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.

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.

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.

scholarly journals Research on Fish Slicing Method Based on Simulated Annealing Algorithm

2021 ◽  
Vol 11 (14) ◽  
pp. 6503
Author(s):  
Shuo Liu ◽  
Hao Wang ◽  
Yong Cai
Keyword(s):  
Simulated Annealing ◽  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Collaborative Optimization ◽  
Sequential Algorithm ◽  
Multiobjective Optimization Problem ◽  
Skilled Workers ◽  
Mutation Strategy ◽  
Annealing Algorithm

Multiobjective optimization is a common problem in the field of industrial cutting. In actual production settings, it is necessary to rely on the experience of skilled workers to achieve multiobjective collaborative optimization. The process of industrial intelligence is to perceive the parameters of a cut object through sensors and use machines instead of manual decision making. However, the traditional sequential algorithm cannot satisfy multiobjective optimization problems. This paper studies the multiobjective optimization problem of irregular objects in the field of aquatic product processing and uses the information guidance strategy to develop a simulated annealing algorithm to solve the problem according to the characteristics of the object itself. By optimizing the mutation strategy, the ability of the simulated annealing algorithm to jump out of the local optimal solution is improved. The project team developed an experimental prototype to verify the algorithm. The experimental results show that compared with the traditional sequential algorithm method, the simulated degradation algorithm designed in this paper effectively improves the quality of the target solution and greatly enhances the economic value of the product by addressing the multiobjective optimization problem of squid cutting. At the end of the article, the cutting error is analyzed.

scholarly journals Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4403 ◽  
Author(s):  
Yang ◽  
Cho
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Wind Turbines ◽  
Wind Farm ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Layout Optimization ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Annealing Algorithm

The optimal layout of wind turbines is an important factor in the wind farm design process, and various attempts have been made to derive optimal deployment results. For this purpose, many approaches to optimize the layout of turbines using various optimization algorithms have been developed and applied across various studies. Among these methods, the most widely used optimization approach is the genetic algorithm, but the genetic algorithm handles many independent variables and requires a large amount of computation time. A simulated annealing algorithm is also a representative optimization algorithm, and the simulation process is similar to the wind turbine layout process. However, despite its usefulness, it has not been widely applied to the wind farm layout optimization problem. In this study, a wind farm layout optimization method was developed based on simulated annealing, and the performance of the algorithm was evaluated by comparing it to those of previous studies under three wind scenarios; likewise, the applicability was examined. A regular layout and optimal number of wind turbines, never before observed in previous studies, were obtained and they demonstrated the best fitness values for all the three considered scenarios. The results indicate that the simulated annealing (SA) algorithm can be successfully applied to the wind farm layout optimization problem.

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 Data Mining Method of Enterprise Human Resource Management Based on Simulated Annealing Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mingwei Xu ◽  
Chuang Li
Keyword(s):  
Data Mining ◽  
Simulated Annealing ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Mining Method ◽  
Nurse Scheduling ◽  
Annealing Algorithm

The human resources department of an enterprise relies on the “mining” of big data when carrying out human resource management and proposes a data mining method for enterprise human resource management based on the simulated annealing algorithm. Applying the simulated annealing algorithm, using the Metropolis algorithm to generate the sequence of solutions to the combinatorial optimization problem, finding the overall optimal solution of the combinatorial optimization problem, using big data directional mining and analysis to help companies establish and find a “radar” system suitable for talents, the maximum tree method is adopted; that is, a special graph is constructed to realize the effective application of data mining technology in enterprise human resource management. The optimization of nurse scheduling in a hospital was used for case analysis. The results show that the target value of the nurse scheduling model is 43.43% lower than the actual manual scheduling target value, the salary cost is reduced by 10.8%, and the nurse’s satisfaction with the shift is increased by 35.24%. After several iterations based on the simulated annealing algorithm, the optimal value of the solution of the simulated annealing algorithm remains unchanged at the 60th generation. Then, the search process is stopped when the 100th generation is reached, and the solution at this time is the optimal optimization value found by the algorithm.

Penjadwalan Produksi untuk Meminimasi Makespan dengan Implementasi Algoritma Simulated Annealing

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Sawaluddin ◽  
Rosnani Ginting
Keyword(s):  
Simulated Annealing ◽  
Production Scheduling ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Job Scheduling ◽  
Initial Temperature ◽  
Heuristic Method ◽  
Reduction Factor ◽  
Annealing Algorithm ◽  
First Job

PT. ABC adalah perusahaan manufaktur yang memproduksi gelas plastik berdasarkan pesanan pelanggan (job order). Perusahaan menerapkan penjadwalan produksi dalam urutan pekerjaan pada pesanan, di mana setiap pekerjaan pertama datang harus diselesaikan terlebih dahulu dari pekerjaan lain (yang memiliki batas waktu kerja yang sama). Ini berdampak pada keterlambatan pengiriman produk ke konsumen. Untuk menghindari keterlambatan pengiriman produk, perlu menjadwalkan produksi di perusahaan untuk meminimalkan waktu penyelesaian produk (makespan). Penelitian ini menggunakan Algoritma Simulated Annealing. Algoritma Simulated Annealing adalah jenis metode heuristik karena memiliki potensi besar untuk menyelesaikan masalah optimisasi, di mana parameter yang digunakan adalah suhu awal (Ti) 2000C, suhu faktor reduksi adalah 0,95, jumlah iterasi adalah 15 kali. Algoritma Simulated Annealing sama dengan 20149,89 menit. Dapat dilihat bahwa dengan menggunakan metode yang diusulkan, ada pengurangan makespan dari 4418,86 menit = 75,65 jam = 3,06 hari. Sehingga penjadwalan pekerjaan dapat dipenuhi tepat waktu dan tidak ada penundaan tanggal jatuh tempo yang ditetapkan 14 hari. Jadi dapat disimpulkan algoritma Annealing Simulasi lebih efektif daripada metode First Come First Served.   PT. ABC is a manufacturing company that produces plastic cups based on customer orders (job order). Companies apply production scheduling in the order of jobs on the order, where every first job comes must be completed first from another job (which has the same working time limit). This has an impact on the delay in delivering products to consumers. To avoid delays in product shipments, it is necessary to schedule production at the company in order to minimize the time of product completion (makespan). This research uses Simulated Annealing Algorithm. The Simulated Annealing algorithm is a type of heuristic method because it has great potential to solve the optimization problem, where the parameters used are the initial temperature (Ti) of 2000C, the reduction factor temperature (s) is 0.95, the number of iterations is 15 times. The Simulated Annealing Algorithm is equal to 20149,89 minutes. It can be seen that by using the proposed method, there is a reduction of makespan of 4418.86 minutes = 75.65 hours = 3.06 days. So that job scheduling can be fulfilled on time and no delay of due date set by 14 days. So it can be concluded Simulated Annealing algorithm is more effective than First Come First Served method.

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.

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