In recent decades, the production speed and efficiency of open shop scheduling problems (OSSP) have increased by considering identical machines for each stage. In addition to the time of the operation, the time of transporting the jobs between the stages is considered significantly. In contrast, the transportation time could be more attention. The nature of machine operations determines the transportation durations between tasks and is independent of distance, therefore eliminating any waiting time for shop activities during transfers. A mixed-integer linear programming approach is proposed to decrease the maximum completion of work time. A novel whale optimization algorithm (WOA) for OSSP is introduced to re-solve extensive OSSP issues within a reasonable computational timeframe. The novelties are based on several factors in the optimal solutions in all small and medium-sized problem in-stances. For solving problem sizes, we consider several metaheuristic algorithms, including particle swarm optimisation (PSO) and differential evolution (DE) methods, for all random large-sized problem instances. The algorithm for OSSP produced better results than PSO, DE, and bucket elimination regarding the spacing, quality, and diversity parameters. Integrating transportation time considerations into OSSP through the WOA algorithm aimed to achieve more effective and practical scheduling solutions in dynamic and complex conditions. The manufacturing industries can benefit from the proposed WOA for OSSP to enhance schedul-ing practices and improve operational efficiency and cost-effectiveness.
This study aims to evaluate the financial performance of 36 companies from different sectors listed on Borsa Istanbul (BIST) Sustainability Index for the years 2021, 2022, and 2023 using multi-criteria decision-making (MCDM) methods. BIST companies represent Turkey's most actively traded and transparent companies. These companies constitute a suitable sample for our study because they provide reliable and accessible financial data and include Turkey's largest and most institutionally developed companies. Eight financial criteria were selected as performance measures, namely Price-to-Earnings ratio (P/E), Return on Capital Employed (ROCE), Tobin’s Q, Accounts Receivable Turnover, Operating Margin, Cost of Goods Sold, Market Capitalization, and Enterprise Value. The Similarity to Ideal-TOPSIS with Distance Entropy (SITDE) method was applied to objectively determine the weights of the criteria, while the Rank Aggregation with Weighted Entropy and Ideal Closeness (RAWEC) method was employed to rank the alternatives. The results of the study, in 2021, during the post-pandemic recovery, companies’ ROCE and Accounts Receivable Turnover were the most influential criteria. Tobin's Q, reflecting long-term investor and market expectations, remained highly important across all years. In 2022 and 2023, Enterprise Value and Market Capitalization emerged as key factors, highlighting the impact of company size and investor confidence on financial performance.
Complexities in uncertain and imprecise data can be resolved due to the proposal of a technique of multicriteria decision-making (MCDM) on the basis of circular intuitionistic fuzzy sets (C-IFS). Circular intuitionistic fuzzy sets are the extension of conventional intuitionistic fuzzy sets and fuzzy sets. The elimination et choix traduisant la réalité (ELECTRE) approach is a solid MCDM tackle that has gained fame in addressing qualitative and quantitative criteria in policy analysis. In this paper, we combine circular intuitionistic fuzzy sets and ELECTRE to evaluate carbon monoxide in urban settings. The core parameters for optimization included emission levels, traffic density, and population exposure, which were evaluated using expert judgments to model environmental decision-making issues. We illustrate the presented intuitionistic fuzzy ELECTRE approach with circular reasoning using a case of high-return zones with high concentrations of carbon monoxide levels. Our findings reveal that the proposed method effectively identified and prioritized three key urban zones as the most critical, a result that was more robust to uncertainty than a conventional fuzzy model. The proposed approach provides a systematic resource in managing air quality and city planning, enabling policymakers to develop targeted strategies for mitigation efforts.
The advancement of autonomous multi-robot systems for planetary exploration missions is critically dependent on their ability to navigate accurately in unstructured and challenging environments, a primary source of localization failure in such contexts is the significant error introduced by unmodeled vehicle dynamics, particularly terrain-induced lateral slip, which severely degrades the performance of standard Simultaneous Localization and Mapping (SLAM) algorithms, this paper presents a terrain-aware cooperative SLAM framework designed to enhance localization accuracy for multi-robot teams operating on undulating surfaces, the proposed approach utilizes an Extended Kalman Filter (EKF) that uniquely integrates inter-robot relative measurements, derived from LiDAR, with a sophisticated motion model that explicitly predicts and compensates for lateral slip based on a 2.5D terrain map, the system's performance is rigorously evaluated within a high-fidelity simulation environment featuring complex topography, realistic sensor and motion noise, and rudimentary communication constraints. Quantitative and qualitative results demonstrate that the proposed framework significantly improves pose estimation accuracy and maintains robust performance over long-term operations, this work validates the efficacy of combining cooperative information sharing with explicit modeling of terrain-motion interaction as a vital strategy for enabling robust multi-robot exploration.
Corporate governance reflects the managerial principles adopted by firms and influences various performance parameters. Companies with robust corporate governance mechanisms tend to establish a high level of trust with their stakeholders. This paper examines corporate governance parameters alongside financial indicators to provide insights into company performance. The primary objective of the study is to develop a robust decision support system for analyzing decision-making models based on corporate governance and financial performance indicators. In this context, a hybrid method combining single-valued triangular neutrosophic (SVTN), step-wise weight assessment ratio analysis (SWARA), and reference-based normalization alternative ranking (RBNAR) is proposed. The weights of the criteria affecting firm performance are determined using the SVTN-SWARA method, while firm performance levels based on these weights are obtained through the RBNAR approach. The SVTN-SWARA-RBNAR hybrid method is applied to thirty-two companies listed in the Borsa Istanbul Corporate Governance Index over a three-year period (2021–2023). The core finding indicates that the SVTN-SWARA-RBNAR method provides accurate and consistent company rankings, with AKSA achieving the highest performance. Among the financial criteria, net profit margin is identified as the most critical indicator. Within corporate governance parameters, the board of directors’ score emerges as the most significant factor. The robustness of the proposed method is further reinforced through sensitivity analyses.
