Activity-based travel scenario analysis with routing problem reoptimization
Activity-based travel scenario analysis and network design using a household activity pattern problem (HAPP) can face significant computational cost and inefficiency. One solution approach, called reoptimization, makes use of an optimal solution of a prior problem instance to find a new solution faster and more accurately. Although the method is generally NP-hard as well, the approximation bound is found to be tighter than a full optimization for several traveling salesman problem variations. To date, however, there have not been any computational studies conducted with the method, nor has there been any meta-heuristics designed with reoptimization in mind, particularly for generalized vehicle routing problems. A generalized, selective household activity routing problem (G-SHARP) is presented as an extension of the HAPP model to include both destination and schedule choice for the purpose of testing reoptimization. Two reoptimization algorithms are proposed: a simple swap heuristic and a new class of evolutionary algorithms designed for reoptimization, dubbed a Genetic Algorithm with Mitochondrial Eve (GAME). The two algorithms are tested against a standard genetic algorithm in a computational experiment involving 100 zones that include 400 potential activities (resulting in a total of 802 nodes per single-traveler household). Five hundred households are synthesized and computationally tested with a base scenario, a scenario where an office land use in one zone is de-zoned, and a scenario where a freeway is added onto the physical network. The results demonstrate the effectiveness of reoptimization heuristics, particularly GAME, and the capability of G-SHARP to capture re-allocations of activities and schedules with respect to spatiotemporal changes.