A novel framework for the parallel solution of combinatorial problems implementing tabu search and simulated annealing algorithms

2016 ◽  
Author(s):  
L. G. Guzman ◽  
E. D. Nino Ruiz ◽  
C. J. Ardila ◽  
D. Jabba ◽  
W. Nieto
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Combinatorial Problems ◽  
Parallel Solution ◽  
Annealing Algorithms

Direct Solution of Fuzzy Project Crashing Problem with Fuzzy Decision Variables using Tabu Search and Simulated Annealing Algorithms

2017 ◽  
Vol 6 (1) ◽  
pp. 17-35 ◽  
Author(s):  
Tolunay Göçken
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Normal Activity ◽  
Transformation Process ◽  
Decision Variable ◽  
Fuzzy Decision ◽  
Project Duration ◽  
Decision Variables ◽  
Important Field ◽  
Annealing Algorithms

Project management is a very important field employed for scheduling activities and monitoring the progress, in competitive and fluctuating environments. Project crashing analysis is concerned with shortening the project duration time by accelerating some of its activities at an additional cost. In reality, because of uncertain environment conditions there can be ambiguity in the parameters of the problem. The uncertainty in the parameters can be modeled via fuzzy set theory. Using fuzzy models give the chance of better project scheduling with more stability under uncertain environmental factors. In this study, a fuzzy project crashing problem with fuzzy decision variable - occurrence time of events - and fuzzy normal activity duration times is handled. The fuzzy project crashing problem is solved without any transformation process by employing a fuzzy ranking method and the tabu search and simulated annealing algorithms.

scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

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