Meta-Heuristics for a Class of Demand-Responsive Transit Systems

The demand-adaptive systems studied in this paper attempt to offer demand-responsive services within the framework of traditional scheduled bus transportation: Users call to request service between two given points and, in so doing, induce detours in the vehicle routes; at the same time, though, a given set of compulsory stops is always served according to a predefined schedule, regardless of the current set of active requests. The model developed to select requests and determine the routing of the vehicle yields a difficult formulation but with a special structure that may be used to develop efficient algorithms. In this paper, we develop, test, and compare several solution strategies for the single line-single vehicle problem that belong to two general meta-heuristic classes, memory-enhanced greedy randomized multistart constructive procedures, and tabu search methods. Hybrid meta-heuristics combining the two methods are also analyzed.