scholarly journals A Proposed Genetic Algorithm Coding for Flow-Shop Scheduling Problems

2007 ◽  
Vol 2 (3) ◽  
pp. 229 ◽  
Author(s):  
Hela Boukef ◽  
Mohamed Benrejeb ◽  
Pierre Borne
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Multi Objective Optimization ◽  
Flowshop Scheduling ◽  
Food Industries ◽  
Shop Scheduling ◽  
Multi Objective

A new genetic algorithm coding is proposed in this paper to solve flowshop scheduling problems. To show the efficiency of the considered approach, two examples, in pharmaceutical and agro-food industries are considered with minimization of different costs related to each problem as a scope. Multi-objective optimization is thus, used and its performances proved.

scholarly journals Solving flow shop scheduling problem based on improved non-dominated genetic algorithm

2021 ◽  
Vol 2078 (1) ◽  
pp. 012005
Author(s):  
Tiankui Wang ◽  
Chunlei Ji ◽  
Yuanfeng Hao ◽  
Jianli He
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Adaptive Method ◽  
Flow Shop Scheduling ◽  
Multi Objective Optimization ◽  
New Production ◽  
Pareto Solution ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Coding Method

Abstract Aiming at the multi-objective problem of flow workshop problem, a multi-objective optimization model was constructed and an improved non-dominated sorting genetic algorithm was proposed. Firstly, aiming at these problems, this paper proposes a two-stage chromosome coding method to adapt to the new production scenarios. Secondly, a new adaptive method is proposed to improve the convergence speed and the superiority of Pareto solution set. Finally, simulation results show that the optimality of the improved non-dominated sorting genetic algorithm is improved greatly.

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.

Sign in / Sign up

Export Citation Format

Share Document