Discussion of “Ant Colony Optimization for Multimode Resource-Constrained Project Scheduling” by Hong Zhang
Bibliographic record
Abstract
The paper used the ant colony optimization (ACO) metaheuristic method to solve multimode resource-constrained project scheduling problems with the objective of minimizing project duration. The author used two small examples to compare the ACO method with two other metaheuristic methods: genetic algorithm (GA) and particle swarm optimization (PSO). For each case study, several runs of each metaheuristic method were conducted and the results were used to report the success rates of the three methods, which were 73, 74, and 80%, on average, for GA, PSO, and ACO, respectively (based on Table 4 in the paper). The author concluded that the proposed ACO algorithm is an effective methodology that can help practitioners plan construction projects. In addition, the author mentioned that further studies will address deep insight into the parameters of the ACO algorithm in addition to other considerations, including multiple objective optimization. In the opinion of the discussers, the results of the three methods on the two small examples are very comparable and, because of the random nature of these processes, their comparative quality is highly problem dependent. All of these methods, however, are highly inefficient to handle large-scale problems of any practical size. As problem size increases (e.g., hundreds of activities) and multiple objectives are considered, all metaheuristic methods exhibit an exponential increase in the solution space (number of possible solutions), which makes the search for an optimum solution a difficult and time consuming task. Kandil and El-Rayes (2005), for example, reported a GA processing time of 55 h for a case study of only 360 activities to reach optimum solution, which was reduced to 9.3 h by using a system of parallel computing with 50 processors. Thus, the focus of future work in this area should be introducing efficient methods to handle practical sized problems. Introducing new metaheuristics or tweaking existing methods to achieve minor performance improvements on small textbook-type problems has no practical value.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".