Capacitated multi-modal network flow models for minimizing total operational cost and CO2e emission. (December 2018)

• Record Type: Journal Article
• Authors: Zhou, Meirong; Duan, Yanting; Yang, Wen; Pan, Yanchun; Zhou, Ming
• Is Part Of: Computers & industrial engineering. Volume 126(2018)
• Page Start: 361
• Page End: 377
• Publication Date: 2018-12
• Subjects: Logistics planning -- Multi-modal network flow -- Multi-objective optimization; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.09.049
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: It explicitly modeled choices of transportation mode and vehicle and their impact on cost/emission. It identified decision trade-offs at operational level to improve operations planning and control. It minimized cost and emission simultaneously through a multi-objective optimization framework. Model variants were tested thoroughly through a set of exemplary cases on real logistics network. While it is NP -hard, the model was solved efficiently for problems of practical scale with Cplex©. Abstract: Green logistics and transportation impose new requirements on distribution planning and flow optimization over complex logistics network. Carbon-dioxide equivalent (CO2e) emission on a road, for instance, depends on the transportation mode and vehicle type selected along the road. The handling cost of a load at a transshipment city also depends on the change of transportation mode and vehicle type from inbound to outbound at the city. Compared with traditional network structure, multi-modal network is characterized by multi-attributes link and dependent transshipment node (in terms of minimizing total cost and emission), which significantly increases problem complexity. This study attempts to address key issues in modeling and analysis of such systems. Analytical models are developed to characterize the problem structure/features explicitly and integrate decisions at single- and multi-objective levels. A set of experiments, designed based on real transportation logistics network, are carried out to verify and validate models' functions, analyze sensitivity of parameters, and evaluate solution convergence behavior. The results indicated that while the problem is NP -hard, solutions via proper analytical models can be derived efficiently for problems of practical scale.