In this work we consider route planning for valuable delivery in an urban environment under the threat of ambushes in which a vehicle, starting from a depot, has to serve a set of predetermined destinations during the day. We provide a method to plan for hardly predictable multi-destination routing extending a minmax flow-based model available for single-destination cases. We then formulate the process of selecting a visiting order as a game to obtain a mixed routing strategy. We analyse the application of the method to a set of simulation scenarios and compare the mixed routing strategy against the best routing. Finally we develop further the methodology introducing a second-level optimization model which reduces the overall risk associated to the proposed mixed routing strategy.2

Planning for the future is inherently risky. In most systems, exogenous driving forces affect any strategy's performance. Uncertainty about the state of those driving forces requires strategies that perform well in the face of a range of possible, even improbable future conditions. This study formalizes the relationship of different methods proposed in the literature for rigorously exploring possible futures and then develops and applies the computational technique of scenario discovery to the policy option of a subsidy for low-income households in downtown Lisbon. The work demonstrates one way in which urban models can be applied to identify robust urban development strategies. Using the UrbanSim model, we offer the first known example of applying computational scenario-discovery techniques to the urban realm. We construct scenarios from combinations of values for presumed exogenous variablespopulation growth rate, employment growth rate, gas prices, and construction costs -using a Latin Hypercube Sample (LHS) experimental design. We then data mine the resulting alternative futures to identify scenarios in which an example policy fails to achieve its goals. This demonstration of concept aims to lead to new practical application of integrated urban models in a way that quantitatively tests the strategic robustness of urban interventions.