catastrophes throughout the world, the COVID-19 outbreak has intensified the severity of disasters. Although the global aid organizations and philanthropists aim to alleviate the adverse impacts, many employed actions are not impactful in dealing with the epidemic outbreak in disasters. However, there is a gap in controlling the epidemic outbreak in the aftermath of disasters. Therefore, this paper proposes a novel humanitarian location-allocation-inventory model by focusing on preventing COVID-19 outbreaks with IoT-based technology in the response phase of disasters. In this study, IoT-based systems enable aid and health-related organizations to monitor people remotely, suspect detection, surveillance, disinfection, and transportation of relief items. The presented model consists of two stages; the first is defining infected cases, transferring patients to temporary hospitals promptly, and accommodating people in evacuation centers. Next, distribution centers are located in the second stage, and relief items are transferred to temporary hospitals and evacuation centers equally regarding shortage minimization. The model is solved by the LP-metric method and applied in a real case study in Salas-e-Babajani city, Kermanshah province. Then, sensitivity analysis on significant model parameters pertaining to the virus, relief items, and capacity has been conducted. Using an IoT-based system in affected areas and evacuation centers reduces the number of infected cases and relief item's shortages. Finally, several managerial insights are obtained from sensitivity analyses provided for healthcare managers.

Ghasemi P., Khalili H.A., Chobar A.P., Safavi S., Hejri F.M.

Disaster management is one of the most important actions to protect the property and lives of the victims. Failure to pay attention to logistical decisions of disaster can have irreversible consequences. Therefore, a multiechelon mathematical model for blood supply chain management in disaster situations is proposed in this research. The proposed supply chain includes supplier, central warehouse, reliable distributor, unreliable distributor, distributor, and affected areas. How the proposed model performs is explained as follows: blood is sent from the supplier to warehouses and distribution centers. Also, the capacity of suppliers is limited. The main objective of the mathematical model is to minimize supply chain costs while maximizing the level of satisfaction in order to meet the demand of the affected area. Hence, this research seeks to decide whether or not to establish a reliable distributor, unreliable distributor, and central warehouse. The amount of blood sent to the centers will also be calculated. One of the contributions of the proposed model is to consider the pre- and postdisaster modes simultaneously. Locating and investigating the flow between centers are also the other contributions of this study. Solving the proposed model using a robust optimization approach is another innovation taken into account in this research. The proposed model is solved using robust optimization, and finally, the results indicate the proper performance of the proposed model.