Mostafa Zandieh; Mostafa Salari Boron
Volume 20, Issue 3 , September 2016, , Pages 127-152
Abstract
In recent years internet shops have had considerable growth. Performance measures are needed for these shops to maximize the use of resources and getting closer to the aims, to provide the identification of strengths and weaknesses along with minimizing resource inputs to improve the situation. Data ...
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In recent years internet shops have had considerable growth. Performance measures are needed for these shops to maximize the use of resources and getting closer to the aims, to provide the identification of strengths and weaknesses along with minimizing resource inputs to improve the situation. Data Envelopment Analysis (DEA) method is a nonparametric method for measuring technical efficiency and performance of a set of units. This study given the importance of assessing the performance of internet shops and using multiple input-oriented CCR model, measure the performance of internet shops. In this study, internet shop production process is divided into two stages: marketability and profitability. So to calculate the efficiency, a two-stage model of data envelopment analysis is used. Based on the data collected, the efficiency of 37 Internet stores evaluated. Results include overall efficiency and efficiency of marketability and profitability stages. After determining the efficiency of marketability and profitability stages, inefficient stages were identified and solutions were proposed to improve it.
Behrooz Dorri Nokorani; Mostafa Zandieh; Mohsen Notash
Volume 18, Issue 4 , January 2015, , Pages 183-203
Abstract
One of the organizations’ fundamental issues is supply chain network design. Optimization of this network can lead to effective management of the whole supply chain. Network design specifies the position, capacity, number and type of network facilities, and transportation network of materials and ...
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One of the organizations’ fundamental issues is supply chain network design. Optimization of this network can lead to effective management of the whole supply chain. Network design specifies the position, capacity, number and type of network facilities, and transportation network of materials and products from the supplier to the customer and vice versa. This research proposes new solution procedure based on Multi-objective Genetic Algorithm (MOGA) and Non-dominated Sorting Genetic algorithm-II (NSGAII) to find the set of Pareto optimal solutions that empowers the decision-makers by alternative solutions. Considering that in this study the level of service is very important, so this modeling was based on satisfying all customer demands. Objectives for network optimization are minimization of total cost and maximization of capacity utilization balance for network facilities that lead to the reduction of customers’ service time (increase service levels). Nine problems were designed from small to large. In order to compare the quality of the obtained Pareto solutions of both algorithms, seven criteria (for multi-objective problems) were used in this study. The results indicated that the solutions produced by NSGAII algorithm have higher quality.
mohammad akbari; Mostafa Zandieha; Behroz Dorri
Volume 17, Issue 3 , September 2013, , Pages 1-21
Abstract
The goal of this research is providing optimization approach for bi-objective scheduling work shifts and job rotation problem in order to exploit efficient performance of employees. In this article scheduling work shifts and job rotation problems had been modeled in one mathematical model with two objective ...
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The goal of this research is providing optimization approach for bi-objective scheduling work shifts and job rotation problem in order to exploit efficient performance of employees. In this article scheduling work shifts and job rotation problems had been modeled in one mathematical model with two objective functions: minimizing labor cost and maximizing number of job rotation. Also human factors (fatigue, learning and forgetting) that have effect on workers performance were modeled. Presented model was mixed integer and nonlinear and genetic algorithm and ε-constraint technique have been used to solve it and gain Pareto sets. To illustrate efficiency of provided algorithm, its performance has been compared to results of LINGO. The results indicated that performance of genetic algorithm is better than that of LINGO in terms of computational time and objective value. To relate relationship between objectives, set of problems have been solved. Obtained Pareto indicated that there is a conflict between objectives. Hence with considering human factors that have effect on workforce’s performance it is needed to plan work shifts and job rotation simultaneously. Results indicated that the proposed optimization approach is capable to provide suitable alternatives while managers try to consider decreasing cost, increasing jobs variety and multi-skilled training.
