This paper explores Community-Based Trip Sharing which uses the structure of communities and commuting patterns to optimize car or ride sharing for urban communities. It introduces the Commuting Trip Sharing Problem (CTSP) and proposes an optimization approach to maximize trip sharing. The optimization method, which exploits trip clustering, shareability graphs, and mixed-integer programming, is applied to a dataset of 9000 daily commuting trips from a mid-size city. Experimental results show that community-based trip sharing reduces daily car usage by up to 44%, thus producing significant environmental and traffic benefits and reducing parking pressure. The results also indicate that daily flexibility in pairing cars and passengers has significant impact on the benefits of the approach, revealing new insights on commuting patterns and trip sharing.

Social influence has been shown to create a Matthew effect in online markets, increasing inequalities and leading to “winner-take-all” phenomena. Matthew effects have been observed for numerous market policies, including when the products are presented to consumers by popularity or quality. This paper studies how to reduce Matthew effects, while keeping markets efficient and predictable when social influence is used. It presents a market strategy based on randomization and segmentation, that ensures that the best products, if they are close in quality, will have reasonably close market shares. The benefits of this market strategy is justified both theoretically and empirically and the loss in market efficiency is shown to be acceptable.

Conservation is an ethic of sustainable use of natural resources which focuses on the preservation of biodiversity, i.e., the degree of variation of life. Conservation planning seeks to reach this goal by means of deliberate actions, aimed at the protection (or restoration) of biodiversity features. In this paper we present an intelligent system to assist conservation managers in planning habitat restoration actions, with focus on the activities to be carried out in the islands of the Great Barrier Reef (QLD) and the Pilbara (WA) regions of Australia. In particular, we propose a constrained optimisation formulation of the habitat restoration planning (HRP) problem, capturing aspects such as population dynamics and uncertainty. We show that the HRP is NP-hard, and develop a constraint programming (CP) model and a large neighbourhood search (LNS) procedure to generate activity plans under budgeting constraints.

With rapid population growth and urbanization, emergency services in various cities around the world worry that the current transportation infrastructure is no longer adequate for large-scale evacuations. This paper considers how to mitigate this issue through infrastructure upgrades, such as the additions of lanes to road segments and the raising of bridges and roads. The paper proposes a MIP model for deciding the most effective infrastructure upgrades as well as a Benders decomposition approach where the master problem jointly plans the upgrades and evacuation routes and the subproblem schedules the evacuation itself. Experimental results demonstrate the practicability of the approach on a real case study, filling a significant need for emergencies services.

This paper considers prescriptive evacuation planning for a region threatened by a natural disaster such a flood, a wildfire, or a hurricane. It proposes a Benders decomposition that generalizes the two-stage approach proposed in earlier work for convergent evacuation plans. Experimental results show that Benders decomposition provides significant improvements in solution quality in reasonable time: It finds provably optimal solutions to scenarios considered in prior work, closing these instances, and increases the number of evacuees by 10 to 15% on average on more complex flood scenarios.

Evacuation planning is a critical aspect of disaster preparedness and response to minimize the number of people exposed to a threat. Controlled evacuations aim at managing the flow of evacuees as efficiently as possible and have been shown to produce significant benefits compared to self-evacuations. However, existing approaches do not capture the delays introduced by diverging and crossing evacuation routes, although evidence from actual evacuations highlights that these can lead to significant congestion. This paper introduces the concept of convergent evacuation plans to tackle this issue. It presents a MIP model to obtain optimal convergent evacuation plans which, unfortunately, does not scale to realistic instances. The paper then proposes a two-stage approach that separates the route design and the evacuation scheduling. Experimental results on a real case study show that the two-stage approach produces better primal bounds than the MIP model and is two orders of magnitude faster; It also produces dual bounds stronger than the linear relaxation of the MIP model. Finally, simulations of the evacuation demonstrate that convergent evacuation plans outperform existing approaches for realistic driver behaviors.

Our society is organized around a number of (interdependent) global systems. Logistic and supply chains, health services, energy networks, financial markets, computer networks, and cities are just a few examples of such global, complex systems. These global systems are socio-technical and involve interactions between complex infrastructures, man-made processes, natural phenomena, multiple stakeholders, and human behavior. For the first time in the history of manking, we have access to data sets of unprecedented scale and accuracy about these infrastructures, processes, natural phenomena, and human behaviors. In addition, progress in high-performancing computing, data mining, machine learning, and decision support opens the possibility of looking at these problems more holistically, capturing many of these aspects simultaneously. This paper addresses emergent architectures enabling controlling, predicting and reaoning on these systems.

We address the problem of power system restoration after a significant blackout. Prior work focus on optimization methods for finding high-quality restoration plans. Optimal solutions consist in a sequence of grid repairs and corresponding steady states. However, such approaches lack formal guarantees on the transient stability of restoration actions, a key property to avoid additional grid damage and cascading failures. In this paper, we show how to integrate transient stability in the optimization procedure by capturing the rotor dynamics of power generators. Our approach reasons about the differential equations describing the dynamics and their underlying transient states. The key contribution lies in modeling and solving optimization problems that return stable generators dispatch minimizing the difference with respect to steady states solutions. Computational efficiency is increased using preprocessing procedures along with traditional reduction techniques. Experimental results on existing benchmarks confirm the feasibility of the new approach.

NICTA is Australia's Information and Communications Technology (ICT) Centre of Excellence. It is the largest organization in Australia dedicated to ICT research. While it has close links with local universities, it is in fact an independent but not-for-profit company in the business of doing research, commercializing that research and training PhD students to do that research. Much of the work taking place at NICTA involves various topics in artificial intelligence. In this article, we survey some of the AI work being undertaken at NICTA.

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.