A mean-risk mixed integer nonlinear program for transportation network protection

This paper focuses on transportation network protection to hedge against extreme events such as earthquakes. Traditional two-stage stochastic programming has been widely adopted to obtain solutions under a risk-neutral preference through the use of expectations in the recourse function. In reality, decision makers hold different risk preferences. We develop a mean-risk two-stage stochastic programming model that allows for greater flexibility in handling risk preferences when allocating limited resources. In particular, the first stage minimizes the retrofitting cost by making strategic retrofit decisions whereas the second stage minimizes the travel cost. The conditional value-at-risk (CVaR) is included as the risk measure for the total system cost. The two-stage model is equivalent to a nonconvex mixed integer nonlinear program (MINLP). To solve this model using the Generalized Benders Decomposition (GBD) method, we derive a convex reformulation of the second-stage problem to overcome algorithmic challenges embedded in the non-convexity, nonlinearity, and non-separability of first- and second-stage variables. The model is used for developing retrofit strategies for networked highway bridges, which is one of the research areas that can significantly benefit from mean-risk models. We first justify the model using a hypothetical nine-node network. Then we evaluate our decomposition algorithm by applying the model to the Sioux Falls network, which is a large-scale benchmark network in the transportation research community. The effects of the chosen risk measure and critical parameters on optimal solutions are empirically explored

airline revenue management

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature.revenue management;seat inventory control;OR techniques;mathematical programming

Resilience-Driven Post-Disruption Restoration of Interdependent Critical Infrastructure Systems Under Uncertainty: Modeling, Risk-Averse Optimization, and Solution Approaches

Critical infrastructure networks (CINs) are the backbone of modern societies, which depend on their continuous and proper functioning. Such infrastructure networks are subjected to different types of inevitable disruptive events which could affect their performance unpredictably and have direct socioeconomic consequences. Therefore, planning for disruptions to CINs has recently shifted from emphasizing pre-disruption phases of prevention and protection to post-disruption studies investigating the ability of critical infrastructures (CIs) to withstand disruptions and recover timely from them. However, post-disruption restoration planning often faces uncertainties associated with the required repair tasks and the accessibility of the underlying transportation network. Such challenges are often overlooked in the CIs resilience literature. Furthermore, CIs are not isolated from each other, but instead, most of them rely on one another for their proper functioning. Hence, the occurrence of a disruption in one CIN could affect other dependent CINs, leading to a more significant adverse impact on communities. Therefore, interdependencies among CINs increase the complexity associated with recovery planning after a disruptive event, making it a more challenging task for decision makers. Recognizing the inevitability of large-scale disruptions to CIs and their impacts on societies, the research objective of this work is to study the recovery of CINs following a disruptive event. Accordingly, the main contributions of the following two research components are to develop: (i) resilience-based post-disruption stochastic restoration optimization models that respect the spatial nature of CIs, (ii) a general framework for scenario-based stochastic models covering scenario generation, selection, and reduction for resilience applications, (iii) stochastic risk-related cost-based restoration modeling approaches to minimize restoration costs of a system of interdependent critical infrastructure networks (ICINs), (iv) flexible restoration strategies of ICINs under uncertainty, and (v) effective solution approaches to the proposed optimization models. The first research component considers developing two-stage risk-related stochastic programming models to schedule repair activities for a disrupted CIN to maximize the system resilience. The stochastic models are developed using a scenario-based optimization technique accounting for the uncertainties of the repair time and travel time spent on the underlying transportation network. To assess the risks associated with post-disruption scheduling plans, a conditional value-at-risk metric is incorporated into the optimization models through the scenario reduction algorithm. The proposed restoration framework is illustrated using the French RTE electric power network. The second research component studies the restoration problem for a system of ICINs following a disruptive event under uncertainty. A two-stage mean-risk stochastic restoration model is proposed to minimize the total cost associated with ICINs unsatisfied demands, repair tasks, and flow. The model assigns and schedules repair tasks to network-specific work crews with consideration of limited time and resources availability. Additionally, the model features flexible restoration strategies including a multicrew assignment for a single component and a multimodal repair setting along with the consideration of full and partial functioning and dependencies between the multi-network components. The proposed model is illustrated using the power and water networks in Shelby County, Tennessee, United States, under two hypothetical earthquakes. Finally, some other topics are discussed for possible future work

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

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