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.

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 simulated annealing algorithm for vehicle scheduling problem

2017 ◽  
Vol 1 (Q4) ◽  
pp. 70-77
Author(s):  
Ha Thi Mai Phan
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Time Window ◽  
Fresh Concrete ◽  
Waiting Times ◽  
Optimal Solution ◽  
Production Scale ◽  
Construction Companies ◽  
Annealing Algorithm ◽  
Concrete Tank

As the construction activity has been growing, the companies that supply fresh concrete expand their production scale to meet their customers’ needs. The more customers, the longer queue tank trucks have to wait to pick up the fresh concrete. The customers are construction companies that have different construction works at the same time while the transportation time is only at night. They have to schedule efficiently the fleet of fresh concrete tank trucks during the night (turning the tank trucks a few turns) with constraints on the time window for the transfer of fresh concrete from the concrete company to the construction site as well as constraints on the waiting time for loading fresh concrete in the company. The scheduling for the fleet of construction company’s tank trucks will be modeled to minimize total transportation costs (fixed, variable) with estimated waiting times and tank truck’s turns several times during the night. The model of logistics problem is NP hard; Therefore, two algorithms are proposed to find the nearly optimal solution: heuristics and simulated annealing algorithm. The results will be compared and analyzed.

scholarly journals A Knowledge-Based Simulated Annealing Algorithm to Multiple Satellites Mission Planning Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Da-Wei Jin ◽  
Li-Ning Xing
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Mission Planning ◽  
Knowledge Based ◽  
Combination Optimization ◽  
Annealing Algorithm ◽  
The Given ◽  
Planning Problems ◽  
Annealing Method

The multiple satellites mission planning is a complex combination optimization problem. A knowledge-based simulated annealing algorithm is proposed to the multiple satellites mission planning problems. The experimental results suggest that the proposed algorithm is effective to the given problem. The knowledge-based simulated annealing method will provide a useful reference for the improvement of existing optimization approaches.

scholarly journals Optimization and Design of Hammerheads and Fenders on Scrap Metal Shredders Based on Improved Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
He Tian ◽  
Guoqiang Wang ◽  
Kangkang Sun ◽  
Zeren Chen ◽  
Chuliang Yan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Dynamic Balance ◽  
Optimal Solution ◽  
Scrap Metal ◽  
Improved Genetic Algorithm ◽  
Multiple Traveling Salesman Problem ◽  
Local Optimal Solution ◽  
Annealing Algorithm

Dynamic unbalance force is an important factor affecting the service life of scrap metal shredders (SMSs) as the product of mass error. Due to the complexity of hammerheads arrangement, it is difficult to take all the parts of the hammerhead into account in the traditional methods. A novel optimization algorithm combining genetic algorithm and simulated annealing algorithm is proposed to improve the dynamic balance of scrap metal shredders. The optimization of hammerheads and fenders on SMS in this paper is considered as a multiple traveling salesman problem (MTSP), which is a kind of NP-hard problem. To solve this problem, an improved genetic algorithm (IGA) combined with the global optimization characteristics of genetic algorithm (GA) and the local optimal solution of simulated annealing algorithm (SA) is proposed in this paper, which adopts SA in the process of selecting subpopulations. The optimization results show that the resultant force of the shredder central shaft by using IGA is less than the traditional metaheuristic algorithm, which greatly improves the dynamic balance of the SMS. Validated via ADAMS simulation, the results are in good agreement with the theoretical optimization analysis.

Optimal Tolerance Allocation Over Multiple Manufacturing Alternatives

1992 ◽  
Author(s):  
Jonathan Cagan ◽  
Thomas R. Kurfess
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Minimum Cost ◽  
Tolerance Allocation ◽  
Hyperbolic Function ◽  
Manufacturing Processes ◽  
Worst Case ◽  
Annealing Algorithm ◽  
Manufacturing Alternatives

Abstract We introduce a methodology for concurrent design that considers the allocation of tolerances and manufacturing processes for minimum cost. Cost is approximated as a hyperbolic function over tolerance, and worst-case stack-up tolerance is assumed. Two simulated annealing techniques are introduced to solve the optimization problem. The first assumes independent, unordered, manufacturing processes and uses a Monte-Carlo simulation; the second assumes well known individual process cost functions which can be manipulated to create a single continuous function of cost versus tolerance with discontinuous derivatives solved with a continuous simulated annealing algorithm. An example utilizing a system of friction wheels over the manufacturing processes of turning, grinding, and saw cutting bar stock demonstrates excellent results.

Simulated Annealing Algorithm Based Single Robot Odor Source Localization Strategy

2014 ◽  
Vol 494-495 ◽  
pp. 1286-1289
Author(s):  
Shi Gang Cui ◽  
Guang Ming Zeng ◽  
Fan Liang ◽  
Jiang Lei Dong
Keyword(s):  
Simulated Annealing ◽  
Source Localization ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Local Maximum ◽  
Function Optimization ◽  
Odor Source ◽  
Gas Source Localization ◽  
Plume Tracing ◽  
Annealing Algorithm

This paper presents a search strategy for single mobile robots to realize the active olfaction (also called odor/gas source localization or plume tracing). The odor source localization is regarded as a kind of dynamic function optimization problem in this article, using the simulated annealing algorithm to calculate the optimal solution of density distribution function, namely the odor source location. The simulation experiments results in indoor ventilated environment show that the robot can track in plume and locate the odor source under the area of the 10m*10m, and it can effectively jump out of local maximum values in the process of search.

Application Research of Improved Simulated Annealing Algorithm in Vehicle Routing Problems

2011 ◽  
Vol 58-60 ◽  
pp. 1031-1036
Author(s):  
Dong Mei Yan ◽  
Cheng Hua Lu
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Simulated Annealing Algorithm ◽  
Memory Function ◽  
Optimal Solution ◽  
Vehicle Routing Problems ◽  
Application Research ◽  
Routing Problems ◽  
Annealing Algorithm ◽  
Optimization Efficiency

This paper analyzed the principle and insufficient of traditional simulated annealing algorithm, and on the basis of the traditional simulated annealing algorithm, this paper used improved simulated annealing algorithm to solve vehicle routing problems. The new algorithm increases memory function, and keeps the current best state to avoid losing current optimal solution while reducing the computation times and accelerating the algorithm speed. The experimental results show that, the algorithm can significantly improve the optimization efficiency, and has faster convergence speed than traditional simulated annealing algorithm.

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