Cooperative Operation of the Fleet Operator and Incentive-aware Customers in an On-demand Delivery System: A Bi-level Approach

TL;DR

This paper introduces a bi-level optimization framework for cooperative operation between fleet operators and incentive-aware customers in on-demand delivery, aiming to reduce costs and improve flexibility.

Contribution

It develops a novel bi-level optimization model and an efficient distributed algorithm to enhance cooperation in on-demand delivery systems.

Findings

Reduces delivery fees for customers

Lowers operational costs for fleet operators

Creates a win-win cooperation scheme

Abstract

In this paper, we study the cooperative operation problem between the fleet operator and incentive-aware customers in an on-demand delivery system. Specifically, the fleet operator offers discounts on transportation costs in exchange of the delivery time flexibility of customers. In order to capture the interaction between the fleet operator and customers, a novel bi-level optimization framework is proposed. By exploiting the strong duality, and the KKT optimality condition of customer optimization problems, we can reformulate the bi-level optimization problem as a mixed integer nonlinear programming problem. Considering the inherent difficulties of MINLP, a computationally efficient algorithm, which combines the merits of Lagrangian dual decomposition and Benders decomposition, is devised to solve the resulting MINLP problem in a distributed manner. Finally, extensive numerical experiments demonstrate that the proposed cooperation scheme can decrease the delivery fees for the customers, and reduce the operation cost of the fleet operator at the same time, thus leading to a win-win situation for both sides.