An Effective Particle Swarm Optimization for Lot-Sizing Problem with Particulate Matter Emission Constraint
Bibliographic record
Abstract
The green supply chain is the reduction of the atmospheric release emissions including gases, vapour, smoke, solid or liquid particles.This atmospheric reduction will concern each stage of the chain: supply, production, distribution, warehousing, transport and delivery.The design of this loop is based on industrial ecological perspectives, particularly in the production, and the transport stage.In this work, we present a lotsizing problem with capacitated one warehouse multi retailers (OWMR) under the minimization of particles matter (PM) emission from production and delivery, knowing that the problem is an NP-hard.We have developed a logistics structure containing a production unit connected to a distribution network characterized by (size, number and location) retailers specializing in a single type of product.Then, we will introduce our mathematical problem modelling using mixed-integer programming and develop an approach based on the metaheuristic called binary particle swarm optimization (BPSO) in this approach; we will study new strategies and techniques concerning the particle swarm parameters.The improved BPSO will be tested on a series of benchmark data sets and compared with CPLEX.According to the experimental results, this approach is effective in minimizing the total cost of the supply chain and promoting green technology by reducing the number of the particles emitted into the air.It also provides a decision support system to answer key questions about when and how much produce and distribute in a sustainable environment.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".