Bibliographic record
Abstract
Numerous literature reviews and research studies have highlighted the increasing environmental concerns of supply chain stakeholders (managers, legislative bodies, customers, etc.). Guaranteeing environmentally conscious supply chain operations is closely linked to an organization’s sustainability and success. A large part of this is the responsible management of product return flows in production and inventory environments. Reverse logistics is inevitable in today’s business environment with the most common reasons being product returns, incorrect product delivery, damaged products, and product exchange programs. Green concepts and should be operationalized in a supply chain context. The literature emphasizes that the modelling of reverse logistics and closed-loop supply chains from a green and/or environmental aspect lacks investigation and development. Mathematical modelling of such systems will assist decisionmaking processes and provided a better understanding of environmentally responsible inventory models. This thesis reviews the literature on the modelling of reverse logistics inventory systems that are based on the economic order/production quantity (EOQ/EPQ) and the joint economic lot size (JELS) settings so as to systematically analyse the mathematics involved in capturing the main characteristics of related processes. The literature is surveyed and classified according to the specific issues faced and modelling assumptions. Special attention is given to environmental issues. There are indications of the need for the mathematics of reverse logistics models to follow current trends in ‘greening’ inventory and supply-chain models. The modelling of waste disposal, greenhouse-gas emissions and energy consumption during production is considered as the most pressing priority for the future of inventory models. Mathematical models for two-level supply chains with different coordination policies, a manufacturing-remanufacturing inventory model and a two-level closed-loop supply chain model with remanufacturing under different coordination are developed in this thesis. Numerical examples are presented and discussed presenting managerial insights and implications. Input-Output system analysis and multi-objective optimization modeling are suggested future research directions.
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 | 0.003 |
| 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 | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".