This paper proposes a Continuous Approximation (CA) model that designs the potentiality of drones as a mode of transportation to supply emergency commodities in a disaster-affected region. The model ...determines the optimal distribution center locations and their corresponding service regions and ordering quantities to minimize the overall distribution cost for the disaster-relief operation. We propose a two-phase CA approach that solves the model efficiently. We conduct extensive sensitivity analysis to reveal insights into how system design varies with key drone design parameters. We use three disaster-prone coastal counties of Mississippi as a testbed to visualize and validate the modeling results.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•Develop restoration plans for interdependent infrastructure networks.•Consider machine movement through a damaged and under repair transportation network.•Computationally analyze using data sets ...representing Juan Diaz, Panama and CLARC.•Apply a rolling horizon method to solve large, complex problem instances.
We consider the problem of restoring services provided by an interdependent set of infrastructures after they were disrupted from an extreme event. Specifically, we select the set of damaged infrastructure arcs for immediate restoration and schedule these on a set of machines (work crews). Our novel contribution is that when we determine the selection and scheduling of these damaged arcs, we explicitly consider the movement of machines through a damaged transportation network that is currently being restored. Previous works failed to consider how machine movement greatly influences the ability to conduct timely restoration due to the interdependence on the transportation network. To model this restoration construct, we propose an interdependent integrated network design and scheduling problem with movement of machines (IINDS-MM). In an IINDS-MM problem, we have a base transportation network and at least one additional infrastructure network layer. For each network layer, we determine what damaged arcs are selected for restoration, which machine will conduct the restoration, and the sequence of tasks assigned to each machine when explicitly considering machine movement through the changing transportation network. We propose a mixed integer programming formulation of the IINDS-MM problem and solve it using a rolling horizon solution procedure. Using realistic data representing Juan Diaz, Panama and the customizable artificial community CLARC data set, we simulate different storm surge levels and possible damage scenarios. We then solve the IINDS-MM problem and deduce insights about machine starting locations, machine capabilities, and the performance of IINDS-MM compared to existing restoration models.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this research we analyze the effectiveness of the current and optimal locations of a set of existing regional assets maintained by the Department of Defense to respond to large-scale emergencies. ...These assets have been incrementally resourced, established, sited over the past 20 years without regard to the entire enterprise and, due to fiscal and political costs, modifications to the current structure must yield significant gains to garner approval. We formulate a multiobjective hierarchical extension of the maximal covering location problem that seeks to maximize coverage of the population within a rapid response window while minimizing modifications to the existing structure. Additionally, we prevent facilities from covering nodes located within close proximity using a modified conditional covering problem (CCP) constraint; this constraint accounts for the large impact radius that can occur in a worst-case scenario. To solve our multiobjective problem, we develop a set of non-inferior solutions using the ε-constraint method. These non-inferior solutions explicitly represent the trade-off between maximizing coverage and minimizing cost, and they offer a decision maker a set of Pareto optimal decisions to consider for implementation. Applying our model and methodology to the current set of assets, we demonstrate that, in the absence of resource constraints, we can improve coverage by more than 15%, approximately 49 million people. Furthermore, with only 23 unit relocations (less than a 30% modification of the entire structure) coverage can exceed 98%, an improvement of an additional 45 million people covered. Finally, we demonstrate additional advantages of implementing the modified CCP constraint.
•Models Chemical Biological Nuclear Radiological Response Enterprise coverage.•Identifies unit moves to cover 99.47% of the continental U.S. population.•Explores non-inferior solutions with comparable coverage and fewer unit moves.•Examines sensitivity of solutions to selected parametric assumptions.
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The projected and current adoption rates of electric vehicles are increasing. Electric vehicles need to be recharged continually over time, and the energy required to ensure that is immense and ...growing. Given that existing infrastructure is insufficient to supply the projected energy needs, models are necessary to help decision makers plan for how to best expand the power grid to meet this need. A successful power grid expansion is one that enables charging stations to service the electric vehicle community. Thus, plans for power expansion need to be coordinated between the power grid and charging station investors. In this paper, we present a two-stage stochastic programming approach that can be used to determine a power grid expansion plan that supports the energy needs, or load, from an uncertain set of electric vehicles geographically dispersed over a region. The first stage determines where to expand the power grid, and the second stage determines where to locate charging stations. The key link between the first and second stage decisions is that charging stations can only be located in areas with sufficient power supply enabled by an expanded power grid. To solve the model, we utilize a hybrid approach that combines Sample Average Approximation and an enhanced Progressive Hedging algorithm. We enhance the Progressive hedging algorithm by applying rolling horizon and variable fixing techniques. To validate the proposed model and gain key insights, we perform computational experiments using realistic data representing the Washington, DC area. Our computational results indicate the robustness of the proposed algorithm while providing a number of managerial insights to the decision makers.
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► We examine a novel class of optimization problems that model restoring infrastructure systems. ► These new problems integrate network design and scheduling decisions. ► We utilize residual network ...optimality conditions in creating a dispatching rule. ► Our models and algorithms are tested on realistic case studies of infrastructure systems. ► The dispatching rule can be utilized in real-time restoration planning activities.
We consider the problem of restoring services provided by infrastructure systems after an extreme event disrupts them. This research proposes a novel integrated network design and scheduling problem that models these restoration efforts. In this problem, work groups must be allocated to build nodes and arcs into a network in order to maximize the cumulative weighted flow in the network over a horizon. We develop a novel heuristic dispatching rule that selects the next set of tasks to be processed by the work groups. We further propose families of valid inequalities for an integer programming formulation of the problem, one of which specifically links the network design and scheduling decisions. Our methods are tested on realistic data sets representing the infrastructure systems of New Hanover County, North Carolina in the United States and lower Manhattan in New York City. These results indicate that our methods can be used in both real-time restoration activities and long-term scenario planning activities. Our models are also applied to explore the effects on the restoration activities of aligning them with the goals of an emergency manager and to benchmark existing restoration procedures.
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Optimizing operations at electric vehicle (EV) battery swap stations is internally motivated by the movement to make transportation cleaner and more efficient. An EV battery swap station allows EV ...owners to quickly exchange their depleted battery for a fully charged battery. We introduce the EV Battery-Swap Station Management Problem (EVB-SSMP), which models battery charging and discharging operations at an EV battery swap station facing nonstationary, stochastic demand for battery swaps, nonstationary prices for charging depleted batteries, and nonstationary prices for discharging fully charged batteries. Discharging through vehicle-to-grid is beneficial for aiding power load balancing. The objective of the EVB-SSMP is to determine the optimal policy for charging and discharging batteries that maximizes expected total profit over a fixed time horizon. The EVB-SSMP is formulated as a finite-horizon, discrete-time Markov decision problem and an optimal policy is found using dynamic programming. We derive structural properties, to include sufficiency conditions that ensure the existence of a monotone optimal policy. Utilizing available demand and electricity pricing data, we design and conduct two main computational experiments to obtain policy insights regarding the management of EV battery swap stations. We compare the optimal policy to two benchmark policies that are easily implementable by swap station managers. Policy insights include the relationship between the minimum battery level and the number of EVs in a local service area, the pricing incentive necessary to encourage effective discharge behavior, and the viability of EV battery swap stations under many conditions.
The online appendix is available at
https://doi.org/10.1287/trsc.2016.0676
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BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Fifty years of research in Networks coincides with 50 years of advances in resilience theory and applications. The purpose of this review is to identify how these two technical communities influenced ...each other in the past and can bolster each other in the future. Advances in resilience theory show that there are at least four ways networks demonstrate resilience: robustness, rebound, extensibility, and adaptability. Research published in Networks and by the broader network optimization community has focused primarily on technical methods for robustness and rebound. We review this literature to organize seminal problems and papers on the ability of networks to manage increasing stressors and return to normal activities after a stressful event. In contrast, the Networks community has made less progress addressing issues for network extensibility and adaptability. Extensibility refers to the ability to stretch current operations to surprising situations and adaptability refers to the ability to sustain operations into the future. We discuss ways to harness existing network optimization methods to study these forms of resilience and outline their limitations. We conclude by providing a research agenda that ensures the Networks community remains central to future advances in resilience while being pragmatic about the limitations of network optimization for achieving this task.
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•A compact interdependent network flow model that has the capability to capture many common interdependence classifications.•The capability to incorporate non-uniform dependency across the network ...layers.•The capability to incorporate dependency within a single layer.•A solution method that exploits a high degree of integrality in LP relaxation solutions.
We consider the problem of optimizing the operations of interdependent infrastructure systems in a resource-constrained environment. In this problem, decisions consist of determining the set of components that will be operational and how services from different infrastructures will be delivered to operational components. We propose an interdependent multi-layered network flow (IMN) model to solve this problem. In this model, interdependent infrastructures are represented by networks and movement of commodities or services by flows. We seek to maximize the reward obtained from operational components minus the cost of routing flows. We show that IMN is NP-hard in the strong sense even in the case of a single-layer network. We further propose families of valid inequalities for the integer programming formulation of IMN, which are then utilized to develop a solution approach for the problem. The solution approach is tested on synthesized data sets of interdependent infrastructure systems. Our computational results demonstrate that our solution approach can obtain high-quality solutions in less computational time when compared to the mixed integer programming (MIP) formulation solved with standard software for most of the instances. We also show the capability of IMN over the previous models in the literature on interdependent infrastructures’ operations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Introduce the concept of flexible release dates for scheduling problems.•Include scheduling elements into integrated network design and scheduling problems.•Develop exact and heuristic solution ...methods for online and offline problems.•Perform computational tests for infrastructure restoration after an extreme event.
We consider scheduling problems with the new concept of flexible release dates under an online optimization framework. A flexible release date is one where the traditional release date of a specific operation can be moved earlier in time, specifically to the completion time of an associated supplementary operation. In this context, we examine two classes of parallel identical machines: those that perform supplementary operations to alter release dates and those that perform installation operations to change the network characteristics. We further consider multi-function machines that can perform both supplementary and installation operations. The release date of an operation is often determined by events outside the knowledge of the decision-maker. Therefore, we consider scheduling problems in an online setting to model the lack of- and evolution of information about the release dates of tasks. Motivated by infrastructure restoration after an extreme event, we consider flexible release dates for an integrated network design and scheduling problem that seeks to improve the performance of a network over time by selecting and scheduling operations that will change the network characteristics. To solve these problems, we propose heuristic dispatching rules whose solutions are benchmarked against the solutions of a mixed integer programming formulation. Using a realistic infrastructure network, we perform computational tests; the results of these tests demonstrate the ability of the dispatching rule to find high-quality solutions in real time and quickly adapt to the arrival of new information. From the analysis of these results, we deduce policy insights regarding the role of flexible release dates and the machine fleet configuration.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
We consider a deterministic integer programming model for determining the optimal operations of multiple plug-in hybrid electric vehicle (PHEV) battery exchange stations over time. The operations ...include the number of batteries to charge, discharge, and exchange at each point in time over a set time horizon. We allow discharging of batteries back to the power grid, through vehicle-to-grid technology. We incorporate the exchange station's dependence on the power network, transportation network, and other exchange stations. The charging and discharging at these exchange stations lead to a greater amount of variability which creates a less predictable and flat power generation curve. We introduce and test three policies to smooth the power generation curve by balancing its load. Further, tests are conducted evaluating these policies while factoring wind energy into the power generation curve. These computational tests use realistic data and analysis of the results suggest general operating procedures for exchange stations and evaluate the effectiveness of these power flattening policies.
•Model the operations of plug-in hybrid electric vehicle battery exchange stations.•Determine the optimal and general charging, discharging, and exchange operations.•Conclude that forced customer service levels are unnecessary with proper pricing.•Examine policies to reduce variability in power generation from PHEVs and wind.•Observe that strict constraints on exchange stations best reduce variability.
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