1A2-E11 Optimization on Job-shop Scheduling Problem with Multiple Purpose Functions(Evolution and Learning for Robotics(2))

Abstract

The Job-shop Scheduling Problem is a classical, but yet modern problem. Because it belongs to the NP-hard problem and it is almost impossible to find out the exact solution, many heuristic methods have been proposed. Recently, Genetic Algorithm (GA), Local Clustering Organization (LCO) and so on have been proposed as a powerful tool to solve the problem. The optimal schedule becomes very important from a viewpoint of cost reduction in the manufacturing industry. This study optimizes the job-shop scheduling with multiple purpose functions by using evolutional computation. Numerical experiments verify that the evolutional computation obtains (quasi-) optimal schedule with multiple purpose functions.