Two-Echelon Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery

In this paper, we propose a tabu search algorithm for the two-echelon vehicle routing problem with time windows and simultaneous pickup and delivery (2E-VRPTWSPD), which is a new variant of the two-echelon vehicle routing problem (2E-VRP) by considering the time windows constraints and simultaneous pickup and delivery. In 2EVRPTWSPD, the pickup and delivery activities are performed simultaneously by the same vehicles through the depot to satellites in the first echelon and satellites to customers in the second echelon, where each customer has a specified time window. To solve this problem, firstly, we formulate the problem with a mathematical model. Then, we implement a variable neighborhood tabu search algorithm with the proposed solution representation of dummy satellites to solve large-scale instances. Dummy satellites time windows are used in our algorithm to speed up the algorithm. Finally, we generate two instance sets based on the existing 2E-VRP and 2E-VRPTW benchmark sets and conduct additional experiments to analyze the performance of our algorithm.

Simultaneous Pickup and Delivery Traveling Salesman Problem considering the Express Lockers Using Attention Route Planning Network

This paper presents a simultaneous pickup and delivery route designing model, which considers the use of express lockers. Unlike the traditional traveling salesman problem (TSP), this model analyzes the scenario that a courier serves a neighborhood with multiple trips. Considering the locker and vehicle capacity, the total cost is constituted of back order, lost sale, and traveling time. We aim to minimize the total cost when satisfying all requests. A modified deep Q-learning network is designed to get the optimal results from our model, leveraging masked multi-head attention to select the courier paths. Our algorithm outperforms other stochastic optimization methods with better optimal solutions and O(n) computational time in evaluation processes. The experiment has shown that reinforcement learning is a better choice than traditional stochastic optimization methods, consuming less power and time during evaluation processes, which indicates that this approach fits better for large-scale data and broad deployment.

Capacitated location routing problem with ‎simultaneous pickup and delivery under the risk of ‎disruption

This paper develops a new mathematical model to study a location-routing problem with simultaneous pickup and delivery under the risk of disruption. A remarkable number of previous studies have assumed that network components (e.g., routes, production factories, depots, etc.) are always available and can permanently serve the customers. This assumption is no longer valid when the network faces disruptions such as flood, earthquake, tsunami, terrorist attacks and workers strike. In case of any disruption in the network, tremendous cost is imposed on the stockholders. Incorporating disruption in the design phase of the network will alleviate the impact of these disasters and let the network resist disruption. In this study, a mixed integer programming (MIP) model is proposed that formulates a reliable capacitated location-routing problem with simultaneous pickup and delivery (RCLRP-SPD) services in supply chain distribution network. The objective function attempts to minimize the sum of location cost of depots, routing cost of vehicles and cost of unfulfilled demand of customers. Since the model is NP-Hard, three meta-heuristics are tailored for large-sized instances and the results show the outperformance of hybrid algorithms comparing to classic genetic algorithm. Finally, the obtained results are discussed and the paper is concluded.