In this paper, we present a Branch and Bound (B&B) algorithm of scheduling (n) jobs on a single machine to minimize the sum total completion time, total tardiness, total earliness, number of tardy jobs and total late work with unequal release dates. We proposed six heuristic methods for account upper bound. Also to obtain lower bound (LB) to this problem we modified a (LB) select from literature, with (Moore algorithm and Lawler's algorithm).  And some dominance rules were suggested. Also, two special cases were derived. Computational experience showed the proposed (B&B) algorithm was effective in solving problems with up to (16) jobs, also the upper bounds and the lower bound were effective in restr

In the petroleum industry, multiphase flow dynamics within the tubing string have gained significant attention due to associated challenges. Accurately predicting pressure drops and wellbore pressures is crucial for the effective modeling of vertical lift performance (VLP). This study focuses on predicting the multiphase flow behavior in four wells located in the Faihaa oil field in southern Iraq, utilizing PIPESIM software. The process of selecting the most appropriate multiphase correlation was performed by utilizing production test data to construct a comprehensive survey data catalog. Subsequently, the results were compared with the correlations available within the PIPESIM software. The outcomes reveal that the Hagedorn and Brown (H

This paper develops a fuzzy multi-objective model for solving aggregate production planning problems that contain multiple products and multiple periods in uncertain environments. We seek to minimize total production cost and total labor cost. We adopted a new method that utilizes a Zimmermans approach to determine the tolerance and aspiration levels. The actual performance of an industrial company was used to prove the feasibility of the proposed model. The proposed model shows that the method is useful, generalizable, and can be applied to APP problems with other parameters.

     The variational iteration method is used to deal with linear and nonlinear differential equations. The main characteristics of the method lie in its flexibility and ability to accurately and easily solve nonlinear equations. In this work, a general framework is presented for a variational iteration method for the analytical treatment of partial differential equations in fluid mechanics. The Caputo sense is used to describe fractional derivatives. The time-fractional Kaup-Kupershmidt (KK) equation is investigated, as it is the solution of the system of partial differential equations via the Boussinesq-Burger equation. By comparing the results that are obtained by the variational iteration method with those obtained by the two-dim

Single crystals of pure and Cu+2,Fe+2 doped potassium sulfate were grown from aqueous solutions by the slow evaporation technique at room temperature. with dimension of (11x9 x4)mm3 and ( 10x 8x 5)mm3 for crystal doping with Cu &Fe respectively. The influence of doping on crystal growth and its structure revealed a change in their lattice parameters(a=7.479 Ã… ,b=10.079 Ã… ,c=5.772 Ã…)for pure and doping (a=9.687 Ã…, b=14.926 Ã… ,c= 9.125 Ã…) & (a=9.638 Ã… , b= 8.045 Ã… ,c=3.271 Ã…) for Cu & Fe respectively. Structure analysis of the grown crystals were obtained by X-Ray powder diffraction measurements. The diffraction patterns were analyzed by the Rietveld refinement method. Rietveld refinement plo

    Cloud computing is a pay-as-you-go model that provides users with on-demand access to services or computing resources. It is a challenging issue to maximize the service provider's profit and, on the other hand, meet the Quality of Service (QoS) requirements of users. Therefore, this paper proposes an admission control heuristic (ACH) approach that selects or rejects the requests based on budget, deadline, and penalty cost, i.e., those given by the user. Then a service level agreement (SLA) is created for each selected request. The proposed work uses Particle Swarm Optimization (PSO) and the Salp Swarm Algorithm (SSA) to schedule the selected requests under budget and deadline constraints. Performances of PSO and SSA with and witho

     Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust EA wit

     In this paper, the propose is to use the xtreme value distribution as the rate of occurrence of the non-homogenous Poisson process, in order to improve the rate of occurrence of the non-homogenous process, which has been called the Extreme value Process. To estimate the parameters of this process, it is proposed to use the Maximum Likelihood method, Method of Moment and a smart method represented by the Artificial Bee Colony:(ABC) algorithm to reach an estimator for this process which represents the best data representation. The results of the three methods are compared through a simulation of the model, and it is concluded that the estimator of (ABC) is better than the estimator of the maximum likelihood method and method of mo

People’s ability to quickly convey their thoughts, or opinions, on various services or items has improved as Web 2.0 has evolved. This is to look at the public perceptions expressed in the reviews. Aspect-based sentiment analysis (ABSA) deemed to receive a set of texts (e.g., product reviews or online reviews) and identify the opinion-target (aspect) within each review. Contemporary aspect-based sentiment analysis systems, like the aspect categorization, rely predominantly on lexicon-based, or manually labelled seeds that is being incorporated into the topic models. And using either handcrafted rules or pre-labelled clues for performing implicit aspect detection. These constraints are restricted to a particular domain or language which is

     The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,