LPPCM: A Low-cost Package Pickup Covering Mechanism for Cooperative Express Services

Abstract

With the swift development of express delivery industry, the increasingly attention has been shifted to express delivery mechanism design. Generally, the revenue of the courier is the difference between the users' express fee and the courier's pickup cost. In order to improve the revenue of courier without increasing the user's express fee, this paper presents a low-cost package pickup covering system to find an optimal Hamiltonian pickup tour for the courier over a subset of packages, where packages who are not on the tour should be covered exactly by one package on the tour. A billing rule discounting the express fee to incentivize users to deliver their packages is also proposed. We formulate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Low-cost Package Pickup Covering (LPPC)</i> problem to maximize the revenue of the courier. Considering the complexity of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LPPC</i> , we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Low-cost Package Pickup Covering Mechanism (LPPCM)</i> to solve the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LPPC</i> problem including problem transformation, hardness analyzing, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Attention Model based on Encoder-Decoder Architecture (AMEDA)</i> model design and model training. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AMEDA</i> is trained by a deep reinforcement learning algorithm in an unsupervised manner and it can directly output the solution based on the given instances. Through extensive simulations, we demonstrate that the average revenue of courier for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AMEDA</i> is at least 10.1% higher than the traditional heuristic local search and is 18.5% lower than the optimal solution on average. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AMEDA</i> provides a desired trade-off between the execution time and solution quality, which is well suited for the large-scale tasks which require quick decisions.