Bibliographic record
Abstract
Inventory systems may be modelled analogously to thermal systems involving multiple flows of capital, labour, energy, and materials among the members of a supply chain. The laws of thermodynamics can be employed to analyze the efficiency of such physical systems by implementing “Exergy Analysis,” a powerful technique which can be used to assess and improve the efficiency of a process, device, and system and to enhance their environmental and economic performance. Traditional exergy analysis methods may not be sufficient for the analysis of certain systems because they do not account for the non-energetic factors such as capital, labour, and environment protection. Extended exergy analysis assigns exergetic equivalents to such non-energetic externalities. Sustainable development is about securing the requirements of today while guarding the needs of future generations. Its target is the improvement of the living styles of humans by protecting their health and environment, and the efficient resources’ consumption while advancing long-term economic growth. In other words, it is the integration of social, environmental, and economic aspects into regulations and policies, which requires actions from everyone on this planet. The production, inventory and logistics of goods have contributed, among other things, towards making our world less sustainable. This thesis, therefore, aims to provide models, methods and decision support tools that can assist in achieving a better level of sustainability through the whole processes of inventory systems. The overall objectives are to analyze the importance of the wise consumptions of physical and human resources in inventory systems. The results of this thesis have significant implications in shifting the “classical” paradigm of inventory systems that are based on the economic performance, which can be measured with financial criteria, such as total costs and profit, to the “non-classical” paradigm that considers the three pillars of sustainable development. The results showed the importance of accounting for the consumed exergy rather than just considering the values in term of monetary units. Computing the exergetic costs can provide more flexibility for managers of supply chains to compute the quantity based on the available resources and not confining this to the capital only.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".