Abolfazl Ranjbar; Farshad Hakimpour; Siamak Talat Ahary
Abstract
Extended Abstract
Introduction
The problem of locating bank branches is classified asNP-Hard problem which can possibly be solved only in exponential time by the increase in the number of banks and the large number of customers, especially when the location model includes various datasets, several ...
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Extended Abstract
Introduction
The problem of locating bank branches is classified asNP-Hard problem which can possibly be solved only in exponential time by the increase in the number of banks and the large number of customers, especially when the location model includes various datasets, several objectives and constraints. As a consequence, we need to use heuristic methods to solve these types of problems. Also, since majority of data and analyses applied in the locating problems are spatial; GIScience’s abilities should be employed besides optimization methods.
Nowadays, to perform particular financial tasks, bank customers often need to be present at their bank. For the sake of its customers, a bank should increase its branches in the city to attract more customers in the race with competing banks. However, establishing new branches is too expensive and banks prefer to carry out an optimal location finding procedure. Such procedures should consider many criteria and objectives including spatial data of customers, new and existing bank branches as well as the level of attraction of banks. Customers often select a bank that is closer to them, has better services or financial records and also consider other human or physical factors. Hence, planning to increase the number of customers for a new branch of a bank considering spatial criteria and various other objectives appears necessary.
Materials & Methods
This paper determines the location of bank branches. Finding an optimum site for branches depends on many factors and these problems are known as NP-hard problems. Despite being approximate methods, meta-heuristic algorithms seem suitable tools for solving NP-hard problems. In this paper, Grey Wolf Optimizer (GWO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Cultural Algorithms (CA) and Invasive Weed Optimization (IWO) are applied for finding the best location for bank branches. From marketing point of view, the aim is to attract more customers while the number of attracted people to a new branch should be acceptable. The new methods have capability to find the optimum location for new branches. The location of a new branch should be as far away as possible from branches of the same bank. The other condition is that the total number of customers for the new branch should not be less than a specified number, while the new branch should not attract customers of old branches of the same bank. To fulfill this propose, a part of the city of Tabriz was selected for implementation.The assumptions for the defined problem can be expressed as the following statements:
a)We consider four different banks (Melli, Mellat, Sepah and Mehr) in our study area.
b)Population density (of people over 15 years of age) is available at the building block level.
c)Banks have infinite capacity for accepting customers.
d)Each customer refers to only one bank.
e)New bank branches should have maximum distance from the branches of the same bank, so that, it attracts minimum number of customers from branches of the same bank.
Conclusion
To evaluate the quality and accuracy of the algorithms, several iterations are performed. The results of statistical and final tests indicate that the accuracy and convergence speed of Invasive Weed Optimization are more than other Algorithms in finding optimal location of bank branches.
Seyyed Hojjat Mousavi; Abolfazl Ranjbar; Mehdi Haseli
Abstract
Due to the changesin land use that is done mostly by human activities, changedetection of landuse and assessment of their environmental impact isessential for future planning and managing the resources. Therefore, the aim of this research is monitoring, detecting andtrending the landuse changes in Abarkooh ...
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Due to the changesin land use that is done mostly by human activities, changedetection of landuse and assessment of their environmental impact isessential for future planning and managing the resources. Therefore, the aim of this research is monitoring, detecting andtrending the landuse changes in Abarkooh basin (1976-2014) in orderto assess the environmental issues such as human stress onearth without considering tolerance capacity, and to identify the regions havingenvironmental stress.In this regard, after classification to identify the type of land uses and applying the base component analysis and tasseled cap functions and difference of images, satellite images data from Landsat, MSS (1976), TM (1990), ETM + (2000 and 2006) and OLI (2014)) sensors, and remote sensing techniques such as supervisory classification and accuracy assessment have been used to monitor the land use changes. The classification results indicate the enhancing of seven typesof land uses including urban lands, agricultural lands, wastelands, rocky lands, rangelands, clayey plain anddesert, and which have the highest accuracy of classification in 2014with kappa coefficient values of82.18%and total accuracy of 0.76. The trending results of changes in land use indicate an upward trend of the area in rangelands (5.65%), rockylands (2.52%),wastelands (3.63%) and agricultural lands (1.04%), and a downward trendof the area in urban land (4.33%), clayey plain (6.89%) and desert (6.03%). From the perspective of base component analysisand tasseled cap functions, 1.748% (306.4912 km2) and 3.989% (699.961 KM2) of the area of the study region were faced with increasing changes of landuse, and in general, the overall trend of the changes of increasing classes is upward. Most of the changes in land use are destructive and devastating, and in terms of spatial changes correspond to the area around human community centers suchas Abarkooh and Mehrdasht cities. It is evident that,due to the continuationof this trend, the Abarkooh basinbecomes a dead inactive ecosystem that lacksany ecological and biological production potential in the near future.