In recent years, sustainable supply chain management has gained increasing attention, and greenhouse gas (GHG) emissions throughout supply chains have been identified as one of the most important sustainability issues. This paper presents an investigation of the problem of transshipment among distribution centers (DCs) in a cold supply chain to achieve sustainable inventory cross-filling. Although transshipment is an effective tool for supply chain pooling, the possibility of increased GHG emissions raises environmental concerns. This study establishes a sustainable cold-chain logistics model that considers GHG emissions from DC storage and transshipment trucks. The new sustainable cold-chain model also reflects laden status and cargo weights of trucks for accurate emission assessment. An optimization model is also developed to minimize both GHG emissions and costs in the cold chain. Numerical simulations are conducted for diverse problem cases to examine important problem characteristics. The result analysis identifies that inventory service levels and demand variability have a strong impact on GHG emissions in transshipment; small p-values in the statistical analysis verify the significance of this effect. The different effects of demand variability and service levels on each emission source are also analyzed. The results demonstrate that transshipment among DCs can effectively reduce both GHG emissions and costs in cold supply chains. This study provides useful models and tools to assess GHG emissions and optimize decisions for the design and operation of transshipment. The proposed models will enable the assessment of sustainable alternatives and achieve sustainability objectives effectively for cold supply chains.

Predictive modeling is important for assessing and reducing energy consumption and CO₂ emissions of light-duty vehicles (LDVs). However, LDV emission datasets have not been fully analyzed, and the rich features of the data pose challenges in prediction. This study aims to conduct a comprehensive analysis of the CO₂ emission data for LDVs and investigate key prediction model characteristics for the data. Vehicle features in the data are analyzed for their correlations and impact on emissions and fuel consumption. Linear and non-linear models with feature selection are assessed for accuracy and consistency in prediction. The main behaviors of the predictive models are analyzed with respect to vehicle data. The results show that the linear models can achieve good prediction performance comparable to that of nonlinear models and provide superior interpretability and reliability. The non-linear generalized additive models exhibit enhanced accuracy but display varying performance with model and parameter choices. The results verify the strong impact of fuel consumption and powertrain attributes on emissions and their substantial influence on the prediction models. The paper uncovers crucial relationships between vehicle features and CO₂ emissions from LDVs. These findings provide insights for model and parameter selections for effective and reliable prediction of vehicle emissions and fuel consumption.

This research investigates an integrated problem of transshipment for cross-filling and imputation for missing demand data. Transshipment for cross-filling has proved effective in mitigating shortages with relatively low inventory, thus reducing resource consumption in inventory management. Although accurate demand data are critical for cross-filling decision making, some demand data are inevitably incomplete. These missing data should be treated for effective transshipment operations. Despite the importance, these missing data issues have not been adequately studied for transshipment problems. This paper addresses how transshipment can be conducted under missing demand conditions. A novel integrated problem is established to combine demand-data imputation processes and transshipment decisions. Imputation strategies and new algorithms suitable for transshipment are developed to handle missing demand data. Diverse demand and transshipment cases are analyzed for cost-effectiveness. The analysis uncovers that conventional straightforward imputation methods result in inferior transshipment decisions. The study also reveals that imputed values should be adjusted to appropriate levels for transshipment to be effective. The strong interplay between imputation processes and shortage prevention is also discovered for transshipment with missing demand. This study demonstrates how inventory transshipment can be carried out successfully with appropriate treatment of missing demand data in practice.

Distribution centers (DCs) typically receive orders from the customers (mostly retail stores) located in their vicinity and deliver the ordered goods the next day morning. To maintain high item fill rate, DCs have to hold a high level of inventory, which will increase inventory cost. As an alternative, cross-filling is that, after closing the daily order receipt, DCs exchange surplus items during the night to reduce the shortage. The economic justification of such cross-filling will depend on the tradeoff between extra transshipment and handling cost versus saved shortage cost. In this paper, as an extension of Rim and Jiang, 2019, vehicles are allowed to drop and pick up items at the intermediate DCs in the route. We present a genetic algorithm to determine the routes and amount to pick up/drop at each DC to minimize the total cost.

Effective predictive modeling is crucial for assessing and mitigating energy consumption and CO₂ emissions in light-duty vehicles (LDVs) throughout the whole value chain of an organization. This study enhances the modeling of LDV CO₂ emissions by developing novel approaches to analyzing vehicle feature datasets. New tree-based machine learning models are developed to increase the accuracy and interpretability in modeling the CO₂ emissions in LDVs. In particular, this study develops a new algorithm called dynamic perturbation additive regression trees (DPART). This new algorithm integrates dynamic perturbation within an iterative boosting framework. DPART progressively adjusts prediction values and explores various tree structures to improve predictive performance with reduced computation time. The effectiveness of the new ensemble-tree-based models is compared to that of other models for the vehicle emission data. The results demonstrate the new models’ capability to significantly improve predicting accuracy and reliability compared to other models. The new models also enable identifying key vehicle features affecting emissions, and thus provide valuable insights into the complex relationships among vehicle features in the dataset.