The multi-vehicle profitable pick up and delivery routing problem with uncertain travel times

Abstract This paper addresses a variant of the known selective pickup and delivery problem with time windows. In this problem, a fleet composed of several vehicles with a given capacity should satisfy a set of customers requests consisting in transporting goods from a supplier (pickup location) to a customer (delivery location). The selective aspect consists in choosing the customers to be served on the basis of the profit collected for the service. Motivated by urban settings, wherein road congestion is an important issue, in this paper, we address the profitable pickup and delivery problem with time windows with uncertain travel times. The problem under this assumption, becomes much more involved. The goal is to find the solution that maximizes the net profit, expressed as the difference between the collected revenue, the route cost and the cost associated to the violation the time windows. This study introduces the problem and develops a solution approach to solve it. Very preliminary tests are performed in order to show the efficiency of developed method to cope with the problem at hand

Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach

The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive enhancements. Nevertheless, its online operations suffer the inherent complexities due to the coupling of vehicle resource allocation among cities and pooled-ride vehicle routing. To tackle these challenges, this study proposes a two-level framework designed to facilitate online fleet management. Specifically, a novel multi-agent feudal reinforcement learning model is proposed at the upper level of the framework to cooperatively assign idle vehicles to different intercity lines, while the lower level updates the routes of vehicles using an adaptive large neighborhood search heuristic. Numerical studies based on the realistic dataset of Xiamen and its surrounding cities in China show that the proposed framework effectively mitigates the supply and demand imbalances, and achieves significant improvement in both the average daily system profit and order fulfillment ratio

Multi-trip pickup and delivery problem, with split loads, profits and multiple time windows to model a real case problem in the constructionindustry

International audienceThis paper presents the first optimization study of multi-site transportation in the construction industry, whichallows mutualizing building material delivery and construction waste removal. This study is inspired by a real-worldproblem encountered in the framework of the French R&D project DILC, in which a pooling platformmust centralize the delivery of building materials to the construction sites and the pickup of their waste, usinga limited and heterogeneous fleet that are allowed to perform multiple trips, under time and capacity limitationconstraints. The problem under study, called the Multi-Trip Pickup and Delivery Problem, with Split loads,Profits and Multiple TimeWindows is a new extension of the vehicle routing problem with pickup and delivery,that considers new realistic constraints specific to the construction industry such as each construction site mayhave a priority on its delivery request or its pickup request or both, with a higher priority level for deliveryrequest, and each construction site may have several time windows. To solve this problem, we propose newinsertion criteria that takes into consideration several aspects of our problem, which we have embedded in aconstruction heuristic. Experiments performed on new real instances have shown the efficiency of our method