Work crew routing problem for infrastructure network restoration

This paper introduces a synchronized routing problem for planning and scheduling restorative efforts for infrastructure networks in the aftermath of a disruptive event. In this problem, a set of restoration crews are dispatched from depots to a road network to restore the disrupted infrastructure network. Two mathematical formulations are presented to scheduling and sequencing disrupted network components to restoration crews and route the crews towards disrupted components to maximize network resilience progress in any given time horizon. In the first formulation, the number of restoration crews assigned to each disrupted component, the arrival time of each assigned crew to each disrupted component and consequently the restoration rate associated with each disrupted component are considered as variables to increase the flexibility of the model in the presence of different disruptive events. Along with the contributions applies in the first formulation, in the second formulation, each disrupted component can be partially active during its restoration process. To find the coordinated routes, we propose a relaxed mixed integer program as well as a set of valid inequalities which relates the planning and scheduling efforts to decision makers policies. The integration of the relaxed formulation and valid inequalities results in a lower bound for the original formulations. We further introduce a feasibility algorithm to derive a strong initial solution for the routing restorative capacity problem. Computational results on gas, water, and electric power infrastructure network instances from Shelby County, TN data, demonstrates both the effectiveness of the proposed model formulation, in solving small to medium scale problems, the strength of the initial solution procedure, especially for large scale problems.

[1]  Junwei Wang,et al.  Data-Driven Resilient Fleet Management for Cloud Asset-enabled Urban Flood Control , 2018, IEEE Transactions on Intelligent Transportation Systems.

[2]  Daniel Bienstock,et al.  Using mixed-integer programming to solve power grid blackout problems , 2007, Discret. Optim..

[3]  Andrea Lodi,et al.  Mathematical programming techniques in water network optimization , 2015, Eur. J. Oper. Res..

[4]  James H. Lambert,et al.  Defining resilience analytics for interdependent cyber-physical-social networks , 2017 .

[5]  Maziar Kasaei,et al.  Arc routing problems to restore connectivity of a road network , 2016 .

[6]  Kash Barker,et al.  Resilience-based network component importance measures , 2013, Reliab. Eng. Syst. Saf..

[7]  Giovanni Sansavini,et al.  A quantitative method for assessing resilience of interdependent infrastructures , 2017, Reliab. Eng. Syst. Saf..

[8]  Elise Miller-Hooks,et al.  Travel time resilience of roadway networks under disaster , 2014 .

[9]  Melih Celik,et al.  The Post-Disaster Debris Clearance Problem Under Incomplete Information , 2015, Oper. Res..

[10]  Linet Özdamar,et al.  Models, solutions and enabling technologies in humanitarian logistics , 2015, Eur. J. Oper. Res..

[11]  B. Obama Presidential Policy Directive 21: Critical Infrastructure Security and Resilience , 2013 .

[12]  Melih Çelik,et al.  Network restoration and recovery in humanitarian operations: Framework, literature review, and research directions , 2016 .

[13]  Maria Paola Scaparra,et al.  A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics , 2014, Comput. Oper. Res..

[14]  Leonardo Dueñas-Osorio,et al.  Interdependent Network Recovery Games , 2020, Risk analysis : an official publication of the Society for Risk Analysis.

[15]  Mehran Mesbahi,et al.  Data-guided control: Clustering, graph products, and decentralized control , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[16]  Yiping Fang,et al.  A Mathematical Framework to Optimize Critical Infrastructure Resilience against Intentional Attacks , 2017, Comput. Aided Civ. Infrastructure Eng..

[17]  John E. Mitchell,et al.  Interdependent network restoration: On the value of information-sharing , 2015, Eur. J. Oper. Res..

[18]  Sang-Hyeok Gang,et al.  Report Card for America's Infrastructure , 2012 .

[19]  Raghav Pant,et al.  Stochastic measures of resilience and their application to container terminals , 2014, Comput. Ind. Eng..

[20]  Wenjun Chris Zhang,et al.  An Integrated Road Construction and Resource Planning Approach to the Evacuation of Victims From Single Source to Multiple Destinations , 2010, IEEE Transactions on Intelligent Transportation Systems.

[21]  Leonardo Dueñas-Osorio,et al.  The Interdependent Network Design Problem for Optimal Infrastructure System Restoration , 2016, Comput. Aided Civ. Infrastructure Eng..

[22]  Zhi-Hua Hu,et al.  Post-disaster debris reverse logistics management under psychological cost minimization , 2013 .

[23]  John E. Mitchell,et al.  Restoring infrastructure systems: An integrated network design and scheduling (INDS) problem , 2012, Eur. J. Oper. Res..

[24]  R. Chris Camphouse,et al.  Infrastructure resilience assessment through control design , 2011, Int. J. Crit. Infrastructures.

[25]  Kash Barker,et al.  Importance measures for inland waterway network resilience , 2014 .

[26]  Mehran Mesbahi,et al.  Efficient Infrastructure Restoration Strategies Using the Recovery Operator , 2017, Comput. Aided Civ. Infrastructure Eng..

[27]  Laura A. Albert,et al.  An integrated network design and scheduling problem for network recovery and emergency response , 2018 .

[28]  Devanandham Henry,et al.  Generic metrics and quantitative approaches for system resilience as a function of time , 2012, Reliab. Eng. Syst. Saf..

[29]  R. Lempert,et al.  Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American west , 2010 .

[30]  Kash Barker,et al.  A review of definitions and measures of system resilience , 2016, Reliab. Eng. Syst. Saf..

[31]  Kash Barker,et al.  Flow-based vulnerability measures for network component importance: Experimentation with preparedness planning , 2016, Reliab. Eng. Syst. Saf..

[32]  Jinliang Ding,et al.  Toward a Resilient Holistic Supply Chain Network System: Concept, Review and Future Direction , 2016, IEEE Systems Journal.

[33]  F. Sibel Salman,et al.  Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity , 2017, Eur. J. Oper. Res..