Advances in Online Delivery: Introducing and Optimizing a Novel
Multi-Objective Function
Abstract
An efficient online delivery system in the dynamic landscape is a
challenging task. The challenges occur due to the difficulty in
generating a proper objective function that can represent the
performance of the delivery system. In this paper, we propose a novel
multi-objective function that represents the utility score and time
required in the delivery process. The utility score takes into
consideration the number of previous orders given by a particular
customer and the Time window methodology is used to achieve the two
objectives. The multi-objective optimization functions are solved and
compared using three multi-objective algorithms. They are Non-dominated
sorting genetic algorithm-II (NSGA-II), strength Pareto evolutionary
algorithm 2 (SPEA2), and indicator-based evolutionary algorithm (IBEA).
The performances are compared extensively and it is found that SPEA2
gives better convergence performance. The proposed objective function
minimizes the limitation of currently available methods for online
delivery systems.