scholarly journals Design and Application of Genetic Algorithm Based on Signal Game and Newsboy Model for Optimizing Supply Chain

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yue Zhao ◽  
Yang Shen ◽  
Jiaqi Yan
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Fitness Function ◽  
Optimal Solution ◽  
Supply Chain Optimization ◽  
Business Decisions ◽  
Efficient Operation ◽  
Maximum Utility ◽  
Newsboy Model ◽  
Decision Making Model

Reasonable communication and cooperation between enterprises are helpful for the efficient operation of a supply chain. To explore the maximum utility of an entire supply chain, we propose a supplier-manufacturer-seller supply-chain game decision-making model. We use the model as the fitness function of a genetic algorithm that calculates the optimal solution and optimizes the total utility parameters. We analyze the theoretical and practical properties of the supply-chain optimization process and implement it in MATLAB, which provides quantitative support and useful references for making business decisions and optimally managing a supply chain.

A Genetic Algorithm for the Production Policy in a Supply Chain under Discrete Demand and Time-Vary Cost

2013 ◽  
Vol 321-324 ◽  
pp. 2137-2140 ◽  
Author(s):  
Bing Chang Ouyang
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Fitness Function ◽  
Optimal Solution ◽  
Just In Time ◽  
Adaptive Genetic Algorithm ◽  
Production Policy ◽  
Production Scheme ◽  
Distribution Requirement ◽  
Local Optimal Solution

Considering discrete demand and time-vary unit production cost under a foreseeable time horizon, this study presents an adaptive genetic algorithm to determine the production policy for one manufacturer supplying single item to multiple warehouses in a supply chain environment. Based on Distribution Requirement Planning (DRP) and Just in Time (JIT) delivery policy, we assume each gene in chromosome represents a period. Standard GA operators are used to generate new populations. These populations are evaluated by a fitness function using the total cost of production scheme. An explicit procedure for obtaining the local optimal solution is provided.

A Natural Evolution Based Numerical Optimisation Framework to Develop and Enhance Airfoil-Slat Arrangement

2019 ◽  
Author(s):  
Sushrut Kumar ◽  
Priyam Gupta ◽  
Raj Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Fuel Efficiency ◽  
Optimal Solution ◽  
Leading Edge ◽  
Initial Population ◽  
Invasive Weed ◽  
Fitness Value ◽  
Leading Edge Slats ◽  
Dynamics Simulations

Abstract Leading Edge Slats are popularly being put into practice due to their capability to provide a significant increase in the lift generated by the wing airfoil and decrease in the stall. Consequently, their optimum design is critical for increased fuel efficiency and minimized environmental impact. This paper attempts to develop and optimize the Leading-Edge Slat geometry and its orientation with respect to airfoil using Genetic Algorithm. The class of Genetic Algorithm implemented was Invasive Weed Optimization as it showed significant potential in converging design to an optimal solution. For the study, Clark Y was taken as test airfoil. Slats being aerodynamic devices require smooth contoured surfaces without any sharp deformities and accordingly Bézier airfoil parameterization method was used. The design process was initiated by producing an initial population of various profiles (chromosomes). These chromosomes are composed of genes which define and control the shape and orientation of the slat. Control points, Airfoil-Slat offset and relative chord angle were taken as genes for the framework and different profiles were acquired by randomly modifying the genes within a decided design space. To compare individual chromosomes and to evaluate their feasibility, the fitness function was determined using Computational Fluid Dynamics simulations conducted on OpenFOAM. The lift force at a constant angle of attack (AOA) was taken as fitness value. It was assigned to each chromosome and the process was then repeated in a loop for different profiles and the fittest wing slat arrangement was obtained which had an increase in CL by 78% and the stall angle improved to 22°. The framework was found capable of optimizing multi-element airfoil arrangements.

scholarly journals Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Novel Mutation ◽  
Fitness Function ◽  
Genetic Disorders ◽  
Optimal Solution ◽  
Global Optimum ◽  
Adaptive Mutation ◽  
Local Optima

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals Solving Approach of Inverse Kinematics for Manipulators Based on Improved Adaptive Niche Genetic Algorithm

2019 ◽  
Vol 37 (3) ◽  
pp. 488-495
Author(s):  
Huizhen Yang ◽  
Xiyang Liu
Keyword(s):  
Genetic Algorithm ◽  
Inverse Kinematics ◽  
Fitness Function ◽  
Optimal Solution ◽  
Solution Approach ◽  
The Poor ◽  
Pose Control ◽  
Simulation Results ◽  
Six Degree Of Freedom ◽  
Speed And Accuracy

Solving the inverse kinematics for a manipulator is of great importance to the manipulator's pose control and trajectory planning. Aiming at the poor generality and difficulty of finding an optimal solution from the multiple inverse kinematics solutions, a novel solution approach based on the modified adaptive niche genetic algorithm is proposed in this study. The principle of 'most suppleness' is integrated into the fitness function such that the only optimal solution can be found; The clustering is introduced into the approach for enhancing the generality and the genetic algorithm is improved for increasing the convergence speed and accuracy. Simulation results based on a six degree of freedom manipulator show that the proposed approach is effective and high precision, and can find the optimal solution.

scholarly journals An Integrated Multi-Echelon Supply Chain Network Design Considering Stochastic Demand: A Genetic Algorithm Based Solution

2017 ◽  
Vol 29 (4) ◽  
pp. 391-400 ◽  
Author(s):  
Sara Nakhjirkan ◽  
Farimah Mokhatab Rafiei
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Large Scale ◽  
Human Life ◽  
Optimal Solution ◽  
Green Supply Chain ◽  
Inventory Routing ◽  
Model Object ◽  
Proposed Model ◽  
Practical Tool

The growing trend of natural resources consumption has caused irreparable losses to the environment. The scientists believe that if environmental degradation continues at its current pace, the prospect of human life will be shrouded in mystery. One of the most effective ways to deal with the environmental adverse effects is by implementing green supply chains. In this study a multilevel mathematical model including supply, production, distribution and customer levels has been presented for routing–location–inventoryin green supply chain. Vehicle routing between distribution centres and customers has been considered in the model. Establishment place of distribution centres among potential places is determined by the model. The distributors use continuous review policy (r, Q) to control the inventory. The proposed model object is to find an optimal supply chain with minimum costs. To validate the proposed model and measure its compliance with real world problems, GAMS IDE/Cplex has been used. In order to measure the efficiency of the proposed model in large scale problems, a genetic algorithm has been used. The results confirm the efficiency of the proposed model as a practical tool for decision makers to solve location-inventory-routing problems in green supply chain. The proposed GA could reduce the solving time by 85% while reaching on the average 97% of optimal solution compared with exact method.

scholarly journals Maritime Supply Chain Optimization by Using Fuzzy Goal Programming

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 234
Author(s):  
Bekir Sahin ◽  
Devran Yazir ◽  
Abdelsalam Adam Hamid ◽  
Noorul Shaiful Fitri Abdul Rahman
Keyword(s):  
Supply Chain ◽  
Goal Programming ◽  
Optimal Solution ◽  
Theoretical Background ◽  
Fuzzy Goal Programming ◽  
Supply Chain Optimization ◽  
Mathematical Algorithm ◽  
Supply Chain Logistics ◽  
Solution Methods ◽  
Fuzzy Goal

Fuzzy goal programming has important applications in many areas of supply chain, logistics, transportation and shipping business. Business management has complications, and there exist many interactions between the factors of its components. The locomotive of world trade is maritime transport and approximately 90% of the products in the world are transported by sea. Optimization of maritime operations is a challenge in order to provide technical, operational and financial benefits. Fuzzy goal programming models attract interests of many scholars, therefore the objective of this paper is to investigate the problem of minimization of total cost and minimization of loss or damage of containers returned from destination port. There are various types of fuzzy goal programming problems based on models and solution methods. This paper employs fuzzy goal programming with triangular fuzzy numbers, membership functions, constraints, assumptions as well as the variables and parameters for optimizing the solution of the model problem. The proposed model presents the mathematical algorithm, and reveals the optimal solution according to satisfaction rank from 0 to 1. Providing a theoretical background, this study offers novel ideas to researchers, decision makers and authorities.

scholarly journals Application of Genetic Algorithm in Time-Based Wireless Sensor Network Schedule Optimization

2021 ◽  
Vol 31.2 (149) ◽  
pp. 29-34
Keyword(s):  
Genetic Algorithm ◽  
Sensor Network ◽  
Fitness Function ◽  
Optimal Solution ◽  
Complex Problem ◽  
Wireless Sensor ◽  
Network Scheduling ◽  
Schedule Optimization ◽  
Wide Range ◽  
Network Schedule

In recent years, wireless sensor networks (WSN) have been particularly interested, studied and applied very strongly. A sensor network is generally limited in resources and energy, which greatly restrict its applicability. Sensor network optimization in practice is a very diverse with a wide range of applications, whereas sensor network scheduling is important in lowering energy consumption and maximizing network lifetime. However, optimization of sensor network schedule a very complex problem with many constraints that is not trivial to solve by analytical methods. This article discusses a heuristical approach using a genetic algorithm to find an optimal solution for network scheduling. The evaluation of fitness function, as well as selection with crossover and mutation operations help to evolve individuals in the population through generations in an optimal direction.

scholarly journals An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ming-Der Yang ◽  
Yeh-Fen Yang ◽  
Tung-Ching Su ◽  
Kai-Siang Huang
Keyword(s):  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Satellite Images ◽  
Optimization Problems ◽  
Fitness Function ◽  
Satellite Image ◽  
Optimal Solution ◽  
Unsupervised Classification ◽  
Shihmen Reservoir ◽  
Spot 5

Genetic algorithm (GA) is designed to search the optimal solution via weeding out the worse gene strings based on a fitness function. GA had demonstrated effectiveness in solving the problems of unsupervised image classification, one of the optimization problems in a large domain. Many indices or hybrid algorithms as a fitness function in a GA classifier are built to improve the classification accuracy. This paper proposes a new index, DBFCMI, by integrating two common indices, DBI and FCMI, in a GA classifier to improve the accuracy and robustness of classification. For the purpose of testing and verifying DBFCMI, well-known indices such as DBI, FCMI, and PASI are employed as well for comparison. A SPOT-5 satellite image in a partial watershed of Shihmen reservoir is adopted as the examined material for landuse classification. As a result, DBFCMI acquires higher overall accuracy and robustness than the rest indices in unsupervised classification.

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