Bibliographic record
Abstract
This thesis presents a new methodology for optimizing earthmoving operations using computer simulation and genetic algorithms. It provides an optimization tool geared towards selection of near-optimum fleet configurations. The optimization used in the selection of fleets accounts for availability of equipment and aims at minimizing the total project cost or duration. The simulation process, in the proposed methodology, utilizes discrete event simulation (DEVS) and object oriented modeling. Different features of object orientation are employed including classes, dynamic data structure and polymorphism. The three-phase simulation approach, rather than process interaction, was employed to control the dynamics of the simulation process and track involved activities. This simulation approach is considered most appropriate for object oriented simulation (OOS). The optimization process uses a developed genetic algorithm to search for a near-optimum fleet configuration that reduces project total cost. The algorithm considers a set of qualitative and quantitative variables that influence the production of earthmoving operations. Qualitative variables represent the models of equipment used in each fleet scenario, whereas, quantitative variables represent the number of equipment involved in each scenario. The proposed methodology accounts for: (1) uncertainties associated with earthmoving operations; (2) optimization of project duration or its total cost, considering equipment availability; and (3) realistic estimates of haulers' travel time. It also makes full use of object oriented features and is implemented in a prototype software system named SimEarth . The system consists of five main components: (1) EarthMoving Simulation Program ( EMSP ); (2) Equipment Cost Application ( ECA ); (3) Equipment Database Application ( EDA ); (4) EarthMoving Genetic Algorithm ( EM_GA ), and (5) Output Reporting Module ( ORM ). Beside these main components, SimEarth is supported by: (a) Hauler's Travel Time Application ( HTTA ), and (b) EarthMoving Markup Application ( EMMA ). All system components are implemented in Microsoft ( MS ) environment except the dynamic sub-module of ORM component, which is implemented utilizing " Proof Animation " software. Five numerical examples were analyzed in order to validate and demonstrate the essential features of the system's components. A comprehensive case study of an actual project was analyzed in order to test the performance of the developed system (including the dynamic interaction among its components) and to illustrate the practical features of the developed methodology. The project involves the construction of a large rockfill dam, located in the northern part of the province of Quebec.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".