A new method for human resource allocation in cloud-based e-commerce using a meta-heuristic algorithm

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ibrahim Al-Shourbaji ◽  
Waleed Zogaan
Keyword(s):  
Resource Allocation ◽  
Human Resource ◽  
Polynomial Time ◽  
Web Applications ◽  
Performance Enhancement ◽  
Imperialist Competitive Algorithm ◽  
Allocation Problem ◽  
Content Type ◽  
Capital Costs ◽  
Competitive Algorithm

PurposeThe human resource (HR) allocation problem is one of the critical dimensions of the project management process. Due to this nature of the problem, researchers are continually optimizing one or more critical scheduling and allocation challenges in different ways. This study aims to optimize two goals, increasing customer satisfaction and reducing costs using the imperialist competitive algorithm.Design/methodology/approachCloud-based e-commerce applications are preferred to conventional systems because they can save money in many areas, including resource use, running expenses, capital costs, maintenance and operation costs. In web applications, its core functionality of performance enhancement and automated device recovery is important. HR knowledge, expertise and competencies are becoming increasingly valuable carriers for organizational competitive advantage. As a result, HR management is becoming more relevant, as it seeks to channel all of the workers’ energy into meeting the organizational strategic objectives. The allocation of resources to maximize benefit or minimize cost is known as the resource allocation problem. Since discovering solutions in polynomial time is complicated, HR allocation in cloud-based e-commerce is an Nondeterministic Polynomial time (NP)-hard problem. In this paper, to promote the respective strengths and minimize the weaknesses, the imperialist competitive algorithm is suggested to solve these issues. The imperialist competitive algorithm is tested by comparing it to the literature’s novel algorithms using a simulation.FindingsEmpirical outcomes have illustrated that the suggested hybrid method achieves higher performance in discovering the appropriate HR allocation than some modern techniques.Practical implicationsThe paper presents a useful method for improving HR allocation methods. The MATLAB-based simulation results have indicated that costs and waiting time have been improved compared to other algorithms, which cause the high application of this method in practical projects.Originality/valueThe main novelty of this paper is using an imperialist competitive algorithm for finding the best solution to the HR allocation problem in cloud-based e-commerce.

Integrating artificial neural network and imperialist competitive algorithm (ICA), to predict the energy consumption for land leveling

2017 ◽  
Vol 11 (4) ◽  
pp. 522-540 ◽  
Author(s):  
Isham Alzoubi ◽  
Mahmoud Delavar ◽  
Farhad Mirzaei ◽  
Babak Nadjar Arrabi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Compressibility Factor ◽  
Imperialist Competitive Algorithm ◽  
Content Type ◽  
Competitive Algorithm ◽  
Land Leveling ◽  
Soil Compressibility ◽  
Artificial Neural

Purpose This work aims to determine the best linear model using an artificial neural network (ANN) with the imperialist competitive algorithm (ICA-ANN) and ANN to predict the energy consumption for land leveling. Design/methodology/approach Using ANN, integrating artificial neural network and imperialist competitive algorithm (ICA-ANN) and sensitivity analysis (SA) can lead to a noticeable improvement in the environment. In this research, effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index on energy consumption were investigated. Findings According to the results, 10-8-3-1, 10-8-2-5-1, 10-5-8-10-1 and 10-6-4-1 multilayer perceptron network structures were chosen as the best arrangements and were trained using the Levenberg–Marquardt method as the network training function. Sensitivity analysis revealed that only three variables, namely, density, soil compressibility factor and cut-fill volume (V), had the highest sensitivity on the output parameters, including labor energy, fuel energy, total machinery cost and total machinery energy. Based on the results, ICA-ANN had a better performance in the prediction of output parameters in comparison with conventional methods such as ANN or particle swarm optimization (PSO)-ANN. Statistical factors of root mean square error (RMSE) and correlation coefficient (R2) illustrate the superiority of ICA-ANN over other methods by values of about 0.02 and 0.99, respectively. Originality/value A limited number of research studies related to energy consumption in land leveling have been done on energy as a function of volume of excavation and embankment. However, in this research, energy and cost of land leveling are shown to be functions of all the properties of the land, including the slope, coefficient of swelling, density of the soil, soil moisture and special weight dirt. Therefore, the authors believe that this paper contains new and significant information adequate for justifying publication in an international journal.

Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm

2018 ◽  
Vol 35 (4) ◽  
pp. 1774-1787 ◽  
Author(s):  
Katayoun Behzadafshar ◽  
Fahimeh Mohebbi ◽  
Mehran Soltani Tehrani ◽  
Mahdi Hasanipanah ◽  
Omid Tabrizi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Vibration ◽  
Imperialist Competitive Algorithm ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Content Type ◽  
Competitive Algorithm ◽  
Spacing Ratio ◽  
Artificial Neural

PurposeThe purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran.Design/methodology/approachFor this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models’ input, and the peak particle velocity (PPV) parameter was used as the models’ output.FindingsAfter modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with theR2 of 0.939 was the most precise model for predicting the PPV in the present study.Originality/valueIn the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

A TWO-RESOURCE ALLOCATION PROBLEM ACCORDING TO AN EXPONENTIAL OBJECTIVE: OPTIMUM DISTRIBUTION OF SEARCHING EFFORT

1994 ◽  
Vol 01 (02) ◽  
pp. 135-146 ◽  
Author(s):  
TETSUO ICHIMORI ◽  
HIROSHI MASUYAMA ◽  
SHIGERU YAMADA
Keyword(s):  
Resource Allocation ◽  
Nonlinear Programming ◽  
Objective Function ◽  
Programming Problem ◽  
Polynomial Time ◽  
Allocation Problem ◽  
Nonlinear Programming Problem ◽  
Resource Allocation Problem ◽  
Optimum Distribution ◽  
Strongly Polynomial

The resource allocation problem has been studied in a variety of applications. This problem usually has only one constraint, i.e., the amount of resource to be allocated is constant. Considering its application areas, however, it is important to treat multiresource problems. In this paper we consider a two-resource allocation problem with an exponential objective function. Though this problem is a nonlinear programming problem, we show that it can be solved in strongly polynomial time.

Design technique for leakage current reduction in surge arrester using gravitational search algorithm and imperialist competitive algorithm

2018 ◽  
Vol 37 (1) ◽  
pp. 357-374 ◽  
Author(s):  
Nurul Ain Abdul Latiff ◽  
Hazlee Azil Illias ◽  
Ab Halim Abu Bakar ◽  
Syahirah Abd Halim ◽  
Sameh Ziad Dabbak
Keyword(s):  
Leakage Current ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Original Design ◽  
Element Analysis ◽  
Content Type ◽  
Competitive Algorithm ◽  
Surge Arresters ◽  
Gravitational Search

Purpose Leakage current is one of the factors, which can contribute towards degradation of surge arresters. Thus, the purpose of this paper is to study on leakage current within surge arresters and improvement on their design. Design/methodology/approach In this work, a three-dimensional model geometry of 11 kV zinc oxide surge arrester was designed in finite element analysis and was applied to calculate the leakage current under normal operating condition and being verified with measurement results. The optimisation methods were used to improve the arrester design by minimising the leakage current across the arrester using imperialist competitive algorithm (ICA) and gravitational search algorithm (GSA). Findings The arrester design in reducing leakage current was successfully optimised by varying the glass permittivity, silicone rubber permittivity and the width of the ground terminal of the surge arrester. It was found that the surge arrester design obtained using ICA has lower leakage current than GSA and the original design of the surge arrester. Practical implications The comparison between measurement and simulation enables factors that affect the mechanism of leakage current in surge arresters to be identified and provides the ideal design of arrester. Originality/value Surge arrester design was optimised by ICA and GSA, which has never been applied in past works in designing surge arrester with minimum leakage current.

Human Resource Allocation in Software Project with Practical Considerations

2015 ◽  
Vol 25 (01) ◽  
pp. 5-26 ◽  
Author(s):  
Jihun Park ◽  
Dongwon Seo ◽  
Gwangui Hong ◽  
Donghwan Shin ◽  
Jimin Hwa ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Human Resource ◽  
Planning Process ◽  
Project Managers ◽  
Time Span ◽  
Allocation Problem ◽  
Software Project ◽  
Resource Allocation Problem ◽  
Project Schedule ◽  
The Cost

Software planning is very important for the success of a software project. Even if the same developers work on the same project, the time span of the project and the quality of software may change based on the project plan. When software managers plan a software project, they strive to allocate human resources in a more efficient way to produce a better software with less cost. The planning process is, however, time-consuming and complicated, especially when the size of the software project is large. Many approaches have been proposed to help software project managers by providing optimal human resource allocations in terms of minimizing the cost. Previous approaches, however, only concentrated on minimizing the cost, and no existing works have considered the practical issues affecting project schedules in practice. We elicited the practical considerations relating to the human resource allocation problem through discussions with a group of software project experts. The practical considerations can affect the project schedule in practice, but their importance has not been taken into consideration in previous approaches. Reflecting the practical considerations, we propose an approach for solving the human resource allocation problem using a genetic algorithm (GA). We compare our approach to an approach that only considers minimization of the time span. Our evaluation shows that the proposed algorithm considers the practical considerations well, in terms of continuous allocation on relevant tasks, minimization of developer multitasking time, and balance of allocation. We also conducted a survey targeting software developers and managers, and the responses showed that practical considerations are as important as minimizing the cost, and our approach would be helpful to software managers. We also investigate the effect of weight factors and coefficient between sub-scores, and find that it is difficult to consider some practical considerations at the same time.

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