scholarly journals Combined Simulated Annealing Algorithm for the Discrete Facility Location Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Jin Qin ◽  
Ling-lin Ni ◽  
Feng Shi
Keyword(s):  
Simulated Annealing ◽  
Facility Location ◽  
Location Problem ◽  
Simulated Annealing Algorithm ◽  
Facility Location Problem ◽  
Location Decision ◽  
Previous Algorithm ◽  
Annealing Algorithm ◽  
Discrete Facility Location ◽  
Better Than

The combined simulated annealing (CSA) algorithm was developed for the discrete facility location problem (DFLP) in the paper. The method is a two-layer algorithm, in which the external subalgorithm optimizes the decision of the facility location decision while the internal subalgorithm optimizes the decision of the allocation of customer's demand under the determined location decision. The performance of the CSA is tested by 30 instances with different sizes. The computational results show that CSA works much better than the previous algorithm on DFLP and offers a new reasonable alternative solution method to it.

Modeling and solution of maximal covering problem considering gradual coverage with variable radius over multi-periods

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1245-1260 ◽  
Author(s):  
Alireza Eydi ◽  
Javad Mohebi
Keyword(s):  
Simulated Annealing ◽  
Facility Location ◽  
Location Problem ◽  
Simulated Annealing Algorithm ◽  
Mixed Integer ◽  
Multiple Time ◽  
Variable Radius ◽  
Maximal Covering ◽  
Annealing Algorithm ◽  
Time Periods

Facility location is a critical component of strategic planning for public and private firms. Due to high cost of facility location, making decisions for such a problem has become an important issue which have gained a large deal of attention from researchers. This study examined the gradual maximal covering location problem with variable radius over multiple time periods. In gradual covering location problem, it is assumed that full coverage is replaced by a coverage function, so that increasing the distance from the facility decreases the amount of demand coverage. In variable radius covering problems, however, each facility is considered to have a fixed cost along with a variable cost which has a direct impact on the coverage radius. In real-world problems, since demand may change over time, necessitating relocation of the facilities, the problem can be formulated over multiple time periods. In this study, a mixed integer programming model was presented in which not only facility capacity was considered, but also two objectives were followed: coverage maximization and relocation cost minimization. A metaheuristic algorithm was presented to solve the maximal covering location problem. A simulated annealing algorithm was proposed, with its results presented. Computational results and comparisons demonstrated good performance of the simulated annealing algorithm.

scholarly journals A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Ali R. Guner ◽  
Mehmet Sevkli
Keyword(s):  
Particle Swarm Optimization ◽  
Facility Location ◽  
Location Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Facility Location Problem ◽  
Discrete Version ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Better Than

A discrete version of particle swarm optimization (DPSO) is employed to solve uncapacitated facility location (UFL) problem which is one of the most widely studied in combinatorial optimization. In addition, a hybrid version with a local search is defined to get more efficient results. The results are compared with a continuous particle swarm optimization (CPSO) algorithm and two other metaheuristics studies, namely, genetic algorithm (GA) and evolutionary simulated annealing (ESA). To make a reasonable comparison, we applied to same benchmark suites that are collected from OR-library. In conclusion, the results showed that DPSO algorithm is slightly better than CPSO algorithm and competitive with GA and ESA.

scholarly journals Multi-Objective Design of Analog Integrated Circuits Using Simulated Annealing with Crossover Operator and Weight Adjusting

2012 ◽  
Vol 7 (1) ◽  
pp. 7-15
Author(s):  
T. O. Weber ◽  
Wilhelmus A. M. V. Noije
Keyword(s):  
Simulated Annealing ◽  
Integrated Circuits ◽  
Pareto Front ◽  
Analog Circuit ◽  
Simulated Annealing Algorithm ◽  
Global Optimum ◽  
Trade Offs ◽  
Adaptive Simulated Annealing ◽  
Annealing Algorithm ◽  
Better Than

This paper approaches the problem of analog circuit synthesis through the use of a Simulated Annealing algorithm with capability of performing crossovers with past anchor solutions (solutions better than all the others in one of the specifications) and modifying the weight of the Aggregate Objective Function specifications in order to escape local minimums. Search for the global optimum is followed by search for the Pareto front, which represents the trade-offs involved in the design and it is performed using the proposed algorithm together with Particle Swarm Optimization. In order to check the performance of the algorithm, the synthesis of a Miller Amplifier was accomplished in two different situations. The first was the comparison of 40 syntheses for Adaptive Simulated Annealing (ASA), Simulate Annealing/Quenching (SA/SQ) and the proposed SA/SQ algorithm with crossovers using a 20-minute bounded optimization with the aim of comparing the solutions of each method. Results were compared using Wilcoxon-Mann-Whitney test with a significance of 0.05 and showed that simulated annealing with crossovers have higher change of returning a good solution than the other algorithms used in this test. The second situation was the synthesis not bounded by time aiming to achieve the best circuit in order to test the use of crossovers in SA/SQ. The final amplifier using the proposed algorithm had 15.6 MHz of UGF, 82.6 dBV, 61º phase margin, 26 MV/s slew rate, area of 980 μm² and current supply of 297 μA in a 0.35 μm technology and was performed in 84 minutes.

scholarly journals An Approximation Algorithm for the Facility Location Problem with Lexicographic Minimax Objective

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ľuboš Buzna ◽  
Michal Koháni ◽  
Jaroslav Janáček
Keyword(s):  
Approximation Algorithm ◽  
Facility Location ◽  
Location Problem ◽  
Optimal Solution ◽  
Facility Location Problem ◽  
Exact Algorithm ◽  
General Purpose ◽  
Median Problem ◽  
Discrete Facility Location ◽  
Number Of Customers

We present a new approximation algorithm to the discrete facility location problem providing solutions that are close to the lexicographic minimax optimum. The lexicographic minimax optimum is a concept that allows to find equitable location of facilities serving a large number of customers. The algorithm is independent of general purpose solvers and instead uses algorithms originally designed to solve thep-median problem. By numerical experiments, we demonstrate that our algorithm allows increasing the size of solvable problems and provides high-quality solutions. The algorithm found an optimal solution for all tested instances where we could compare the results with the exact algorithm.

Research on Printed Wiring Boards Manufacture Based on Optimization

2013 ◽  
Vol 312 ◽  
pp. 506-510
Author(s):  
Jiang Ze Hu ◽  
An Ni Peng ◽  
Xin Zhong Lu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Shortest Path ◽  
Ant Colony Algorithm ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Printed Wiring Boards ◽  
Tool Switching ◽  
Annealing Algorithm ◽  
Better Than

The efficiency E of a punch for making printed wiring boards is decided by its cost and time. The weight of cost and time is different in different companies. In this paper, we establish a bi-object programming model to measure E. We use an algorithm for obtaining an order of tool switching. Considering how to calculate the shortest path for every tool, we compare three modern optimizationalgorithms. We find Ant colony Algorithm is best in computing the shortest path, but it will cost a lot of time in running. Genetic Algorithm is better than Simulated Annealing Algorithm in time and cost. We make some improvements on these algorithms.

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