Many industrial systems involve multiple criteria and objectives, and they are very complex problems in computational science, such as task scheduling. We propose bi-criteria and bi-objective scheduling problems, which are solved by two nature-inspired evolutionary algorithms, such as Simulated Annealing (SA) and Bee Algorithm (BA). This problem is characterized by scheduling a batch of tasks on multiple machines, and it is fundamental because the solution should focus on the simultaneous optimization of two conflicting objectives: the makespan minimization and the total tardiness minimization. This problem is NP-Hard, and therefore, two evolutionary methods were used to search for solutions intelligently in this huge, very complex space. In this research, A mathematical model of the scheduling problem was developed based on the above objectives. Here, we proposed a tailored tune-up of SA and BA, both of which have been specifically developed and implemented to solve the proposed model for integrated scheduling and delivery, geared for the bifunctional nature of the problem. Quantitative results indicate that the Bee Algorithm (BA) achieves a more diverse Pareto front, with an average improvement of approximately 12–18 % in solution diversity compared to Simulated Annealing (SA). In contrast, SA converges faster, reducing computational time by about 30–40 % for large problem instances (n ≥ 80). Overall, BA provides better trade-offs between objectives, while SA offers superior computational efficiency. The results showed that both algorithms can generate solutions that are balanced and time-efficient.
Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s
... Show MoreIn this paper, the complexes of Shiff base of Methyl -6-[2-(diphenylmethylene)amino)-2-(4-hydroxyphenyl)acetamido]-2,2-dimethyl-5-oxo-1-thia-4-azabicyclo[3.2.0]heptane-3-carboxylate (L) with Cobalt(II), Nickel(II), Cupper(II) and Zinc(II) have been prepared. The compounds have been characterized by different means such as FT-IR, UV-Vis, magnetic moment, elemental microanalyses (C.H.N), atomic absorption, and molar conductance. It is obvious when looking at the spectral study that the overall complexes obtained as monomeric structure as well as the metals center moieties are two-coordinated with octahedral geometry excepting Co complexes that existed as a tetrahedral geometry. Hyper Chem-8.0.7
... Show MoreCopper Zinc Sulphide (Cu0.5Zn0.5S) alloy and thin films were fabricated in a vacuum. Nano crystallized (CZS) film with thick 450±20 nm was deposit at substrates glasses using thermal evaporation technique below ~ 2 × 10− 5 mbar vacuum to investigated the films structural, morphological and optical properties depended on annealing temperatures ( as-deposited, 423, 523 and 623) K for one hour. The influences annealed temperature on structurally besides morphologically characteristics on these films were investigated using XRD and AFM respectively. XRD confirms the formation a mixed hexagonal phase of CuS-ZnS in (102) direction with polycrystalline in nature having very fine crystallites size varying from (5.5-13.09) nm. AFM analys
... Show MoreThe extracting of personal sprite from the whole image faced many problems in separating the sprite edge from the unneeded parts, some image software try to automate this process, but usually they couldn't find the edge or have false result. In this paper, the authors have made an enhancement on the use of Canny edge detection to locate the sprite from the whole image by adding some enhancement steps by using MATLAB. Moreover, remove all the non-relevant information from the image by selecting only the sprite and place it in a transparent background. The results of comparing the Canny edge detection with the proposed method shows improvement in the edge detection.
AgInSe2 (AIS) thin films solar cell involving of n-type AgInSe2 and Si of p-type substrate by using thermal evaporation method. The influence of annealing of the preparation AgInSe2 were considered to find the best properties of solar device. Thin film AIS have been deposited under the vacuum of 1.5*10-6 Torr with (400) nm thickness at R.T and annealing temperatures (473,573) K. Polycrystalline tetragonal structure for AIS thin films from XRD and increasing of surface roughness from AFM, energy gap values decreasing with increasing annealing temperatures, all films were negative type, I-V characteristics show increasing of efficiency with increasing of annealing temperatures.
In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as the Bayes method. The comparison was made using the mean error squares (MSE), where the best estimator is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).
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