Research Directions in Information Systems for Humanitarian Logistics

This article systematically reviews the literature on using IT (Information Technology) in humanitarian logistics focusing on disaster relief operations. We first discuss problems in humanitarian relief logistics. We then identify the stage and disaster type for each article as well as the article’s research methodology and research contribution. Finally, we identify potential future research directions

A review of operations research methods applicable to wildfire management

Across the globe, wildfire-related destruction appears to be worsening despite increased fire suppression expenditure. At the same time, wildfire management is becoming increasingly complicated owing to factors such as an expanding wildland-urban interface, interagency resource sharing and the recognition of the beneficial effects of fire on ecosystems. Operations research is the use of analytical techniques such as mathematical modelling to analyse interactions between people, resources and the environment to aid decision-making in complex systems. Fire managers operate in a highly challenging decision environment characterised by complexity, multiple conflicting objectives and uncertainty. We assert that some of these difficulties can be resolved with the use of operations research methods. We present a range of operations research methods and discuss their applicability to wildfire management with illustrative examples drawn from the wildfire and disaster operations research literature

Facility location optimization model for emergency humanitarian logistics

Since the 1950s, the number of natural and man-made disasters has increased exponentially and the facility location problem has become the preferred approach for dealing with emergency humanitarian logistical problems. To deal with this challenge, an exact algorithm and a heuristic algorithm have been combined as the main approach to solving this problem. Owing to the importance that an exact algorithm holds with regard to enhancing emergency humanitarian logistical facility location problems, this paper aims to conduct a survey on the facility location problems that are related to emergency humanitarian logistics based on both data modeling types and problem types and to examine the pre- and post-disaster situations with respect to facility location, such as the location of distribution centers, warehouses, shelters, debris removal sites and medical centers. The survey will examine the four main problems highlighted in the literature review: deterministic facility location problems, dynamic facility location problems, stochastic facility location problems, and robust facility location problems. For each problem, facility location type, data modeling type, disaster type, decisions, objectives, constraints, and solution methods will be evaluated and real-world applications and case studies will then be presented. Finally, research gaps will be identified and be addressed in further research studies to develop more effective disaster relief operations

Robust Modeling Framework for Transportation Infrastructure System Protection Under Uncertainty

This dissertation presents a modelling framework that will be useful for decision makers at federal and state levels to establish efficient resource allocation schemes to transportation infrastructures on both strategic and tactical levels. In particular, at the upper level, the highway road network carries traffic flows that rely on the performance of individual bridge infrastructure which is optimized through robust design at lower level. A system optimization model is developed to allocate resources to infrastructure systems considering traffic impact, which aims to reduce infrastructure rehabilitation cost, long term economic cost including travel delays due to realization of future natural disasters such as earthquakes. At the lower level, robust design for each individual bridge is confined by the resources allocated from upper level network optimization model, where optimal rehabilitation strategies are selected to improve its resiliency to hedge against potential disasters. The above two decision making processes are interdependent, thus should not be treated separately. Thus, the resultant modeling framework will be a step forward in the disaster management for transportation infrastructure network. This dissertation first presents a novel formulation and a solution algorithm of network level resource allocation problem. A mean-risk two-stage stochastic programming model is developed with the first-stage considering resources allocation and second-stages shows the response from system travel delays, where the conditional value-at-risk (CVaR) is specified as the risk measure. A decomposition method based on generalized Benders decomposition is developed to solve the model, with a concerted effort on overcoming the algorithmic challenges imbedded in non-convexity, nonlinearity and non-separability of first- and second- stage variables. The network level model focusing on traffic optimization is further integrated into a bi-level modeling framework. For lower level, a method using finite element analysis to generate a nonlinear relationship between structural performances of bridges and retrofit levels. This relationship was converted to traffic capacity-cost relationship and used as an input for the upper-level model. Results from the Sioux Falls transportation network demonstrated that the integration of both network and FE modeling for individual structure enhanced the effectiveness of retrofit strategies, compared to linear traffic capacity-cost estimation and conventional engineering practice which prioritizes bridges according to the severity of expected damages of bridges. This dissertation also presents a minimax regret formulation of network protection problem that is integrated with earthquake simulations. The lower level model incorporates a seismic analysis component into the framework such that bridge columns are subject to a set of ground motions. Results of seismic response of bridge structures are used to develop a Pareto front of cost-safety-robustness relationship from which bridge damage scenarios are generated as an input of the network level model

OPTIMIZATION MODELS AND METHODOLOGIES TO SUPPORT EMERGENCY PREPAREDNESS AND POST-DISASTER RESPONSE

This dissertation addresses three important optimization problems arising during the phases of pre-disaster emergency preparedness and post-disaster response in time-dependent, stochastic and dynamic environments. The first problem studied is the building evacuation problem with shared information (BEPSI), which seeks a set of evacuation routes and the assignment of evacuees to these routes with the minimum total evacuation time. The BEPSI incorporates the constraints of shared information in providing on-line instructions to evacuees and ensures that evacuees departing from an intermediate or source location at a mutual point in time receive common instructions. A mixed-integer linear program is formulated for the BEPSI and an exact technique based on Benders decomposition is proposed for its solution. Numerical experiments conducted on a mid-sized real-world example demonstrate the effectiveness of the proposed algorithm. The second problem addressed is the network resilience problem (NRP), involving an indicator of network resilience proposed to quantify the ability of a network to recover from randomly arising disruptions resulting from a disaster event. A stochastic, mixed integer program is proposed for quantifying network resilience and identifying the optimal post-event course of action to take. A solution technique based on concepts of Benders decomposition, column generation and Monte Carlo simulation is proposed. Experiments were conducted to illustrate the resilience concept and procedure for its measurement, and to assess the role of network topology in its magnitude. The last problem addressed is the urban search and rescue team deployment problem (USAR-TDP). The USAR-TDP seeks an optimal deployment of USAR teams to disaster sites, including the order of site visits, with the ultimate goal of maximizing the expected number of saved lives over the search and rescue period. A multistage stochastic program is proposed to capture problem uncertainty and dynamics. The solution technique involves the solution of a sequence of interrelated two-stage stochastic programs with recourse. A column generation-based technique is proposed for the solution of each problem instance arising as the start of each decision epoch over a time horizon. Numerical experiments conducted on an example of the 2010 Haiti earthquake are presented to illustrate the effectiveness of the proposed approach