scholarly journals THE APPLICATION OF THE GENETIC ALGORITHM TO MULTI-CRITERIA WAREHOUSES LOCATION PROBLEMS ON THE LOGISTICS NETWORK

Transport ◽  
2018 ◽  
Vol 33 (3) ◽  
pp. 741-750 ◽  
Author(s):  
Mariusz Izdebski ◽  
Ilona Jacyna-Gołda ◽  
Mariusz Wasiak ◽  
Roland Jachimowski ◽  
Michał Kłodawski ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Location Problem ◽  
Labour Force ◽  
Raw Material ◽  
Initial Population ◽  
Optimization Criteria ◽  
Logistics Network ◽  
Local Taxes ◽  
Transition Costs ◽  
Crossover And Mutation

This paper presents multi-criteria warehouses location problem in the logistics network. In order to solve this problem the location model was developed. The limitations and optimization criteria of the model were determined. Optimization criteria refer to transportation costs, costs associated with warehouses, e.g.: local taxes, expenditure on starting the warehouse, the constant costs, the labour force costs, the purchase costs of the additional land for the expansion, the transition costs of the raw material via the warehouses. The final location of warehouse facilities was obtained using a genetic algorithm. The genetic algorithm was developed in order to solve the multi-criteria warehouses location problem. This paper describes the stages of the genetic algorithm i.e. the stage of designating the initial population, the crossover and mutation process, the adaptation function. In this paper, the process of calibration of this algorithm was presented. The results of the genetic algorithm were compared with the random results.

Scheduling Flexible Production Lines with No Intermediate Buffers by Hybrid Algorithms

2012 ◽  
Vol 557-559 ◽  
pp. 2229-2233
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Initial Population ◽  
Flexible Production ◽  
Scheduling Problems ◽  
Production Lines ◽  
Solution Quality ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Better Than

This paper is concerned about the scheduling problems in flexible production lines with no intermediate buffers. The optimization objective is to minimizing the makespan. The mathematical models are presented. Since the problem is NP-hard, a hybrid algorithm, based on genetic algorithm and tabu search, is put forward for solving the models. In this algorithm, the method of generating the initial population is proposed and the crossover and mutation operators, tabu list, and aspiration rule are newly designed. The performance of the hybrid algorithm is compared with that of the traditional genetic algorithm. The computational results show that satisfactory solutions can be obtained by the hybrid algorithm and it performs better than the genetic algorithm in terms of solution quality.

GPS Signal Acquisition Based on an Improved Genetic Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2909-2912
Author(s):  
Min Shi ◽  
Cong Cong Tian ◽  
Qing Ming Yi
Keyword(s):  
Genetic Algorithm ◽  
Initial Population ◽  
Signal Acquisition ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Acquisition Performance ◽  
Signal Parameters ◽  
Dynamic Search ◽  
Gps Signal Acquisition ◽  
Crossover And Mutation

In order to solve the problem of GPS signal acquisition, an acquisition algorithm based on an improved genetic algorithm (IGA) is proposed in this paper. The IGA employs the technologies including the dynamic search range of parameter, the adaptive probabilities of crossover and mutation, and the small section method for generation of an initial population to search Doppler shift and code phase. Simulations and experiment results show that the proposed method can acquire the signal parameters precisely and rapidly. Consequently, the acquisition performance is improved.

Data Mining of Bayesian Network Structure Using a Semantic Genetic Algorithm-Based Approach

2007 ◽  
pp. 42-53 ◽  
Author(s):  
Sachin Shetty ◽  
Min Song ◽  
Mansoor Alam
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Bayesian Network ◽  
Network Structure ◽  
Initial Population ◽  
Network Structures ◽  
Bayesian Network Structure ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Intuitive Interpretation

A Bayesian network model is a popular formalism for data mining due to its intuitive interpretation. This chapter presents a semantic genetic algorithm (SGA) to learn the best Bayesian network structure from a database. SGA builds on recent advances in the field and focuses on the generation of initial population, crossover, and mutation operators. In SGA, we introduce semantic crossover and mutation operators to aid in obtaining accurate solutions. The crossover and mutation operators incorporate the semantic of Bayesian network structures to learn the structure with very minimal errors. SGA has been proven to discover Bayesian networks with greater accuracy than existing classical genetic algorithms. We present empirical results to prove the accuracy of SGA in predicting the Bayesian network structures.

Sequencing Mixed-Model Assembly Lines to Minimize the Variation of Parts Consumption by Hybrid Genetic Algorithms

2012 ◽  
Vol 566 ◽  
pp. 253-256
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Hybrid Genetic Algorithm ◽  
Initial Population ◽  
Assembly Lines ◽  
Mixed Model Assembly Lines ◽  
Mutation Operators ◽  
Sequencing Problems ◽  
Crossover And Mutation ◽  
Mixed Model Assembly

This paper is concerned about the sequencing problems in mixed-model assembly lines. The optimization objective is to minimizing the variation of parts consumption. The mathematical models are put forward. Since the problem is NP-hard, a hybrid genetic algorithm is newly-designed for solving the models. In this algorithm, the new method of forming the initial population is presented, the hybrid crossover and mutation operators are adopted, and moreover, the adaptive probability values for performing the crossover and mutation operations are used. The optimization performance is compared between the hybrid genetic algorithm and a genetic algorithm proposed in early published literature. The computational results show that satisfactory solutions can be obtained by the hybrid genetic algorithm and it performs better in terms of solution’s quality.

Assembly Sequence Generation and Optimization for Compliant Assemblies Based on Genetic Algorithm

2011 ◽  
Vol 230-232 ◽  
pp. 978-981
Author(s):  
Yan Feng Xing ◽  
Yan Song Wang ◽  
Xiao Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Initial Population ◽  
Assembly Sequence ◽  
Constraint Function ◽  
Assembly Modeling ◽  
Sequence Generation ◽  
Assembly Sequences ◽  
Assembly Sequence Generation ◽  
Crossover And Mutation

This paper proposes a genetic algorithm to generate and optimize assembly sequences for compliant assemblies. An assembly modeling is presented to describe the geometry of the assembly, which includes three sets of parts, relationships and joints among the parts. Based on the assembly modeling, an assembly sequence is denoted as an individual, which is assigned an evaluation function that consists of the fitness and constraint functions. The fitness function is used to evaluate feasible sequences; in addition, the constraint function is employed to evolve unfeasible sequences. The genetic algorithm starts with a randomly initial population of chromosomes, evolves new populations by using reproduction, crossover and mutation operations, and terminates until acceptable sequences output. Finally an auto-body side assembly is used to illustrate the algorithm of assembly sequence generation and optimization.

Scheduling for the Flexible Job-Shop Problem Based on Genetic Algorithm (GA)

2012 ◽  
Vol 457-458 ◽  
pp. 616-619
Author(s):  
Shun Cheng Fan ◽  
Jin Feng Wang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Initial Population ◽  
Flexible Job Shop ◽  
Chromosome Representation ◽  
Local Optimal Solution ◽  
Crossover And Mutation ◽  
Flexible Job Shop Problem

In this paper, we analyze the characteristics of the flexible job-shop scheduling problem(FJSP). A novel genetic algorithm is elaborated to solve the FJSP. An improved chromosome representation is used to conveniently represent a solution of the FJSP. Initial population is generated randomly. The relevant selection, crossover and mutation operation is also designed. It jumped from the local optimal solution, and the search area of solution is improved. Finally, the algorithm is tested on instances of 4 jobs and 6 machines. Computational results prove the proposed genetic algorithm effective for solving the FJSP.

Data Mining of Bayesian Network Structure Using a Semantic Genetic Algorithm-Based Approach

2008 ◽  
pp. 1081-1090
Author(s):  
Sachin Shetty ◽  
Min Song ◽  
Mansoor Alam
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Bayesian Network ◽  
Network Structure ◽  
Initial Population ◽  
Network Structures ◽  
Bayesian Network Structure ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Intuitive Interpretation

A Bayesian network model is a popular formalism for data mining due to its intuitive interpretation. This chapter presents a semantic genetic algorithm (SGA) to learn the best Bayesian network structure from a database. SGA builds on recent advances in the field and focuses on the generation of initial population, crossover, and mutation operators. In SGA, we introduce semantic crossover and mutation operators to aid in obtaining accurate solutions. The crossover and mutation operators incorporate the semantic of Bayesian network structures to learn the structure with very minimal errors. SGA has been proven to discover Bayesian networks with greater accuracy than existing classical genetic algorithms. We present empirical results to prove the accuracy of SGA in predicting the Bayesian network structures.

Optimization of PID Controller Parameters Based on Improved Genetic Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 1189-1192
Author(s):  
Ying Chen ◽  
Yong Jie Ma ◽  
Wen Xia Yun
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Optimization Design ◽  
Selection Strategy ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
New Approach ◽  
Effective Selection ◽  
System Requirements ◽  
Crossover And Mutation

The current Proportion Integration Differentiation(PID) optimization design methods are often difficult to consider the system requirements for quickness,reliability and robustness.So an Improved Genetic Algorithm(IGA) is proposed.The new method of generating the initial population,adaptive change of crossover and mutation probability and effective selection strategy are used to optimize the parameters of PID controller. The simulation experiments with Matlab prove the new approach is valid.

scholarly journals An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1194
Author(s):  
Thejus Pathmakumar ◽  
Madan Mohan Rayguru ◽  
Sriharsha Ghanta ◽  
Manivannan Kalimuthu ◽  
Mohan Rajesh Elara
Keyword(s):  
Genetic Algorithm ◽  
Mobile Robots ◽  
Numerical Simulations ◽  
Labour Force ◽  
Successful Implementation ◽  
Multi Objective Optimization ◽  
Metallic Surfaces ◽  
Industrial Sites ◽  
Experimental Trials ◽  
Time And Energy

The hydro blasting of metallic surfaces is an essential maintenance task in various industrial sites. Its requirement of a considerable labour force and time, calls for automating the hydro blasting jobs through mobile robots. A hydro blasting robot should be able to cover the required area for a successful implementation. If a conventional robot footprint is chosen, the blasting may become inefficient, even though the concerned area is completely covered. In this work, the blasting arm’s sweeping angle is chosen as the robot’s footprint for hydro blasting task, and a multi-objective optimization-based framework is proposed to compute the optimal sweeping arc. The genetic algorithm (GA) methodology is exploited to compute the optimal footprint, which minimizes the blasting time and energy simultaneously. Multiple numerical simulations are performed to show the effectiveness of the proposed approach. Moreover, the strategy is successfully implemented on our hydro blasting robot named Hornbill, and the efficacy of the proposed approach is validated through experimental trials.

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