Integrated Optimization for Stock Levels and Cross-Training Schemes with Simulation-Based Genetic Algorithm

2016 ◽  
Author(s):  
Hasan Huseyin Turan ◽  
Shaligram Pokharel ◽  
Andrei Sleptchenko ◽  
Tarek Y. Elmekkawy
Keyword(s):  
Genetic Algorithm ◽  
Integrated Optimization ◽  
Simulation Based ◽  
Cross Training ◽  
Training Schemes

Simulation-based optimisation for worker cross-training

2021 ◽  
Vol 16 (3) ◽  
pp. 185
Author(s):  
Johannes Karder ◽  
Andreas Beham ◽  
Viktoria A. Hauder ◽  
Klaus Altendorfer ◽  
Michael Affenzeller
Keyword(s):  
Simulation Based ◽  
Cross Training

Simulation-based optimisation for worker cross-training

2021 ◽  
Vol 16 (3) ◽  
pp. 185
Author(s):  
Johannes Karder ◽  
Andreas Beham ◽  
Viktoria A. Hauder ◽  
Michael Affenzeller ◽  
Klaus Altendorfer
Keyword(s):  
Simulation Based ◽  
Cross Training

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2015 ◽  
pp. 334-347 ◽  
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

scholarly journals Genetic Algorithm for Job Scheduling with Maintenance Consideration in Semiconductor Manufacturing Process

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Seungchul Lee ◽  
Jun Ni
Keyword(s):  
Genetic Algorithm ◽  
Semiconductor Manufacturing ◽  
Job Scheduling ◽  
Optimization Methods ◽  
Optimization Method ◽  
Scheduling Problems ◽  
Simulation Based ◽  
Sequencing Problems ◽  
Maintenance Activities ◽  
Single Cluster

This paper presents wafer sequencing problems considering perceived chamber conditions and maintenance activities in a single cluster tool through the simulation-based optimization method. We develop optimization methods which would lead to the best wafer release policy in the chamber tool to maximize the overall yield of the wafers in semiconductor manufacturing system. Since chamber degradation will jeopardize wafer yields, chamber maintenance is taken into account for the wafer sequence decision-making process. Furthermore, genetic algorithm is modified for solving the scheduling problems in this paper. As results, it has been shown that job scheduling has to be managed based on the chamber degradation condition and maintenance activities to maximize overall wafer yield.

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