Manufacturing system design by considering multiple machine replacements under discounted costs
Bibliographic record
Abstract
This article investigates the effect of equipment replacement on the design phase of multi-machine manufacturing systems, given a finite horizon and discounted costs. For the most part, the manufacturing system design literature has focused on the design issue, ignoring equipment replacement and its economic impact. The design phase generally consists of equipment selection, process routing, and layout decisions. The authors propose an explicit mathematical form for the operating costs of equipment and their salvage values based on their previous experience of life cycle costing projects. The design phase of cellular manufacturing systems, the so-called cell formation problem, is used. The problem is formulated as a non-linear mixed-integer programming model and solved using a proposed branch-and-bound algorithm. The algorithm employs a depth-first branching strategy in conjunction with a bounding procedure with a heuristic method. Selected numerical examples demonstrate the applicability of the model and verify the performance of the proposed algorithm. The results enable the best equipment mix and product process routes to be chosen based on the given horizon and economic factors; in addition, information is obtained about which equipment should be replaced and at what time point this replacement should occur.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".