This research introduces the Urban Traffic Mobility Optimization Model (UTMOM), a data-driven methodology for analyzing two distinctive urban traffic datasets through the integration of data mining and mathematical modeling. Designed to decode the complexities of urban mobility patterns, UTMOM meticulously evaluates daily traffic dynamics with a focus on reducing discrepancies and underscoring variations in traffic intensity, particularly during peak times. Our findings unveil pivotal insights into the differences across datasets, providing a substantial contribution to the realms of traffic management and urban planning. UTMOM delves into the intricacies of traffic flow variations, emphasizing the critical importance of comprehending fluctuations in traffic volume across diverse times and locations. By incorporating detailed graphical representations and statistical validations, including ANOVA analysis, our study delivers a comprehensive evaluation of UTMOM’s precision in reflecting real-world traffic scenarios. These insights affirm the value of data-informed strategies in optimizing traffic flow and alleviating congestion. Positioned as a valuable asset for traffic engineers, data scientists, and urban planners, UTMOM advocates for advanced modeling techniques to improve urban mobility. Beyond enriching academic discourse on traffic analysis, UTMOM offers actionable intelligence for enhancing the efficiency and sustainability of urban transportation systems. Through this in-depth investigation, our aim is to catalyze the development of innovative solutions to traffic challenges, steering towards smoother and more sustainable urban environments.

Efficient warehouse management is essential for optimizing inventory, minimizing transportation costs, and enhancing overall performance. This research introduces a novel Mixed-Integer Nonlinear Programming (MINLP) model to address the Storage Location Assignment Problem (SLAP) in warehouse management. Integrating multi-criteria decision-making with strategic production planning, our model advances warehouse operations by allocating storage locations to products strategically, focusing on reducing transportation distances and maximizing storage efficiency. The distinctive innovation of this study is the nuanced application of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) results to strategic storage location assignments, enhancing the model's capability to consider a comprehensive evaluation of inventory attributes, including physical characteristics and perishability. This approach evolves TOPSIS's application in warehouse management, enabling it to consider both physical characteristics and perishability of products. The outcomes of TOPSIS, including product classifications and preferences, serve as vital inputs to the mathematical model, facilitating a comprehensive evaluation of storage locations that encompasses spatial, demand-related, and physical aspects of inventory. Additionally, our research introduces a versatile decision support system, adaptable to various operational requirements. This system enhances practical decision-making in warehouse management, accommodating scenarios based on single or multiple criteria, including the cube-per-order index (COI). The research results highlight the significant impact of this innovative approach in enhancing warehouse management. By addressing the complexities of storage location assignment and integrating multiple criteria, we achieve more efficient and cost-effective warehouse operations. The approach has been shown to be adaptable and practical, making it a valuable contribution to the field of logistics and warehouse management.

The fact that the transport costs directly affect the countries' economies all around the world and that the transport costs increase depending on the energy resources steered the countries into developing combined transport strategies that will decrease the transport costs. In this study, an assessment is made for the selection of locations of the logistics centers which will contribute to decreasing the transport costs, develop logistics sector strategies in Turkey, and thereby enable to identify the strategies for the fastest, safest, and the most economic transport utilizing integration of modes of transport with each other. Nonetheless, an integrated logistics model which includes freight capacity and selection of the optimum location is proposed for the efficient and useful logistics center. In this logistics model, a freight model, which is a criterion for optimal location selection, has been obtained by using regression analysis. According to this freight model, freight carrying capacity is determined for each alternative region. Optimum location selection has been made utilizing AHP (Analytic Hierarchy Process)-VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) method. We aim to contribute to the literature by making systematically estimating variable carrying capacities and by being decisive in the logistics center location selection. The proposed logistics model has been applied for a logistics center to be established, and effective and valid results have been obtained.

In this study, a novel integrated method including CRITIC (Criteria Importance Through Inter-Criteria Correlation) and ORESTE (Organisation, Rangement Et Synth&e De DonnCes Relarionnelles) methods from MCDM (multi-criteria decision making) methods and aiming to solve the problem of inconsistency in the ORESTE method has been proposed. Since the ORESTE method only considers the ordering of alternatives and criteria, a conflict may occur due to the different ordering of each alternative in the criteria. To solve the conflict problem in the second step of the ORESTE method, it is proposed to create the preference density matrix of the alternatives by using the CRITIC weights of the criteria, and to calculate the net flow values with reference to the PROMETHEE II (Preference Ranking Organization Method for Encrichment Evaluations II) method (C-ORESTE III). The CRITIC method is used because the relationship between the criteria is as important as the alternatives in calculating the normalized preference intensities. To test the validity and applicability of the proposed method, an application is made to the logistics center location problem within the scope of green logistics.