Hamid Reza Aghamiri; Esmaeil Mehdizadeh; Habib Reza Gholami
Volume 10, Issue 1 , July 2023, , Pages 53-66
Abstract
Today, the proper and effective performance of employees is one of the keys to the success of organizations. Good performance refers to high efficiency, quality, profitability, and customer orientation. One of the most important duties of human resource managers is to design and establish employee performance ...
Read More
Today, the proper and effective performance of employees is one of the keys to the success of organizations. Good performance refers to high efficiency, quality, profitability, and customer orientation. One of the most important duties of human resource managers is to design and establish employee performance evaluation systems. Since qualitative indices have a major share of these indices, judgmental methods are generally used for ranking them. Decision makers assign weights to these indices based on their attitudes and rank the employees. Hence, these methods fail to fully explain the performance of organizations’ employees and are influenced by some degrees and levels of ambiguity. Fuzzy logic methods are highly useful for resolving the ambiguities in these alternatives. In this paper, we propose an employee performance evaluation method with a type-2 fuzzy ranking approach. In our proposed method, a job ID is designed based on optimal models while an employee ranking method is developed and explained using the trapezoidal interval type-2 fuzzy ranking model introduced by Chen et al. 2012. In the end, the proposed method is utilized for the performance evaluation of employees in a real company.
Arezoo Osati; Esmaeil Mehdizadeh; Sadoullah Ebrahimnejad
Abstract
The purpose of this paper is to optimize the integrated problem of lot-sizing and scheduling in a flexible job-shop environment considering energy efficiency. The main contribution of the paper is simultaneously considering lot-sizing and scheduling decisions, while accounting for energy efficiency. ...
Read More
The purpose of this paper is to optimize the integrated problem of lot-sizing and scheduling in a flexible job-shop environment considering energy efficiency. The main contribution of the paper is simultaneously considering lot-sizing and scheduling decisions, while accounting for energy efficiency. In order to achieve this objective, a mathematical model has been developed for integrated optimization of scheduling and lot-sizing problems. The developed model uses a big bucket approach and is presented as a mixed integer nonlinear problem (MINLP). The BARON solver in GAMS software has been used to solve the proposed MINLP model. By defining the relative optimality limit (OPTCR) of 0.05 for the termination criterion in BARON solver, GAMS has not been able to solve large problems at a specified time to achieve relative optimality. Therefore, due to the NP-hard nature of the problem, a new genetic-based evolutionary algorithm has been developed to solve the problem of large scale. In the developed algorithm, a different approach (instead of cross-over and mutation operators) is used to generate a new solution. By presenting and solving various problems, the efficiency of this algorithm for solving big problems is shown. Comparing the values of the objective function obtained from the genetic algorithm and the exact method shows that, especially in large problems, the genetic algorithm has been able to achieve a better solution than GAMS software in a limited time. It has also been shown that energy efficiency has a significant effect on the solution of the problem.