A GENERAL MODEL FOR JOB SHOP PROBLEMS USING IMUNE-GENETIC ALGORITHM AND MULTIOBJECTIVE OPTIMIZATION TECHNIQUES

2009 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
General Model ◽  
Job Shop ◽  
Optimization Techniques

scholarly journals Performance Analysis of Opposition Based Particle Swarm Optimization with Cauchy Distribution in Minimizing Makespan Time in Job Shop Scheduling

2020 ◽  
Vol 8 (6) ◽  
pp. 360-366
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Particle Swarm ◽  
Cauchy Distribution ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems

In the contemporary circumstances, manual solving of job shop scheduling problem (JSSP) is quite time consuming and inaccurate. The main intention of this paper is to analyze the performance of various optimization techniques in JSSP in order to minimize makespan time. This paper aims to analyze four optimization techniques viz, particle swarm optimization (PSO), genetic algorithm (GA), opposition based genetic algorithm(OGA) and opposition based particle swarm optimization with Cauchy distribution (OPSO CD) in addition to the existing optimization techniques applied in various research papers on combinatorial optimization problems viz., JSSP. A comparative study of these optimization techniques were conducted and analyzed to find out the most effective optimization technique on solving JSSP. Results show that OPSO CD is found to possess minimum makespan time in comparison with other algorithms for JSSP.

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 Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hongjing Wei ◽  
Shaobo Li ◽  
Huafeng Quan ◽  
Dacheng Liu ◽  
Shu Rao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficient ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1392 ◽  
Author(s):  
Iram Parvez ◽  
JianJian Shen ◽  
Mehran Khan ◽  
Chuntian Cheng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Stochastic Programming ◽  
Short Interval ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Fuzzy Logics ◽  
Spinning Reserve ◽  
Lagrange Relaxation ◽  
Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

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