Eren Erman Ozguven

Stochastic Humanitarian Inventory Control Model for Disaster Planning

The impacts of disasters have recently attracted increased attention from researchers and policy makers. However, there has been little consensus about how an efficient inventory management model can be developed for postdisaster conditions. Victims of a disaster are generally gathered into shelters during and after a severe disaster to ensure their security. Many evacuees do not have the financial resources to leave the disaster area or to find food, drugs, and other necessities. Hence, their vital needs should be supplied efficiently throughout the disaster and postdisaster periods. Without an adequate stock of goods, satisfying the daily requirements of the victims without disruption might be problematic. To solve this problem, humanitarian inventory control models that can aid in adequately responding to a disaster or a humanitarian crisis are needed. In this context, response represents preparedness, planning, assessment, appeal, mobilization, procurement, transportation, warehousing, and distribution. This paper is concerned with the development of a subproblem of the general humanitarian supply chain problem: an efficient and quick-response humanitarian inventory management model able to determine the safety stock that will prevent disruptions at a minimal cost. The humanitarian inventory management problem is first mathematically formulated as a version of the Hungarian Inventory Control Model. A solution to this time-dependent stochastic model is then proposed by using the p-level efficient points algorithm. The single commodity case results are given, and a sensitivity analysis of the model vis-à-vis various model parameters that affect safe inventory levels is conducted.

Metadata-based Needs Assessment for Emergency Transportation Operations with a Focus on an Aging Population: A Case Study in Florida

Abstract In the aftermath of disasters, evacuating aging victims and maintaining an optimal flow of critical resources in order to serve their needs becomes problematic, especially for Gulf Coast states in the USA such as Florida, where more than 6.9 million (36.9%) of the population are over age 50. Scanning the literature, there is no substantial prior work that has synthesized the requirements for a multi-modal emergency needs assessment that could facilitate the safe and accessible evacuation of aging people, and optimize the flow of resources into the affected region to satisfy the needs of those who remain. This paper presents a review of the aging population-focused emergency literature utilizing a knowledge base development methodology supported with a geographic information system-based case study application set in Florida. Importance is given to both ensuring the resiliency of the transportation infrastructure and meeting the needs of aging populations. As a result of this metadata-based analysis, critical research needs and challenges are presented with planning recommendations and future research directions. Results clearly indicate that transportation agencies should focus on clear and fast dissemination of disaster-related information to the aging populations. The use of paratransit services for evacuating aging people, especially those living independently and/or in rural areas, is also found to be of paramount importance.

Use of Regional Transportation Planning Tool for Modeling Emergency Evacuation

Evacuation modeling and analysis are concerned primarily with identifying the types of traffic movements associated with a disaster evacuation, as well as the estimation of evacuation and clearance times. Thus, an efficient evacuation planning model is important in determining evacuation times, identifying critical locations in the transportation network, and assessing traffic operations strategies and evacuation policies. In this paper various scenarios, including a hurricane, a toxic chemical leak, dirty bombs, and a nuclear event, are studied to understand the evacuation and highway network effects of the evacuating population. Unlike corridor studies or bottleneck studies found in the literature, a network model with equilibrium assignment is used. The scenarios are tested with a case study of Northern New Jersey, modeled with the North Jersey Regional Transportation Model–Enhanced, a large-scale travel demand model of the region. The results presented in this paper focus on the effect of several assumptions and input data on the evacuation estimates, giving planners an idea of the necessary considerations for evacuation planning with a modeling context. The experience with this study shows that regional planning models are suitable tools to model evacuation; however, the modeler must be careful in their use. Multiple methodologies can be used, and assumptions, such as time of day, notice or no-notice, passengers per car, and background traffic in the network, have wide-ranging effects.

Emergency inventory management for disasters--a review.

There has been a recent surge in the publication of academic literature examining various aspects of emergency inventory management for disasters. This article contains a timely literature review of these studies, beginning with an exposition of the characteristics of storage and delivery options for emergency supplies, with a particular emphasis on the differences between emergency inventories and conventional inventory management. Using a novel classification scheme and a comprehensive search of the inventory related literature, an overview of the emergency inventory management studies is also presented. Finally, based on this extensive review, a discussion is presented based on the critical issues and key findings related to the emergency inventory management field, and include suggestions for future research directions.

Case Study-Based Evaluation of Stochastic Multicommodity Emergency Inventory Management Model

Emergency disaster management has emerged as a vital tool for reducing the harm and alleviating the suffering that disasters worldwide cause their victims. A significant task of planners involved in emergency disaster management is the ability to plan for and satisfy the vital needs of the people located in emergency shelters, such as the Superdome in New Orleans, Louisiana, which was used as a shelter during Hurricane Katrina. This task requires determination of a way to reduce the uncertainties associated with emergency operations and to estimate the expected costs of delivery and consumption of vital supplies throughout these operations. This paper attempts to address these issues by application of a case study–based approach to demonstrate the usefulness of a stochastic humanitarian inventory control model and estimation of the minimum safety stock levels of emergency inventories. The emergency inventory management problem is discussed, and previous emergency inventory studies are reviewed to identify the need for a stochastic emergency inventory management model. After introduction of the mathematical formulation for the model, the formulation is applied to a number of realistic case studies built on the basis of the experiences in recent major disasters, such as Hurricane Katrina. The paper concludes with a summary of lessons learned for the model when it is applied to a wide range of scenarios drawn from real-life experiences and used to create emergency inventory management strategies for different types of disasters.

Nonparametric Bayesian Estimation of Freeway Capacity Distribution from Censored Observations

Previous studies have been made of the usefulness and effectiveness of survival analysis in transportation and traffic engineering studies with incomplete data in which the Kaplan–Meier estimate is proposed for determining traffic capacity distribution. However, well-known estimators like Kaplan–Meier and Nelson–Aalen have several disadvantages that make it difficult to obtain the traffic capacity distribution. First, neither estimator is defined for all values of traffic flows possible. That is, the maximum flow followed by a breakdown defines the final point of the estimated distribution curve. Therefore, parametric fitting tools have to be applied to obtain the remaining portion of the curve. Moreover, the discontinuity and nonsmoothness of the Kaplan–Meier and Nelson–Aalen estimates make it difficult to ensure the robustness of the estimation. In this paper the Kaplan–Meier and Nelson–Aalen nonparametric estimators are used to obtain the traffic capacity function of four freeway sections. Then a Bayesian nonparametric estimator, which is shown to be a Bayesian extension of the Kaplan–Meier estimator, is introduced for estimating the capacity distribution. This estimator assumes a Dirichlet process prior for the survival function under the minimization of a squared-error loss function. The results indicate that the curves obtained by using the Bayesian estimation method are smoother than those obtained with the other estimator. This smoothness also ensures the continuity in the vicinity of censored observations. Furthermore, the Bayesian estimates can be obtained for any traffic flow value regardless of the availability of data only for certain ranges of observations (including censored data).

A Comparative Methodology for Estimating the Capacity of a Freeway Section

The random characteristics of the traffic flow make it essential to have a random component and therefore add a stochastic meaning to the deterministic parameters. This paper aims to improve the conventional deterministic approach to the freeway capacity by estimating parameters of the various probability distribution functions that are likely to represent the probabilistic nature of freeway traffic capacity. Firstly, Maximum Likelihood Estimation method is applied to estimate the capacity distribution function. Then, confidence intervals for the capacity distribution function are calculated using Bayesian statistics techniques that can address the difficult problem of censored data. Finally, a comparative analysis has been conducted between the estimations of deterministic and probabilistic models to come up with a conclusion regarding spatial and temporal characteristics of freeway capacity. The analysis results indicate that including stochasticity in the model estimation results in better representation of observed data and thus improve understanding of real-life situations.

Minimizing Carbon Dioxide Emissions Due to Container Handling at Marine Container Terminals via Hybrid Evolutionary Algorithms

Considering a rapidly increasing seaborne trade and drastic climate changes due to emissions, produced by oceangoing vessels and container handling equipment, marine container terminal operators not only have to improve effectiveness of their operations to serve the increasing demand, but also to account for the environmental impact associated with the terminal operations. This paper proposes a novel mixed integer mathematical model for the berth scheduling problem, which minimizes the total service cost of vessels, including the total carbon dioxide emission cost due to container handling. The latter pollutant is a primary greenhouse gas that causes global warming. A Hybrid Evolutionary Algorithm, which deploys a set of local search heuristics, is developed to solve the problem. Computational experiments showcase that the optimality gap of the proposed solution algorithm does not exceed 1.61%. It is further shown that the application of additional local search heuristics allows efficient discovery of promising solutions throughout the search process. Results from numerical experiments also indicate that changes in the carbon dioxide emission cost may significantly affect the design of berth schedules. The developed mathematical model and the proposed solution algorithm can thus be adopted as effective planning tools by the marine container terminal operators and improve the environmental sustainability of the terminal operations.

Simultaneous Perturbation Stochastic Approximation Algorithm for Solving Stochastic Problems of Transportation Network Analysis: Performance Evaluation

Stochastic optimization has become one of the important modeling approaches in transportation network analysis. For example, for traffic assignment problems based on stochastic simulation, it is necessary to use a mathematical algorithm that iteratively seeks out the optimal, the suboptimal solution, or both, because an analytical (closed-form) objective function is not available. Therefore, efficient stochastic approximation algorithms that can find optimal or suboptimal solutions to these problems are needed. The method of successive averages (MSA), a well-known algorithm, is used to solve both deterministic and stochastic equilibrium assignment problems. As found in previous studies, the MSA has questionable convergence characteristics, especially when the number of iterations is not sufficiently large. In fact, the stochastic approximation algorithm is of little practical use if the number of iterations to reduce the errors to within reasonable bounds is arbitrarily large. An efficient method to solve stochastic approximation problems is the simultaneous perturbation stochastic approximation (SPSA), which can be a viable alternative to the MSA because of its proven power to converge to sub-optimal solutions in the presence of stochasticities and its ease of implementation. The performance of MSA and SPSA algorithms is compared for solving traffic assignment problems with varying levels of stochastic-ities on a small network. The utmost importance is given to comparison of the convergence characteristics of the two algorithms as well as to the computational times. A worst-case scenario is also studied to check the efficiency and practicality of both algorithms in terms of computational times and accuracy of results.

Optimal capacity design under k-out-of-n and consecutive k-out-of-n type probabilistic constraints

We formulate and solve probabilistic constrained stochastic programming problems, where we prescribe lower and upper bounds for