Vehicle routing with time windows
Operations Research
A new optimization algorithm for the vehicle routing problem with time windows
Operations Research
Computers and Operations Research
A Heuristic for the Vehicle Routing Problem with Time Windows
Journal of Heuristics
A Branch-and-Cut Procedure for the Vehicle Routing Problem with Time Windows
Transportation Science
Evolutionary Algorithms for the Vehicle Routing Problem with Time Windows
Journal of Heuristics
Robust Branch-and-Cut-and-Price for the Capacitated Vehicle Routing Problem
Mathematical Programming: Series A and B
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics
Transportation Science
Formulations and exact algorithms for the vehicle routing problem with time windows
Computers and Operations Research
Mathematical Programming: Series A and B
Vehicle routing scheduling using an enhanced hybrid optimization approach
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
Hi-index | 0.00 |
This paper addresses a vehicle routing and scheduling problem arising in Flight Ticket Sales Companies for the service of free pickup and delivery of airline passengers to the airport. The problem is formulated under the framework of Vehicle Routing Problem with Time Windows (VRPTW), with the objective of minimizing the total operational costs, i.e. fixed start-up costs and variable traveling costs. A 0---1 mixed integer programming model is presented, in which service quality is factored in constraints by introducing passenger satisfaction degree functions that limit time deviations between actual and desired delivery times. The problem addressed in this paper has two distinctive characteristics--small vehicle capacities and tight delivery time windows. An exact algorithm based on the set-partitioning model, concerning both characteristics, is developed. In the first phase of the algorithm the entire candidate set of best feasible routes is generated, and then the optimal solution is obtained by solving the set-partitioning model in the second phase. Finally, we use four actual instances to illustrate application of the proposed algorithm. Moreover, the proposed algorithm is applied to a random instance containing more orders to verify the general effectiveness of the proposed algorithm even if the number of passengers increases in future.