Optimal control and cooperative game theory based analysis of a by-product synergy system
Bibliographic record
Abstract
In this paper, we propose a framework to analyse the setting up of an industrial symbiosis system. The establishment of such a system entails implementation of expensive technologies by the companies so as to convert wastes into energy and other mutually beneficial materials. Much of the literature on modeling industrial symbiosis do not consider this important aspect of high conversion costs. Further, such a cooperative effort will be sustainable only if the high cost of implementing technologies is compensated by sharing of the benefits in a fair and satisfactory manner. Towards this end, first at a general level, we addres the issue of the optimal design of an industrial symbiosis network of companies in a given region. The materials processing rates depend on the choice of the new technology. We first propose an optimal control problem to determine the best choice of technology for the companies. Then, we recommend the nucleolus-based cooperative game theory approach to determine the best share of the benefits among the participating companies as this method minimizes the unhappiness of the members. A hypothetical numerical example of three companies is provided to illustrate the model. Each company has a choice of 3 technology options. The planning horizon is assumed to be 5 years. The numerical example brings out clearly the benefit of cooperation. The benefit of by-product synergy is that companies 1, 2 and 3 can realize an increase in their NPVs by 44%, 33% and 21% respectively. The nucleolus based sharing of NPVs guarantees the acceptance by companies of the distribution of the additional profits.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".