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The paper introduces a bio-inspired approach to solve the route allocation problem (RAP) in the transportation networks. The approach extends a well-known meta-heuristics algorithm with the real life constraints that are dealt with in the scheduling process (i.e. the uniform distribution of routes diversity for vehicles, the average distance travelled in a month, the driver’s rest between subsequent trips etc.). The paper is focusing on the engineering aspects of employing bio-inspired algorithms (which proved to have near-optimal results for toy-like problems) to a real-life application domain. The approach proved to be capable of preserving the software components (agents) to the complexity and dynamics of the situation when the RAP requires incremental extensions of constraints to reflect the traffic conditions in the transportation network. [ABSTRACT FROM AUTHOR]

We investigate in this paper the recovery of the local volatility surface in a parametric framework similar to that of Coleman, Li and Verma [1]. The quality of a surface is assessed through a functional which is optimized; the specificity of the approach is to separate the optimization on the parametric space (performed with any suitable optimization algorithm) from the computation of the functional where we use an adjoint formulation similar to that of the optimal control; the procedure can thus incorporate information from any derivative contract compatible with the adjoint approach. The procedure was implemented and was shown to perform satisfactory on real-world data. [ABSTRACT FROM AUTHOR]