Modelling industrial construction operations using a multi‐agent resource allocation framework
Bibliographic record
Abstract
Purpose Modelling construction resources and their dynamic interactions and constraints are a challenging problem. The allocation of these resources to competing activities is usually a function required in any scheduling process. Performing such allocation under a dynamic and diverse set of constraints adds more complexity to the problem. This study seeks a structured approach for representing resources and their allocation to different activities through the use of an agent‐oriented modelling framework. Design/methodology/approach A model is developed for a real case of assembly operations of industrial construction modules. The model follows a multi‐agent resource allocation structure and is implemented within an agent‐based simulation environment. The model is used to evaluate the effects of different optimization algorithms and modelling parameters on the generation of a construction schedule. Different experiments run through the model and their results are analyzed and discussed. Findings The model showed sensitivity only under large and continuous workloads. Overall the structured approach followed in developing the model provided a flexible medium for experimenting with different elements of the resource allocation problem. Research limitations/implications The work is limited to the studied case and the results cannot be generalized beyond similar cases. The modelling approach used in the study provides a platform that can facilitate future research in construction resource allocation strategies. Originality/value The presented work demonstrates a new approach for modelling construction resource allocation problems that enables structured experimentation with alternative allocation algorithms. It also presents a novel way for modelling modular industrial construction operations.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".