Green Transportation Model in Logistics Considering the Carbon Emissions Costs Based on Improved Grey Wolf Algorithm

Abstract

The use of new energy vehicles in transportation can effectively promote the development of green logistics. This study selects heavy–duty diesel trucks as traditional logistics vehicles and heavy–duty electric trucks as new energy logistics vehicles. A green transportation model considering carbon emission costs is established to analyze whether new energy logistics vehicles should be used in long–distance freight delivery and how to arrange the use of two types of logistics vehicles. The model is solved using a grey wolf optimization algorithm, which incorporates good point sets, dynamic adaptive inertia weights, and memory–guided location update equations. The model is then applied to three logistics companies in Zhejiang province, China. In addition, considering the time constraints of the logistics industry, the model is used to simulate the arrangement of logistics transport companies for two types of vehicles in long–distance transportation of goods under realistic situations. Finally, this paper studies the future arrangements for long–distance transportation of goods by logistics companies considering the growing popularity of charging piles and advancements in production technology for new energy vehicles. The results show that the involvement of more new energy logistics vehicles in long–distance transport results in lower transportation costs and reduced pollution generated during transportation.

Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution

“…In order to prove the effectiveness of the dynamic inertia weight based on the Cauchy distribution and logarithmic function proposed in this paper, three different types of inertia weights are selected from other improved Gray Wolf Optimizers (GWOs) in the literature for comparison. This paper calls the GWO algorithm improved by these three inertia weight strategies GWO1 [48], GWO2 [49], and GWO3 [50], respectively. The comparative test results of the algorithm improved by the Cauchy nonlinear weighting strategy and the GWO algorithm improved by these three weighting strategies are shown in Table 9, and the best experimental results are shown in bold.…”

Sustainable Supply Chain Management of Cocoa Beans in Indonesia: A Review

“…The first aspect involves applying relevant knowledge, such as econometrics and statistics, to analyze green transportation. The second aspect is to develop an environmentally friendly transportation model using intelligent optimization algorithms to solve it (Lu & Li, 2023).…”

“…There are key performance indicators (KPIs) and metrics used to measure the effectiveness of sustainable supply chain practices. This could include tracking carbon emissions reductions (Huisingh et al, 2015;Lu & Li, 2023;Su et al, 2017), waste reduction percentages (Johannes, 2018;Widiyanto et al, 2023), supplier compliance scores (Asif et al, 2019;Er Kara & Oktay Fırat, 2018), and more. Sustainability KPIs and metrics are benchmarks that businesses set to measure the sustainability of their supply chain, employees, partners and suppliers.…”