Store Sites Research Articles

Drug supply management at first-level public health facilities: Case of Pyay District, Myanmar.

First-level public health facilities (PHFs) serve as primary providers of essential medicines, necessitating critical attention to drug availability and quality assurance. This study aimed to examine the status of functional areas within the drug supply chain management framework and assess the overall capability maturity at first-level PHFs. The cross-sectional study was conducted among 183 drug store sites from six townships of Pyay District. Only situational analysis was exercised to determine the existing situations. The overall capability maturity was determined according to the definitions of levels of the Capability Maturity Module Tool. 58.47% lacked formal drug supply management training, with 23.5% not undergoing performance reviews. Drug forecasting predominantly relied on a pen-paper system (91.6%) and factors like patient load (87.39%), drug consumption (85.71%), and disease prevalence (64.71%). Store site analysis revealed that 65.03% exhibited marginal capability, lacking standardized drugstores and employing unstandardized procedures. Storage practices varied, with 48.69% storing drugs conveniently and others categorizing them by drug type (32.79%) or using the first-expired-first-out system (40.98%). Approximately 42.69% reported having expired drugs. Concerning transportation costs, 37.16% incurred expenses exceeding 20,000 Kyats per time, with management staff often covering the costs. Waste management methods included burial pits (49.18%), incineration (62.84%), and sharp pits (55.19%). A majority (78.14%) used safety boxes, and 57.38% implemented a color-coded system for waste bins. The logistics management information system was entirely paper-based (100%). On average, assessments of drug quality conditions and physical damages scored 46.51% and 48.20%, respectively. The overall supply chain maturity at first-level public health facilities is at a marginal capability level (36.35%). While some basic drug supply chain management procedures were in place, they were not consistently followed, and many systems remain manual. The findings underscored significant inconsistencies in the management functions of supplied drugs, with poor adherence to Standard Operating Procedure guidelines.

The Site/Group Extended Data Format and Tools.

Comparative sequence analysis permits unraveling the molecular processes underlying gene evolution. Many statistical methods generate candidate positions within genes, such as fast or slowly evolving sites, coevolving groups of residues, sites undergoing positive selection, or changes in evolutionary rates. Understanding the functional causes of these evolutionary patterns requires combining the results of these analyses and mapping them onto molecular structures, a complex task involving distinct coordinate referential systems. To ease this task, we introduce the site/group extended data format, a simple text format to store (groups of) site annotations. We developed a toolset, the SgedTools, which permits site/group extended data file manipulation, creating them from various software outputs and translating coordinates between individual sequences, alignments, and three-dimensional structures. The package also includes a Monte-Carlo procedure to generate random site samples, possibly conditioning on site-specific features. This eases the statistical testing of evolutionary hypotheses, accounting for the structural properties of the encoded molecules.

Investigating online shopping intention and customer participation with risk understanding and evidence available on the site: a study of digikala store

PurposeIn online shopping, there is often a risk due to the inability to check the products. As a result, it can reduce the impact of factors affecting the intention to buy online, which is a significant point in e-commerce. The purpose of this research is to investigate the impact of the evidence and the perceived usefulness of a store site on the existing risk and online participation of customers for online shopping.Design/methodology/approachThe statistical population of this research is people who have had the experience of online shopping at the Digikala store. The research method was descriptive-survey and the samples were randomly selected. A questionnaire was also used to collect information. Structural and statistical analysis of the model was done by SmartPLS3 software.FindingsIn this research, the authors found that the perceived usefulness (benefits of the site) has a favorable effect on the online participation of customers. It can also reduce the risk of online shopping. This research suggests good ways to increase sales to those who have a store site. The present research provides useful findings for those who sell their products online.Originality/valueThis research specifically examines the interaction of customers with sellers and can be a help for progress in e-commerce. In this study, perceived risk and participation as mediating variables, information and perceived usefulness on the site as independent variables and online shopping intention a dependent variables. This research was done about Digikala's online store. The results were obtained with the participation of customers in completing the questionnaire. Then the theoretical model and the background of the research are examined and then the hypotheses are evaluated according to the statistical results and finally, the research results and limitations are stated.

PARAMETRIC CLASSIFICATION OF THE CONSUMER PROPERTIES OF THE GOODS FOR THE APPLICATION OF SEARCH FILTERS IN THE INTERNET STORE

The development of information technologies, among which the Internet took one of the key places, the emergence and rapid growth of electronic commerce became the basis for the emergence of a new direction in the modern concept of interaction marketing – Internet marketing. Consumer validity and feasibility of using search filters in the Internet store is an important prerequisite for the effectiveness of the Internet store. After all, depending on the marketing validity and accuracy of the ranking of the applied product filters, it primarily depends on whether the buyer will find the product he needs on the store’s website and whether the purchase itself will be completed. That is, the quality of the parametric classification of the product’s consumer properties carried out by marketers directly affects the number of sales, and therefore the effectiveness of marketing companies’ promotion of goods via the Internet. The growth of competition in the field of electronic business and trade on the Internet requires increasing attention at all stages of product promotion, including the appropriate search engine optimization of the Internet store site and the appropriate setting of product filters. The article examines the necessity and expediency of conducting a parametric classification of the consumer qualities of the product in the process of developing a sequence of product filters that will be displayed on the main page of the online store. In addition, the author’s scheme of the sequence of the formation of the influence of the parametric classification of the consumer qualities of the product on the effectiveness of the online store is proposed, with the determination of positive and negative consequences at each of its stages, practical examples of the development of the parametric classification of the product are given and its results are compared with the product filters of the operating online store according to the corresponding a group of goods.

Web scraping with excel

Web scraping is a process that is particularly important in fields such as Business Intelligence in the modern age. Web scrapping is a technology that allow us to extract structured data from text such as HTML. Web scrapping is extremely useful in situations where doesisn’t provided in readable format such as JSON or XML. The use of web scrapping to gather data allows us to gather prices in near real time from retail store sites and provide further details, web scrapping can also be used to gather intelligence of illicit businesses such as drug marketplaces in the darknet to provide law enforcement and researchers valuable data such as drug prices and varieties that would be unavailable with conventional methods. It has been found that using a web scraping program would yield data that is far more thorough, accurate, and consistent than manual entry Web scraping with Excel is the process of automatically extracting data from websites and importing it into Excel for analysis and reporting purposes. Excel's built-in web scraping tool, Power Query, allows users to connect to a website, select the data they want to extract, and transform the data into a usable format. However, it's important to be aware of the potential legal and ethical issues surrounding web scraping, and to use discretion when extracting data from websites KEYWORDS Scraping, Excel, Data, structure.

Comparison of SAW and Topsis Methods in The Selection of The Best Online Bike Shops

The need for information technology in this pandemic era is greatly increasing. Many people meet their needs by doing buying and selling transactions as if they were online. Through a decision support system using the SAW (Simple Additive Weighting) and TOPSIS (Technique for Others Reference by Similarity to Ideal Solution), methods can provide the best decision in choosing the best online bicycle store site. On the result of the calculation, it is obtained that the results of the two are appropriate. The SAW and TOPSIS methods produced the same ranking, namely the Rodalink site got the highest ranking with a value of 1.019 on the SAW method of 0.833 on the TOPSIS method followed by serbasepeda, united, and cycles. The results of comparing calculations using these 2 methods are considered the SAW method is the most relevant method.

Artificial intelligence-driven identification of morin analogues acting as CaV1.2 channel blockers: Synthesis and biological evaluation

Morin is a vasorelaxant flavonoid, whose activity is ascribable to CaV1.2 channel blockade that, however, is weak as compared to that of clinically used therapeutic agents. A conventional strategy to circumvent this drawback is to synthesize new derivatives differently decorated and, in this context, morin-derivatives able to interact with CaV1.2 channels were found by employing the potential of PLATO in target fishing and reverse screening. Three different derivatives (5a-c) were selected as promising tools, synthesized, and investigated in in vitro functional studies using rat aorta rings and rat tail artery myocytes. 5a-c were found more effective vasorelaxant agents than the naturally occurring parent compound and antagonized both electro- and pharmaco-mechanical coupling in an endothelium-independent manner. 5a, the series’ most potent, reduced also Ca2+ mobilization from intracellular store sites. Furthermore, 5a≈5c > 5b inhibited Ba2+ current through CaV1.2 channels. However, compound 5a caused also a concentration-dependent inhibition of KCa1.1 channel currents.

GeoBERT: Pre-Training Geospatial Representation Learning on Point-of-Interest

Thanks to the development of geographic information technology, geospatial representation learning based on POIs (Point-of-Interest) has gained widespread attention in the past few years. POI is an important indicator to reflect urban socioeconomic activities, widely used to extract geospatial information. However, previous studies often focus on a specific area, such as a city or a district, and are designed only for particular tasks, such as land-use classification. On the other hand, large-scale pre-trained models (PTMs) have recently achieved impressive success and become a milestone in artificial intelligence (AI). Against this background, this study proposes the first large-scale pre-training geospatial representation learning model called GeoBERT. First, we collect about 17 million POIs in 30 cities across China to construct pre-training corpora, with 313 POI types as the tokens and the level-7 Geohash grids as the basic units. Second, we pre-train GeoEBRT to learn grid embedding in self-supervised learning by masking the POI type and then predicting. Third, under the paradigm of “pre-training + fine-tuning”, we design five practical downstream tasks. Experiments show that, with just one additional output layer fine-tuning, GeoBERT outperforms previous NLP methods (Word2vec, GloVe) used in geospatial representation learning by 9.21% on average in F1-score for classification tasks, such as store site recommendation and working/living area prediction. For regression tasks, such as POI number prediction, house price prediction, and passenger flow prediction, GeoBERT demonstrates greater performance improvements. The experiment results prove that pre-training on large-scale POI data can significantly improve the ability to extract geospatial information. In the discussion section, we provide a detailed analysis of what GeoBERT has learned from the perspective of attention mechanisms.

Analysis of Manufacturing Companies by Weight Sum Method

In this from analysis EDAS method is the most ideal solution Short-distance and negative-best The solution with the longest distance from the solution Determines, but the comparison of these distances Does not consider importance. From the result it is seen that Market turmoil ranks first, with management support having the lowest ranking. This article addresses the issue of customer complexity, Customer center, innovation, service diversity and Explores the relationship between business performance and. The study of 332 manufacturing companies provides the basis for empirical inquiry. There are two broad types of financing research dealing with the use of rates Read Corporate Structure. Increased market complexity and impending Competitiveness is their position in the freight-service continuum by expanding the business power to expand Is a traditional service to manufacturing-manufacturing companies. World-class quality of life, production plays a key role in improving companies. However, linearity in the system they are combined with standard Production and consumption patterns. This is a measure of performance and Store site status report is not financial. This research science includes macroeconomics; Financial risk management, audit scenarios, equity returns, investment decisions, financial results and Good Corporate Management There are 147 models of manufacturing companies listed on the Indonesian Stock Exchange. Sustained performance in organizational research is one of the primary ideas. Its importance and effectiveness of the many advances that have appeared over the years are, despite criticisms, a difficult concept to apply in a scientifically rigorous manner. Multi-Objective Optimization Issues Mostly big dominance Lead to unpaid packages, Because of the size of the problem or the number of goals to create a complete non-dominant set Requires significant calculation time Related at the same time Most of the solutions are unsuitable for the decision maker (DM) Shows that Manufacturing Companies Alternative: Perceived benefits, technical resources, management support, computer perspective, market turmoil, peer pressure. Evaluation Preference: Technology, Organization, Environment. From the result it is seen that Market turbulence and is got the first rank whereas is the Management support got is having the lowest rank

Analysis of Manufacturing Companies by Weight Sum Method

In this from analysis EDAS method is the most ideal solution Short-distance and negative-best The solution with the longest distance from the solution Determines, but the comparison of these distances Does not consider importance. From the result it is seen that Market turmoil ranks first, with management support having the lowest ranking. This article addresses the issue of customer complexity, Customer center, innovation, service diversity and Explores the relationship between business performance and. The study of 332 manufacturing companies provides the basis for empirical inquiry. There are two broad types of financing research dealing with the use of rates Read Corporate Structure. Increased market complexity and impending Competitiveness is their position in the freight-service continuum by expanding the business power to expand Is a traditional service to manufacturing-manufacturing companies. World-class quality of life, production plays a key role in improving companies. However, linearity in the system they are combined with standard Production and consumption patterns. This is a measure of performance and Store site status report is not financial. This research science includes macroeconomics; Financial risk management, audit scenarios, equity returns, investment decisions, financial results and Good Corporate Management There are 147 models of manufacturing companies listed on the Indonesian Stock Exchange. Sustained performance in organizational research is one of the primary ideas. Its importance and effectiveness of the many advances that have appeared over the years are, despite criticisms, a difficult concept to apply in a scientifically rigorous manner. Multi-Objective Optimization Issues Mostly big dominance Lead to unpaid packages, Because of the size of the problem or the number of goals to create a complete non-dominant set Requires significant calculation time Related at the same time Most of the solutions are unsuitable for the decision maker (DM) Shows that Manufacturing Companies Alternative: Perceived benefits, technical resources, management support, computer perspective, market turmoil, peer pressure. Evaluation Preference: Technology, Organization, Environment. From the result it is seen that Market turbulence and is got the first rank whereas is the Management support got is having the lowest rank.

The Effect of Promotion, Product Review, and Security on Purchasing Decision

This research uses qualitative methods through field surveys of a sample of Tokopedia Online Store site is one of the Indonesian marketplaces for buying and selling online. and the most visited in 2021. Promotions, Product Reviews, Security of the Tokopedia Application can affect purchase intentions using the Tokopedia Application. The purpose of this study was to determine the effect of promotions, product reviews, and application security on purchasing decisions using the Tokopedia application at the Bekasi Civil Society Communication Forum. The population of 1771 members, 100 samples, and research samples were obtained using the Slovin formula with a standard error of 10%. There were 95 respondents who appeared. The results of this study indicate that there is a positive effect of promotional variations (X1) on purchasing decisions (Y) from these variables. Product evaluation variable (X2) has a positive effect on purchasing decisions (Y). There is a positive effect of the security variable (X3) on purchasing decisions (Y). The Adjusted R-squared value of 0.720 indicates that 70.2% of the variation in purchasing decisions can be explained by the three independent variables in the regression equation.

Site Selection via Learning Graph Convolutional Neural Networks: A Case Study of Singapore

Selection of store sites is a common but challenging task in business practices. Picking the most desirable location for a future store is crucial for attracting customers and becoming profitable. The classic multi-criteria decision-making framework for store site selection oversimplifies the local characteristics that are both high dimensional and unstructured. Recent advances in deep learning enable more powerful data-driven approaches for site selection, many of which, however, overlook the interaction between different locations on the map. To better incorporate the spatial interaction patterns in understanding neighborhood characteristics and their impact on store placement, we propose to learn a graph convolutional network (GCN) for highly effective site selection tasks. Furthermore, we present a novel dataset that encompasses land use information as well as public transport networks in Singapore as a case study to benchmark site selection algorithms. It allows us to construct a geospatial GCN based on the public transport system to predict the attractiveness of different store sites within neighborhoods. We show that the proposed GCN model outperforms the competing methods that are learning from local geographical characteristics only. The proposed case study corroborates the geospatial interactions and offers new insights for solving various geographic and transport problems using graph neural networks.

Locating facilities under competition and market expansion: Formulation, optimization, and implications

As the ongoing battle between brick‐and‐mortar stores and e‐commerce shops escalates, managers of the former are becoming more cautious regarding their strategic store site selection and decoration decisions, particularly if foreseeable competition from rival companies exists. This paper investigates a bilevel competitive facility location (BCFL) problem, where two companies, a leader and a follower, plan to enter a market sequentially. Each company has a budget to open and design facilities. The goal is to maximize expected revenue that is forecasted through a discrete choice model. To reflect a practical environment, we further consider a situation with elastic demand, explaining the market expansion effect when customers are offered better service due to the opening of new facilities. We formulate the problem as a nonlinear 0‐1 bilevel program. Because of the bilevel structure and the market expansion effect, this problem is so challenging that we are unaware of any exact algorithms in the literature. Motivated by this gap, we develop an exact framework that leverages the state‐of‐the‐art value‐function–based approach. However, this framework requires solving a mixed‐integer nonconvex optimization problem (MINOP) at each iteration, which is computationally prohibitive even for medium‐scale instances. To mitigate the intractability, we propose a new framework that avoids MINOP and tackles instances with hundreds of location variables. Finally, we conduct extensive computational studies to show the efficiency and effectiveness of our method as well as provide insightful guidance for managers to have win–win/dominate outcomes and choose an appropriate market size function when dealing with expansion decisions in chained business operations.

Mitofusin 2 positively regulates Ca2+ signaling by tethering the sarcoplasmic reticulum and mitochondria in rat aortic smooth muscle cells.

Mitochondria buffer cytosolic Ca2+ increases following Ca2+ influx from extracellular spaces, and Ca2+ release from intracellular Ca2+ store sites under physiological circumstances. Therefore, close contact of mitochondria with the sarcoplasmic reticulum (SR) is required for maintaining Ca2+ homeostasis. Mitofusin 2 (Mfn2) localizes in both mitochondrial and SR membranes and is hypothesized to optimize the distance and Ca2+ transfer between these organelles. However, the physiological significance of Mfn2 in vascular smooth muscle cells (VSMCs) is poorly understood. In the present study, the role of Mfn2 in the physical and functional couplings between SR and mitochondria was examined in rat aortic smooth muscle cells (rASMCs) by confocal and electron microscope imaging. When Mfn2 was knocked down using siRNA in rASMCs, the mean distance between these organelles was extended from 16.2 to 21.6 nm. The increase in the cytosolic Ca2+ concentration ([Ca2+]cyt) induced by 100 nM arginine vasopressin (AVP) was not affected by Mfn2 siRNA knockdown, whereas cytosolic Ca2+ removal was slower after Mfn2 knockdown. Following the AVP-induced [Ca2+]cyt increase, mitochondrial Ca2+ uptake and Ca2+ refill into the SR were attenuated by Mfn2 knockdown. In addition, Mfn2-knockdown cells exhibited a loss of mitochondrial membrane potential (ΔΨmito) and lower ATP levels in mitochondria. Moreover, Mfn2 knockdown inhibited cell proliferation. In contrast, Mfn2 overexpression increased ΔΨmito and cell growth. This study strongly suggests that Mfn2 is responsible for SR-mitochondria Ca2+ signaling by tethering mitochondria to SR, thereby regulating ATP production and proliferation of VSMCs.

Sentiment Analysis of User Reviews Based On Naïve Bayes Classifier Algorithm with Hyperparameter Optimization: A Case Study On Application "Kredit Pintar"

The development of information and communication technology makes it easy for people to take advantage of their sophistication in various sectors, especially industry and the economy. However, due to the Covid-19 pandemic in Indonesia, it has an impact on the socio-economic community. To meet their needs, most people use online loan platforms because they feel they have easy requirements. One of the most trusted online loan platforms and protected by the OJK is "Kredit Pintar", which is the number one platform that is most widely used by the public to borrow money in 2021. From the facts, the purpose of this research is to find out the reviews of the community of online loan application users “Kredit Pintar” through sentiment analysis based on text data in the form of public comments taken from the Google Play Store site application of “Kredit Pintar”. The data used are 1374 reviews with positive and negative class classifications. This research was conducted by using an analytical text mining method using the Naïve Bayes Classifier algorithm and applying Hyperparameter Optimization by comparing two models of GaussianNB and MultinomialNB functions by Phyton programming. The results of the classification of training and testing data show that based on performance evaluation, the best model uses the MultinomialNB function with an accuracy of 97.08% for training data and 90.54% for testing data.

Web Scraping or Web Crawling: State of Art, Techniques, Approaches and Application

Web scraping or web crawling refers to the procedure of automatic extraction of data from websites using software. It is a process that is particularly important in fields such as Business Intelligence in the modern age. Web scrapping is a technology that allow us to extract structured data from text such as HTML. Web scrapping is extremely useful in situations where data isn’t provided in machine readable format such as JSON or XML. The use of web scrapping to gather data allows us to gather prices in near real time from retail store sites and provide further details, web scrapping can also be used to gather intelligence of illicit businesses such as drug marketplaces in the darknet to provide law enforcement and researchers valuable data such as drug prices and varieties that would be unavailable with conventional methods. It has been found that using a web scraping program would yield data that is far more thorough, accurate, and consistent than manual entry. Based on the result it has been concluded that Web scraping is a highly useful tool in the information age, and an essential one in the modern fields. Multiple technologies are required to implement web scrapping properly such as spidering and pattern matching which are discussed. This paper is looking into what web scraping is, how it works, web scraping stages, technologies, how it relates to Business Intelligence, artificial intelligence, data science, big data, cyber securityو how it can be done with the Python language, some of the main benefits of web scraping, and what the future of web scraping may look like, and a special degree of emphasis is placed on highlighting the ethical and legal issues. Keywords: Web Scraping, Web Crawling, Python Language, Business Intelligence, Data Science, Artificial Intelligence, Big Data, Cloud Computing, Cybersecurity, legal, ethical.

ANALISIS RAGAM PRODUK DAN KUALITAS INFORMASI TERHADAP MINAT BERBELANJA DISITUS ONLINE STORE SHOPEE (STUDI PADA PT REYCOM DOCUMENT SOLUSI DI JAKARTA)

The title of this research is Analysis of Product Variety and Quality of Information on Shopping Interests on the shopee online store site (Study on Employees of PT Reycome Document Solusi in Jakarta). Does the variety of products and quality of information affect the interest in shopping at the Shopee online store site? The purpose of this study was to determine the Analysis of Product Variety and Quality of Information on Shopping Interests.The population in this study were online users, namely employees of PT Reycome Document Solusi in Jakarta who had made purchases on the online Store Shopee site. According to the opinion of the roots, the sample requirements used in this study were 64 people. This research is an associative study using a quantitative approach. This research is categorized as survey research, in which the research instrument is a questionnaire. Validity test using CFA (Confirmatory Factor Analysis Model), using the SPSS application. The results of this study indicate that Product Variety has no significant effect on Shopping Interest on Shopee's online store site with test results (t) of 0.970 greater than 0.05, and information quality has a significant effect on Shopping Interest on Shopee's online store sites with test results (t) of 0.003 is less than 0.05. Thus the Information Quality variable (X2) is the most dominant variable influencing the shopping interest on the Shopee online store site.

Development E-Commerce Applications

Buying and selling activities carried out electronically have now become delightful activities for many people. Also called online shopping, this is considered to make it easier for sellers and buyers and seems more practical. Not only making online shops in various types of shopping applications, but many business actors also create e-commerce themed websites to expand their sales network of products and services. For website creation, many templates can be used, one of which is Prestashop CMS, which is an e-commerce platform used to build online store sites. In Prestashop, there are already many features that can be used, and it also makes it easier for users to build the website they want to create. Even though many people have switched to online shop users. But there are too many shops or malls that they still visit directly to see first hand the quality of the desired goods.

Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.

Convenience store locations prioritization: a fuzzy TOPSIS-GRA hybrid approach

PurposeThe objective of the study is to develop a methodology to strategically rank store locations using criteria such as population, store site characteristics, economic considerations, competition and so on to select the most optimal retail convenience store location.Design/methodology/approachA case of National Capital Region, India, for a 24-h convenience store was considered for the study and the major criteria that affect the performance of a convenience store are identified, such as population characteristics, economic criteria, competition, consumer accessibility, store size, total cost, site attractiveness and security. Fuzzy AHP is utilized to find the weightage for each criteria and a combination of fuzzy TOPSIS and grey relational analysis (GRA) is applied to rank the alternative using these criteria weight. Further, results obtained are compared with results from fuzzy TOPSIS and fuzzy VIKOR methods. Sensitivity analysis is also performed for ensuring the robustness of the framework.FindingsIt is observed that outcomes do not change under various settling coefficient values, demonstrating that the methodology is very robust. The developed framework will be quite useful to diverse retailers looking to expand and generate substantial profits.Research limitations/implicationsA large sample size of number of locations encourages generalization of results. Strategic ranking of the selected locations is carried out on a few selected criteria. The study was limited by the designated geographical area.Originality/valueThe study contributes to the few available articles on convenience store selection using combination of fuzzy AHP, fuzzy TOPSIS and GRA for a developing country.