This research investigates the relationships between investors’ behavioral biases and compares their relative importance. For this purpose, a survey is conducted, and analytical methods are used. The sample for this study has been 512 individual investors of the Tehran Stock Exchange who completed an online questionnaire. The respondents replied about their behavior in different situations to analyze the prevalence of asymmetric discounting, mental accounting, shifting risk preference, loss aversion, regret aversion, overconfidence, proxy decision making, ambiguity aversion bias, anchoring, and herd behavior as significant fields of behavioral biases in their investment decisions. The data is analyzed using two different analytical techniques. A model based on structural equations is designed and tested to analyze the relations between these fields. Another integrated method, the DEMATEL-based analytic network process, is also used to prioritize and rank these behavioral biases. Finally, the results are compared and confirmed by each other. Analyzing the results proves the existence of 19 positive and statistically significant relations between these fields. Thus, an increase or decrease in the intensity of a particular field of behavioral biases in one’s decisions significantly affects the intensity of other fields. The present study finds that shifting risk preference, anchoring, loss aversion, and regret aversion are the most important fields of behavioral biases based on their prevalence among investors and their correlations with other biases.

The intention of this paper is to propose some similarity measures between Fermatean fuzzy sets (FFSs). Firstly, we propose some score based similarity measures for finding similarity measures of FFSs and also propose score based cosine similarity measures between FFSs. Furthermore, we introduce three newly scored functions for effective uses of Fermatean fuzzy sets and discuss some relevant properties of cosine similarity measure. Fermatean fuzzy sets introduced by Senapati and Yager can manipulate uncertain information more easily in the process of multi-criteria decision making (MCDM) and group decision making. Here, we investigate score based similarity measures of Fermatean fuzzy sets and scout the uses of FFSs in pattern recognition. Based on different types of similarity measures a pattern recognition problem viz. personnel appointment is presented to describe the use of FFSs and its similarity measure as well as scores. The counterfeit results show that the proposed method is more malleable than the existing method(s). Finally, concluding remarks and the scope of future research of the proposed approach are given.

Considering the acceleration in the development of alternative energy sources due to climate change and the net zero carbon commitments made in this direction, there are different assessments of how the capacity of the refining industry will change in the next two decades. Refinery companies are trying to adapt to altering conditions while also trying to determine their investment strategies. Project portfolio selection problem is one of the relevant issues to be considered in line with these changes. In this article, research has been undertaken to determine which criteria refinery companies take into consideration while selecting their project portfolios. Based on the identified criteria, it is also aimed to carry out a study that will guide sector practitioners in project selection. For this purpose, interviews were conducted with industry experts. The criteria were accredited by applying categorical content analysis to the data obtained and their importance weights were identified accordingly. The most deterministic criteria were abstracted from the findings and applied to a multi-criteria decision-making (MCDM) framework, namely fuzzy MULTIMOORA to suggest a decision support tool that ranks the projects against themselves. Some of the prominent outcomes of the study are also discussed, along with the previous studies and comparative results of the proposed decision support tool.

Vendor-managed inventory (VMI) is one of effective and crucial tools to alleviate the demand volatility of stocks problems, reduce time and operating cost in healthcare sector. VMI strategy becomes a necessity for both suppliers and hospitals to sustainably develop and to cope with stock availability and overall reliability process by sharing information. The process and management of VMI is a complicated work which needs substantial degrees of collaboration, expertise, and information sharing. This paper purposes a comprehensive multi-criteria decision making (MCDM) to select the best potential supplier for VMI collaboration in healthcare organization. The study developed MCDM framework consists of (i) Fuzzy Delphi approach to select the appropriate evaluation criteria for VMI supplier selection (ii) Fuzzy Step-wise Weight Assessment Ration Analysis (SWARA) method to determine the relative importance weight of evaluation criteria, (ii) Fuzzy Complex Proportional Assessment of Alternatives (COPRAS) to compare, rank and select the best appropriated supplier. An empirical case study was applied for a local famous public hospital and the best potential supplier was selected. The study reveals that the most evaluation criteria when selecting supplier for VMI in healthcare sector are institutional trust, information sharing and exchanging as well as information technology.

There are huge amounts of emissions associated with construction industry during its different stages from cradle till building demolition. This study presents a methodology that integrates multi-objective optimization and multi-criteria decision making (MCDM) in order to enable construction decision-makers to select the most sustainable construction alternatives. Four objectives functions are investigated, which are: construction time, lifecycle cost, environmental impact and primary energy in order to construct the Pareto front. A novel hybrid MCDM is designed based on seven multi-criteria decision making techniques to select the best solution among the set of the Pareto optimal solutions. Sensitivity analysis is performed in order to determine the most sensitive attribute and construction stages that influence environmental emissions. The analysis illustrates that WSM, COPRAS and TOPSIS provided the best rankings of the alternatives, primary energy is the most sensitive attribute for different MCDM methods. Moreover, PROMETHEE II is the most robust MCDM method.

Effective transshipment network is currently recognized as an important success determinant for most manufacturing organizations, because the transshipment management has significant impact on cost and environmental impact. Due to the complexity of the multi-criteria transshipment problem for infectious waste management (IWM) for this case, forty hospitals and three candidate disposal municipalities in Northeastern Thailand, a novel holistic approach (combination of fuzzy AHP, transshipment model and DEA) was developed for solving this problem. We first utilized the fuzzy AHP technique to calculate the location weights of each candidate disposal municipalities. Secondly, a new cost-based transshipment model was formulated and solved in order to provide the set of feasible solutions. These solutions can be viewed as decision making units (DMUs), inputs and outputs. Finally, DEA-CCR model was applied to calculate the efficiency scores of candidate DMUs. The study results demonstrated that the proposed holistic approach can help decision makers (DMs) to choose a suitable transshipment network for IWM. The major advantage of the proposed holistic approach is that both costs and environmental impacts under constraints are focused on simultaneously. Future work will apply the developed approach with other real-world complex problems to enhance the validity of the research output further. For large-size transshipment problems in which an exact solution cannot be found, meta-heuristics must be applied.

In the era of digital manufacturing and highly competitive environment, it is desirable to deliver the right item, right quantity at right time at minimal cost. Under this volatile market environment, the inventory should be readily available at the manufacturing level at the lowest possible cost. Many industries have been conventionally employing traditional ABC analyses based on a single criterion of annual consumption cost for classification of inventory items in spite of other criteria such as unit cost, consumption rate, average inventory cost that may be important in inventory classification. To address such problems, incorporation of Multi-criteria decision making (MCDM) methods is considered an advantage. The present article focuses on a new approach to categorize inventory items using Modified similarity-based method. The proposed method is applied to the inventory data of raw materials from a renowned conveyor belt manufacturing company of West Bengal, India. By using Modified similarity-based method, the items are classified in A, B and C categories. Results obtained from the said method using R program are compared with those of well recognized TOPSIS and AHP methodologies to validate the application of this method for inventory classification.

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.

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.

An enormous amount of heat is released at the contact surface during machining of a component/partdue to friction, rubbing action and cutting forces generated. Cutting fluids are generally applied to provide lubrication and cooling at the tool and workpiece interface. They also play a beneficial role in machining operations, and enhance job shop’s productivity, tool life and quality of the finished parts/products. In addition to these, they act as an important contributor in optimizing a machining operation. However, a cutting fluid suitable for a particular machining requirement may not be equally good for other applications and hence, there is a need for selection of the appropriate type of cutting fluid with a view to facilitate superlative and uncomplicated machining operation. Several factors, such as material ofthe cutting tool, operator’s safety, compatibility with the machine tool, reliability and rancidity of the cutting fluid, and cost may all combine to limit the effectiveness or applicability of a cutting fluid. Therefore, the present study explores the potentiality of a modified similarity-based method, which is a multi-criteria decision making tool, in solving cutting fluid selection problems. To illustrate the procedural steps of this method, two real time problems are solved. The results obtained highly corroborate with the opinions of the experts in the related field, demonstrating the applicability of the said method.