A multi-objective optimization approach for resource assignment and task scheduling problem: Application to maritime domain awareness

2012 ◽  
Author(s):  
Olfa Dridi ◽  
Saoussen Krichen ◽  
Adel Guitouni
Keyword(s):  
Task Scheduling ◽  
Optimization Approach ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Resource Assignment ◽  
Multi Objective ◽  
Maritime Domain Awareness ◽  
And Task

scholarly journals Improved genetic algorithm for 3D printing multi-objective optimization task scheduling

2021 ◽  
Vol 336 ◽  
pp. 02020
Author(s):  
Haobin Zhao ◽  
Hongbin Yu
Keyword(s):  
3D Printing ◽  
Task Scheduling ◽  
Related Problem ◽  
Problem Definition ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Problem Model ◽  
Multi Objective ◽  
Optimization Task

For the 3D printing multi-objective optimization task scheduling problem, the problem model is established from the three aspects of related problem definition, constraint conditions, and objective function, which lays the foundation for subsequent research and solution.

Multi-Objective Optimization Approach with Job-Based Encoding Method for Semiconductor Final Testing Scheduling Problem

2012 ◽  
Vol 622-623 ◽  
pp. 152-157
Author(s):  
Yi Sun ◽  
Xin Wei ◽  
Shigeru Fujimura ◽  
Gen Ke Yang
Keyword(s):  
Parallel Machines ◽  
Flow Shop ◽  
Optimization Approach ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Penalty Cost ◽  
Multi Objective ◽  
Testing Stage ◽  
Encoding Method

The semiconductor final testing scheduling problem (SFTSP) is a variation of the complex scheduling problem, which deals with the arrangement of the job sequence for the final testing process. In this paper, we present an actual SFTSP case includes almost all the flow-shop factors as reentry characteristic, serial and batch processing stages, lot-clusters and parallel machines. Since the critical equipment needs to be utilized efficiently at a specific testing stage, the scheduling arrangement is then playing an important role in order to reduce both the makespan and penalty cost of all late products in total final testing progress. On account of the difficulty and long time it takes to solve this problem, we propose a multi-objective optimization approach, which uses a lot-merging procedure, a new job-based encoding method, and an adjustment to the non-dominated sorting genetic algorithm II (NSGA-II). Simulation results of the adjusted NSGA-II on this SFTSP problem are compared with its traditional algorithm and much better performance of the adjusted one is observed.

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