Cognitive biases significantly influence decision-making by distorting how individuals perceive and evaluate outcomes over time. This systematic review synthesizes research from various domains, including behavioral economics, psychology, and health, to explore six time-related biases affecting intertemporal judgments and trade-offs. We analyze the underlying mechanisms of each bias, map their interrelationships, and uncover their impacts on both individual choices and societal decisions. Drawing upon empirical evidence, we propose tailored strategies to mitigate the adverse effects of these biases. Our findings contribute to the literature not only by enhancing the understanding of time-related cognitive biases but also by providing practical insights for improving decision-making and policy design aimed at promoting long-term well-being. The review concludes by highlighting critical gaps in the literature and outlining a future research agenda to further investigate and address biases in intertemporal decision-making.

Daily life is shaped by countless decisions, most of which involve intertemporal trade-offs. Time preference plays a crucial role in how individuals evaluate alternatives across time and often leads them to make choices that compromise long-term interests. For instance, people generally prefer smaller, sooner rewards over larger, later ones mainly because of their tendency to discount future benefits. Intertemporal judgments are further affected by numerous biases that systematically distort perceptions of time and value, altering trade-offs and often producing deviations from normative models such as exponential discounting. Consequently, decisionmaking frequently departs from the principles of rational choice, underscoring the importance of investigating time preferences and associated biases to enhance immediate decision outcomes while safeguarding long-term welfare. Given the pervasive role of time in almost every aspect of life, investigating these phenomena can enhance choices and contribute to the overall wellbeing of individuals and societies. Despite the well-established importance and prevalence of the topic, several key aspects remain underexplored.

This dissertation investigates how time preference and time-related cognitive biases affect judgments and preferences in decision analysis, and discusses strategies to mitigate their negative impacts. Guided by this main objective, the study integrates descriptive and prescriptive perspectives to build a more comprehensive understanding of intertemporal decision-making and its various real-world implications. Methodologically, it adopts a mixed approach that combines systematic literature reviews, conceptual analysis, and experimental studies, thereby drawing on both qualitative and quantitative evidence. This design enables the investigation of mechanisms underlying intertemporal trade-offs, the exploration of their role in multi-objective decision frameworks, and the empirical testing of their effects under controlled conditions.....

The dynamic nature of the healthcare technology industry necessitates constant improvement and optimization of supply chain processes to maintain competitiveness. Manufacturing unit processes, as critical components of the supply chain, directly influence production efficiency, lead times, and overall supply chain performance. Therefore, a strategic focus on optimizing these processes can significantly enhance the responsiveness and cost-effectiveness of the entire supply chain. However, for large enterprises, determining the unit where optimization will be implemented is intricate, considering the multifaceted nature of the decision-making process. The complexity arises from the presence of diverse criteria, each assigned varying importance levels, along with multiple alternative stages to consider. This paper introduces a decision-making framework tailored for such complexities, employing a synergistic blend of two multi-criteria decision-making methods: Best Worst Method (BWM) and ELECTRE III. The application of the proposed framework is demonstrated through a practical case study involving a prominent healthcare technology company, Philips. First, six experts are carefully selected and interviewed to provide a set of criteria with their respective importance weights. Then, using this information, eight alternative processes within the manufacturing unit are ranked using ELECTRE III. The analysis results reveal that process complexity is the top priority for decision-makers when deciding which manufacturing units require optimization first. The findings delve into the intricacies of optimizing production processes in large health technology companies and offer practical solutions and further recommendations.

Earthquakes are hazardous natural disasters, and they may cause severe damage and losses where they occur. In addition to their devastating effects, they may trigger following disasters like tsunamis and fires. Post-earthquake fires are known as the most dangerous secondary disasters and generally cause much more damage than the damage caused by the earthquake itself. The difficulty in determining and responding to ignition sources, the lack of equipment and workforce, and obstacles like collapsed buildings that block the ways to reach fires may cause catastrophic disasters after an earthquake. In recent years, Unmanned Aerial Vehicle technologies (UAVs) have shown promising performance in post-disaster response operations. Parallel to technological improvements, they have been used for many purposes, like fire-fighting, victim location detection, base station support, and material distribution in disaster areas. To manage a possible response and improve the performance of UAVs in post-earthquake fire areas, it is crucial to be prepared in advance. This study proposes an artificial neural network-based clustering approach for unmanned aerial vehicle use in post-earthquake fire areas. After conducting a detailed literature review covering post-earthquake fires, usage of UAVs in disasters, and some aspects of Self Organizing Maps, the methodology used for clustering the neighborhoods regarding their post-earthquake fire risk similarities is introduced. A real-life application is carried out to identify and cluster the regions and provide preliminary information to the decision-makers on possible interventions. Neighborhoods of Tuzla district, one of the riskiest districts in terms of post-earthquake fires in Istanbul, are clustered with Self-Organizing Maps (SOM). In a possible post-earthquake fire disaster, the Tuzla district can be divided into three areas, and UAVs can be organized more efficiently and quickly based on this cluster information. The results of this real-life application can guide decision-makers by showing which regions have similarities for UAV response in possible post-earthquake fires and where they can be intervened together. The authorities can benefit from the findings of this study while preparing disaster plans, intervention actions, and post-disaster humanitarian activities.

Strategic investments are crucial for software companies as they determine the direction and growth of the businesses. Parallel to continuous improvements in information technologies, increased customer expectations, and competitive environments, deciding between new investment strategies has become even more important. Despite their prevalence, making these decisions is generally challenging, especially when there are multiple alternatives, conflicting objectives, and a group of decision-makers with different priorities. In this study, we consider this complex issue as a multi-criteria decision-making problem by focusing on a real-life case study from a software company that specializes in selling tools for online education and assessment. We propose a two-level methodology integrating the Best-Worst method (BWM) and Elimination and Choice Translating Reality (ELECTRE-III). In the first step, six criteria that play a role in software investment decisions are defined based on an extensive literature review and expert interviews. Then a group of experts compared these criteria by using BWM, and importance weights were calculated. In the third step, the ELECTRE-III method is utilized to evaluate a set of investment alternatives and provide a ranking. We conclude with insights for both researchers and practitioners who are concerned about strategic investment decisions.

Non-ergonomic working conditions are the leading causes of musculoskeletal disorders that seriously affect human health. REBA is widely used tool due to its convenience and consideration of all body parts. However, it heavily relies on the subjective judgments of the assessor, leading to inconsistencies in results, and lacks sensitivity in detecting small changes in ergonomic risk factors. Therefore, there is a need to improve the REBA method by integrating it with new technologies. While a few studies have proposed integrating ergonomic risk measurement tools with ANNs, there is a research gap in comparing different types of neural networks and membership functions to determine the most effective approach for improving the performance of REBA. Additionally, there is a need to apply these integrations to real-life case studies to demonstrate their effectiveness in practice. This study proposes a comparative neural network and neuro-fuzzy-based REBA method that includes various types of neural networks and membership functions. The proposed method is applied to service employee who have experienced increased workloads due to the Covid-19 pandemic. The results show that the neuro-fuzzy method is more accurate than the REBA and provides greater flexibility in defining which member belongs to which risk level cluster. This study is critical because it addresses research gaps in integrating neural networks and REBA and applies these integrations to a real-life case study. By comparing different types of neural networks and membership functions, the study provides insights into which approaches are most effective for improving the performance of REBA.

One of the main causes of the significant commercial vehicle traffic in the city region is last-mile deliveries. Parcel lockers, which are one of the easiest and most environmentally friendly solutions for last-mile delivery, are one of the most studied subjects recently. The parcel locker ensures consumer privacy while being quick and efficient. Its full-time service can effectively address the issue of student and office worker pickup. In this paper, the location of a parcel locker intended to be established in the most convenient location in Beşiktaş district of İstanbul, Turkey has been determined. This problem can be solved using a multi-criteria decision-making (MCDM) structure due to the availability of numerous aspects that must be considered while choosing the optimum location. Additionally, the benefit of fuzzy logic is employed to translate expert opinions into mathematical expressions and incorporate them into decision-making processes. To choose the ideal location for the parcel locker, a novel model integrating the Bayesian Best Worst Method (B-BWM) and Pythagorean fuzzy Weighted Aggregated Sum Product Assessment (PF-WASPAS) approaches is proposed for the first time in the literature. Additionally, a sensitivity analysis is conducted to evaluate the model’s robustness. As a consequence, the suggested model effectively identifies the best location for a parcel locker in Istanbul.

Earthquakes are the leading natural disasters that seriously affect human life. Furthermore, earthquakes are natural disasters that have the ability to trigger a second disaster in addition to the damages they cause. From this point of view, post-earthquake fires are defined as the one of the most dangerous secondary disasters after an earthquake and often cause even more serious dangers. For this reason, government officials and relevant decision-makers should effectively determine post-earthquake fire risks and take necessary precautions. In this study, we consider the problem of determining the fire risk after an earthquake as a multi-criteria decision problem and present a two-level framework for risk assessment. The main and sub-criteria are determined by a detailed literature review and Modified Delphi method is employed to gain and consolidate expert opinions. Firstly, the importance weights of the criteria for post-earthquake fire risk problem are determined by the interval valued neutrosophic-Analytical Hierarchy Process (IVN-AHP) methodology. Then, interval valued neutrosophic TOPSIS (IVN-TOPSIS) method is used to rank the districts in Anatolian side of Istanbul according to their post-earthquake fire risks. The proposed risk assessment methodology is utilized with real life data to determine the most risky districts of Istanbul, Turkey. The result of proposed methodology is tested and validated with sensitivity analysis. A comparative analysis also is conducted to further validate the robustness and effectiveness of the proposed methodology. The proposed integrated methodology is intended to be a useful tool for risk assessment and to provide decision makers with a reliable assessment.