delivering perishable products to customers as soon as possible and with the minimum cost has been always a challenge for producers and has been emphasized over recent years due to the global market becoming more competitive. in this paper a multi-objective mix integer non-linear programming model is proposed to maximize both profits of a distributer and the total freshness of the several produ...

We demonstrate how the stochastic global optimization scheme of Simulated Annealing can be used to evaluate optimum parameters in the problem of DNA breathing dynamics. The breathing dynamics is followed in accordance with the stochastic Gillespie scheme with the denaturation zones in double stranded DNA studied as a single molecule time series. Simulated Annealing is used to find the optimum v...

The dynamic single row facility layout problem (DSRFLP) is defined as the of arranging facilities along a straight line during multi-period planning horizon with objective minimizing sum material handling and rearrangement costs. cost products flow costs center-to-center distances between facilities. In this paper, we focus on metaheuristic algorithms for problem. main contributions paper are t...

Proteins or genes that have similar sequences are likely to perform the same function. One of the most widely used techniques for sequence comparison is sequence alignment. Sequence alignment allows mismatches and insertion/deletion, which represents biological mutations. Sequence alignment is usually performed only on two sequences. Multiple sequence alignment, is a natural extension of two-se...

in this paper, we introduce a methodology for safety stock placement in supply chain. the problem has two objectives: minimization of good's delivery time and inventory cost. because the problem belongs to np-hard category, we develop a proper simulated annealing algorithm (sa) for solve the proposed methodology in large scaled problem. for verify the performance and efficiency of the meth...

Two major classes of optimization techniques are the deterministic gradient methods and stochastic annealing methods. Gradient descent algorithms are greedy algorithms, which are subject to a fundamental limitation of being easily trapped in local minima of the cost function. Hopfield networks usually converge to a local minimum of energy function. Because of its deterministic input-output rela...