Empirical analysis of optimization methods for the real-world dial-a-ride problem

Abstract

This paper deals with solving the Dial-a-Ride Problem (DARP) for an on-demand delivery start-up company which delivers products to its customers from their corresponding pick-up points within guaranteed time intervals. The primary goal of the company is to minimize its operational costs while fulfilling the orders under the constraints on time window, duration, carrier capacity and ride time. This problem is formulated as the real-world DARP, and two methods are empirically evaluated by using Mixed Integer Programming (MIP) and Genetic Algorithm (GA) frameworks. The experiments are done on the simulated data provided by the company. The results show that a heuristic approach is more suitable for the real-world problem to meet the time window limitations.