A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systemsopen access
- Authors
- Meilanitasari, Prita; Shin, Seung-Jun
- Issue Date
- Aug-2021
- Publisher
- MDPI
- Keywords
- flexible manufacturing systems; job shop scheduling; sequence learning; sequence prediction; uncertainty
- Citation
- PROCESSES, v.9, no.8, pp.1 - 26
- Indexed
- SCIE
SCOPUS
- Journal Title
- PROCESSES
- Volume
- 9
- Number
- 8
- Start Page
- 1
- End Page
- 26
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/141391
- DOI
- 10.3390/pr9081391
- ISSN
- 2227-9717
- Abstract
- This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.
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