Bilevel Optimization for On-Demand Multimodal Transit Systems

This study explores the design of an On-Demand Multimodal Transit System (ODMTS) that includes segmented mode switching models that decide whether potential riders adopt the new ODMTS or stay with their personal vehicles. It is motivated by the desire of transit agencies to design their network by taking into account both existing and latent demand, as quality of service improves. The paper presents a bilevel optimization where the leader problem designs the network and each rider has a follower problem to decide her best route through the ODMTS. The bilevel model is solved by a decomposition algorithm that combines traditional Benders cuts with combinatorial cuts to ensure the consistency of mode choices by the leader and follower problems. The approach is evaluated on a case study using historical data from Ann Arbor, Michigan, and a user choice model based on the income levels of the potential transit riders

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

This paper presents a response model for the aftermath of a Mass-Casualty Incident (MCI) that can be used to provide operational guidance for regional emergency planning as well as to evaluate strategic preparedness plans. A mixed integer programming (MIP) formulation is proposed for the combined ambulance dispatching, patient-to-hospital assignment, and treatment ordering problem. T he goal is to allocate effectively the limited resources during the response so as to improve patient outcomes, while the objectives are to minimize the overall response time and the total flow time required to treat all patients, in a hierarchical fashion. The model is solved via exact and MIP-based heuristic solution methods. The applicability of the model and the performance of the new methods are challenged on realistic MCI scenarios. We consider the hypothetical case of a terror attack at the New York Stock Exchange in Lower Manhattan with up to 150 trauma patients. We quantify the impact of capacity-based bottlenecks for both ambulances and available hospital beds. We also explore the trade-off between accessing remote hospitals for demand smoothing versus reduced ambulance transportation times

Alternate risk measures for emergency medical service system design

The stochastic nature of emergency service requests and the unavailability of emergency vehicles when requested to serve demands are critical issues in constructing valid models representing real life emergency medical service (EMS) systems. We consider an EMS system design problem with stochastic demand and locate the emergency response facilities and vehicles in order to ensure target levels of coverage, which are quantified using risk measures on random unmet demand. The target service levels for each demand site and also for the entire service area are specified. In order to increase the possibility of representing a wider range of risk preferences we develop two stochastic optimization models involving alternate risk measures. Our first model includes integrated chance constraints (ICCs), whereas the second one incorporates ICCs and a second order dominance constraint. We propose solution methods for our stochastic optimization problems and present extensive numerical results demonstrating their computational effectiveness

Integrating Academic and Everyday Learning Through Technology: Issues and Challenges for Researchers, Policy Makers and Practitioners

This paper builds on work undertaken over a number of years by a group of international researchers with an interest in the potential of connecting academic and everyday practices and knowledge. Drawing extensively on literature and our own work, we first discuss the challenges around defining informal learning, concluding that learning is multidimensional and has varying combinations of formal and informal attributes. We then highlight the potential of technology for integrating formal and informal learning attributes and briefly provide some exemplars of good practice. We then discuss in depth the challenges and issues of this approach to supporting learning from the perspective of pedagogy, research, policy and technology. We also provide some recommendations of how these issues may be addressed. We argue that for the learner, integration of formal and informal learning attributes should be an empowering process, enabling the learner to be self-directed, creative and innovative, taking learning to a deeper level. Given the complexity of the learning ecosystem, this demands support from the teacher but also awareness and understanding from others such as parents, family, friends and community members. We present a conceptual model of such an ecosystem to help develop further discussions within and between communities of researchers, policy makers and practitioners

Fare inspection patrols scheduling in transit systems using a Stackelberg game approach

This study analyzes the scheduling of unpredictable fare inspections in proof-of-payment transit systems, where the transit operator chooses a collection of patrol paths (one for each patrol) every day with some probability in order to avoid any regularity that could be exploited by opportunistic passengers. We use a Stackelberg game approach to represent the hierarchical decision-making process between the transit operator and opportunistic passengers, whose decision on whether to evade the fare depends on the inspection probabilities set by the transit operator. Unlike previous work, we use an exact formulation of the inspection probabilities that allows us to develop new heuristics for the fare inspection scheduling problem, and to assess their solution quality in terms of their optimality gap

Revenue management and demand side management in the energy field

International audiencePricing models for demand side management methods are traditional used to control electricity demand which became quite irregular recently and resulted in inefficiency in supply. We propose bilevel models to explore the relation and between energy suppliers and customers who are connected to a smart grid. This approach enables to integrate customer response into the optimization process of supplier who aims to maximize revenue or minimize capacity requirements. Numerical results are given