Amir-Mohammad Golmohammadi; Alireza Goli; Hasan Rasay
Volume 3, Issue 1 , May 2022, , Pages 48-61
Abstract
Purpose: The study aims to present a mathematical model for reducing system costs by adequately locating the required warehouses and routing vehicles that carry the products from the warehouses within a time window.Methodology: Concerning the specific nature of the location-routing problem, the consumption ...
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Purpose: The study aims to present a mathematical model for reducing system costs by adequately locating the required warehouses and routing vehicles that carry the products from the warehouses within a time window.Methodology: Concerning the specific nature of the location-routing problem, the consumption of fuel and the depreciation of vehicles are directly affected by the distance covered. The model proposed in this research seeks to minimize the undue length of the distance that vehicles have to travel. Moreover, to approximate the model to real-world conditions as much as possible, the 'time window' concept is employed to determine the maximum allowable time for the distribution of goods.Findings: Three metaheuristic algorithms, including NSGA-II, PAES, and MOICA, are used to solve the proposed model. Several problems of different sizes are introduced and solved to evaluate the efficiency of the solutions. Then, the results are compared regarding the SM, MID, and QM criteria. The comparative results suggest the superiority of the MOICA algorithm for big-size problems.Originality/Value: Setting a time window to reduce the distance travelled by the vehicles gets the model close to real-world conditions. It also makes it possible to estimate the costs more accurately.
Marziyeh Nourahmadi; Hojjatollah Sadeqi
Volume 2, Issue 2 , September 2021, , Pages 180-194
Abstract
Purpose: One of the issues that significantly impact how people invest is the behavioural characteristics of investors. Given the importance of this issue, investors should be able to categorize investors into different classes and recommend investments appropriate to the personality type of the same ...
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Purpose: One of the issues that significantly impact how people invest is the behavioural characteristics of investors. Given the importance of this issue, investors should be able to categorize investors into different classes and recommend investments appropriate to the personality type of the same class for each class. One of the solutions that can be used for this purpose is clustering. Clustering is one of the unsupervised learning methods and has a descriptive nature. In this method, the data are allocated based on a similarity criterion so that the data in each cluster are most similar and the least comparable to the data in other clusters.Methodology: This study identifies a group of investors with similar ability and willingness to accept risk using K-means clustering and Affinity propagation clustering. We also show how to allocate assets effectively using investor characteristics and clustering techniques.Findings: Use silhouette coefficient to evaluate two clustering methods to select the best method for data clustering. The k-means coefficient was equal to 0.17, and the Affinity propagation clustering was equal to 0.097. Therefore, we choose the k-means method as the optimal clustering method. Using the K-means clustering method, we cluster investors based on financial, behavioural, and demographic characteristics, and according to the clustering results, we divide individuals into seven categories with low to high-risk acceptance.Originality/Value: All calculations in this study were performed by Python 3.8. Investment managers and stock advisors can use the results of this study.
