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.
Cadmium sulfide and Aluminum doped CdS thin films were prepared by thermal evaporation technique in vacuum on a heated glass substrates at 373K. A comparison between the optical properties of the pure and doped films was made through measuring and analyzing the transmittance curves, and the effect of the annealing temperature on these properties were estimated. All the films were found to exhibit high transmittance in the visible/ near infrared region from 500nm to 1100nm.The optical band gap energy was found to be in the range 2.68-2.60 eV and 2.65-2.44 eV for CdS and CdS:Al respectively , with changing the annealing temperature from room temperature to 423K.Optical constants such as refractive index, extinction coefficient, and complex di
... Show MoreIn the present work, pulsed laser deposition (PLD) technique was applied to a pellet of Chromium Oxide (99.999% pure) with 2.5 cm diameter and 3 mm thickness at a pressure of 5 Tons using a Hydraulic piston. The films were deposited using Nd: YAG laser λ= (4664) nm at 600 mJ and 400 number of shot on a glass substrate, The thickness of the film was (107 nm). Structural and morphological analysis showed that the films started to crystallize at annealing temperature greater than 400 oC. Absorbance and transmittance spectra were recorded in the wavelength range (300-
4400) nm before and after annealing. The effects of annealing temperature on absorption coefficient, refractive index, extinction coefficient, real and imaginary parts of d
SnS has been widely used in photoelectric devices due to its special band gap of 1.2-1.5 eV. Here, we reported on the fabrication of SnS nanosheets and the effect of synthesis condition together with heat treatment on its physical properties. The obtained band gap of the SnS nanosheets is in the rage of 1.37-1.41 eV. It was found that the photo-current density of a thin film comprised of SnS nanosheets could be enhanced significantly by annealing treatment. The maximum photo-current density of the stack structure of FTO/SnS/CdS/Pt was high as 389.5 mu A cm(-2), rendering its potential application in high efficiency solar hydrogen production.
Effect of the thermal annealing at 400oC for 2 hours and Argon laser radiation for half hour on the optical properties of AgAlS2 thin films, prepared on glass slides by chemical spray pyrolysis at 360oC with (0.18±0.05) μm thickness .The optical characteristics of the prepared thin films have been investigated by UV/Vis spectrophotometer in the wavelength range (300 – 1100)nm .The films have a direct allow electronic transition with optical energy (Eg) values decreased from (2.25) eV for untreated thin films to (2.10) eV for the annealed films and to (2.00) eV for the radiated films. The maximum value of the refractive index (n) for all thin films are given about (2.6). Also the extinction coefficient (K) and the real and imaginary d
... Show MoreThis study evaluated the extent to which obturation materials bypass fractured endodontic instruments positioned in the middle and apical thirds of severely curved simulated root canals using different obturation techniques. Sixty resin blocks with simulated root canals were used, each with a 50° curvature, a 6.5 mm radius of curvature, and a length of 16.5 mm, prepared to an ISO #15 diameter and taper. Canals were shaped using ProTaper Universal files (Dentsply Maillefer) attached to an X-smart Plus endo motor (Dentsply), set at 3.5 Ncm torque and 250 rpm, up to size S2 at working length. To simulate fractures, F2 and F3 files were weakened 3 mm from the tip, then twisted to break in the apical and middle sections of the canal, re
... Show MoreThirty three specimens of the blue-cheeked bee-eater were collected at central and southern Iraq from April 1997 to October 2000. Two nematodes Hadjelia truncata and Syphaciella capensis, were recovered from the alimentary tract. Reporting these two nematodes represents the first record for Iraq as well as a new host record.
Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed
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