Logic-based MultiObjective Optimization for Restoration Planning

Gong, Jun; Lee, Earl E.; Mitchell, John E.; Wallace, William A.

doi:10.1007/978-0-387-88617-6_11

Cited by 20 publications

(8 citation statements)

References 13 publications

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“…References (Lee, Mitchell, and Wallace 2007;Gong et 2009;Cavdaroglu et al 2011) are the closest related work and a detailed review. Our approach fundamentally differs from these earlier studies: It uses the more accurate LDCM for power systems, it scales to large instances, it models cyclic interdependencies.…”

Section: Related Workmentioning

confidence: 99%

Last-Mile Restoration for Multiple Interdependent Infrastructures

Coffrin

Hentenryck

Bent

2021

AAAI

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This paper considers the restoration of multiple interdependent infrastructures after a man-made or natural disaster. Modern infrastructures feature complex cyclic interdependencies and require a holistic restoration process. This paper presents the first scalable approach for the last-mile restoration of the joint electrical power and gas infrastructures. It builds on an earlier three-stage decomposition for restoring the power network that decouples the restoration ordering and the routing aspects. The key contributions of the paper are (1) mixed-integer programming models for finding a minimal restoration set and a restoration ordering and (2) a randomized adaptive decomposition to obtain high-quality solutions within the required time constraints. The approach is validated on a large selection of benchmarks based on the United States infrastructures and state-of-the-art weather and fragility simulation tools. The results show significant improvements over current field practices.

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Section: Related Workmentioning

confidence: 99%

Last-Mile Restoration for Multiple Interdependent Infrastructures

Coffrin

Hentenryck

Bent

2021

AAAI

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“…different variants of the problem have been employed, which are solved using either exact methods [20,21] or heuristic and meta-heuristic approaches [22,23]. In particular, some studies [9,16,24] focus on the restoration scheduling of several networks that are interdependent physically or operationally.…”

Section: Resource Allocationmentioning

confidence: 99%

Deep Learning-based Resource Allocation for Infrastructure Resilience

Alemzadeh¹,

Talebiyan²,

Talebi³

et al. 2020

Preprint

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From an optimization point of view, resource allocation is one of the cornerstones of research for addressing limiting factors commonly arising in applications such as power outages and traffic jams. In this paper, we take a data-driven approach to estimate an optimal nodal restoration sequence for immediate recovery of the infrastructure networks after natural disasters such as earthquakes. We generate data from td-INDP, a high-fidelity simulator of optimal restoration strategies for interdependent networks, and employ deep neural networks to approximate those strategies. Despite the fact that the underlying problem is NP-complete, the restoration sequences obtained by our method are observed to be nearly optimal. In addition, by training multiple models-the so-called estimators-for a variety of resource availability levels, our proposed method balances a trade-off between resource utilization and restoration time. Decision-makers can use our trained models to allocate resources more efficiently after contingencies, and in turn, improve the community resilience Besides their predictive power, such trained estimators unravel the effect of interdependencies among different nodal functionalities in the restoration strategies. We showcase our methodology by the real-world interdependent infrastructure of Shelby County, TN.

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“…In the transportation logistics domain, they include food distribution [110], bicycle sharing [73,74], lock scheduling [123], and supply chain scheduling [115]. Other applications are project scheduling [70], robust call center scheduling [28], task scheduling for satellites [129], course timetabling [19], wind turbine maintenance scheduling [42], queuing design and control [113,114], service restoration planning for infrastructure networks [50], and sports scheduling [23,94,95,120,121].…”

Section: Other Scheduling and Logistics Problemsmentioning

confidence: 99%

Logic-Based Benders Decomposition for Large-Scale Optimization

Hooker

2019

Springer Optimization and Its Applications

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Logic-based Benders decomposition (LBBD) is a substantial generalization of classical Benders decomposition that, in principle, allows the subproblem to be any optimization problem rather than specifically a linear or nonlinear programming problem. It is amenable to a wide variety large-scale problems that decouple or otherwise simplify when certain decision variables are fixed. This chapter presents the basic theory of LBBD and explains how classical Benders decomposition is a special case. It also describes branch and check, a variant of LBBD that solves the master problem only once. It illustrates in detail how Benders cuts and subproblem relaxations can be developed for some planning and scheduling problems. It then describes the role of LBBD in three large-scale case studies. The chapter concludes with an extensive survey of the LBBD literature, organized by problem domain, to allow the reader to explore how Benders cuts have been developed for a wide range of applications.

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Logic-based MultiObjective Optimization for Restoration Planning

Cited by 20 publications

References 13 publications

Last-Mile Restoration for Multiple Interdependent Infrastructures

Last-Mile Restoration for Multiple Interdependent Infrastructures

Deep Learning-based Resource Allocation for Infrastructure Resilience

Logic-Based Benders Decomposition for Large-Scale Optimization

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