Multi-Objective Criteria in Hybrid Flow Shop Scheduling Using Improved Genetic Algorithm

2017 ◽  
Vol 11 (2) ◽  
pp. 537-544
Author(s):  
M. L. Brabin Nivas ◽  
T. Prabaharan
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Hybrid Flow Shop Scheduling

A fuzzy logic based methodology for multi-objective hybrid flow shop scheduling with multi-processor tasks problems and solving with an efficient genetic algorithm

2021 ◽  
pp. 1-13
Author(s):  
Orhan Engin ◽  
Mustafa Kerim Yılmaz
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Processing Time ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Due Dates ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Hybrid Flow Shop Scheduling

In the conventional scheduling problem, the parameters such as the processing time for each job and due dates are usually assumed to be known exactly, but in many real-world applications, these parameters may very dynamically due to human factors or operating faults. During the last decade, several works on scheduling problems have used a fuzzy approach including either uncertain or imprecise data. A fuzzy logic based tool for multi-objective Hybrid Flow-shop Scheduling with Multi-processor Tasks (HFSMT) problem is presented in this paper. In this study, HFSMT problems with a fuzzy processing time and a fuzzy due date are formulated, taking Oğuz and Ercan’s benchmark problems in the literature into account. Fuzzy HFSMT problems are formulated by three-objectives: the first is to maximize the minimum agreement index and the second is to maximize the average agreement index, and the third is to minimize the maximum fuzzy completion time. An efficient genetic algorithm(GA) is proposed to solve the formulated fuzzy HFSMT problems. The feasibility and effectiveness of the proposed method are demonstrated by comparing it with the simulated annealing (SA) algorithm 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.

Study on Multi-Objective Optimization of Hybrid Flow Shop Scheduling Problem

2014 ◽  
Vol 1082 ◽  
pp. 529-534
Author(s):  
Zheng Ying Lin ◽  
Wei Zhang
Keyword(s):  
Flow Shop ◽  
Economic Benefits ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Hybrid Flow Shop Scheduling

Due to several mutual conflicting optimized objectives in the hybrid flow shop scheduling problem, its optimized model, including three objectives of make-span, flow-time and tardiness, was firstly set up, instead of the single optimized objective. Furthermore, in order to improve the optimized efficiency and parallelism, after comparing the normal multi-objective optimized methods, an improved NSGA-II algorithm with external archive strategy was proposed. Finally, taking a piston production line as example, its performance was tested. The result showed that the multi-objective optimization of hybrid flow shop scheduling based on improved NSGA-II provided managers with a set of feasible solutions for selection in accordance to their own preference. Therefore the decision could be made more scientific and efficient, and thus brings to the factory more economic benefits.

Adaptive Genetic Algorithm for Hybrid Flow-Shop Scheduling

2013 ◽  
Vol 753-755 ◽  
pp. 2925-2929
Author(s):  
Xiao Chun Zhu ◽  
Jian Feng Zhao ◽  
Mu Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Adaptive Genetic Algorithm ◽  
Shop Scheduling ◽  
Mutation Operators ◽  
Hybrid Flow Shop Scheduling ◽  
Encoding Method ◽  
Crossover And Mutation

This paper studies the scheduling problem of Hybrid Flow Shop (HFS) under the objective of minimizing makespan. The corresponding scheduling simulation system is developed in details, which employed a new encoding method so as to guarantee the validity of chromosomes and the convenience of calculation. The corresponding crossover and mutation operators are proposed for optimum sequencing. The simulation results show that the adaptive Genetic Algorithm (GA) is an effective and efficient method for solving HFS Problems.

Sign in / Sign up

Export Citation Format

Share Document