On the value of optimal myopic solutions for dynamic routing and scheduling problems in the presence of user noncompliance

The most common approach for modeling and solving routing and scheduling pr oblems in, a dynamic setting is to solve, as close to optimal as possible, a series of deterministic, myopic models. The argument is most often made t hat, if the data changes then we should simply reoptimize. We use the setti ng of the load matching problem that arises in truckload trucking to compar e the value of optimal myopic solutions versus varying degrees of greedy, s uboptimal myopic solutions in the presence of three forms of uncertainty: c ustomer demands, travel times, and of particular interest, user noncomplian ce. A simulation environment is used to test different dispatching strategi es under varying Levels of system dynamism. An important issue we consider is that of user noncompliance, which is the effect of optimizing when users do not adopt all of the recommendations of the model. Our results show tha t (myopic) optimal solutions only slightly outperform greedy solutions unde r relatively high levels of uncertainty, and that a particular suboptimal s olution actually outperforms optimal solutions under a wide range of condit ions.