Progressive damage assessment and network recovery after massive failures

After a massive scale failure, the assessment of damages to communication networks requires local interventions and remote monitoring. While previous works on network recovery require complete knowledge of damage extent, we address the problem of damage assessment and critical service restoration in a joint manner. We propose a polynomial algorithm called Centrality based Damage Assessment and Recovery (CeDAR) which performs a joint activity of failure monitoring and restoration of network components. CeDAR works under limited availability of recovery resources and optimizes service recovery over time. We modified two existing approaches to the problem of network recovery to make them also able to exploit incremental knowledge of the failure extent. Through simulations we show that CeDAR outperforms the previous approaches in terms of recovery resource utilization and accumulative flow over time of the critical service

On critical service recovery after massive network failures

This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large-scale disruption. We give a formulation of the problem as a mixed integer linear programming and show that it is NP-hard. We propose a polynomial time heuristic, called iterative split and prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. ISP's decisions are guided by the use of a new notion of demand-based centrality of nodes. We performed extensive simulations by varying the topologies, the demand intensity, the number of critical services, and the disruption model. Compared with several greedy approaches, ISP performs better in terms of total cost of repaired components, and does not result in any demand loss. It performs very close to the optimal when the demand is low with respect to the supply network capacities, thanks to the ability of the algorithm to maximize sharing of repaired resources

Optimal Cutting of Lumber and Particleboards into Dimension Parts: Some Algorithms and Solution Procedures

This paper describes some algorithms and procedures that can be used for determining the optimal cutting of lumber or composite boards into dimension or furniture parts. Methodologies are described for various production scenarios: 1) cutting when the direction of the grain matters (e.g., lumber), 2) cutting composite boards where grain direction does not matter, 3) rip-first cutting, 4) crosscut-first cutting, and 5) a combination of rip-first and crosscut-first. An algorithm for optimizing the cutting of all lumber types while at the same time satisfying a given order of dimension parts is also described. The models can be used interactively for comprehensive optimization of cutting a mix of lumber as shown by the two-stage decision model, or the double knapsack algorithms could be used as stand alone models for optimizing the cutting of individual lumber