A Simplified Online Solution for Simulation-Based Optimization of Earthmoving Operations
Bibliographic record
Abstract
A Simplified Online Solution for Simulation-Based Optimization of Earthmoving Operations Yasser Mohamed, Mostafa Ali Pages 368-376 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Daily field management of earthmoving operations requires quick and regular decisions for allocating available equipment to different jobs on a project. A general foreman's day starts with matching several activities with a suitable set of available equipment to achieve the highest productivity and lowest unit cost. Usually, this decision-making process needs to be quick, and depends to a great extent on the foreman's experience, which varies from one individual to another. Many analytical solutions with various degrees of sophistication and optimization exist to address such decisions. However, adopting any of these solutions is contingent on how accessible and easy-to-use the solution is. This paper discusses the development process of a webaccessible solution for evaluating earthmoving fleet composition and allocation scenarios. The solution relies on discrete event simulation and an optimization backend engine, but introduces the user with a simplified and easy-to-use interface that is accessible from any mobile device, and is customized to specific user's needs. The developed system gathers most equipment and site-related input data from a company's information systems to minimize user input. The user mainly needs to formulate equipment and job combinations and allocation scenarios (e.g. soil type, quantity, and hauling distance), according to equipment availability each day. Then the system will evaluate the productivity and unit cost estimates for each scenario, allowing the user to choose the most suitable one. It may also be used to automatically recommend the optimum solution given an available list of equipment. The paper presents the process followed in prototyping the proposed system in collaboration with a major Canadian earthmoving contractor, and customizing it to the user needs within the company. It also describes the overall structure of the developed system and its core simulation model. Keywords: Earthmoving, Resource Allocation, Simulation, Optimization DOI: https://doi.org/10.22260/ISARC2013/0040 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".