Multi-Objective Two-Echelon City Dispatching Problem With Mobile Satellites and Crowd-Shipping

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 9; pp. 15340 - 15353
• Main Authors: Lan, Yu-Lin, Liu, Fagui, Ng, Wing W. Y., Gui, Mengke, Lai, Chengqi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Companies; Costs; Crowd-shipping; Customer satisfaction; Dispatching; Evolutionary algorithms; Logistics; multi-directional search; multi-objective optimization; Multiple objective analysis; Optimization; Pareto optimization; Route planning; Satellites; Shipping; Sustainable development; two-echelon; Vehicle routing
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3140351

Recently, a two-echelon city dispatching model with mobile satellites (2ECD-MS) has been proposed to reduce costs effectively. However, in addition to costs, speeds of delivery to customers are increasingly demanding in urban dispatching. This work extends 2ECD-MS to 2ECD-MS-CS by adopting the crowd-shipping model in the second-echelon dispatching, which uses occasional drivers of private vehicles to deliver parcels to improve the delivery speed. Furthermore, existing works generally consider the optimization from a single aspect, e.g., the delivery company. However, the sustainable development of a logistics company must also focus on other subjects in logistics activities, such as customers and delivery employees. So, we define a multi-objective model considering company cost, customer satisfaction, and income satisfaction of crowd-shippers simultaneously. The multi-objective optimization problem of 2ECD-MS-CS is solved by a multi-directional evolutionary algorithm (MDEA). In MDEA, multiple neighborhood operators are designed and combined with the multi-directional search strategy to fully explore the Pareto Front. Finally, we generate 40 new 2ECD-MS-CS instances based on existing common vehicle routing datasets. Experimental results show that 2ECD-MS-CS reduces the average cost by 3.4% and improves the delivery speed by 42% against 2ECD-MS in 40 instances with different customer scales, numbers of mobile satellites, and geographic scopes. The proposed MDEA outperforms several popular multi-objective optimization algorithms in both convergence and diversity. These illustrate the advantages of 2ECD-MS-CS especially in terms of delivery speed and the effectiveness of the proposed MDEA.