scholarly journals Genetic Algorithm Modification for Production Scheduling

2013 ◽  
Vol 38 (4) ◽  
pp. 299-309 ◽  
Author(s):  
Tomasz Brzęczek ◽  
Dariusz Nowak
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Completion Time ◽  
Job Shop ◽  
Formal Models ◽  
Theoretical Part ◽  
Solution Algorithms

Abstract Scheduling of production soundly affects its capacity especially if system does complex production jobs. In the theoretical part of the article an overview of the scheduling methods proposed in the literature was presented. In this paper it was stated a variant of job shop problem, in which jobs can overlap in some machines and omit others. Authors designed and presented here genetic algorithm to optimize solution of such a problem. The algorithm finds jobs sequence priority and in accordance with it schedules operations and calculates their completion time. An adequate problem was met in an examined plant, where 20 production jobs consisted of 11 to 20 operations assigned to at most 15 machines. Such big parameter numbers are crucial for big formal models and their solution algorithms. The designed algorithm proved to deal with parameters scale, as it found the schedule with 23,8% shorter jobs completion time in comparison with FIFO heuristic, that has been used so far by the plant.

scholarly journals PENJADWALAN MESIN DENGAN MENGGUNAKAN ALGORITMA JADWAL NON-DELAY DI PT. SNN SEKSI WORKSHOP

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Inna Kholidasari ◽  
Keyword(s):  
Production Process ◽  
Production Scheduling ◽  
Completion Time ◽  
Job Shop ◽  
Machine Scheduling ◽  
Spare Parts ◽  
First In First Out ◽  
A Company

Production scheduling is the most important part in carrying out the production process that will be carried out on a production floor. Scheduling activities are carried out before the production process begins to ensure the smooth running of the production process. If the production scheduling is not done properly, there will be obstacles in the production process and will cause losses to the company. This study aims to determine the production machine scheduling in a company engaged in the manufacture of spare parts for automotive products. This company implements a job shop production process and uses the First In First Out method in completing its work. Due to the large number of products that have to be produced, there are often two or more products that must be worked on at the same time and machine. This condition causes some products to have to wait for the associated machine to finish operating and causes long product turnaround times. This problem is solved by making a production machine scheduling using the Non-Delay method. By applying this method, the makespan of completion time can be minimized.

Minimizing Makespan in Job Shop Scheduling Problem Using Genetic Algorithm

2015 ◽  
Vol 813-814 ◽  
pp. 1183-1187 ◽  
Author(s):  
Aathi Muthiah ◽  
R. Rajkumar ◽  
B. Muthukumar
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Setup Time ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Knowing That ◽  
Batch Sizes

- Scheduling is an important tool for manufacturing and engineering, where it can have a major impact on the productivity of a process. In manufacturing, the purpose of scheduling is to minimize the production time and costs. Production scheduling aims to maximize the efficiency of the operation and reduce costs. We keep all of our machines well-maintained to prevent any problems, but there is on way to completely prevent down-time. With redundant machines we have the security of knowing that we are not going to be in trouble meeting our deadlines if a machine has any unexpected down-times. Finally we can work to get our batch sizes as small as is reasonably possible while also reducing the setup time of each batch. This allows us to eliminate a sizable portion of each part waiting while the rest of the parts in the batch are being machined.

Energy-Aware Production Scheduling in Flow Shop and Job Shop Environments Using a Multi-Objective Genetic Algorithm

2019 ◽  
Vol 31 (2) ◽  
pp. 82-97
Author(s):  
Pablo Vallejos-Cifuentes ◽  
Camilo Ramirez-Gomez ◽  
Ana Escudero-Atehortua ◽  
Elkin Rodriguez Velasquez
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Job Shop ◽  
Flow Shop ◽  
Energy Aware ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

scholarly journals MANUFACTURING INDUSTRY JOB SHOP FLOOR DECISIONS THROUGH HYBRID GENETIC ALGORITHM

2021 ◽  
Vol 9 (2) ◽  
pp. 62-76
Author(s):  
Dr. Nageswara Rao. M, Et. al.
Keyword(s):  
Genetic Algorithm ◽  
Completion Time ◽  
Processing Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Hybrid Genetic Algorithm ◽  
Shop Floor ◽  
Scheduling Problems ◽  
Priority Rules ◽  
Simultaneous Scheduling

This article addresses flexible manufacturing system (FMS) Performance is likely to improve with employment of various resources efficiently. Initially simultaneous scheduling problems are solved by means of priority rules like first come first serve (FCFS), shortest processing time (SPT) and longest processing time (LPT) to find out the operational completion time for 120 problems. Later gene rearrangement genetic algorithm (HGA) is implemented for same set of problems with makespan as objective and the results are compared with the results of priority rules. The results are performed well by using HGA.  The same HGA is used to find the finest optimal sequence that minimize the operational completion time.  

A GENETIC ALGORITHM APPROACH FOR SOLVING INBOARD OUTER FIXED LEADING EDGE-DRIVE RIB 1 PRODUCTION SCHEDULING AT PT DIRGANTARA INDONESIA

JOURNAL ASRO ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 120
Author(s):  
Heri Awalul Ilhamsah ◽  
Indra Cahyadi ◽  
Ari Yulianto
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Job Shop ◽  
Leading Edge ◽  
Optimization Techniques ◽  
Genetic Operators ◽  
Roulette Wheel Selection ◽  
Np Hard Problem ◽  
Genetic Algorithm Approach ◽  
The Given

The scheduling of production floor is a sophisticated problem which seeks the optimal task allocation to certain resources under a number of constraints. The use of optimization techniques facilitates the determination of acceptable solutions that considered optimized for a specific problem. This paper proposes production scheduling solution based on job priority in a Non-Deterministic Polynomial-time hard (NP-hard) problem. The case study was taken from the Spirit Aerosystem Project, particularly in the Inboard Outer Fixed Leading Egde - Drive Rib 1 component production process. The problem consists of finding the machine operations schedule, taking into account the precedence constraints. The main objective is to minimize total delays or tardiness. The genetic algorithm was employed to determine the optimized production scheduling solution. The parameter for genetic operators in this study consists of a roulette wheel selection, 1 elitist chromosome, partially-mapped crossover mutation and 1 point mutation. The termination condition was achieved when there has been no improvement in the population for 30 iterations.The results show that the algorithm is capable to generate optimum production schedule with minimum tardiness for the given problem. Keywords: Genetic algorithm, job shop problem, scheduling problem

scholarly journals Improved Whale Optimization with Buffer Setup Time

2019 ◽  
Vol 9 (2) ◽  
pp. 675-680
Keyword(s):  
Genetic Algorithm ◽  
Completion Time ◽  
Job Shop ◽  
Setup Time ◽  
Industrial Applications ◽  
Fitness Score ◽  
Whale Optimization ◽  
Highly Effective ◽  
Optimum Solution

The job shop problem is widespread in industrial applications due to lateness in getting a better solution. Buffer plays an important role in the completion of jobs. Proper utilization of buffer by fixing optimal buffer setup time can lead to optimal completion time so that lateness is minimized. This work introduced Improved Whale Optimization with Buffer Setup time Technique (IWOBS) for setting optimal buffer setup time. Using tabu movements, an optimum solution for the nearest buffer setup time is derived and the genetic algorithm is equipped to find fitness score to gather the best buffer setup time with respect to problem consideration. The proposed IWOBS algorithm is proved to be highly effective compared to other existing algorithms

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