Annals of Operations Research

Paul, Shubhra; Davis, Lauren B.

doi: 10.1007/s10479-021-04146-5pmid: N/A

Food banks are nonprofit hunger relief organizations that collect donations from donors and distribute food to local agencies that serve people in need. Donors consist of local supermarkets, manufacturers, and community organizations. The frequency, quantity, and type of food donated by each donor can vary each month. In this research, we propose a technique to identify the supply behavior of donors and cluster them based on these attributes. We then develop a predictive ensemble model to forecast the contribution of different donor clusters. Our study shows the necessary behavioral attributes to classify donors and the best way to cluster donor data to improve the prediction model.

Dubey, Rameshwar

doi: 10.1007/s10479-022-05021-7pmid: 36345351

The design and management of the humanitarian supply chain are the most critical aspects of the humanitarian aid supply chain. Despite enormous interest among the academic community and the practitioners, the design of a humanitarian supply chain is still not well understood. Most of the publications have attempted to address the mechanisms of the humanitarian relief operations. However, the elements of the humanitarian supply chain designs are not well understood in an integrated manner. In this special issue, we have accepted the articles based on six factors that shape the design and management of the humanitarian supply chain and the influencing factors (see Fig. 4). We have noted the research gaps and offered rich directions for future research.

John, Lijo; Gurumurthy, Anand; Mateen, Arqum; Narayanamurthy, Gopalakrishnan

doi: 10.1007/s10479-020-03778-3pmid: N/A

The uncertainty associated with the location, severity and timing of disaster makes it difficult for the humanitarian organization (HO) to predict demand for the aid material and thereby making the relief material procurement even more challenging. This research explores whether options contract can be used as a mechanism to aid the HO in making procurement of relief material less challenging by addressing two main issues: inventory risk for buyers and over-production risk for suppliers. Furthermore, a contracting mechanism is designed to achieve coordination between the HO and aid material suppliers in the humanitarian supply chain through optimal pricing. The options contract is modelled as a stylized version of the newsvendor problem that allows the HO to adjust their order quantity after placing the initial order at the beginning of the planning horizon. This flexibility helps to mitigate the risk of both overstocking and understocking for the HO as well as the risk of overproduction for the supplier. Our results indicate that the optimal values for decision parameters are not “point estimates” but a range of prices, which can facilitate negotiation between the two parties for appropriate selection of contract parameters under an options contract. The results imply that options contract can aid in the decentralized approach of fixing the prices between the HO and the supplier, which in turn would help in achieving systemic coordination.

Atsiz, Eren; Balcik, Burcu; Gunnec, Dilek; Sevindik, Busra Uydasoglu

doi: 10.1007/s10479-020-03909-wpmid: N/A

Disasters may cause significant damages and long-lasting failures in lifeline infrastructure networks (such as gas, power and water), which must be recovered quickly to resume providing essential services to the affected communities. While making repair plans, it is important to consider the interdependencies among network components to minimize recovery times. In this paper, we focus on post-disaster repair operations of multiple interdependent lifeline networks, which involve functional dependencies. We assume that each network component, whether damaged or not, becomes nonfunctional if it depends on another nonfunctional component, and it is recovered when all components that it depends on become functional. We introduce a post-disaster coordinated infrastructure repair routing problem, in which dedicated repair teams of each lifeline infrastructure travel through a road network to visit the sites with damaged network components. We present a mixed integer programming model that assigns repair teams to the sites and constructs routes for each team in order to minimize the sum of the recovery times for all network components. We develop a constructive heuristic and a simulated annealing algorithm to solve the proposed coordinated routing problem. We test the performance of the proposed solution algorithms on a set of instances that are developed based on two interdependent lifeline networks (e.g., power and gas). The computational results show that our heuristics can quickly find high-quality solutions. Our results also indicate that coordinating repair operations can significantly improve the overall recovery time of interdependent infrastructure networks.

Farrokhizadeh, Elmira; Seyfi-Shishavan, Seyed Amin; Satoglu, Sule Itir

doi: 10.1007/s10479-021-03978-5pmid: N/A

In natural disasters, having a capable network of collecting and distributing crucial items such as blood is one of the major concerns. However, due to damage to the infrastructure after disasters, mobile blood collecting facilities (blood mobiles) are usually required. This paper aims to decide the locations of mobile facilities in each period for collecting donated blood, plan the blood distribution from the fixed and mobile facilities to the main blood centers, as well as from blood centers to the hospitals and field-hospitals, under uncertain conditions. To do so, a multi-period, bi-objective mixed-integer mathematical model is developed under a multiple-scenario, aiming to minimize the unsatisfied blood demand as well as the total cost of the network. In the proposed model, the blood group compatibility matrix, failure rate of the facilities, and patients’ urgency levels are considered. An augmented ε-constraint method is applied to solve this bi-objective model. Due to the complex nature of the proposed blood supply chain model, the Lagrangian relaxation approach is used to solve the proposed model. An expected Istanbul earthquake is considered, and the blood supply planning through the Red Crescent’s European branch is performed utilizing the proposed model to examine its validity. According to the numerical results, the mobile facilities' locations in each period under each scenario are determined, the unsatisfied demand in each hospital and field-hospital for each blood type are reported, and the tradeoff between the supply chain costs and unsatisfied demand are discussed in detail. Finally, to illustrate the robustness of the proposed model, a detailed sensitivity analysis is performed. According to the study results, opening new blood centers near the high-demand sub-districts for faster testing and supply, increasing the hospitals' capacities, and usage of drones and helicopters for blood distribution are suggested can be considered as managerial insights.

Gao, Xuehong

doi: 10.1007/s10479-019-03506-6pmid: N/A

In planning for a large-scale disaster, potential relief centers to accommodate evacuees need to be identified. The quantities of emergency commodities are prepared and stocked at relief centers in advance for possible disasters. In the event of a disaster, due to the different disaster severities and uncertain environment, some relief centers inevitably have surplus commodities, whereas some relief centers are still unmet. To use any surplus commodities effectively, a multi-commodity rebalancing process is necessary to rebalance the commodities among relief centers. However, various uncertainties make the multi-commodity rebalancing process extremely challenging, including uncertain demand and transportation-network availability. By recognizing those practical uncertainties, a bi-level stochastic mixed-integer nonlinear programming model is proposed to formulate this multi-commodity rebalancing problem. The upper-level objective is to minimize the total dissatisfaction level, which is measured by the expected total weighted unsatisfied demand, and the lower-level objective is to minimize the expected total transportation time. Finally, a case study on the Great Sichuan Earthquake in China is implemented; their results show that the proposed model facilitates effective decision-making in the practice of multi-commodity rebalancing.

Jana, Rabin K.; Sharma, Dinesh K.; Mehta, Peeyush

doi: 10.1007/s10479-021-04267-xpmid: 34539018

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.

Khorsi, Maliheh; Chaharsooghi, Seyed Kamal; Husseinzadeh Kashan, Ali; Bozorgi-Amiri, Ali

doi: 10.1007/s10479-022-04757-6pmid: N/A

Every year, natural disasters such as earthquakes, floods, volcanos, etc. cause millions of victims. So a quick response to these disasters is vital to reduce their negative consequences. Vehicle routing models can make important contributions to faster response, and thus, save lives. This paper proposes a vehicle routing problem to deliver relief resources from origins to destinations in response to disasters. For this purpose, a multi-period, multi-depot, multi-trip mixed-integer linear programming model is developed. Minimizing the sum of arrival times is considered as a service-based objective function for the increase of the survival rate. For the first time, the problem is solved using a grouping metaheuristic algorithm. Then its performance is compared with two other grouping algorithms. To evaluate the solution method, the algorithms are implemented on various test problems and compared statistically. Additionally, to show the validity of the model, sensitivity analyses are performed and managerial insights are given.

Seraji, Hasti; Tavakkoli-Moghaddam, Reza; Asian, Sobhan; Kaur, Harpreet

doi: 10.1007/s10479-021-04003-5pmid: N/A

Humanitarian logistics is an integral part of disaster relief operations, which involves the phases of preparedness, disaster operations, and post-disaster operations. Integrating the planning and execution between phases minimizes the gaps in providing relief to the affected population. This paper presents a two-stage multi-objective mathematical model for integrated decision-making during the preparation and response phases. The proposed model is developed to jointly optimize the location of emergency shelters (and/or depots) and coordinate the movement of relief vehicles between the disaster site and emergency shelters. Focusing on the optimal distribution of relief supplies to the emergency shelters, the proposed model aims to minimize the operational, distributive injustice, and dissatisfaction costs. To address the computational complexity of the introduced model, two multi-objective meta-heuristics, namely multi-objective vibration damping optimization and non-dominated sorting genetic algorithm (NSGA-II), are used. A comprehensive sensitivity analysis is conducted to study the impacts of variations in key parameters on model output under different scenarios. Our results suggests that the employed solution algorithms outperform the traditional optimization methods in achieving the Pareto-Front solutions.

Stallkamp, Christoph; Diehlmann, Florian; Lüttenberg, Markus; Wiens, Marcus; Volk, Rebekka; Schultmann, Frank

doi: 10.1007/s10479-020-03800-8pmid: N/A

A shortage of water leads to severe consequences for populations. Recent examples like the ongoing water shortage in Kapstadt or in Gloucestershire in 2007 highlight both the challenges authorities face to restore the water supply and the importance of installing efficient preparedness measures and plans. This study develops a proactive planning approach of emergency measures for possible impairments of water supply systems and validates this with a case study on water contamination in the city of Berlin. We formulate a capacitated maximal covering problem as a mixed-integer optimization model where we combine existing emergency infrastructure with the deployment of mobile water treatment systems. The model selects locations for mobile water treatment systems to maximize the public water supply within defined constraints. With the extension to a multi-objective decision making model, possible trade-offs between the water supply coverage and costs, and between the coverage of differently prioritized demand points are investigated. Therefore, decision makers benefit from a significantly increased transparency regarding potential outcomes of their decisions, leading to improved decisions before and during a crisis.