Alger

Abstract The assembly line is generally known as the last stage of the production processes. It constitutes the main production paradigm of the manufacturing industry. Thus, the performance of the assembly line problem has an important impact on the global performance of the entire production system. Among others, due to demand rate fluctuation. It’s important to quickly rebalance the assembly line and obtain an effective solution for ALB problem. For these reasons, this article proposes an adaptive generalized simulated annealing using fuzzy inference system to solve simple assembly line balancing problem of type I (SALBP-I). The objective of the problem is to minimize the number of stations for a predefined cycle time of workstations in an existing assembly line. Moreover, the performance of our approach is analyzed using a well-known data set of the SALBP-I.

Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the annealing process in metallurgy to approximate the global minimum of an optimization problem. The SA has many parameters which need to be tuned manually when applied to a specific problem. The tuning may be difficult and time-consuming. This paper aims to overcome this difficulty by using a self-tuning approach based on a machine learning algorithm called Hidden Markov Model (HMM). The main idea is allowing the SA to adapt his own cooling law at each iteration, according to the search history. An experiment was performed on many benchmark functions to show the efficiency of this approach compared to the classical one.

This work consist mainly in highlighting the effect of the step by step tracking system using on the performance of a solar water heating system. Using data measurement obtained from renewable energy systems development in arid region of Ghardaia characterized respectively by temperate and arid climates. The results obtained by the considered system are compared to those obtained by a fixed system with a seasonal inclination change and to those obtained by two mechanisms with a continuous tracking around a vertical axis and that around an inclined axis. In following are presented the considered system and used in this study. As results it is observed that the orientation of the solar water heater collector three times by day lead to increase its thermal efficiency and this work leads also Evaluating the flat plate solar collector optimum azimut angle for several Algerian climate.

The Simulated Annealing (SA) is a stochastic local search algorithm. It is an adaptation of the Metropolis-Hastings Monte Carlo algorithm. SA mimics the annealing process in metallurgy to approximate the global optimum of an optimization problem and uses a temperature parameter to control the search. The efficiency of the simulated annealing algorithm involves the adaptation of the cooling schedule. In this paper, we integrate Hidden Markov Model (HMM) in SA to iteratively predict the best cooling law according to the search history. Experiments performed on many benchmark functions show that our proposed scheme outperforms other SA variants in term of quality of solutions.

Simulated annealing (SA) is a local search algorithm. It mimics the annealing process used in the metallurgy to approximate the global optimum of an optimization problem and uses the temperature to control the search. Unfortunately, the effectiveness of simulated annealing drops drastically when dealing with a large-scale optimization problem. This is due in general to a premature convergence or a stagnation. The both phenomenon's can be avoided by a good balance between exploitation and exploration. This paper focuses on this problem encountered by simulated annealing and try to heal it using the fuzzy logic controller by dynamically adapting the neighborhood structure of the simulated annealing. Empirical analysis was conducted on many numerical benchmark functions. The experimental results show that the dynamic controlling of neighborhood structure improves the quality of solution compared with the classical simulated annealing.

The simulated annealing (SA) is amongst the well-known algorithms for stochastic optimization. Unfortunately, its major weakness is the slow rate of convergence, leading to a large period of poor improvement towards a global optimum. In this paper, we present a self-adaptive approach to enhance the SA performance during the run using Hidden Markov Model (HMM). Experiments have been performed on many benchmark functions.