Stochastic Optimization of the Repair Shops Location Problem Using Particle Swarm Optimization Algorithm
Bibliographic record
Abstract
The optimization of facility location decisions is critical for the success of a supply chain in a market since it can contribute to long-term performance of the supply chain. In the last two decades, the number of research in this field has been growing to address more realistic problems such as incorporating uncertainties in repair time and demand. In this paper, a particle swarm optimization algorithm (PSO) is employed to locate repair shops in a stochastic environment. The problem aim is to decide about the location and the capacity of local repair shops as well as identifying the capacity of central repair shop to minimize total expected cost. It is assumed that customers select the closest local repair shop. In the local repair shops, services are available to repair customer’s broken items and a number of spare parts are stored to supply customers’ needs. Additionally, each repair shop is allowed to open some servers, depending on the number of customers, to serve its customers. If a stock-out happens, a customer should wait until the part is repaired in that shop. When a local repair shop is unable to repair a part, the part is sent to the central repair shop to be repaired. The central repair shop follows similar strategy for spare part inventory. The contribution of this paper is to employ a meta-heuristic solution approach based on particle swarm optimization for locating repair shops problem. In order to evaluate the performance of the employed solutions approach, its result is compared to other methods and differences are highlighted.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".