scholarly journals Mixed Replenishment Policy for ATO Supply Chain Based on Hybrid Genetic Simulated Annealing Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Huang ◽  
Yan Jin ◽  
Bo Huang ◽  
Han-Guang Qiu
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Genetic Simulated Annealing Algorithm ◽  
Replenishment Policy ◽  
Annealing Algorithm ◽  
Searching Ability

Timely components replenishment is the key to ATO (assemble-to-order) supply chain operating successfully. We developed a production and replenishment model of ATO supply chain, where the ATO manufacturer adopts both JIT and (Q,r) replenishment mode simultaneously to replenish components. The ATO manufacturer’s mixed replenishment policy and component suppliers’ production policies are studied. Furthermore, combining the rapid global searching ability of genetic algorithm and the local searching ability of simulated annealing algorithm, a hybrid genetic simulated annealing algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is given to illustrate the rapid convergence of the HGSAA and the good quality of optimal mixed replenishment policy obtained by the HGSAA. Finally, by comparing the HGSAA with GA, it is proved that the HGSAA is a more effective and reliable algorithm than GA for solving the optimization problem of mixed replenishment policy for ATO supply chain.

scholarly journals Degree Reduction of S-λ Curves Using a Genetic Simulated Annealing Algorithm

Symmetry ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 15 ◽  
Author(s):  
Jing Lu ◽  
Xinqiang Qin
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Degree Reduction ◽  
Practical Applications ◽  
Genetic Simulated Annealing Algorithm ◽  
Global Optimal ◽  
Annealing Algorithm

The S-λ Curves have become an important research subject in computer aided geometric design (CAGD), which owes to its good geometric properties (such as affine invariance, symmetry, and locality). This paper presents a new method to approximate an S-λ curve of degree n by using an S-λ curve of degree n-1. We transform this degree reduction problem into the function optimization problem first, and then using a new genetic simulated annealing algorithm to determine the global optimal solution of the optimization problem. The method can be used to approximate S-λ curves with fixed or unconstrained endpoints. Examples are given to verify the effectiveness of the presented algorithm; and these numeric examples show that the algorithm is not only easy to implement, but also offers high precision, which makes it valuable in practical applications.

scholarly journals A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yanhui Li ◽  
Hao Guo ◽  
Lin Wang ◽  
Jing Fu
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Inventory Routing ◽  
Logistics System ◽  
Routing Problem ◽  
Problem Model ◽  
Inventory Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.

Three Dimensional Offline Packing Optimization Problem Based on Genetic Simulated Annealing Algorithm

2015 ◽  
Vol 744-746 ◽  
pp. 1919-1923
Author(s):  
Zhan Zhong Wang ◽  
Jing Fu ◽  
Lan Fang Liu ◽  
Rui Rui Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Three Dimensional ◽  
Packing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Final Optimization ◽  
Packing Optimization

In this paper, we try to solve 3D offline packing optimization problem by combining two methods-genetic algorithm’ global performance and simulated annealing algorithm’ local performance. Given Heuristic rules in loading conditions, we use the optimal preservation strategy and the roulette wheel method to choose selection operator, integrating simulated annealing algorithm into genetic algorithm , and achieving code programming and algorithms by Matlab.This paper carries out an actual loading in a vehicle company in Changchun City, then makes a contrast between the final optimization results and each suppliers’ current packing data.The experimental results show that the algorithm has a certain validity and practicability in multiple container packing problem.

Study on Hybrid Genetic Simulated Annealing Algorithm for Multi-Vehicle and Multi-Cargo Loading Problem

2010 ◽  
Vol 171-172 ◽  
pp. 167-170 ◽  
Author(s):  
Xiao Bo Wang ◽  
Jin Ying Sun ◽  
Chun Yu Ren
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Solution Quality ◽  
Genetic Simulated Annealing Algorithm ◽  
Algorithm Construct ◽  
Loading Problem ◽  
Annealing Algorithm ◽  
Crossover And Mutation

This paper studies multi-vehicle and multi-cargo loading problem under the limited loading capacity. Hybrid genetic simulated annealing algorithm is used to get the optimization solution. Firstly, adopt hybrid coding so as to make the problem more succinctly. On the basis of cubage-weight balance algorithm, construct initial solution to improve the feasibility. Adopt the improved non-uniform mutation so as to enhance local search ability of chromosomes. Secondly, through utilizing Boltzmann mechanism of simulated annealing algorithm, control crossover and mutation operation of genetic algorithm, search efficiency so as to improve the solution quality of algorithm. Finally, the example can be shown that the above model and algorithm is effective and can provide for large-scale ideas to solve practical problems.

scholarly journals Simulated annealing method for equilibrium placement problem

2021 ◽  
pp. 152-158
Author(s):  
Igor Kozin ◽  
Natalia Maksyshko ◽  
Yaroslav Tereshko
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Placement Problem ◽  
Fragmentary Structure ◽  
Simulating Annealing ◽  
Set Of Permutations ◽  
Annealing Algorithm ◽  
The Traveling Salesman Problem

The paper proposes a modification of the simulated annealing algorithm as applied to problems that have a fragmented structure. An algorithm for simulating annealing for the traveling salesman problem is considered and its applicability to the optimization problem on a set of permutations is shown. It is proved that the problem of equilibrium placement of point objects on a plane has a fragmentary structure and, therefore, reduces to an optimization problem on a set of permutations. The results of numerical experiments for various types of algorithms for finding the optimal solution in the equilibrium placement problem are presented.

Study on Adaptive Genetic Simulated Annealing Algorithm in Association Rules Mining

2014 ◽  
Vol 701-702 ◽  
pp. 77-82
Author(s):  
Fei Jiang
Keyword(s):  
Simulated Annealing ◽  
Association Rules ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Rule Mining ◽  
Web Log ◽  
Genetic Simulated Annealing Algorithm ◽  
Crossover Probability ◽  
Adaptive Crossover ◽  
Searching Ability

Premature occurring in the process of web log association rule mining by genetic algorithm may lead to no globally optimal solution. In order to avoid such a situation, this paper proposes Web log mining algorithm of association rules based on adaptive genetic strategy, simulated annealing. Based on genetic simulated annealing strategy, the algorithm ensures the diversity of the population from one generation to the application of parallel processing technology. In addition, this algorithm introduces the adaptive crossover probability and mutation probability so as to improve the algorithm's global searching ability. Experimental results show that this algorithm can significantly enhance mining speed and can effectively avoid premature, thus of better strong global convergence.

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