Showing 4 results for Alinezhad
Mehdi Dadehbeigi, Ali Taherinezhad, Alireza Alinezhad,
Volume 0, Issue 0 (IJIEPR 2024)
Abstract
Today, data mining and machine learning are recognized as tools for extracting knowledge from large
datasets with diverse characteristics. With the increasing volume and complexity of information in various
fields, decision-making has become more challenging for managers and decision-making units. Data
Envelopment Analysis (DEA) is a tool that aids managers in measuring the efficiency of the units under
their supervision. Another challenge for managers involves selecting and ranking options based on specific
criteria. In such cases, the selection of an appropriate multi-criteria decision-making (MCDM) technique
is crucial. With the spread of COVID-19 and the significant financial, economic, and human losses it caused,
data mining has once again played a role in improving outcomes, predicting trends, and reducing these
losses by identifying patterns in the data. This paper aims to assess and predict the efficiency of countries
in preventing and treating COVID-19 by combining DEA and MCDM models with machine learning models.
By evaluating decision-making units and utilizing available data, decision-makers are better equipped to
make effective decisions in this area. Computational results are presented in detail and discussed in depth.
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Volume 23, Issue 2 (IJIEPR 2012)
Abstract
The problem of staff scheduling at a truck hub for loading and stripping of the trucks is an important and difficult problem to optimize the labor efficiency and cost. The trucks enter the hub at different hours a day, in different known time schedules and operating hours. In this paper, we propose a goal programming to maximize the labor efficiency via minimizing the allocation cost. The proposed model of this paper is implemented for a real-world of a case study and the results are analyzed.
Ali Mohtashami, Alireza Alinezhad,
Volume 28, Issue 3 (IJIEPR 2017)
Abstract
In this article, a multi objective model is presented to select and allocate the order to suppliers in uncertainty condition and in a multi source, multi customer and multiproduct case in a multi period state at two levels of supply chain. Objective functions considered in this study as the measures to evaluate suppliers are cost including purchase, transportation and ordering costs, timely delivering, shipment quality or wastages which are amongst major quality aspects, partial and general coverage of suppliers in respect of distance and finally suppliers weights making the products orders amount more realistic. The major limitations are price discount for products by suppliers which are calculated using signal function. In addition, suppliers weights in the fifth objective function is calculated using fuzzy Topsis technique. Lateness and wastes parameters in this model are considered as uncertain and random triangular fuzzy number. Finally the multi objective model is solved using two multi objective algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Particle Swarm Optimization (PSO) and the results are analyzed using quantitative criteria Taguchi technique was used to regulate the parameters of two algorithms.
Amin Amini, Alireza Alinezhad, Davood Gharakhani,
Volume 35, Issue 2 (IJIEPR 2024)
Abstract
The selection of a sustainable supplier is a multi-criteria decision-making issue that covers a range of criteria (quantitative-qualitative). Selecting the most eco-friendly suppliers requires balancing tangible and intangible elements that may be out of sync. The problem gets more complicated when volume discounts are taken into account, as the buyer needs to decide between two issues: 1) What are the best sustainable suppliers? 2) Which amount needs to be bought from each of the selected eco-friendly suppliers? In current study a combined attitude of best-worst method (BWM) ameliorated via multi-objective mixed integer programming (MOMIP) and rough sets theory is developed. The aim of this work is to contemporaneously ascertain the order quantity allocated to these suppliers in the case of multiple sourcing, multiple products with multiple criteria and with capacity constraints of suppliers and the number of suppliers to employ. In this situation, price reductions are offered by suppliers based on add up commerce volume, not on the amount or assortment of items acquired from them. Finally, a solution approach is proposed to solve the multi-objective model, and the model is demonstrated using a case study in Iran Khodro Company (IKCO). The results indicate that ISACO is the most sustainable supplier and the most orders are assigned to this supplier.
