Optimizing the Vehicle Scheduling Problem for Just-in-Time Delivery Considering Carbon Emissions and Atmospheric Particulate Matter

Bingtao Quan, Sujian Li and Kuo-Jui Wu
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Bingtao Quan: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Sujian Li: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Kuo-Jui Wu: School of Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan

Sustainability, 2022, vol. 14, issue 10, 1-19

Abstract: The coordinated development of companies and ecological protection are possible only with increasing environmental awareness. Therefore, this study aims to investigate how companies can achieve sustainable development. It is found that the scientific implementation of the vehicle scheduling problem (VSP) for just-in-time (JIT) delivery in the raw material procurement logistics of iron and steel companies can reduce the carbon emissions in the VSP process and, taking into account the negative correlation between weather conditions and PM10, can effectively reduce PM10. On this basis, a multiobjective optimization model is constructed with the objectives of minimizing carbon emissions and PM10 along with the traditional objective of cost optimization. A greedy algorithm with high computational efficiency and an embedded genetic algorithm (GA) is used to further improve the response time of the VSP. Verification shows that in practice, the model enables companies to effectively reduce not only logistics costs but also PM10 and carbon emissions; in theory, the model expands the applicability of JIT to all value-added activities, exploring all value-added activities in different spatial and temporal dimensions to achieve the optimal combination of company cost, environmental effects, and weather dimensions.

Keywords: just-in-time; vehicle scheduling problem; carbon emission; atmospheric particulate matter (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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