This paper deals with the multi-objective single-machine scheduling problem in agro-food industry. To solve this problem, a new hybrid algorithm is proposed. This new algorithm named SHGA/SA is composed of two well-known metaheuristics: genetic algorithms and simulated annealing. The results show that our new approach can be used to solve the single-machine scheduling problem efficiently and in a short computational time. Also, the results show that the hybrid algorithm outperforms both the GA and SA.
Handwriting is considered as one of the most delicate and complex human activities. This habit requires a certain level of evolution of the language, the control of the graphic space and a certain degree of affective and praxis development. The production of a meaningful and readable writing involves a variety of motor commands generated by the brain and sent to the muscles to define, with an extreme precision, the motion of each joint at a given time. In this paper, two models characterizing the handwriting process are proposed. Using the activities of the forearm muscles, called the ElectroMyoGraphic signals (EMG), the first model is based on the coordinates of the pen-tip moving on (x,y) plan and the second model is defined from the velocity of the pen-tip. The parameters' estimation of both models is determined from the Recursive Least Square algorithm (RLS). The comparison of responses of two proposed structures shows the interest of the velocity to model the complex biological process. Indeed, the model based on the velocity shows best results then the model bases on the coordinates of the pen-tip.
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