Mathematical Models and Solution Procedures in the Design and Scheduling of Manufacturing Systems with Distributed Layouts
Bibliographic record
Abstract
This thesis addresses two distinct problems in facility design and scheduling for manufacturing firms operating in volatile environments and producing multiple batches of products. In regards to the facility layout problem, a new comprehensive mathematical model that integrates layout configuration and production planning in the design of dynamic distributed layouts is formulated. The model incorporates a number of important manufacturing attributes such as demand fluctuation, system reconfiguration, lot splitting, work load balancing, alternative routings, machine capability and tooling requirements. In addition, the model allows the optimization of several cost elements in an integrated manner. These include material handling, machine relocation, setup, inventory carrying, in-house production and subcontracting costs. With respect to the scheduling problem, a mathematical formulation for scheduling of manufacturing systems with distributed layouts is developed. The objective of scheduling model is the minimization of the weighted sum of makespan and total traveling distance by the products. Thus on one hand, the problem is to find a schedule of operations on machines (the sequence and starting times of the various operations) which minimizes the overall finishing time or makespan. On the other hand, the problem is to find assignment of jobs to the machines such that total distance traveled by parts is minimized. Optimal solutions for the proposed mathematical models can only be found for small size problems due to NP-complexity. To solve both models for larger-size problems, two hybrid metaheuristics for solving the facility design model and a genetic algorithm for the scheduling model have been developed. All proposed algorithms are thoroughly examined with an emphasis on solution convergence, solution quality and algorithm robustness. For both cases, we provide numerical results to support various managerial insights. In particular in facility design problem, we draw a managerial insight as to how high product variety and high volatility in the production environment can be accommodated without harm to operational efficiency or cost. Similarly in the scheduling study, we show that linking scheduling and material handling performance can contribute to the development of accurate models to obtain a schedule that can also greatly enhance system performance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".