A two-stage model for a day-ahead paratransit planning problem

We consider a dynamic planning problem for paratransit transportation. The focus is on a decision to take one day ahead: which requests to serve with own vehicles, and which requests to subcontract to taxis? We call this problem the day-ahead paratransit planning problem. The developed model is a non-standard two-stage integer recourse model. Both stages consist of two consecutive optimization problems: the clustering of requests into routes, and the assignment of these routes to vehicles. To solve this model, a genetic algorithm approach is used. Computational results are presented for randomly generated data sets

A dynamic day-ahead paratransit planning problem

Abstract We consider a dynamic planning problem for the transport of elderly and disabled people. The focus is on a decision to make one day ahead: which requests to serve with own vehicles, and which ones to assign to subcontractors, under uncertainty of late requests which are gradually revealed during the day of operation. We call this problem the Dynamic Day-ahead Paratransit Planning problem. The developed model is a nonstandard two-stage recourse model in which ideas from stochastic programming and online optimization are combined: in the first stage clustered requests are assigned to vehicles, and in the dynamic second-stage problem an event-driven approach is used to cluster the late requests once they are revealed and subsequently assign them to vehicles. A genetic algorithm is used to solve the model. Computational results are presented for randomly generated data sets. Furthermore, a comparison is made to a similar problem we studied earlier in which the simplifying but unrealistic assumption has been made that all late requests are revealed at the beginning of the day of operation.

Fast heuristics for a dynamic paratransit problem

In a previous paper we developed a non-standard two-stage recourse model for the dynamic day-ahead paratransit planning problem. Two heuristics, which are frequently applied in the recourse model, contain many details which leads to large CPU times to solve instances of relatively small size. In this paper we simplify both heuristics to decrease CPU time considerably while maintaining the quality of the obtained solutions as much as possible. Numerical experiments on (semi-)realistic instances, inspired by practice, show that our recourse model with fast heuristics provides acceptable solutions within reasonable time.

A dynamic day-ahead paratransit planning problem

Abstract We consider a dynamic planning problem for the transport of elderly and disabled people. The focus is on a decision to make one day ahead: which requests to serve with own vehicles, and which ones to assign to subcontractors, under uncertainty of late requests which are gradually revealed during the day of operation. We call this problem the Dynamic Day-ahead Paratransit Planning problem. The developed model is a nonstandard two-stage recourse model in which ideas from stochastic programming and online optimization are combined: in the first stage clustered requests are assigned to vehicles, and in the dynamic second-stage problem an event-driven approach is used to cluster the late requests once they are revealed and subsequently assign them to vehicles. A genetic algorithm is used to solve the model. Computational results are presented for randomly generated data sets. Furthermore, a comparison is made to a similar problem we studied earlier in which the simplifying but unrealistic assumption has been made that all late requests are revealed at the beginning of the day of operation.