Asset Liability Management (ALM) can be overseen using financial ratios derived from financial statements. These statements provide a comprehensive picture of a company's status and necessitate analysis to evaluate performance. This research aims to analyze financial ratios to describe the financial condition, measure business development over time, and evaluate the achievement of the company's objectives. An optimization analysis of financial ratios is performed using the Weighted Goal Programming (WGP) model, which addresses multiple objectives by applying weights based on their priorities. The Best-Worst Method (BWM) was used to determine the priority weights of deviation variables from each financial ratio target. Financial ratios were selected based on their impact on profit using factor analysis. The constructed WGP model aims to minimize deviations in Return on Assets, Operating Ratios, Operating Income Ratio, Total Assets Turnover, and Current Ratio. Computational calculations to solve the WGP model are performed using Python, with pseudocode provided. A case study on a company in the garment and textile sector was conducted and found that the Operating Ratio, Return on Assets, Operating Income Ratio, and Current Ratio still need improvement by developing strategies to achieve the targets. Sensitivity analysis was also employed to assess the resilience of the model in response to alterations in data.

One of the primary concerns in most decision making problems is the uncertainty associated with the input parameters. The existence of uncertainty may lead to some unrealistic results, which may make the final decision even more difficult. This paper presents an application of robust optimization technique to a recently developed model named Best-Worst method. The resulted robust approach is formulated as a linear programming where it can be solved using any commercial software package. The proposed model has been implemented on several instances which exist in the literature and the preliminary results have indicated that a small perturbation may influence the final ranking, significantly.

Search for lost or hidden things is a very interesting and complicated issue. This problem concentrates on the study of how to exploit resources to discover a target with unknown location. On the other hand, search problem may be formulated as a difficult decision problem because it is affected by various crucial decision factors such as search cost, search time, the probability of discovering, etc. In this paper, a new multi-criteria decision making (MCDM) approach on the basis of best-worst method (BWM) and simple additive weighting (SAW) is suggested to rank potential locations of lost or hidden targets. BWM is a novel subjective weighting technique and compared to the most common subjective method, analytic hierarchy process (AHP), requires fewer comparisons and gives more trustworthy outcomes. In this paper, BWM is used to gain the criteria weights and SAW is employed to rank the locations regarding the decision factors. This study demonstrates that BWM is easier and works better than AHP, also perfect agreement in the results of COPRAS, TOPSIS and SAW is observed. The suggested approach is very easy as well as flexible and provides an efficient method which can be developed to tackle other decision problems.

The present study aimed at identifying and evaluating the factors affecting the service experience at the touch points of banking services. These factors were prioritized to help the managers understand the most important factors for achieving the favorable service experience. In this study, the theoretical foundations and interviews with customers were used to identify the factors forming the service experience at touch points. Then, the Best-Worst method (BMW) was used to evaluate and determine the significance of each identified factor. Sixteen customers participated in the interview and ten customers participated in the study by the BMW method. Six touch points and 34 factors forming the service experience were created at these points of interview analysis. The research findings showed that ‘service process’ and ‘interaction customer-employees’ for creating a favorable experience are significant in banking services. In addition, the findings showed that bank managers should allocate their resources for improving them to achieve the competitive advantage.

Developing innovation, based on knowledge and technology, as a driving force of the economy, is necessary for survival and is required in having strong interactions within the globalized world of business. Innovation and technology development require an intertwined network of organizational interactions between public and private sector. The activities and interactions of these firms are the reasons for innovation development in the framework of innovation systems. Following strategies is of crucial necessity and importance in industries such as aerospace and remotely-piloted helicopters (RPH) with their complex characteristics, costly and time-consuming processes. Understanding the business environment and identifying the success factors is a significant step towards adopting innovative strategies and planning for technology development. The aim of this article is to evaluate the key success factors in technological innovation development of remotely-piloted helicopters (RPH) industry. The methodology used in this article is Best-Worst method which is considered as one of the most prominent and effective MCDM methods. Based on a case study and by reviewing the extant and relevant literature, the key success factors of technological innovation development of remotely-piloted helicopters (RPH) industry in Iran were identified. Then by applying the “Best-Worst” method and the experts’ opinions, the key success factors were analyzed and prioritized. Finally, some suggestions are made by considering the results of the study.

This study presents a comprehensive performance evaluation framework for 20 leading medical laboratories worldwide using an integrated Best-Worst Method (BWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The assessment incorporates ten critical criteria encompassing clinical accuracy, operational efficiency, research output, cost-effectiveness, and technological advancement. BWM was employed to determine optimal criterion weights through systematic pairwise comparisons, followed by TOPSIS for objective laboratory ranking based on relative closeness to ideal solutions. Results indicate that Memorial Sloan Kettering Labs (USA) and MD Anderson Cancer Center Labs (USA) consistently rank highest across multiple scenarios, demonstrating superior performance in clinical accuracy and quality accreditation. The analysis reveals significant performance variations across countries and laboratory categories, with academic/research institutions generally outperforming commercial laboratories. Sensitivity analysis confirms the robustness of rankings across different weighting scenarios. This framework provides healthcare administrators, policymakers, and laboratory managers with a validated tool for benchmarking and strategic decision-making in medical laboratory services optimization.