Electric Vehicle Routing Problems with Stochastic Demands and Dynamic Remedial Measures

Abstract

With the rapid development of e-commerce, logistic enterprises must better predict customer demand to improve distribution efficiency, so as to deliver goods in advance, which makes logistics stochastic and dynamic. In order to deal with this challenge and respond to the concept of “green logistics,” an electric vehicle routing problem with stochastic demands (EVRPSD) and proactive remedial measures is investigated, and an EVRPSD model with probability constraints is established. At the same time, a hybrid heuristic algorithm, combining a saving method and an improved Tabu search algorithm, is proposed to solve the model. Moreover, two insertion strategies with the greedy algorithm for charging stations and dynamic nodes are introduced. Finally, a large number of experimental data show that the heuristic algorithm proposed in this paper is feasible and effective.

Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation

“…Customers' demands also play an important role in terms of stochasticity and dynamism within the E-VRP formulation. For instance, Ge et al [138] propose an E-VRP with stochastic demands and proactive corrective measures. They develop a model with probability constraints that is solved by a hybrid heuristic algorithm combining a sparing method and an improved TS algorithm.…”

Multi-Depot Capacitated Vehicle Routing Problem by Using Stable Marriage and K-Means Clustering to Minimize Number of Unserved Customers and Total Travel Distance

“…Ge, Zhu, and Jin [14] developed an EVRP model for solving the stochastic demand and dynamic demand. In it, there were two tiers of warehouses: the central warehouse, and the front warehouses.…”

“…Moreover, several goals can be derived from this main goal, due to the specific objective of the study. Some VRP studies aimed to minimize total cost [13], where the cost structure is constructed from the fixed cost and the variable cost [14]. In the GVRP study, the goal is minimizing pollution [8].…”

“…The demand is static so that it is predetermined before the routing process starts. It is different from the dynamic demand where the customers are known but the requested quantity is unknown [12] or the stochastic demand where there are two types of customers: the predetermined customers and customers that exist during the routing process [14]. In this work, unserved customers are allowed.…”

Data‐Driven Robust Optimization for Solving the Heterogeneous Vehicle Routing Problem with Customer Demand Uncertainty

“…Ge et al studied electric vehicle routing problem with stochastic demands (EVRPSDs) and proactive remedial measures, and a model with probability constraints is established. And a hybrid heuristic algorithm, combining a saving method and an improved Tabu search algorithm, is proposed to solve the model [4]. Goodson developed methods to estimate and exactly calculate the expected cost of a priori policies for the multicompartment vehicle routing problem with stochastic demands [5].…”