Super‐Efficiency‐Malmquist Model‐Based Efficiency Evaluation of Logistics Distribution Center considering Truck Traffic Restriction
Bibliographic record
Abstract
Combining the super‐efficiency model based on data envelopment analysis (DEA) with the Malmquist index model, this paper evaluated the efficiency of the logistics distribution center comprehensively considering the truck traffic restriction and provided decision suggestions to improve the efficiency of the logistics distribution center. This paper takes 20 logistics distribution centers as the research objects and uses economic factors, transportation factors, quality of distribution center business activities, and quality of customer service as the primary input indicators; selects eight indicators such as construction cost, transportation cost, labor cost, road facilities, accessibility, business demand, number of laborers, and customer satisfaction as the secondary input indicators; chooses distribution time and profit as the output indicators; and measures the static efficiency of logistics distribution centers from two perspectives, including the traditional unconstrained super‐efficiency model and the truck‐ restricted conditions, using the super‐efficiency model of data envelopment analysis (DEA). The Malmquist index model was used to measure the dynamic efficiency and change trend efficiency of the logistics distribution center, and a unified and comprehensive analysis was also made. The results of the case study show that the average efficiency of the logistics distribution center in the driving and nondriving restriction area is 0.872 and 0.914, respectively, and the average efficiency in the driving restriction area is about 4.5% lower than that of the nondriving restriction area, and variance is 1.58 times of the latter. Therefore, it can be concluded that the measures of truck driving restriction have an impact on the efficiency of the logistics distribution center, and the results of the super‐efficiency model with the restriction constraint have a greater impact on the logistics efficiency of the logistics distribution center than the traditional unconstrained super‐efficiency model. According to the evaluation results, suggestions on reasonable assignment of labor and other resources input are put forward for logistics distribution centers in areas where driving is restricted to improve efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".