Meng Zhang

As the market of perishable goods in China continues to expand, reducing quality loss during transportation has become an urgent issue for the industry. Multimodal transport, as a key approach to optimizing the transportation structure and lowering logistics costs, has been increasingly adopted in practice. However, multimodal transport involves multiple transfers, and continuous vibration from transportation equipment throughout the transport process, together with impacts during transfer operations, can easily increase the loss of perishable goods, making it highly significant in practice to consider vibration loss during transportation in route planning. Since different transportation equipment generates different levels of vibration acceleration, this study considers the vibration losses caused by road, rail, and air transport. A bi-objective route optimization model for fresh produce multimodal transport is established, aiming to minimize total cost while maximizing product quality satisfaction. A hybrid algorithm combining an improved Strength Pareto Evolutionary Algorithm and a multi-objective adaptive large neighborhood search algorithm is designed to solve the model. The effectiveness of the model and algorithm is verified through case analysis, followed by sensitivity analysis on different time-sensitive factors of vibration damage and vibration acceleration. The results show that, compared with the multi-objective adaptive large neighborhood search algorithm and the non-dominated sorting genetic algorithm, the proposed algorithm can obtain solutions with lower total costs or higher product quality satisfaction. The minimum total cost of the multimodal transport scheme obtained by the proposed algorithm is reduced by 0.27% and 2.4%, respectively, compared with the multi-objective adaptive large neighborhood search algorithm and the non-dominated sorting genetic algorithm, while the maximum satisfaction is increased by 2.1% and 0.35%, respectively.

Appropriate transportation management significantly influences both operational costs and associated risks of diffusible chemicals distribution. Urban diffusible chemicals distribution differs distinctly from conventional distributions, presenting significant challenges for routing arrangement. This study investigates urban diffusible chemicals distribution, proposing a routing optimization model that incorporates urban rescue team points while accounting for response times with potential blockages, rescue team capabilities, and their impact on routing decision-making. A composite solution method is proposed, encompassing model pre-processing techniques and a two-stage solution framework. In Stage I, an Improved Non-dominated Sorting Genetic Algorithm-II (Improved NSGA-II) is conducted to generate the Pareto frontier; and in Stage II, a post-processing procedure is implemented to facilitate the decision-maker's selection of appropriate solutions. The method is tested by computational experiments, the results show that the Improved NSGA-II can enhance the Hypervolume by up to 20.20% (average 6.99%). Finally, sensitivity analysis on the population and crossover probability related parameters is reported. Furthermore, several insights governing the cost and risk of diffusible chemicals distribution are concluded, including selecting routes with densely distributed RT points and prioritizing regions where RT points maintain substantial rescue capabilities.

The efficiency of the same vehicle can vary in different regions, posing unique challenges and implications for electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) within a country. However, most studies have regarded countries as single entities, and it is difficult to assess differences in efficiency between similar entities by simply using the life cycle analysis (LCA) method. To provide the specific environmental efficiency of vehicles in each region, in this study, we used data from 100 cities in 30 provinces in China (4 provinces are not discussed due to a lack of data) and constructed a new road congestion indicator that simulated different road conditions at different times and in different regions. A more effective method, which consisted of LCA, two-stage data envelopment analysis (DEA) and a slack-based model (SBM), was integrated to reflect the phases of LCA more clearly. The results show that the well-to-wheel (WTW) emission range of internal combustion engine vehicles (ICEVs) is 288.28–217.40 CO₂-eq g/km, while it is 248.20–26.67 CO₂-eq g/km for EVs, which means the WTW carbon emissions of EVs are generally lower than those of ICEVs (except in Heilongjiang Province). Furthermore, it was concluded that provinces with a high proportion of hydropower and a high degree of power autonomy could adjust the proportion of thermal power and inter-provincial power transmission to enhance environmental sustainability, and this would not change provincial environmental efficiency. The analysis suggests that provinces should consider both environmental protection and electricity sustainability when planning their own power development, rather than only focusing on improving environmental efficiency.