Selecting an optimum combination of items from a set of items, known as knapsack problems, is an important issue in the decision making domain. In this paper, a new approach is developed to solve a Multiple Attribute Knapsack Problem (MAKP) in which each combination of items is evaluated using some quantitative and qualitative attributes. The assumed qualitative attributes cannot be measured by a mathematical formulation but by a DM/expert. In this paper, a GENETIC Algorithm (GA) model has been developed to generate different combinations as the sequential population of the GA model. To rate the qualitative attributes for each chromosome (or combination) in the population, a Neural Network (NN) model has been developed. The ratings (or scores) resulted from quantitative attributes (by NN) and qualitative attributes (by mathematical formulation) for each chromosome form a row of a decision matrix. Having the decision matrix and known weights of attributes, the combinations in each population are ranked by applying a MADM model. The ranks obtained for each chromosome shows the fitness of that chromosome. Using the GA model, the best combination is achieved. The results of conducted experiments show the capability of the proposed approach to deal with MAKP problems.

Uncertain renewable energy supplies, load demands and the non-linear characteristics of some components of photovoltaic (PV) systems make the design problem not easy to solve by classical optimization methods, especially when relevant meteorological data are not available. To overcome this situation, modern methods based on artificial intelligence techniques have been developed for sizing PV systems. However, simple methods like worst month method are still largely used in sizing simple PV systems. In the present study, a method for sizing remote PV systems based on GENETIC ALGORITHMS has been compared with two classical methods, worst month method and loss of power supply probability (LPSP) method. The three methods have been applied to a PV lighting system with orientation due south and inclination angles between 0o and 90o in Adrar city (south Algeria). Because measured data for the chosen location were not available, a year of synthetic hourly meteorological data of this location, generated by PVSYST software, have been used in the simulation. GENETIC ALGORITHMS and worst month methods give results close to each other between 0o and 60o but the system is largely oversized by the worst month method when the tilted angle is over 60o. The results obtained by LPSP method show that the system is very undersized. Hence, a proposition has been made to improve results obtained by this method.

This paper presents a new mathematical programming model for the bi-criteria mixed-model assembly line balancing problem in a just-in-time (JIT) production system. There is a set of criteria to judge sequences of the product mix in terms of the effective utilization of the system. The primary goal of this model is to minimize the setup cost and the stoppage assembly line cost, simultaneously. Because of its complexity to be optimally solved in a reasonable time, we propose and develop two evolutionary meta-heuristics based on a GENETIC algorithm (GA) and a memetic algorithm (MA). The proposed heuristics are evaluated by the use of random iterations, and the related results obtained confirm their efficiency and effectiveness in order to provide good solutions for medium and large-scale problems.

The familiar multivariate process monitoring and control procedure is the Hotelling’s T2 control chart, a direct analog of the univariate shewhart  chart. But, its efficiency for detecting small to moderate shifts in the process mean is poor. To improve the power of chart, this paper presents the variable sampling intervals (VSI) scheme. It is assumed that the length of time the process remains in control has exponential distribution. The chart is modeled using Markov chains and is optimized using GENETIC algorithm optimization method. The results show that the T2 chart with variable ratio sampling scheme is quicker than the classical one in detecting almost all shifts in the process mean.

In this paper, optimization of Boeing 747 wing has accomplished for cruise condition (Mach number=0.85 and flight altitude=35000 ft) and an optimal wing shape have been proposed. Optimization problem has two objectives and is constrained for this research. Objective functions are minimization of wing weight and drag force that as well as confining design parameters, two functional constrains are applied. The first functional constrain is fuel tank volume in the aircraft wing that supply the required fuel. The second functional constrain is lift coefficient that should be equal to initial lift coefficient. Design parameters are root chord, wing span and wing sweep angle. Non-dominating GENETIC algorithm has been used in optimization process until finding one optimal solution; set of solutions (pare to front) are obtained for two objective unctions. Finally a criterion for selecting a best solution for the aircraft on the pareto frontier is addressed.

Assembly line balancing problems are important problems in production management systems. Based on the nature of systems the jobs processing time may be exact, stochastic, or fuzzy. In this article a GENETIC algorithm approach for both type of fuzzy assembly line balancing problems is introduced. The reported results and comparisons show that proposed algorithm is promising.

In many industries, manufacturers for various reasons, have to collect products are used by customers. Then, depending on the situation returns, required to process the decision taken on the product. In this paper the problem of optimizing inventory control and product planning in the environment has been integrated reverse logistics. Logistics network to consist of two stages. In the first stage returns using the qualitative thresholds defined, separated and sent to the appropriate lines recovery or disposal, are subject to quality inspection. In the second stage having different amounts sent to the line, a mixed integer optimization algorithm (MILP) to lower the total cost of our network. Model with a view to minimizing the costs, the type of problems that are NP-Hard, the problem increases exponentially. Therefore, in this study, GENETIC Algorithm is used to solve the model.