An evolutionary algorithm for joint bi-criteria location-scheduling problem
Bibliographic record
Abstract
A new case of joint location and scheduling (ScheLoc) problem is considered. It deals with selecting a non-fixed number of locations for identical parallel executors (machines) from a given set of available sites. Simultaneously, a schedule for a set of tasks is sought. For every task, it comprises an executor carrying-out the task and the moment of time when the performance of the task is started. The locations for executors and the schedule are evaluated by two criteria: the sum of task completion times and investment costs incurred when locations for executors are selected and launched. It is justified that the joint optimization problem is strongly NP-hard. In consequence, a heuristic algorithm Alg_BC is proposed, which uses the general scheme of NSGA II provided for the multi-criteria optimization. The performance of Alg_BC is evaluated for small instances by exact solutions determined by the Matlab solver. The sensitivity analysis for bigger instances is also provided, which among others, allows examining the influence of both component criteria on results generated by the evaluated algorithm. A case study dealing with the evacuation of citizen groups from danger zones is provided as an example of the investigated bi-criteria ScheLoc problem. The usefulness of Alg_BC is confirmed as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.002 | 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".