Solving a Multi-Objective No-Wait Flow Shop Problem by a Hybrid Multi-Objective Immune Algorithm
Bibliographic record
Abstract
Multiprocessor Scheduling: Theory and Applications 196The multi-objective flow shop scheduling problem has been addressed by some of papers.Murata et al. (1996) proposed a multi objective genetic algorithm to tackle flow shop scheduling problem.They considered the problem with two objectives of minimizing makespan and total tardiness and then they investigated the problem with respect to minimizing makespan, total tardiness and total flowtime as objectives.Ponnambalam et al. (2004)proposed a TSP-GA multi objective algorithm for flow shop scheduling where they use a weighted sum of multiple objectives (i.e.minimizing makespan, mean flow time and machine idle time).Toktas et al. (2004) considered the two machine flow shop scheduling problem by minimizing makespan and maximum earliness as objectives.Ravindran et al. (2005) proposed three heuristic algorithms to solve the flow shop scheduling problem which in makespan and total flow time have been considered as objectives.This chapter deals with a multi-objective no-wait flow shop scheduling problem.The weighted mean completion time and the weighted mean tardiness are to be optimized simultaneously.To tackle this problem, an effective multi-objective immune algorithm (MOIA) is designed for searching locally Pareto-optimal frontier.The rest of this chapter is organized as follows: Basic definitions of multi-objective optimization are presented in Section 2. Section 3 gives the problem definition.In Section 4, the background of immune algorithm is described and then the proposed algorithm is given.The experimental results are provided in Section 5. Finally, In Section 6 we conclude. How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following:
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".