Abstract
The logistics facility location is always involved with great deals of investment. Its construction and operation also bring out a huge amount of the greenhouse gas (GHG) emission due to the consumption of building materials, energy, the running of trucks, and other logistics equipment. Particularly, trucking activities in the urban logistics networks (ULN) are a major source of GHG. This paper aims to formulate an eco-facility location model to minimize both the total cost of ULN construction and operation and the GHG emissions of truck trips. Based on the mathematical relations of GHG emissions rates and several macroscopic factors, which we obtained by multivariate regression analysis on a large set of empirical trucking data in our previous research, the data-driven emissions rates estimation function is acquired. Then, we link the estimation function of each trip purpose by various kinds of logistics facilities through a qualitative analysis. The eco-facility location problem is modeled by integrating the pure facility location model and the GHG emissions function. The problem is first converted to a biobjective mixed-integer program, and the Particle Swarm Optimization algorithm is applied to solve the model. Through experiments with real case, the effectiveness of the models and algorithms is verified. The eco-facility location model for ULN tends to obtain the environment-friendly location decision. Our analytical results also verify the hypothesis that locations of facility do impact the relevant truck-related GHG emissions, especially to transfer transport, as well as inbound and outbound freight.
Highlights
The logistics activity in urban areas is responsible for a significant portion of the global greenhouse gas (GHG) emissions [1]
Aiming to fill such research gap, we have investigated a large set of empirical truck trajectory data, developed a trip purpose imputation matrix to classify truck-related GHG emissions, and explored how the macroscopic trip would affect overall GHG emissions associated with each type of truck trips
We try to develop a new horizon to cut down the GHG emissions in urban logistics network (ULN) by optimizing the facility location decisions
Summary
The logistics activity in urban areas is responsible for a significant portion of the global greenhouse gas (GHG) emissions [1]. Due to the rapid development of E-commerce, the related GHG emissions share is increasing continuously. Reducing GHG emissions in logistics sector is crucial to sustainable urban development. Since the urban logistics network (ULN) is the backbone for carrying out all the logistics activities and satisfying the logistics demand of the whole city, investigating the emission sources of logistics activity in urban, finding out the relationship of the GHG emissions rate and the corresponding source, and constructing an optimal model considering both economic and environmental factors are necessary to reduce the logistics-related GHG emissions. The node stands for the facility, and the arc refers to the distribution path or flow in each supply-demand pair. It is obvious that the GHG emissions in ULN come from the facilities (nodes in ULN) and distribution flows (arcs in ULN), which include
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