Manufacturing cell scheduling using genetic algorithms

2000 ◽  
Vol 214 (2) ◽  
pp. 159-163 ◽  
Author(s):  
G C Onwubolu
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Performance Measure ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Manufacturing Cells ◽  
Data Set ◽  
Manufacturing Cell ◽  
Combinatorial Optimization Problems ◽  
Genetic Algorithm Procedure

This paper presents a new approach to the scheduling of manufacturing cells which have flow-shop configuration. The approach is based on the genetic algorithm, which is a meta-heuristic for solving combinatorial optimization problems. The performance measure demonstrated in this paper is the optimization of the mean flow time. The procedure developed automatically computes the make-span. A flexible manufacturing cell schedule is used as a case study. The genetic algorithm procedure was used to solve a published data set for simple scheduling problems. The genetic algorithm procedure was further used to solve large flow-shop scheduling problems having machine sizes of up to 30 and job sizes of up to 100 in very reasonable computation time. The results show that the genetic-algorithm-based heuristic is promising for scheduling manufacturing cells.

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS 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.

Solving Flow Shop Scheduling Problems with Blocking by using Genetic Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 20-38
Author(s):  
Harendra Kumar ◽  
Pankaj Kumar ◽  
Manisha Sharma
Keyword(s):  
Genetic Algorithm ◽  
Completion Time ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Working Process ◽  
Shop Scheduling ◽  
Mutation Operators ◽  
And Inversion ◽  
Present Algorithm

Flow shop scheduling problems have been analyzed worldwide due to their various applications in industry. In this article, a new genetic algorithm (NGA) is developed to obtain the optimum schedule for the minimization of total completion time of n-jobs in an m-machine flow shop operating without buffers. The working process of the present algorithm is very efficient to implement and effective to find the best results. To implement the proposed algorithm more effectively, similar job order crossover operators and inversion mutation operators have been used. Numerous examples are illustrated to explain proposed approach. Finally, the computational results indicate that present NGA performs much superior to the heuristics for blocking flow shop developed in the literature.

Optimization of Two-Stage Hybrid Flow Shop Scheduling Problems Using Genetic Algorithm

2015 ◽  
Vol 766-767 ◽  
pp. 962-967
Author(s):  
M. Saravanan ◽  
S. Sridhar ◽  
N. Harikannan
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling

The two-stage Hybrid flow shop (HFS) scheduling is characterized n jobs m machines with two-stages in series. The essential complexities of the problem need to solve the hybrid flow shop scheduling using meta-heuristics. The paper addresses two-stage hybrid flow shop scheduling problems to minimize the makespan time with the batch size of 100 using Genetic Algorithm (GA) and Simulated Annealing algorithm (SA). The computational results observed that the GA algorithm is finding out good quality solutions than SA with lesser computational time.

scholarly journals Pengembangan Algoritma Non Delay Pada Kasus Penjadwalan Non-Permutation Hybrid Flowshop Untuk Minimasi Mean Flowtime

2018 ◽  
Vol 19 (2) ◽  
pp. 148
Author(s):  
Siti Muhimatul Khoiroh
Keyword(s):  
Production Scheduling ◽  
Manufacturing Industry ◽  
Success Factors ◽  
Flow Shop ◽  
Flow Time ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Mean Flow Time ◽  
Permutation Flow Shop

Production scheduling is one of the key success factors in the production process. Scheduling approach with Non-Permutation flow shop is a generalization of the traditional scheduling problems Permutation flow shop for the manufacturing industry to allow changing the job on different machines with the flexibility of combinations. This research tries to develop a heuristic approach that is non-delay algorithm by comparing Shortest Processing Time (SPT) and Largest Remaining Time (LRT) in the case of non-permutation flow shop to produce minimum mean flow time ratio. The result of simulation shows that the SPT algorithm gives less mean flow time value compared to LRT algorithm which means that SPT algorithm is better than LRT in case of non-permutation hybrid flow shop.

scholarly journals Application of genetic algorithm to industrial scheduling and problems of parameters evaluation

2021 ◽  
Vol 47 ◽  
Author(s):  
Edgaras Šakurovas ◽  
Narimantas Listopadskis
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Flow Shop ◽  
Np Hard ◽  
Scheduling Problems ◽  
Optimal Parameters ◽  
Algorithm Performance ◽  
Hard Problems ◽  
Np Hard Problems ◽  
Real World Problems

Genetic algorithms are widely used in various mathematical and real world problems. They are approximate metaheuristic algorithms, commonly used for solving NP-hard problems in combinatorial optimisation. Industrial scheduling is one of the classical NP-hard problems. We analyze three classical industrial scheduling problems: job-shop, flow-shop and open-shop. Canonical genetic algorithm is applied for those problems varying its parameters. We analyze some aspects of parameters such as selecting optimal parameters of algorithm, influence on algorithm performance. Finally, three strategies of algorithm – combination of parameters and new conceptualmodel of genetic algorithm are proposed.

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