Real-Time Optimization of Production Scheduling: Strategies, Models, and Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
<p>In practice, manufacturing systems are highly dynamic and continuously facing unexpected events and interruptions. This puts production managers in charge of making frequent updates to the ongoing plans and schedules to cope with these changes. This is done by adopting different scheduling strategies and optimization models. Despite several attempts in the literature, the need for models that can minimize the effect of changes (i.e., stochastic and robust models) or react to them using real-time information (i.e., real- time optimization models) is still felt. </p> <p>This dissertation includes three main contributions with different optimization models and heuristic algorithms that can help managers deal with different unexpected events and interruptions on the shop floor. In the first contribution (Chapter 2), the case of stochastic deterioration-based failures in a single machine production system is considered. The machine’s degradation is modeled as a multi-state system. The obtained formulations are then integrated into an optimization model that jointly optimizes the production sequences, machine inspections, and condition-based maintenance actions. The results showed an average improvement of about 35% in total expected costs when information about the machine’s degradation level was used. </p> <p>In the second contribution (Chapter 3), the case of unexpected new job arrivals and random machine breakdowns in a flexible job shop production environment is considered. The effect of random machine breakdowns on the processing durations is formulated and then integrated into a dynamic optimization model. The proposed model investigates how real-time updates can be utilized to improve scheduling decisions based on unexpected arrivals, the availability of machines (downtimes and recovery times), and the completion times of operations. </p> <p>Finally, in Chapter 4, the case of integrating production scheduling and condition-based preventive maintenance (PM) planning in a flexible job shop production system is addressed. The study considers the case of stochastic machine degradation, random machine breakdowns, minimal repairs, condition-based PM, due date changes, and new job arrivals. The reliability of machines is modeled as a multi-state system, in which the obtained formulations are incorporated into an integrated dynamic optimization model. The developed model aims to study the effects of different RTS policies on each of the considered cases and empirically quantify the potential benefits of using real-time information to enhance scheduling decisions. </p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it