Multi-Class Based Revenue Optimization on Distribution Processing Capacity Allocation for Third Party Warehousing
Bibliographic record
Abstract
Distribution processing of 3PW is an important value-added service of profit source. The article examines that the research on distribution processing is so important to 3PW companies. Firstly, the paper considers a 3PW company with stochastic warehousing demands and invariable process ability scheme. Then, a stochastic programming model is developed on capacity allocation in combination with Job Shop problem. Secondly, based on revenue management methods, multi-classed models are transformed to linear integer programming models with robust optimization. Finally, the feasibility of model and method are confirmed by digital simulation and capacity allocation policy is determined by calculation. Distribution processing, jointing production and delivery plays the role of a `bridge' and `link'. It realizes "Time utility" and "Place utility" and contributes great profit to 3PW. Also, it has been confirmed that the revenue contributed by distribution processing is not less than which is contributed by warehousing and transportation by practical data of Chinese companies in recent years. Warehousing companies grew significantly faster than the sole transport companies and integrated logistics companies (NDRC China 2006). Specifically, revenue created by the distribution processing department grows much faster at 107.3% as investigated by National Development and Reform Commission of China in 2006. However, costs have been increased 23.8% (NDRC China 2006). Under such circumstances, it's not always feasible to keep the growth of profit only by reducing costs (Lin 2007). Revenue Management (RM) is highly effective tools (Mcgill 1999) which can help companies sell their products or services to right customers at right price and at right time. In this manner, it is able to achieve greatest revenue (Boyd 2003). Few literatures focused on Distribution Processing profits. Distribution processing job scheduling with simulation (Chen 2001) was studied without revenue consideration. Capacity allocation model for container logistics Revenue Management with empty container transportation (Bu 2005), extended RM application and guided many literatures focus on this topic. However, there are apparent differences between ocean shipping industry, hotel and human resource industry. In addition, 3PW revenue optimizations (Lin 2007) directly considered such difference and applied RM in warehousing industry successfully and are the fundamental part of warehousing distribution processing. Thus, Revenue Management was getting focused (Lin 2009) without considering the complexity of multi-classed demand.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".