This paper introduces an approach to dynamic vehicle routing problems with time windows (VRPTW) and discusses the real world variation of vehicles routing problem, which involves scheduling the transport of containers for collection, delivery and positioning. The variation of VRPTW studied is a containers transport problem with triangulation (TP) where multi-objective optimization and rescheduling need to be taken into account. The importance of efficient multi-objective optimization and rescheduling are illustrated and possible metrics to support scheduling and the advantages of suing a Pareto optimization approach are explained. A hybrid CSP/GA framework including local search and Pareto optimization is introduced as the optimization algorithm.

The combination of Petri nets (PNs) as an analysis tool for discrete-event dynamic systems and artificial intelligence heuristic search has been shown to be a promising way to solve flexible manufacturing systems (FMS) scheduling problems. However, the NP hard nature of the problem obscures the PN capability of reasoning about the behavior of the system. In this paper, two techniques to alleviate this drawback are presented: a systematic method to avoid the generation of futile paths within the search graph and a novel hybrid stage-search algorithm. The new algorithm is based on the application of A* guided by a PN-based heuristic within a limited local search frame. An optimization policy is applied to maintain, under evaluation, only the most promising paths. For each system state, the algorithm is able to decide whether an enabled operation should be applied and to maintain this decision until new information forces reconsideration. This eliminates permutation paths and useless scheduling sequences. Experimental results show that the algorithm's cost does not grow exponentially with the size of the problem. Comparison with previous work is given to show the superiority of our approach and the potential of PN-based heuristic search.

The combination of Petri net (PN) and AI to solve flexible manufacturing systems (FMS) scheduling problems has been proven to be a promising approach. However, the NP-hard nature of the problem prevents the PN capability of reasoning about the behavior of a practical system. To overcome this drawback, we propose two techniques: a systematic method to avoid the generation of unpromising paths within the search graph, and a stage-search based algorithm. The algorithm developed is based on the application of the A* algorithm and the PN-based heuristics. The search is performed within a limited local search window where an optimization policy is applied to evaluate the most promising paths. For each state, the algorithm is able to decide whether an enabled operation is applied, and to maintain the decision until new system information makes the reconsideration meaningful. Comparison with previous work is presented to show the superiority of the proposed approach.

The basic algorithms involved in reason maintenance in the standard ATMS is known to have a computational complexity that is exponential in the worst case. Yet, also in average‐case problem solving, the ATMS often lays claim to a major part of the computational effort spent by a problem solver/ATMS system. In this paper, we argue that within the limits of the worst‐case computational complexity, it is possible to improve on the average‐case complexity of reason maintenance and query processing by eliminating computation that is of no relevance to the problem solver's performance. To this purpose, we present a set of algorithms designed to control the effort spent by the ATMS on label updating. The basic idea underlying these algorithms is that of lazy evaluation: labels are not automatically maintained on all datums but are computed only when needed (either directly or indirectly) by the problem solver. The algorithms have been implemented in the LazyRMS with which we have experimented in the context of model‐based diagnosis; our experiments show a substantial saving in the computational effort spent on reason maintenance.