Making concurrent decisions on assembly plans and supply chain configurations (SCCs) is a desirable strategy in today's competitive global market. Such concurrent decisions help reduce decision iterations for shortened lead time and lower the total cost for manufacturers and their supply chains. However, there lacks deep understanding of the interaction between assembly plans and SCCs, as well as methodologies for integrating them. In this paper, we consider an assembly planning problem that simultaneously decomposes an assembly product to modules and assigns the modules to suppliers. The joint decision is formulated as an optimization problem of finding the assembly decomposition and the corresponding supply chain network with the lowest total supply chain cost. We develop a coordinated decision-making method by integrating assembly AND/OR graphs with supply chain cost information. A dynamic programing (DP) based algorithm is developed to efficiently solve the combined optimization problem. Case studies demonstrate that the proposed method can find optimal solutions for the integrated decision-making problem efficiently and provide a tool to plan assemblies from the perspective of the total supply chain cost in the early stages of product development.

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.