A Tabu search-based approach for scheduling job-shop type flexible manufacturing systems

1997 ◽  
Vol 48 (3) ◽  
pp. 264-277 ◽  
Author(s):  
R Logendran ◽  
A Sonthinen
Keyword(s):  
Tabu Search ◽  
Flexible Manufacturing ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Job Shop

scholarly journals A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1391
Author(s):  
Prita Meilanitasari ◽  
Seung-Jun Shin
Keyword(s):  
Flexible Manufacturing ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Future Research ◽  
Relevant Research ◽  
Future Directions ◽  
Individual Product ◽  
Sequencing And Scheduling ◽  
Level Scheduling

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.

Scheduling approaches for random job shop flexible manufacturing systems

1991 ◽  
Vol 29 (5) ◽  
pp. 1053-1067 ◽  
Author(s):  
JIM HUTCHISON ◽  
KEONG LEONG ◽  
DAVID SNYDER ◽  
PETER WARD
Keyword(s):  
Flexible Manufacturing ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Job Shop
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