journal article·2022-03-16Open access

A Robust Scheduling Framework for Re-Manufacturing Activities of Turbine Blades

Lei Liu, Marcello Urgo

Applied Sciences· Volume 12· Issue 6

Abstract

Refurbished products are gaining importance in many industrial sectors, specifically high-value products whose residual value is relevant and guarantee the economic viability of the re-manufacturing at an industrial level, e.g., turbine blades for power generation. In this paper, we address the robust scheduling scheme of re-manufacturing activities for turbine blades. Parts entering the process may have very different wear states or presence of defects. Thus, the repair process is affected by a significant degree of uncertainty. The paper investigates the uncertainties and discusses how they affect the scheduling performance of the re-manufacturing system. We then present a robust scheduling framework for the re-manufacturing scheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches in the re-manufacturing scheduling area, which will be a guideline for the planning and scheduling of re-manufacturing activities of turbine blades. A case study approach was adopted to examine how re-manufacturers design their scheduling strategies.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Manufacturing Process and Optimization · Process Optimization and Integration · Scheduling and Optimization Algorithms

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical Engineering, Politecnico di Milano, 20133 Milano, Italy

Funding

Marie Curie

References (42)

Show source-provided references
1. Product recovery decisions within the context of extended producer responsibility10.1016/j.jengtecman.2013.11.002 ↗
2. Design, management and control of demanufacturing and remanufacturing systems10.1016/j.cirp.2017.05.001 ↗
3. Research on data mining technology for the connotation and measurement of uncertainty for reassembly dimensions
4. Industry 4.0 technologies: Implementation patterns in manufacturing companies10.1016/j.ijpe.2019.01.004 ↗
5. Magruk, A. (2021). Analysis of Uncertainties and Levels of Foreknowledge in Relation to Major Features of Emerging Technologies—The Context of Foresight Research for the Fourth Industrial Revolution. Sustainability, 13.10.3390/su13179890 ↗
6. A review of the state of the art in tools and techniques used to evaluate remanufacturing feasibility10.1016/j.jclepro.2014.06.014 ↗
7. A descriptive framework for additive remanufacturing systems10.1504/IJRAPIDM.2014.066043 ↗
8. Remanufacturing scheme design for used parts based on incomplete information reconstruction10.1186/s10033-020-00457-z ↗
9. Liu, L., and Urgo, M. (2022). Scheduling Remanufacturing Activities for the Repair of Turbine Blades: An Approximate Branch and Bound Approach to Minimize a Risk Measure. Selected Topics in Manufacturing, Springer.10.1007/978-3-030-82627-7_3 ↗
10. A systematic review of decision-making in remanufacturing10.1016/j.cie.2020.106681 ↗
11. Production planning and control for remanufacturing: Industry practice and research needs10.1016/S0272-6963(00)00034-6 ↗
12. A systematic literature review of remanufacturing scheduling10.1080/00207543.2013.774491 ↗
13. Remanufacturing process planning10.1016/j.procir.2014.06.087 ↗
14. A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling10.1016/j.asoc.2015.08.051 ↗
15. Ducker, M. (2022, January 19). The Fall of the F-Class Turbine. Available online: https://www.power-eng.com/emissions/policy-regulations/the-fall-of-the-f-class-turbine/#gref.
16. (2022, March 08). GE H Series Power Generation Gas Turbine. Available online: https://en.wikipedia.org/wiki/Gas_turbine.
17. Wikipedia (2021, December 19). Turbine Blade. Available online: https://en.wikipedia.org/wiki/Turbine_blade.
18. End-of-Life Decision Making with Uncertain Product Return Quantity10.1115/1.4007394 ↗
19. Bermejo, J., Calinescu, A., Efstathiou, J., and Schirn, J. (1997, January 10–11). Dealing with uncertainty in manufacturing: The impact on scheduling. Proceedings of the Thirty-Second International Matador Conference, Manchester, UK.10.1007/978-1-349-14620-8_24 ↗
20. Robustness measures and robust scheduling for job shops10.1080/07408179408966626 ↗
21. A branch-and-bound approach for the single machine maximum lateness stochastic scheduling problem to minimize the value-at-risk10.1007/s10696-018-9316-z ↗
22. Resource-constrained project scheduling with renewable and non-renewable resources and time-resource tradeoffs10.1016/S0360-8352(96)00212-4 ↗
23. Artigues, C., Demassey, S., and Neron, E. (2013). Resource-Constrained Project Scheduling: Models, Algorithms, Extensions and Applications, John Wiley & Sons.
24. Measures of problem uncertainty for scheduling with interval processing times10.1007/s00291-012-0306-3 ↗
25. Min–max regret criterion-based robust model for the permutation flow-shop scheduling problem10.1080/0305215X.2019.1607848 ↗
26. The model of maintenance planning and production scheduling for maximising robustness10.1080/00207543.2018.1492752 ↗
27. Law, A.M., Kelton, W.D., and Kelton, W.D. (2000). Simulation Modeling and Analysis, McGraw-Hill.
28. Robust production control against propagation of disruptions10.1016/j.cirp.2011.03.047 ↗
29. Min–max and min–max regret versions of combinatorial optimization problems: A survey10.1016/j.ejor.2008.09.012 ↗
30. Computing and minimizing the relative regret in combinatorial optimization with interval data10.1016/j.disopt.2005.07.001 ↗
31. Robust optimization for resource-constrained project scheduling with uncertain activity durations10.1007/s10696-012-9147-2 ↗
32. Conditional value-at-risk for general loss distributions10.1016/S0378-4266(02)00271-6 ↗
33. Minimizing value-at-risk in single-machine scheduling10.1007/s10479-016-2251-z ↗
34. A branch-and-bound approach to schedule a no-wait flow shop to minimize the CVaR of the residual work content10.1016/j.cie.2019.01.002 ↗
35. Sarykalin, S., Serraino, G., and Uryasev, S. (2008). Value-at-risk vs. conditional value-at-risk in risk management and optimization. State-of-the-art Decision-Making Tools in the Information-Intensive Age, Informs.10.1287/educ.1080.0052 ↗
36. Project portfolio selection and scheduling optimization based on risk measure: A conditional value at risk approach10.1007/s10479-019-03214-1 ↗
37. Bounding the project completion time distribution in PERT networks10.1287/opre.33.4.862 ↗
38. Determining the optimal sequences and the distributional properties of their completion times in stochastic flow shops10.1016/0305-0548(95)00083-6 ↗
39. Markov and Markov-regenerative PERT networks10.1287/opre.34.5.769 ↗
40. Angius, A., Horváth, A., and Urgo, M. (2021). A Kronecker Algebra Formulation for Markov Activity Networks with Phase-Type Distributions. Mathematics, 9.10.3390/math9121404 ↗
41. Stochastic project management: Multiple projects with multi-skilled human resources10.1007/s10951-018-0592-y ↗
42. A predictive–reactive approach for the sequencing of assembly operations in an automated assembly line10.1016/j.rcim.2021.102201 ↗