DETERMINING THE AREA SIZES OF EACH PRODUCT CATEGORY IN A DEPARTMENT STORE USING MULTI-CRITERIA DECISION MAKING METHODOLOGIES

Author(s):  
Gulcin Dinc Yalcin ◽  
Zehra Kamisli Ozturk

In a department store, customers have the opportunity to reach a wide range of consumer goods from different product categories within a single store area. Store layouts generally show the size and location of each department, any permanent structures, fixture locations, and customer traffic patterns. Determining the area sizes to be allocated to each product category and the layout of these areas in the department store is a strategic planning decision problem. The layout problem has been studied in the literature with different approaches where the sizes of the areas are known. The first purpose of this paper is to determine the area sizes of each product category.   Customers decide to go to a department store for several reasons including the quality of products, services, location, etc. These reasons have been studied in the literature. However, “for which product categories do customers decide to go to a department store” is an open question. The second purpose of this paper is to find the frequency of product categories from the viewpoint of the customers. Therefore, our aim is to obtain the required results in a systematic way with multi-criteria decision making methodologies. For this purpose, we perform the Analytic Network Process (ANP) and the Analytic Hierarchy Process (AHP) from the viewpoints of department managers and customers, respectively.   In the ANP model, several tangible and intangible criteria such as product costs, the demands of customers, sales history, overall inventory, floor space and relationship with suppliers are chosen, and the intersections between them are specified. Pairwise comparisons are made by department store managers. The ANP outcome is the weight of each product category, and these weights are considered the percentage of the area size within the store from the viewpoint of the department stores. In the AHP model, a simple model is constructed to define the customers’ preference for each product category. Pairwise comparisons between product categories are made by the customers. Therefore, the outcome of the AHP model is the weight of each product category, and this is the preference of each product category from the viewpoint of the customers. The outcomes show that these weights may be different. This is an expected situation since even if a product category is preferred by some as the driver to visit a department store, the footprint of that category in the actual store may be small. The outcome from customers provides feedback to department store managers on which product category should be diversified as well as the area sizes of those categories.

2020 ◽  
Vol 9 (6) ◽  
pp. 393 ◽  
Author(s):  
Meisam Moharrami ◽  
Amin Naboureh ◽  
Thimmaiah Gudiyangada Nachappa ◽  
Omid Ghorbanzadeh ◽  
Xudong Guan ◽  
...  

Landslides are one of the most detrimental geological disasters that intimidate human lives along with severe damages to infrastructures and they mostly occur in the mountainous regions across the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potential areas that are prone to landslides and could have an impact on decreasing the possible damages. The application of the fuzzy best-worst multi-criteria decision-making (FBWM) method was applied for LSM in Austria. Further, the role of employing a few numbers of pairwise comparisons on LSM was investigated by comparing the FBWM and Fuzzy Analytical Hierarchical Process (FAHP). For this study, a wide range of data was sourced from the Geological Survey of Austria, the Austrian Land Information System, Humanitarian OpenStreetMap Team, and remotely sensed data were collected. We used nine conditioning factors that were based on the previous studies and geomorphological characteristics of Austria, such as elevation, slope, slope aspect, lithology, rainfall, land cover, distance to drainage, distance to roads, and distance to faults. Based on the evaluation of experts, the slope conditioning factor was chosen as the best criterion (highest impact on LSM) and the distance to roads was considered as the worst criterion (lowest impact on LSM). LSM was generated for the region based on the best and worst criterion. The findings show the robustness of FBWM in landslide susceptibility mapping. Additionally, using fewer pairwise comparisons revealed that the FBWM can obtain higher accuracy as compared to FAHP. The finding of this research can help authorities and decision-makers to provide effective strategies and plans for landslide prevention and mitigation at the national level.


Processes ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 252 ◽  
Author(s):  
Chia-Nan Wang ◽  
Ying-Fang Huang ◽  
I-Fang Cheng ◽  
Van Nguyen

Suppliers are extremely important in business operations. The supplier ensures the supply of materials, raw materials, commodities, etc. in sufficient quantity, quality, stability, and accuracy to meet the requirements of production and business with low costs and on-time deliveries. Therefore, selecting and managing good suppliers is a prerequisite for organizing the production of quality products as desired, according to the schedule, and with reasonable prices and competitiveness in the market. It is also important to gain the support of suppliers in order to continue to improve and achieve more as a business. The evaluation and selection of a supplier is a Multi-Criteria Decision-Making (MCDM) issue, in which the decision-maker is faced with both qualitative and quantitative factors. In this research, the authors propose an MCDM model using a hybrid of Supply Chain Operations Reference metrics (SCOR metrics), the Analytic Hierarchy Process (AHP) model, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach for supplier evaluation and selection in the gas and oil industry. Using literature reviews on SCOR metrics, all criteria that impact supplier selection are defined in the first stage, the AHP model is applied to determine the weight of each factor in the second stage, and the optimal supplier is presented in final stage using the TOPSIS model. As a result, Decision-Making Unit 5 (DMU-05) is found to be the best supplier for the gas and oil industry in this research. The contribution of this work is to propose a new hybrid MCDM model for supplier selection in the gas and oil industry. This research also introduces a useful tool for supplier selection in other industries.


Author(s):  
Salimov Vagif Hasan Oglu

Multi criteria decision making problem was considered. Review of existing multi criteria decision making methods was presented. Methods of solving this problem can be divided into two large groups: methods using the aggregation of all alternatives according to all criteria and the solution of the obtained one-criterion problem, the second group is associated with the procedure of pairwise comparisons. Promethee method have been considered with details. This method is based on the pairwise comparison of alternatives and specific aggregation procedures. The preference function are considered for minimization and maximization cases. As practice problem the job selection is considered. Three important criteria are used: salary, time, risk. The results of all computations are presented.


2015 ◽  
Vol 735 ◽  
pp. 41-49 ◽  
Author(s):  
Arash Azaryoon ◽  
Musa Hamidon ◽  
Ashraf Radwan

In this study, a knowledge-based system has been developed for selection of non-conventional machining processes using a hybrid multi-criteria decision making Method. This approach is a combination ofDEMATEL(Decision Making Trial and Evaluation Laboratory),ANP(Analytic Network Process) andVIKOR(VlseKriterijumska Optimizacija I Kompromisno Resenje, in Serbian, meaning Multi-criteria Optimization and Compromise Solution) methods which evaluates different types of quantitative and qualitative measures of performance and economic factors, and ultimately provides a set of capable processes in order of priority. Twelve machining processes, eight group of workpiece material and eighteen shape features have been investigated in this study. What separates this approach from others is that, this hybrid method considers the influence of factors in the network relation map as well as their relative importance. Moreover, unlike other popular ranking methods such as TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution), it is not just based on two reference points, namely ideal and inferior points; instead, it proposes a compromise solution and not just a single ranking score. Observations have shown that the developed system works satisfactorily, yields acceptable results and makes accurate decisions as well. It also provides a comparative study among the alternative processes by utilizing graphical features for better analysis and judgment of acceptable alternatives.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Henry Lau ◽  
Yung Po Tsang ◽  
Dilupa Nakandala ◽  
Carman K.M. Lee

PurposeIn the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.Design/methodology/approachA novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.FindingsThroughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.Originality/valueA novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.


2016 ◽  
Vol 16 (4) ◽  
pp. 1019-1033 ◽  
Author(s):  
Mariana Madruga de Brito ◽  
Mariele Evers

Abstract. This paper provides a review of multi-criteria decision-making  (MCDM) applications to flood risk management, seeking to highlight trends and identify research gaps. A total of 128 peer-reviewed papers published from 1995 to June 2015 were systematically analysed. Results showed that the number of flood MCDM publications has exponentially grown during this period, with over 82 % of all papers published since 2009. A wide range of applications were identified, with most papers focusing on ranking alternatives for flood mitigation, followed by risk, hazard, and vulnerability assessment. The analytical hierarchy process (AHP) was the most popular method, followed by Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Simple Additive Weighting (SAW). Although there is greater interest in MCDM, uncertainty analysis remains an issue and was seldom applied in flood-related studies. In addition, participation of multiple stakeholders has been generally fragmented, focusing on particular stages of the decision-making process, especially on the definition of criteria weights. Therefore, addressing the uncertainties around stakeholders' judgments and endorsing an active participation in all steps of the decision-making process should be explored in future applications. This could help to increase the quality of decisions and the implementation of chosen measures.


Author(s):  
Salimov Vagif Hasan Oglu

The article is devoted to the problem of multi-criteria decision-making. Methods for solving this problem can be divided into two large groups:methods using the aggregation of all alternatives according to all criteria and the solution of the resulting single-criterion problem. The second group isassociated with the procedure of pairwise comparisons and stepwise aggregation. The first group includes methods: weighted average sum,product and their various modifications, the second group includes - AHP, ELECTRE, TOPSIS, PROMETHEE, ELECTRE. For many problemsassessment of the criteria implemented by experts and presented in linguistic form. The effective approach for dealing with linguistic information is fuzzyset theory proposed by L. Zadeh. In this paper is proposed fuzzy ELECTRE method. This method is presented in details. As application problem is usedthe equipment selection problem The issues of practical implementation of this method are discussed in details. The results of the solution test problem at all stages are presented.


2021 ◽  
Author(s):  
Hemant Kumar Pandey ◽  
Vishal Kumar Singh ◽  
Sudhir Kumar Singh

Abstract The present study illustrates the delineation of the groundwater potential zones in one of the most critical and drought affected areas under Bundelkhand region of Uttar Pradesh. Hydrological evaluations were carried out in district Mahoba using GIS tools and remote sensing data which ultimately yielded several thematic maps, such as lineament density, land use/land cover, drainage density, lithology, slope, geomorphology, wetness index (WTI), altitude and soil. CartoDEM data which have spatial resolution of 30m i.e. equivalent to one arc second were used to create digital elevation model, drainage density, altitude, WTI and slope. The thematic layers were assigned relative weightages as per their groundwater potential prospects under multi-criteria decision making (MCDM) method through analytical hierarchy process (AHP). To recognize the groundwater potential zone, weighted overlay analysis was performed using ArcMap software. Additionally, for testing of the Dempster-Shafer model, 16 borewells in high potential areas have been selected. Based on the probability of the groundwater occurrence, the belief factor was equated. Further combining the weighted layers, groundwater potential zones were obtained. The groundwater potential maps illustrate five zones having different potential in the Mahoba district. According to the AHP model the north-west side of the study area is characterized with very good potential zones whereas the north-east and south-east region constitute medium and poor groundwater potential zones respectively. It reflects that more than 50% of the area is having medium groundwater potential while 30 percent of the area falls under low potential zone. 10% of the study area falls under very good groundwater potential zones. According to the DS model, very high groundwater zones constitute only 7% and the remaining area falls under poor potential. Overall accuracy of the DS model was higher than AHP model.


Author(s):  
Mahmoud Modiri ◽  
Mohammad Dashti

Today, IS supplier selection is one of the most critical steps in the outsourcing process; the success of outsourcing is highly dependent on the selection of IS suppliers. This paper proposes a new hybrid fuzzy multi-criteria decision-making (MCDM) model, which uses decision-making trial and evaluation laboratory (DEMATEL) technique, analytic network process (ANP), and Vlse Kriterijumska Optimizacija I Kompromisno Resenje(VIKOR) to evaluate four potential suppliers using seven factors and five decision makers using a realistic case study. the results showed that Service support is importance for outsourcing. The proposed model can help practitioners improve their decision making process.


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