Capacity and Operational Performance Optimization under External Constraints with Uncertainty
Bibliographic record
Abstract
This thesis presents novel models to investigate the significant impacts of globalization, demand uncertainty, competition, and external emissions regulations on firms' profitability, operational performance, and decision-making processes. The models relax common simplifying assumptions (i.e., deterministic lead-times, exogenous emissions permit price) to better represent real market characteristics and applications. Specifically, these models are developed in the context of maritime shipments of fuels, such as Liquefied Natural Gas (LNG), to supply global energy markets. The transport capacity investment problem is studied in terms of a mixed integer nonlinear model to maximize a single firm's profit. Using a proposed heuristic algorithm, the model determines the optimal number and size of tankers to deliver a single product under demand uncertainty. The model incorporates economies of scale, lost sales penalties, and the possibility of leasing extra capacity. Results show that tanker size and capacity utilization decrease if demand uncertainty increases. Moreover, tanker sizes are found to decrease as economies of scale decrease. This model is extended to a duopoly setting under competition constrained by the cap-and-trade policy. It is treated as a nonlinear Cournot game and solved analytically to maximize firms' individual profits by determining their optimal production volumes. Furthermore, it provides a set of cap-and-trade policy characteristics (i.e., market cap, cap allocation rate, permit price) leading to the maximum total profit and increasing the trading possibility. Moreover, individual bounds on the ranges of these characteristics are determined within which trading occurs. This model is modified for the carbon tax policy and is further extended to an oligopoly natural gas (NG) market to satisfy price-sensitive NG demand in the Chinese power sector. The supplied NG volume replaces a fraction of the coal consumption for electricity generation to investigate the economic and environmental implications of replacing coal with imported NG in its life cycle through an integrated Cournot game and life cycle assessment approach. The model is able to find a carbon price range under various sources of uncertainty involved in the market at which the import of NG to generate electricity is economically preferred compared to the coal-dominant Chinese power mix outlook.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".