Multiobjective Route Optimization for Multimodal Cold Chain Networks Considering Carbon Emissions and Food Waste

Yong Peng, Yali Zhang, Dennis Z. Yu () and Yijuan Luo
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Yong Peng: School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Yali Zhang: School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Dennis Z. Yu: The David D. Reh School of Business, Clarkson University, Potsdam, NY 13699, USA
Yijuan Luo: Port and Shipping Maritime Affairs Center, Qijiang District, Chongqing 401420, China

Mathematics, 2024, vol. 12, issue 22, 1-27

Abstract: The cold chain logistics industry faces significant challenges in terms of transportation costs and carbon emissions. It is imperative to plan multimodal transportation routes efficiently to address these issues, minimize food waste, and reduce carbon emissions. This paper focuses on four key optimization objectives for multimodal cold chain transport: minimizing total transportation time, costs, carbon emissions, and food waste. To tackle these objectives, we propose a high-dimensional multiobjective route optimization model for multimodal cold chain networks. Our approach involves the development of a multiobjective evolutionary algorithm, utilizing Monte Carlo simulation and a one-by-one selection strategy. We evaluate the proposed algorithm’s performance by analyzing various convergence and distribution indicators. The average values for the minimum total transportation time, transportation cost, carbon emission cost, and cargo loss rate derived from the proposed algorithm ultimately converge to 6721.7, 5184.4, 301.5, and 0.21, respectively, demonstrating the effectiveness of the algorithmic solution. Additionally, we benchmark our algorithm against the existing literature to showcase its efficiency in solving high-dimensional multi-objective route optimization problems. Furthermore, we investigate the impact of different parameters, such as carbon tax rates, temperature, and cargo activation energy, on carbon emissions, and food waste. Moreover, we conduct a real-world case study to apply our approach to solving a practical business problem related to multimodal cold chain transportation. The insights gained from this research offer valuable decision-making support for multimodal carriers in developing low-carbon and environmentally friendly transportation strategies to efficiently transport perishable goods.

Keywords: multimodal cold chain; multiobjective optimization; carbon emissions; food waste (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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