The shifting bottleneck procedure for job shop scheduling
Management Science
Computational feasibility of multi-criteria models of production, planning and scheduling
Computers and Industrial Engineering
Guided Local Search with Shifting Bottleneck for Job Shop Scheduling
Management Science
Decomposition methods for large job shops
Computers and Operations Research
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Computers and Operations Research
ISADS '99 Proceedings of the The Fourth International Symposium on Autonomous Decentralized Systems
Multicriteria Scheduling: Theory, Models and Algorithms
Multicriteria Scheduling: Theory, Models and Algorithms
Scheduling jobs on parallel machines with setup times and ready times
Computers and Industrial Engineering
A self-adaptive agent-based fuzzy-neural scheduling system for a wafer fabrication factory
Expert Systems with Applications: An International Journal
A two-stage hybrid memetic algorithm for multiobjective job shop scheduling
Expert Systems with Applications: An International Journal
Rule-based scheduling in wafer fabrication with due date-based objectives
Computers and Operations Research
Dynamic supply chain scheduling
Journal of Scheduling
Computers and Industrial Engineering
International Journal of Fuzzy System Applications
International Journal of Intelligent Information Technologies
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In this research, we model a semiconductor wafer fabrication process as a complex job shop, and adapt a Modified Shifting Bottleneck Heuristic (MSBH) to facilitate the multi-criteria optimization of makespan, cycle time, and total weighted tardiness using a desirability function. The desirability function is implemented at two different levels of the MSBH: the subproblem solution procedure level (SSP level) and the machine criticality measure level (MCM level). In addition, we suggest two different methods of choosing the critical toolgroup at the MCM level: (1) the Local MCM approach, which chooses the critical toolgroup based on local desirability values from the SSP level and (2) the Global MCM approach, which chooses the critical toolgroup based on its impact on the desirability of the entire disjunctive graph. Results demonstrate the desirability-based approaches' ability to simultaneously minimize all three objectives.