Multi-Objective Simulated Annealing for a Maintenance Workforce Scheduling Problem: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this chapter, we proposed a multi-objective simulated annealing (MOSA) algorithm to solve a real maintenance workforce scheduling problem (MWSP) with the aim of simultaneously minimizing the workforce cost and the flow time of the work requests. The latter objective is equivalent to the maximization of the equipment availability because by increasing the flow time of a work request the unscheduled shutdown of the corresponding asset will increase too. Workforces have different proficiencies and are grouped into a number of teams called “Field Groups†(or FG for short). Labour requirements are provided from internal and external resources as regular time, overtime and contract. We use a MOSA algorithm introduced in the literature namely Suppapitnarm-MOSA to solve the MWSP. In this method, an archive set stores all the non-dominated/Pareto solutions between each of the multiple objectives. The acceptance probability of a new solution depends on whether or not it is added to the set of potentially Pareto optimal set. However, all objectives affect the acceptance probability of a non-improver solution. The developed MOSA uses the swapping adjacent pair strategy to explore the feasible solution. One of the main differences between the current study and previous ones is that we consider the precedence relations between FGs to do a given work request, in addition to the traditional interference relations between work requests that must be scheduled for a given FG. This extra assumption is a big obstacle to generating the feasible or neighbourhood solutions. Hence, the single solution-based meta-heuristics such as SA or Tabu search seem to be the unique alternatives to solve this problem. This is because population-based operators, such as crossover in Genetic Algorithm, lead to infeasible solutions most of the time. To overcome this drawback, we introduce a recursive-sequential approach to construct the sequence of works for each FG with the aim of identifying the infinite loops resulting from consecutive interference and precedence relations. Because the Pareto optimal set cannot be obtained in real-sized problems, a lower bound was developed separately for each objective function and the obtained Pareto front is compared with these lower bounds. The obtained results show that the developed MOSA is a robust method to solve the MWSP. Our reasoning is that the developed MOSA always converges to a small region of the feasible space, very close to the lower bound of one of the objective functions while the relative difference between the obtained results and the lower bound of another objective function doesn’t increase significantly when the size of the problem increases.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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