A dynamic model for road protection against flooding

This paper focuses on the problem of identifying optimal protection strategies to reduce the impact of flooding on a road network. We propose a dynamic mixed-integer programming model that extends the classic concept of road network protection by shifting away from single-arc fortifications to a more general and realistic approach involving protection plans that cover multiple components. We also consider multiple disruption scenarios of varying magnitude. To efficiently solve large problem instances, we introduce a customised GRASP heuristic. Finally, we provide some analysis and insights from a case study of the Hertfordshire road network in the East of England. Results show that optimal protection strategies mainly involve safeguarding against flooding events that are small and likely to occur, whereas implementing higher protection standards are not considered cost-effective

Reliable hub network design: Formulation and solution techniques

In this paper, we investigate the issue of unreliability in hub location planning. A mixed integer nonlinear programming model is formulated for optimally locating p uncapacitated hubs, each of which can fail with a site-specific probability. The objective is to determine the location of hubs and the assignment of demand nodes to hubs in order to minimize expected demand weighted travel cost plus a penalty if all hubs fail. A linear version of the model is developed employing a specialized flow network called a probability lattice to evaluate compound probability terms. A Tabu search algorithm is proposed to find optimal to near optimal solutions for large problem instances. A parallel computing strategy is integrated into the Tabu search process to improve performance. Experimental results carried out on several benchmark instances show the efficiency of our linearized model and heuristic algorithm. Compared to a standard hub median model that disregards the potential for hub failures, our model produces solutions that serve larger numbers of customers and at lower cost per customer

Use of OR in earthquake operations management: A review of the literature and roadmap for future research

To reduce human losses and minimize social and economic disruption caused by large-scale earthquakes, effective planning and operational decisions need to be made by responsible agencies and institutions across all pre- and post-disaster stages. Operations Research (OR), which encompasses a broad array of quantitative and analytical methods for systematic decision making, has garnered a considerable amount of attention in the disaster operations management literature over the past few decades. The purpose of this review is to highlight and discuss main lines of research involving the use of OR techniques applied specifically to earthquakes disasters. As part of our review, we identify existing research gaps and propose a roadmap to guide future work and enhance the real-world applicability of OR to earthquake operations management. We emphasize the need for (i) developing models that are specifically tailored to earthquake operation management, including the need to contend with cascading effects and secondary disasters caused by aftershocks; (ii) greater stakeholder involvement in problem identification and methodological approach to enhance realism and adoption of OR models by practitioners; (iii) more holistic planning frameworks that combine decision making across multiple disaster stages; (iv) integration of OR methods with real- and near real-time information systems, while confronting the problem of dealing with missing and incomplete data; (v) greater use of use of multi-methodology and interdisciplinary approaches, including behavioral OR and Soft OR techniques as well as seismology and earthquake engineering expertise; and (vi) improved data generation defined at appropriate scales and better probability estimation of earthquake scenarios