Browsing by Author "Pamucar, D."

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An Interval Rough Improved Ordinal Priority Approach-Based Decision Support System To Redesign Postal and Logistics Networks
(Elsevier Ltd, 2025) Pamucar, D.; Dobrodolac, M.; Simic, V.; Lazarevic, D.; Görçün, Ö.F.
Postal and logistics companies are essential subjects in the economy, providing services of the corresponding assortment for a wide range of business and private users. Service providers strive to meet the needs of users and, at the same time, make as much profit as possible. The efficiency of each of the subsystems in companies from this area significantly impacts the sustainability of postal and logistics systems. Rural areas, which are characterized by a smaller number of users and services and a low level of system efficiency, can have an additional negative impact on sustainability. As a result, optimization tasks become complex but also necessary to solve. The paper proposes an interval rough improved Ordinal Priority Approach - Power Schweiyer-Sklar Combined Compromise Solution (I-OPA - PSS'CoCoSo) methodology for prioritizing different models of solving the problem of inefficient network units. Methodological novelties are: a) A new approach for defining the lower and upper limits of interval rough numbers is proposed, which is based on nonlinear Bonferroni functions; b) The classic OPA linear model is improved through the implementation of a new concept for defining relational relationships between criteria; c) The CoCoSo method is improved through the implementation of nonlinear PSS and implementation of a novel function for the integration of aggregate strategies. The application of the interval rough I-OPA - SSP'CoCoSo methodology is demonstrated through a case study on the example of a public postal operator operating in the territory of the Republic of Serbia. Since this is a system with a highly developed infrastructure and network throughout the entire country, this further implies the applicability of the methodology to smaller systems or sectors within larger companies that deal with parcel deliveries and other logistics activities. A new aggregation function is introduced to define the compromise index of the alternatives as well as eliminate the anomaly of the original function. The simulation of different scenarios is enabled depending on the degree of risk. The proposed methodology enables decision-making in conditions of incomplete and imprecise criteria values. In accordance with the aforementioned, this approach contributes to improving the accuracy of modeling expert opinions, and consequently, in making the final decision. © 2025 Elsevier Ltd
Strategic Analysis of E-Trade Platforms in Automotive Spare Part Sector: a T-Spherical Fuzzy Perspective
(Elsevier B.V., 2025) Görçün, Ö.F.; Chatterjee, P.; Aytekin, A.; Korucuk, S.; Pamucar, D.
E-trade platforms are software applications that enable businesses to conduct online sales and manage their digital storefronts. These platforms provide a range of tools and features to facilitate the creation, operation, and management of an online business. This study comprehensively evaluates e-trade platforms within the automotive spare parts industry, examining various critical aspects to identify the optimal platform. The evaluation includes an in-depth analysis of the current state of the platforms, exploration of potential strategies and approaches for improvement, and identification and analysis of challenges and barriers. To address these issues, the study employs problem-solving within the framework of expert evaluations based on criteria defined by an extensive literature review. T-Spherical fuzzy (T-SF) subjective weighting approach and T-SF-weighted aggregated sum product assessment (WASPAS) method are used for this purpose. The analysis reveals that “security” is the most crucial criterion, with Amazon emerging as the most prominent e-trade platform. The findings indicate that prioritizing security, discounts, and delivery time will enable e-commerce platforms to gain a competitive edge. The study evaluates international e-commerce platforms, identifying weaknesses in critical business areas key competitive advantage factors, and offering forward-thinking recommendations. This research has significant implications for the rapid and effective development of logistical partnerships with e-trade platforms across various industries. Additionally, it serves as a foundational basis and template for future research in the e-commerce sector, particularly within the automotive spare parts industry. © 2025 Elsevier Inc.
Citation - Scopus: 69
Warehouse Site Selection for the Automotive Industry Using a Fermatean Fuzzy-Based Decision-Making Approach
(Elsevier Ltd, 2023) Saha, A.; Pamucar, D.; Gorcun, O.F.; Raj, Mishra, A.
The automotive industry is one of the most competitive sectors, and it requires a well-structured logistics system to meet the industry' vital requirements such as just-in-time, lean and agile supply chain operations, productivity and sustainability. Well-located and well-designed warehouses can make reaching these aims for the automotive industry possible and more accessible. Hence, determining a location for a warehouse is a highly critical, tactical, and managerial resolution for the automotive industry, as there is a strong correlation between well-located warehouses and the well-structured logistics network in the automotive industry. Although the WSS is a significant decision-making problem, we observed four critical and severe gaps in the existing literature: (1) the authors preferred to apply traditional objective & subjective frames, and they overlooked existing highly complicated uncertainties. (2) The number of studies focusing on the WSS problem in the automotive industry is surprisingly scarce. (3) It is not sufficiently clear how these factors used in the previous studies were determined, which causes doubts about their reliability. (4) there is no satisfactory evidence of which approaches were used to identify the factors in the previous papers. By considering these gaps, we propose two approaches which can be accepted as a novelty of the paper. First is the extension of the Delphi techniques based on the Fermetean fuzzy sets (FFs) used for identifying the criteria. It also combines the two traditional approaches (i.e., literature review and professionals' evaluations to identify the criteria) with the FF-Delphi technique. The second is the Double Normalized MARCOS approach based on FFs (FF- DN MARCOS) implemented to identify the weights of the criteria and ranking performance of the alternatives. The proposed model was implemented to identify the best warehouse location for the automotive manufacturing company. The results show that the C1 “energy availability & cost” criterion is the most influential criterion and the C5 proximity to port and customs criterion is the second most crucial factor. Then we executed a comprehensive sensitivity analysis, and the results approved the suggested model's validity and robustness despite excessive modifications in the criteria weights. © 2022 Elsevier Ltd