A novel integer linear model for reliability-centric service composition in cloud manufacturing
Bibliographic record
Abstract
Cloud manufacturing, as a service-oriented approach for manufacturing systems, bridges physical resources with customer demands through resource sharing and smart manufacturing technologies, thereby enhancing production efficiency, service quality, and sustainability. Within this paradigm, the service composition problem, an NP-hard optimization challenge, has received sustained attention, and Quality of Service (QoS) metrics that combine cost, time, and reliability are commonly used to assess competing compositions. Because the resulting mixed-integer nonlinear formulation scales poorly beyond small instances, we focus on sequential compositions and transform the nonlinear reliability term into an additive logarithmic surrogate; the associated fixed-point weight is found with a bisection procedure that solves a series of mixed-integer linear programs of unchanged size. Cost is represented through time-dependent and time-independent pricing parameters, and the bilinear expression that arises is linearized with auxiliary inequalities, enabling precise cost analysis and a rational link between pricing and reliability. Experiments on 70 synthetic benchmark instances, reaching 240 tasks and 240 providers, show that the proposed approach reproduces global-solver results on small problems and improves the median solution quality over a tuned simulated annealing metaheuristic by 4%–16% while reducing run time by up to two orders of magnitude. Sensitivity analysis of the pricing parameters reveals how economic incentives steer service assignments between speed-dominated and reliability-dominated regimes. These findings demonstrate that reliability-aware, multi-criteria service selection can be computed at an industrial scale with modest computational resources, providing a practical decision support tool and a basis for extending QoS optimization to more complex workflow topologies and real-world data sets and can be replicated through a scenario wise approach to serve as a plug and play core for future stochastic or rolling horizon cloud manufacturing planners.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".