Showing papers in "Socio-economic Planning Sciences in 2012"

Optimization models in emergency logistics: A literature review

Abstract: Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics. Disaster operations can be performed before or after disaster occurrence. Short-notice evacuation, facility location, and stock pre-positioning are drafted as the main pre-disaster operations, while relief distribution and casualty transportation are categorized as post-disaster operations. According to these operations, works in the literature are broken down into three parts: facility location, relief distribution and casualty transportation, and other operations. For the first two parts, the literature is structured and analyzed based on the model types, decisions, objectives, and constraints. Finally, through the content analysis framework, several research gaps are identified and future research directions are proposed.

Disaster relief routing: Integrating research and practice

Abstract: Disaster relief presents many unique logistics challenges, with problems including damaged trans- portation infrastructure, limited communication, and coordination of multiple agents. Central to disaster relief logistics is the distribution of life-saving commodities to beneficiaries. Operations research models have potential to help relief agencies save lives and money, maintain standards of humanitarianism and fairness and maximize the use of limited resources amid post-disaster chaos. Through interviews with aid organizations, reviews of their publications, and a literature review of operations research models in transportation of relief goods, this paper provides an analysis of the use of such models from the perspective of both practitioners and academics. With the complexity of disaster relief distribution and the relatively small number of journal articles written on it, this is an area with potential for helping relief organizations and for tremendous growth in operations research.

Modeling integrated supply chain logistics in real-time large-scale disaster relief operations

Abstract: The goal of this research is to develop a comprehensive model that describes the integrated logistics operations in response to natural disasters. We propose a mathematical model that controls the flow of several relief commodities from the sources through the supply chain and until they are delivered to the hands of recipients. The structure of the network is in compliance with FEMA's complex logistics structure. The proposed model not only considers details such as vehicle routing and pick up or delivery schedules; but also considers finding the optimal locations for several layers of temporary facilities as well as considering several capacity constraints for each facility and the transportation system. Such an integrated model provides the opportunity for a centralized operation plan that can eliminate delays and assign the limited resources to the best possible use. A set of numerical experiments is designed to test the proposed formulation and evaluate the properties of the optimization problem. The numerical analysis shows the capabilities of the model to handle the large-scale relief operations with adequate details. However, the problem size and difficulty grows rapidly by extending the length of the operations or when the equity among recipients is considered. In these cases, it is suggested to find fast solution algorithms and heuristic methods in future research.

Pre-positioning and dynamic delivery planning for short-term response following a natural disaster

Abstract: Natural disasters often result in large numbers of evacuees being temporarily housed in schools, churches, and other shelters. The sudden influx of people seeking shelter creates demands for emergency supplies, which must be delivered quickly. A dynamic allocation model is constructed to optimize pre-event planning for meeting short-term demands (over approximately the first 72 h) for emergency supplies under uncertainty about what demands will have to be met and where those demands will occur. The model also includes requirements for reliability in the solutions – i.e., the solution must ensure that all demands are met in scenarios comprising at least 100 α % of all outcomes. A case study application using shelter locations in North Carolina and a set of hurricane threat scenarios is used to illustrate the model and how it supports an emergency relief strategy.

Facility location under demand uncertainty: Response to a large-scale bio-terror attack

Abstract: In the event of a catastrophic bio-terror attack, major urban centers need to efficiently distribute large amounts of medicine to the population. In this paper, we consider a facility location problem to determine the points in a large city where medicine should be handed out to the population. We consider locating capacitated facilities in order to maximize coverage, taking into account a distance-dependent coverage function and demand uncertainty. We formulate a special case of the maximal covering location problem (MCLP) with a loss function, to account for the distance-sensitive demand, and chance-constraints to address the demand uncertainty. This model decides the locations to open, and the supplies and demand assigned to each location. We solve this problem with a locate-allocate heuristic. We illustrate the use of the model by solving a case study of locating facilities to address a large-scale emergency of a hypothetical anthrax attack in Los Angeles County.

Transportation in disaster response operations

Abstract: Disasters are extraordinary situations that require significant logistical deployment to transport equipment and humanitarian goods in order to help and provide relief to victims. An efficient response helps to reduce the social, economic and environmental impacts. In this paper, we define and formulate a practical transportation problem often encountered by crisis managers in emergency situations. Since optimal solutions to such a formulation may be achieved only for very small-size instances, we developed an efficient genetic algorithm to deal with realistic situations. This algorithm produces near optimal solutions in relatively short computation times and is fast enough to be used interactively in a decision-support system, providing high-quality transportation plans to emergency managers.

Location of temporary depots to facilitate relief operations after an earthquake

Abstract: The type of humanitarian logistics problem of interest is an earthquake with significant damage, prioritized items for delivery, and an extensive time period over which supplies need to be delivered. The problem of interest is an outgrowth of a recent paper by [10] , where they focused on supplying relief items from a central depot for a prolonged period of time. The drawback of their approach is that long travel distances of vehicles are required between demand points and the central depot. In this paper, we propose the location of temporary depots around the disaster-affected area, along with the required vehicles and resources, to improve logistical efficiency. A two-phase heuristic approach is proposed; it locates temporary depots and allocates covered demand points to an open depot in Phase I, and explores the best logistics performance under the given solution from Phase I in Phase II. Results from computational experiments and an earthquake case study are used to illustrate the benefits of this approach.

A framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology

Abstract: There are many opportunities and challenges in area of Indian technical education due to liberalization and globalization of economy. One of these challenges is how to assess performance of technical institutions based on multiple criteria. This paper is focused on performance evaluation and ranking of seven Indian Institute of Technology (IITs) in respect to stakeholders’ preference using an integrated model consisting of fuzzy AHP and COPRAS. Findings based on 2007–2008 data show that performance of two IITs need considerable improvement. To the best of our knowledge it is one of few studies that evaluates performance of technical institutions in India.

Locating fire stations: An integrated approach for Belgium

Abstract: This paper demonstrates the potential of a decision-support system developed for Belgium by a consortium of universities and a private firm, in the framework of a public call by the Ministry of the Interior. The system is designed to provide the Belgian emergency management administration with a complete decision-aid tool for the location of fire-stations. The originality of the project is that it includes a risk-modeling approach developed at a national scale. This analysis involves a multiscale GIS system which includes a thorough representation of the physical, human and economic spatial realities, a risk modeling approach, an adequate optimal location and allocation model (taking into account both queuing and staffing problems). The final result is an interactive operational tool for defining locations, equipment allocations, staffing, response times, the cost/efficiency trade-off, etc. which can be used in an assessment as well as a prospective context. It has numerous functionalities including rapid modification of the modeling conditions to allow for quick scenario analysis, multiscale analysis, and prospective analysis.

A transshipment model for distribution and inventory relocation under uncertainty in humanitarian operations

Abstract: The number of disasters and humanitarian crises which trigger humanitarian operations is ever-expanding. Unforeseen incidents frequently occur in the aftermath of a disaster, when humanitarian organizations are already in action. These incidents can lead to sudden changes in demand. As fast delivery of relief items to the affected regions is crucial, the obvious reaction would be to deliver them from neighbouring regions. Yet, this may incur future shortages in those regions as well. Hence, an integrated relocation and distribution planning approach is required, considering current demand and possible future developments. For this situation, a mixed-integer programming model is developed containing two objectives: minimization of unsatisfied demand and minimization of operational costs. The model is solved by a rolling horizon solution method. To model uncertainty, demand is split into certain demand which is known, and uncertain demand which occurs with a specific probability. Periodically increasing penalty costs are introduced for the unsatisfied certain and uncertain demand. A sensitivity analysis of the penalty costs for unsatisfied uncertain demand is accomplished to study the trade-off between demand satisfaction and logistical costs. The results for an example case show that unsatisfied demand can be significantly reduced, while operational costs increase only slightly.

An optimization model for volunteer assignments in humanitarian organizations

Abstract: One of the challenges facing humanitarian organizations is that there exist limited decision technologies that are tailored specifically to their needs. While employee workforce management models have been the topic of extensive research over the past decades, very little work has yet concentrated on the problem of managing volunteers for humanitarian organizations. This paper develops a multi-criteria optimization model to assist in the assignment of volunteers to tasks, based upon a series of principles from the field of volunteer management. In particular, it offers a new volunteer management approach for incorporating the decision maker's preferences and knowledge into the volunteer assignment process, thus allowing him or her to closely examine the tradeoffs between potentially conflicting objectives. Test results illustrate the model's ability to capture these tradeoffs and represent the imprecision inherent in the work of humanitarian organizations, and thus demonstrate its ability to support efficient and effective volunteer management.

Dynamic vehicle routing with anticipation in disaster relief

Abstract: Pickup and delivery problems (PDP), where locations may both receive and send goods, are an extension of the classical vehicle routing problem. This paper considers the application of a routing and scheduling problem for forwarding agencies handling less-than-truckload freight in disasters. The approach evaluates the benefits of dynamic optimization anticipating varying travel times (i.e., the availability of connections in this case) as well as unknown orders (i.e., the integration of demand regions on short-notice) in the specific environment of emergencies. The objective is to avoid delays and increase equipment utilization. We model a multi-stage mixed integer problem which is able to operate under variable demand and transport conditions.

Modeling emergency medical response to a mass casualty incident using agent based simulation

Abstract: Emergency managers have to develop plans for responding to disasters within their jurisdiction. This includes coordinating multiple independent agencies participating in the response. While much of this is currently done by use of intuition and expert judgment, models can be used to test assumptions and examine the impact of policies and resource levels. The autonomous nature of responders as well as the rapidly changing information during a disaster suggests that agent based models can be especially suited for examining policy questions. In this work, we built an agent based model of a given urban area to simulate the emergency medical response to a mass casualty incident (MCI) in that area. The model was constructed from publicly available geographic information system and data regarding available response resources (such as ambulances, EMS personnel and hospital beds). Three different agent types are defined to model heterogeneous entities in the system. By simulating various response policies, the model can inform emergency responders on the requirements and response protocols for disaster response and build intuition and understanding in advance of facing actual incidents that are rare in the day-to-day operating experiences.

Incorporating recycling into post-disaster debris disposal

Abstract: Although large amounts of disaster-generated debris significantly strain landfill capacities, until recently existing policy provided no financial incentive to consider other disposal alternatives such as recycling. In 2007, the U.S. Federal Emergency Management Agency (FEMA) released a new pilot program that provides incentives for communities to recycle by allowing them to retain revenue from the sale of disaster debris. This first-ever policy offers significant financial benefits for communities seeking to cleanup in an environmentally responsible way but requires reexamining existing assumptions and decision processes that are based on prior reimbursement programs. This paper presents a decision model with recycling incentives for locating temporary disposal and storage reduction (TDSR) facilities in support of disaster debris cleanup operations. A facility location model is proposed to incorporate the unique assumptions, objectives, and constraints of disaster recovery in light of FEMA’s new policy.

A Location-based Comparison of Health Care Services in Four U.S. States with Efficiency and Equity

Abstract: This paper examines the efficiency and equality in geographic accessibility provided by hospitals. We use the criteria efficiency, availability of the service, and equality. Quantitative measures are defined for all criteria, and are measured using a geographical information system. We then compare existing locations with optimal locations satisfying two objectives, one that minimizes hospital–patient distance, and another that captures as many patients as possible within a pre-specified time or distance. The results of our study indicate that the existing locations provide near-optimal geographic access to health care. Some potential for improvement is indicated.

Case study in disaster relief: A descriptive analysis of agency partnerships in the aftermath of the January 12th, 2010 Haitian earthquake

Abstract: This study analyzes the disaster response and recovery efforts following the January 12th, 2010 Haitian earthquake through the eyes of 18 different relief agencies. Focusing on the formation and maintenance of partnerships after the catastrophic earthquake, this paper explores the concepts of cooperation, mutual understanding, and connectivity among agencies responding to the earthquake. The case study is based on results from interviews and interactions with 18 agencies during a month-long trip to Haiti in the summer of 2010. Of the agencies interviewed, it was found that agencies that had no partnerships or presence in Haiti prior to the earthquake were most likely to build new clinics, orphanages, and schools. Additionally, we found that agencies were more likely to develop new partnerships from new contacts rather than dormant contacts. By studying the partnerships between local and international agencies, it was found that their relationships were less stable than partnerships between international agencies. This study hopes to increase understanding and applicability of research in disaster relief networks by providing a new perspective into how agencies work together.

Pre-positioning hurricane supplies in a commercial supply chain

Abstract: Inventory control for retailers situated in the projected path of an observed hurricane or tropical storm can be challenging due to the inherent uncertainties associated with storm forecasts and demand requirements. In many cases, retailers react to pre- and post-storm demand surge by ordering emergency supplies from manufacturers posthumously. This wait-and-see approach often leads to stockout of the critical supplies and equipment used to support post-storm disaster relief operations, which compromises the performance of emergency response efforts and proliferates lost sales in the commercial supply chain. This paper proposes a proactive approach to managing disaster relief inventories from the perspective of a single manufacturing facility, where emergency supplies are pre-positioned throughout a network of geographically dispersed retailers in anticipation of an observed storm's landfall. Once the requirements of a specific disaster scenario are observed, supplies are then transshipped among retailers, with possible direct shipments from the manufacturer, to satisfy any unfulfilled demands. The manufacturer's pre-positioning problem is formulated as a two-stage stochastic programming model which is illustrated via a case study comprised of real-world hurricane scenarios. Our findings indicate that the expected performance of the proposed pre-positioning strategy over a variety of hurricane scenarios is more effective than the wait-and-see approach; currently used in practice.

Capacity shortfall and efficiency determinants in Brazilian airports: Evidence from bootstrapped DEA estimates

Abstract: This paper reports on the use of different approaches for assessing efficiency related-issues in 63 major Brazilian airports. Starting out with the bootstrapping technique presented in Simar and Wilson (1998, 2004), several DEA estimates were generated, allowing the use of confidence intervals and bias correction in central estimates to test for significant differences in efficiency levels, returns-to-scale, and input-decreasing/output-increasing potentials. The findings corroborate anecdotal and empirical evidence regarding a capacity shortfall within Brazilian airports, where infrastructure slack is virtually inexistent, regardless of the airport type and location.

Locating farmers’ markets with an incorporation of spatio-temporal variation

Abstract: The recent drive among consumers to purchase fresh, healthy and environmentally friendly food has brought about a renewed interest in farmers’ markets. However, ensuring the success of a farmers’ market is not an easy task. Unlike general grocery stores, farmers’ markets often have very limited hours of service and are distributed sparsely in space. Both spatial and temporal constraints that people experience in their daily lives limit their accessibility to a farmers’ market. This research incorporates such constraints in the service provision planning of farmers’ markets. In particular, two models are proposed to select locations and the associated service schedules for a fixed number of farmers’ markets. A case study is conducted in Tucson, Arizona to demonstrate the merits of the new approach.

Specialty care single and multi-period location–allocation models within the Veterans Health Administration

Abstract: Optimal location of specialty care services within any healthcare network is increasingly important for balancing costs, access to care, and patient-centeredness. Typical long-range planning efforts attempt to address a myriad of quantitative and qualitative issues, including within-network access within reasonable travel distances, space capacity constraints, costs, politics, and community commitments. To help inform these decisions, single and multi-period mathematical integer programs were developed that minimize total procedure, travel, non-coverage, and start-up costs to increase network capacity subject to access constraints. These models have been used to help the Veterans Health Administration (VHA) explore relationships and tradeoffs between costs, coverage, service location, and capacity and to inform larger strategic planning discussions. Results indicate significant opportunity to simultaneously reduce total cost, reduce total travel distances, and increase within-network access, the latter being linked to better care continuity and outcomes. An application to planning short and long-term sleep apnea care across the VHA New England integrated network, for example, produced 10–15% improvements in each performance measure. As an example of further insight provided by these analyses, most optimal solutions increase the amount of outside-network care, contrary to current trends and policies to reduce external referrals.

Assessing geographic coverage of the essential air service program

Abstract: Essential air service (EAS) is a federally funded program that helps provide commercial air transport service from smaller, geographically remote communities in the United States. While critics of this program frequently cite the underutilization of EAS connections as being an indicator of wasteful public spending, recent studies suggest that the spatial configuration of EAS subsidized airports may also contribute to systemic inefficiencies. The purpose of this paper is to explore the prospects for reducing EAS allocations while meeting existing geographic service needs. The analysis of this public sector service is structured using classic location coverage problems to examine whether there are any system inefficiencies. This enables an objective assessment to be carried out, using spatial optimization modeling approaches. The subsequent analysis provides the basis for a number of public policy insights, including the evaluation of service redundancies, the impact of geographic proximity guidelines and the potential for expanding coverage of the EAS program.

Improving access to health facilities in Nouna district, Burkina Faso

Abstract: The Nouna health district in Burkina Faso, has a population of approximately 275,000 people living in 281 villages, and is served by 25 health facilities, as of 2006. For many people, the time and effort required in traveling to a health facility, which may demand a journey of many kilometers over poor roads on foot, is a deterrent to seeking proper medical care. In this study we examine how access to health facilities in Nouna may be improved by considering the configuration of the road network in addition to the locations of the facilities. We model the situation as a facility location–network design problem and draw conclusions about how best to improve the physical access of the health facilities. Our model shows the extent to which access can be improved when the road network is considered along with facility locations, in contrast to facility locations considered alone.

Dynamic redistribution of mitigation resources during influenza pandemics

Abstract: The Institute of Medicine (IOM) has pointed out that the existing pandemic mitigation models lack the dynamic decision support capability. In this paper, we present a simulation optimization model to generate dynamic strategies for distribution of limited mitigation resources, such as vaccines and antivirals, over a network of regional outbreaks. The model has the capability to redistribute the resources remaining from previous allocations in response to changes in the pandemic progress. The model strives to minimize the impact of ongoing outbreaks and the expected impact of potential outbreaks, considering measures of morbidity, mortality, and social distancing, translated into the societal and economic costs of lost productivity and medical services. The model is implemented on a simulated H5N1 outbreak involving four counties in the state of Florida, U.S. with over four million inhabitants. The performance of our strategy is compared to that of a myopic distribution strategy. Sensitivity analysis is performed to assess the impact of variability of some critical factors on policy performance. The methodology is intended to support public health policy on effective distribution of limited mitigation resources.

Inventory policies for humanitarian aid during hurricanes

Abstract: In this work we present a stochastic programming model minimizing costs, to support the decision process of inventory policy which best satisfies the demand for food in shelters when hurricane winds are about to impact a town. In this model we consider perishable products as well as the first in first out (FIFO) system for their consumption. In order to make the model closer to reality ordering cost is time-varying and we add a penalty cost in case the shortage exceeds a known limit for two days in a row. Finally the cost to dispose of expired food is greater than the purchase cost of the product since throwing away food has ethical implications. Starting from a stochastic programming model, we present a procedure to transform it to a deterministic mixed integer programming model (MIP) with non-convex objective function over its entire domain, which closely states the situation in reality. Preliminary computational results and discussion are presented.

Analyzing the volume and nature of emergency medical calls during severe weather events using regression methodologies

Abstract: An effective emergency medical service (EMS) response to emergency medical calls during extreme weather events is a critical public service. Nearly all models for allocating EMS resources focus on normal operating conditions. However, public health risks become even more critical during extreme weather events, and hence, EMS systems must consider additional needs that arise during weather events to effectively respond to and treat patients. This paper seeks to characterize how the volume and nature of EMS calls are affected during extreme weather events with a particular focus on emergency preparedness. In contrast to other studies on disaster relief, where the focus is on delivery of temporary commodities, we focus on the delivery of routine emergency services during blizzards and hurricane evacuations. The dependence of emergency service quality on weather conditions is explored through a case study using real-world data from Hanover County, Virginia. The results suggest that whether it is snowing is significant in nearly all of the regression models. Variables associated with increased highway congestion, which become important during hurricane evacuations, are positively correlated with an increased call volume and the likelihood of high-risk calls. The analysis can aid public safety leaders in preparing for extreme weather events.

Using announcement options in the bid construction phase for disaster relief procurement

Abstract: This paper presents an analysis of the bid construction phase of procurement auctions in disaster relief and humanitarian logistics. Substitution and partial fulfillment options are presented in formulations to allow bidders with fewer inventories to offer substitute item types and partial bids in auctions. During the auction announcement phase, a coordinating platform for disaster locations (i.e., auctioneer) allows substitution and partial fulfillment options to the relief suppliers (i.e., bidders) when acceptable. Thus, suppliers with fewer inventories can offer substitute item types and participate in more auctions by partially bidding. A genetic algorithm, a simulated annealing algorithm and an integer program are used for the analysis of the bid construction phase with different announcement options. Heuristic solution techniques and an IP formulation help understand the dynamics of the bid construction problem. It is shown that the addition of substitution and partial fulfillment options is essential to diversify and increase the usable capacity of the supplier base. Additionally, the partial fulfillment option enables better usage of supplier inventories in an environment with scarce supplies.

What is a strategic acquisition? Decision modeling in support of foreclosed housing redevelopment

Abstract: This paper describes and develops a model for calculating location-based strategic values of foreclosed properties considered for acquisition and redevelopment by community development corporations (CDCs). A property’s strategic value refers to its proximity to site-specific neighborhood amenities and disamenities (e.g. schools, public transit, distressed properties), given the relative importance of that proximity to CDC organizational and community objectives. We operationalize the concept of strategic value, and apply this concept to a salient public sector decision problem. Using data and value assessments from a CDC engaged in foreclosed housing redevelopment, we compute measures of strategic value for a set of acquisition candidates. We show that strategic values can differ in systematic ways depending on the types of amenities and disamenities identified as relevant for CDC acquisition decisions, the relative importance assigned to those amenities and disamenities, and the utility maximization objectives of the CDC. We conclude by proposing a multi-criteria decision model for foreclosed housing acquisition and redevelopment which incorporates a theory of residential housing impacts for which strategic value measures are a special case.

Consider the non-adopter: Developing a prediction model for the adoption of household-level broadband access

Abstract: We explore the decision process for the household-level adoption of broadband Internet access. Our aim, of determining the barriers to household-level broadband adoption and how to best overcome those barriers, guides our analysis in an effort to better inform broadband policy development and implementation. We introduce and rely on data collected from 3101 New Jersey households under the National Telecommunications and Information Administration's nationwide Broadband Technology Opportunity Program. Following MATH, the Model of Technology Adoption in Households, extended with a moderating control variable, we model the conditions under which a household is more or less likely to adopt household-level broadband Internet access. We specify an original two-step model that first estimates from demographic determinants a linear latent variable of the responding household's propensity to not adopt high-speed broadband Internet access, and then regresses that propensity variable on behavioral and attitudinal measures about broadband and computer use and familiarity. Analyzing those outcomes generates three empirical findings that help inform the efficient implementation of policies to establish universal broadband access: (1) demographically, household-level broadband adoption in New Jersey is colorblind: race and ethnicity, in and of themselves, do not predict household-level broadband adoption; (2) behaviorally, the strongest facilitator for household-level broadband adoption is computer use by the household decision-maker; and (3) structurally, the strongest barrier to such adoption is lack of resources. Decomposing and better understanding the phenomenon of non-adoption will help to inform planning efforts to maximize household-level broadband adoption.

Mitigating shortage and distribution costs in damaged water networks

Abstract: Recent events have sparked renewed interest in disaster mitigation for public infrastructures. Presidential Decision Directive 63 identifies water distribution as being among the most vital and vulnerable of our large-scale infrastructures. Water distribution networks are vulnerable to threats such as chemical and biological contamination, cyber attacks on computer-based management systems, and physical destruction from acts of nature and intentional attack. This research develops methods for configuring the undamaged portion of the water network to mitigate the consequences of physical destruction. The approach is to find a hydraulically feasible residual network that can be pressurized to meet the demand of a subset of demand sectors. Demand sectors not pressurized then receive water through truck distribution from pressurized sectors. The objective is to minimize weighted water shortage and water truck distribution costs by identifying sectors to pressurize along with an assignment of unpressurized sectors to pressurized sectors for water delivery by truck. The paper develops an optimization model, describes a solution method, and presents computational results for three example networks.