Shortage of gasoline is a common phenomenon during onset of forecasted disasters like hurricanes. Prediction of future gasoline shortage can guide agencies in pushing supplies to the correct regions ...and mitigating the shortage. We demonstrate how to incorporate social media data into gasoline supply decision making. We develop a systematic approach to examine social media posts like tweets and sense future gasoline shortage. We build a four-stage shortage prediction methodology. In the first stage, we filter out tweets related to gasoline. In the second stage, we use an SVM-based tweet classifier to classify tweets about the gasoline shortage, using unigrams and topics identified using topic modeling techniques as our features. In the third stage, we predict the number of future tweets about gasoline shortage using a hybrid loss function, which is built to combine ARIMA and Poisson regression methods. In the fourth stage, we employ Poisson regression to predict shortage using the number of tweets predicted in the third stage. To validate the methodology, we develop a case study that predicts the shortage of gasoline, using tweets generated in Florida during the onset and post landfall of Hurricane Irma. We compare the predictions to the ground truth about gasoline shortage during Irma, and the results are very accurate based on commonly used error estimates.
•Population, Dispersion and Equity Criteria in public facility location.•Generation of good, implementable solutions for a location problem.•Modeling a problem from the viewpoint of an OR/MS ...practitioner.
From a practical perspective, the paper demonstrates that the appropriate use of dispersion, population, and equity criteria can lead to fairly good solutions with respect to the p-median objective. The only stipulation is that the decision maker verifies (through simple constraint checks) that the chosen locations meet the dispersion, population, and equity criteria. An empirical investigation is conducted to obtain appropriate values for these parameters. From a location science perspective, a new location model that accounts for equity and efficiency simultaneously is studied and analyzed. Specifically, the p-maxian problem with side constraints on dispersion, population, and equity is developed, its NP-completeness established, and valid inequalities and bounds derived. Computational tests show encouraging results.
In this paper we develop a methodological framework for designing the daily distribution and replenishment operations of petroleum products over a weekly planning horizon by taking into account the ...perspectives of both the transporter and its customers. The proposed approach considers the possibility of having late deliveries due to the variability of the customers’ demands and expected time windows. We first develop an inventory model for the customers to identify the optimal order quantities and time windows. Then, we solve a sequence of mixed-integer optimization models for designing distribution routes based on the order quantities and time windows selected by the inventory models. We design the optimization models so that late deliveries are balanced among the customers in order to mitigate the overall customer dissatisfaction. We test the proposed approach by solving a set of instances adapted from the literature. The empirical results show that the proposed approach can be used for designing the distribution plan for delivering petroleum products in conditions where the operational capabilities of the transporter are limited for generating optimal on-time plans.
Hurricanes are a type of natural disaster for which it is possible to plan for prepositioning of supplies to improve the efficiency of the post-disaster relief effort. This paper develops a model for ...prepositioning supplies in such a setting. Our model has a distinguishing feature the possible destruction of supply points during the disaster event. To gain insight into our model, we develop a series of theoretical properties. To test the applicability of our model a series of computational tests are performed. From such tests we conclude that it is possible to solve relatively large instances of the problem utilizing standard optimization software. A methodology based on creation of clusters of demand points is proposed for solving even larger problems. Finally we study sensitivity of the results with respect to key parameters. These investigations provide important policy implications.
► Disaster relief modeling. ► Planning for a Hurricane disaster. ► Modeling destruction of supply points. ► Clustering to solve large-scale problems.
► Identifying gaps in disaster operations management literature. ► Evaluation of recent disaster operations management contributions. ► OR/MS applications in disaster operations management.
Potential ...consequences of disasters involve overwhelming economic losses, large affected populations and serious environmental damages. Given these devastating effects, there is an increasing interest in developing measures in order to diminish the possible impact of disasters, which gave rise to the field of disaster operations management (DOM). In this paper we review recent OR/MS research in DOM. Our work is a continuation of a previous review from Altay and Green (2006). Our purpose is to evaluate how OR/MS research in DOM has evolved in the last years and to what extent the gaps identified by Altay and Green (2006) have been covered. Our findings show no drastic changes or developments in the field of OR/MS in DOM since the publication of Altay and Green (2006). Additionally to our comparative analysis, we present an original evaluation about the most common assumptions in recent OR/MS literature in DOM. Based on our findings we provide future research directions in order to make improvements in the areas where lack of research is detected.
The aim of this paper is to develop a robust methodology for the dispatching and routing of emergency vehicles (EVs) in a post-disaster environment with the support of data fusion. In this work, we ...consider an earthquake scenario with a large number of casualties needing medical attention. Given an influx of information (regarding casualties, road, traffic conditions, etc.), data are fused to provide estimates of the entities under consideration. We use this information to dispatch and route EVs to casualty pickup locations, followed by delivery to appropriate hospitals. Key factors here include patient priorities, clustering criteria, and distance. Similarly, factors affecting the dispatching of EVs from patient locations to hospitals include waiting time at hospital emergency rooms, hospital capacity, and distance. Routes must be generated for EVs by accounting for real-world road networks, existing road damage, congestion, and related issues. We develop a dispatching and routing simulation model, and utilize a case study to evaluate the performance of our proposed methodology.
This paper discusses the problem of predicting the length of Traveling Salesman Problem (TSP) tour in dynamic and uncertain environments. Our findings, which are guided by extensive simulation runs, ...provide statistical estimations for the tour length under different scenarios. One of the applications that can benefit from these estimates includes warehouse order picking, which has seen increased importance due to online shopping. The utility of statistical estimates for TSP tour length for order picking is demonstrated on a common warehouse layout.
Panic-buying and shortages of essential commodities is common during early phases of a disaster or an epidemic. The goal of this paper is develop a methodology which includes social media information ...in optimization models of searching essential commodities during disasters and improves the efficiency of search. Specific contributions in the data processing of social media posts include the development of an event localizer that probabilistically infers the location and time of shortage of commodity based on social media information. Contributions in the mathematical model development include an integer programming formulation of the resultant search problem on a graph, with the two objective different objective functions: (a) Maximizing probability of finding the commodity (b) Minimizing expected time to find the commodity given the commodity is found. The methodology is validated using a case study on gasoline search during the Hurricane Irma evacuations. We found that social media posts can predict shortage at gas station for four major cities of Florida accurately with a MAPE of 12%. We also found that addition of social media information to the search process improved the average search time by 41.74%.
•Optimization model for an efficient search of an essential commodity.•Can social media posts can significantly improve the search efficiency?•Efficient processing of social media posts for use in a mathematical model.•Gasoline search in Hurricane Irma evacuation.
In recent years, considerable effort in the field of operations research has been paid to optimizing airline operations, including the logistics of an airline’s fleet of aircraft. We focus on the ...problem of aircraft routing, which involves generating and selecting a particular route for each aircraft of a sub-fleet that is already assigned to a set of feasible sequences of flight legs. Similar studies typically focus on long-term route planning. However, stochastic events such as severe weather changes, equipment failures, variable maintenance times, or even new regulations mandated by the Federal Aviation Administration (FAA) play havoc on these long-term plans. In addition, these long-term plans ignore detailed maintenance requirements by considering only one or two of the primary maintenance checks that must be performed on a regular, long-term basis. As a result, these plans are often ignored by personnel in airline operations who are forced on a daily basis to develop quick, ad hoc methods to address these maintenance requirements and other irregular events. To address this problem, we develop an operational aircraft maintenance routing problem formulation that includes maintenance resource availability constraints. We propose a branch-and-price algorithm for solving this problem, which, due to the resource constraints, entails a modification of the branch-on, follow-on branching rule typically used for solving similar problems. Through computational testing, we explore the efficiency of this solution approach under a combination of heuristic choices for column (route) generation and selection.
•Review of papers in prepositioning of supplies and assets for natural disasters.•Categorization of prepositioning literature in the natural disaster domain.•Research gap identification in ...prepositioning supplies and assets for natural disasters.
Prepositioning of assets and supplies prior to a disaster strike accelerates the response activities as it reduces the supply chain burden associated with humanitarian relief items. Unlike prior survey papers on pre-disaster and post-disaster humanitarian logistics, our paper has a specific focus on prepositioning of assets and supplies in the domain of natural disasters. The first aim of our paper is to review the main Operations Research and Management Science (OR/MS) journal papers published between 2000 and 2018 on this topic. We have statistically analyzed these papers based on contributions in different journals, number of papers per year, and type of disaster. We have also categorized the papers based on their decision variables into three categories: Allocation papers (“A”), Location papers (“L”), and Location-Allocation papers (“LA”). After that, we have assessed our current literature based on some of the methodological issues in Humanitarian Operations that gathered by Kovacs and Moshtari (2018). The second aim of our paper is research gap identification. Our key findings in this domain are that there is a lack of papers that: consider demand-side costs in their proposed model objectives; deal with uncertainty in funding, budget, asset and supply quantities, and infrastructure; considering prepositioning as a risk mitigation strategy; take reliability into account for reducing the risk of loss; consider prepositioning of medical staff and emergency crew; discuss the best time to start prepositioning of supplies and assets in confronting a foreseen disaster; use social media to better prepare for upcoming disasters.
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