A Multi-Objective Water Cycle Algorithm for the BI-Objective Multi-Mode Project Resource Renting Problem
Bibliographic record
Abstract
A resource renting problem is a project scheduling problem in which the required resources should be rented, and the goal is to find a schedule and resource renting plan such that the total cost of the resources minimises.Traditionally, the model of a resource renting problem contains single-mode activities and a single objective function.This research aims to present a new mathematical model for a bi-objective multi-mode resource renting problem.The objectives are to minimise the project makespan and also the total cost of resources, including the time-independent resource procurement costs and time-dependent resource renting costs, simultaneously.A novel evolutionary algorithm, namely the Multi-Objective Water Cycle Algorithm (MOWCA), is employed to solve this NP-hard problem.In order to evaluate the proposed algorithm, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) is applied, too.A set of instances is selected from the digital library of project scheduling problems to analyse the performances of evolutionary algorithms.The results of the experimentation are quite satisfactory.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".