We present a cutting plane algorithm for two-stage stochastic linear programs with recourse. Motivated by Benders' decomposition, our method uses randomly generated observations of random variables to construct statistical estimates of supports of the objective function. In general, the resulting piecewise linear approximations do not agree with the objective function in finite time. However, certain subsequences of the estimated supports are shown to accumulate at supports of the objective function, with probability one. From this, we establish the convergence of the algorithm under relatively mild assumptions.

This paper presents a general purpose algorithm for real-time traffic control at an intersection. Our methodology, based on dynamic programming, allows optimization of a variety of performance indices such as delay, stops and queue lengths. Furthermore, optimal phase sequencing is a direct by-product of this new approach. These features make the new methodology a powerful tool for intersection control. We demonstrate the usefulness of the approach by a simulation experiment in which our intersection control algorithm is interfaced with a well established simulation package called TRAF-NETSIM. Our study compares the controlled optimization of phases methodology with fully-actuated as well as semi-actuated control. We show that consistent reductions in delay may be possible by adopting the new algorithm.

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