Understanding stakeholder participation in post-disaster recovery (case study: Nagapattinam, India)

Stakeholder participation is widely acknowledged as a critical component of post-disaster recovery because it helps create a shared understanding of local hazard risk and vulnerability, improves recovery and mitigation decision efficacy, and builds social capital and local resilience to future disasters. But approaches commonly used to facilitate participation and empower local communities depend on lengthy consensus-building processes which is not conducive to time-constrained post-disaster recovery. Moreover, these approaches are often criticized for being overly technocratic and ignoring existing community power and trust structures. Therefore, there is a need for more nuanced, analytical and applied research on stakeholder participation in planning for post-disaster recovery. This research examines participatory behavior of three stakeholder groups (government agencies, non-local non-government organizations, local community-based organizations) in three coastal village communities of Nagapattinam (India) that were recovering from the 2004 Indian Ocean tsunami. The study found eight different forms of participation and non-participation in the case study communities, ranging from 'transformative' participation to 'marginalized' non-participation. These forms of participation and non-participatory behavior emanated from the negotiation of four factors, namely stakeholder power, legitimacy, trust, and urgency for action. The study also found that the time constraints and changing conditions of recovery pose particular challenges for how these factors operated on the ground and over the course of recovery. Finally, the study uses these insights to suggest four strategies for recovery managers to use in the short- and long-term to facilitate more effective stakeholder participation in post-disaster recovery.

Scheduling of track inspection and maintenance activities in railroad networks

The U.S. railroad companies spend billions of dollars every year on railroad track maintenance in order to ensure safety and operational efficiency of their railroad networks. Besides maintenance costs, other costs such as train accident costs, train and shipment delay costs and rolling stock maintenance costs are also closely related to track maintenance activities. Optimizing the track maintenance process on the extensive railroad networks is a very complex problem with major cost implications. Currently, the decision making process for track maintenance planning is largely manual and primarily relies on the knowledge and judgment of experts. There is considerable potential to improve the process by using operations research techniques to develop solutions to the optimization problems on track maintenance. In this dissertation study, we propose a range of mathematical models and solution algorithms for three network-level scheduling problems on track maintenance: track inspection scheduling problem (TISP), production team scheduling problem (PTSP) and job-to-project clustering problem (JTPCP). TISP involves a set of inspection teams which travel over the railroad network to identify track defects. It is a large-scale routing and scheduling problem where thousands of tasks are to be scheduled subject to many difficult side constraints such as periodicity constraints and discrete working time constraints. A vehicle routing problem formulation was proposed for TISP, and a customized heuristic algorithm was developed to solve the model. The algorithm iteratively applies a constructive heuristic and a local search algorithm in an incremental scheduling horizon framework. The proposed model and algorithm have been adopted by a Class I railroad in its decision making process. Real-world case studies show the proposed approach outperforms the manual approach in short-term scheduling and can be used to conduct long-term what-if analyses to yield managerial insights. PTSP schedules capital track maintenance projects, which are the largest track maintenance activities and account for the majority of railroad capital spending. A time-space network model was proposed to formulate PTSP. More than ten types of side constraints were considered in the model, including very complex constraints such as mutual exclusion constraints and consecution constraints. A multiple neighborhood search algorithm, including a decomposition and restriction search and a block-interchange search, was developed to solve the model. Various performance enhancement techniques, such as data reduction, augmented cost function and subproblem prioritization, were developed to improve the algorithm. The proposed approach has been adopted by a Class I railroad for two years. Our numerical results show the model solutions are able to satisfy all hard constraints and most soft constraints. Compared with the existing manual procedure, the proposed approach is able to bring significant cost savings and operational efficiency improvement. JTPCP is an intermediate problem between TISP and PTSP. It focuses on clustering thousands of capital track maintenance jobs (based on the defects identified in track inspection) into projects so that the projects can be scheduled in PTSP. A vehicle routing problem based model and a multiple-step heuristic algorithm were developed to solve this problem. Various side constraints such as mutual exclusion constraints and rounding constraints were considered. The proposed approach has been applied in practice and has shown good performance in both solution quality and efficiency.