Using the Memetic Algorithm for Multi Objective Job Shop Scheduling Problems

2012 ◽  
Vol 544 ◽  
pp. 245-250 ◽  
Author(s):  
Guo Hui Zhang
Keyword(s):  
Search Strategy ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Shop Scheduling ◽  
Multi Objective

The multi objective job shop scheduling problem is well known as one of the most complex optimization problems due to its very large search space and many constraint between machines and jobs. In this paper, an evolutionary approach of the memetic algorithm is used to solve the multi objective job shop scheduling problems. Memetic algorithm is a hybrid evolutionary algorithm that combines the global search strategy and local search strategy. The objectives of minimizing makespan and mean flow time are considered while satisfying a number of hard constraints. The computational results demonstrate the proposed MA is significantly superior to the other reported approaches in the literature.

scholarly journals AIS-SFHM APPROACH FOR OPTIMIZATION OF MULTI OBJECTIVE JOB SHOP PROBLEMS

2017 ◽  
Vol 13 (7) ◽  
pp. 6363-6368
Author(s):  
Chandrasekaran Manoharan
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Approach ◽  
Flow Time ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Mean Flow ◽  
Shop Scheduling ◽  
Multi Objective

The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems. The JSP problem is a scheduling problem, where a set of ‘n’ jobs must be processed or assembled on a set of ‘m’ dedicated machines. Each job consists of a specific set of operations, which have to be processed according to a given technical precedence order. Job shop scheduling problem is a NP-hard combinatorial optimization problem.  In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. The hybrid approach of Sheep Flocks Heredity Model Algorithm (SFHM) is used for finding optimal makespan, mean flow time, mean tardiness. The hybrid SFHM approach is tested with multi objective job shop scheduling problems. Initial sequences are generated with Artificial Immune System (AIS) algorithm and results are refined using SFHM algorithm. The results show that the hybrid SFHM algorithm is an efficient and effective algorithm that gives better results than SFHM Algorithm, Genetic Algorithm (GA). The proposed hybrid SFHM algorithm is a good problem-solving technique for job shop scheduling problem with multi criteria.

Optimization of Total Holding Cost in Job Shop Scheduling by Using Hybrid Algorithm

2014 ◽  
Vol 591 ◽  
pp. 176-179
Author(s):  
S. Gobinath ◽  
C. Arumugam ◽  
G. Ramya ◽  
M. Chandrasekaran
Keyword(s):  
Hybrid Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems ◽  
Holding Cost ◽  
Hard Problems

The classical job-shop scheduling problem is one of the most difficult combinatorial optimization problems. Scheduling is defined as the art of assigning resources to tasks in order to insure the termination of these tasks in a reasonable amount of time. Job shop scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Mathematical and heuristic methods are the two major methods for resolving JSP. Job shop Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions. In this paper, a Hybrid algorithm combined artificial immune system and sheep flock heredity model algorithm is used for minimizing the total holding cost for different size benchmark problems. The results show that the proposed hybrid algorithm is an effective algorithm that gives better results than other hybrid algorithms compared in literature. The proposed hybrid algorithm is a good technique for scheduling problems.

The Simulation Optimization for Job-Shop Scheduling Based on Plant Simulation Using Genetic Algorithm

2012 ◽  
Vol 217-219 ◽  
pp. 1444-1448
Author(s):  
Xiang Ke Tian ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems ◽  
Plant Simulation

The job-shop scheduling problem (JSP), which is one of the best-known machine scheduling problems, is among the hardest combinatorial optimization problems. In this paper, the key technology of building simulation model in Plant Simulation is researched and also the build-in genetic algorithm of optimizing module is used to optimize job-shop scheduling, which can assure the scientific decision. At last, an example is used to illustrate the optimization process of the Job-Shop scheduling problem with Plant Simulation genetic algorithm modules.

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