Markov decision process for mixed-model assembly line design under process time uncertainty

The industry is increasingly confronted with the challenge of process duration uncertainty in production systems. These variations are particularly problematic for manufacturers that utilize Multi-Manned Mixed-Model Assembly Lines, as they can cause significant disruptions that may stop the production line. Our study explores the benefit of walking workers to dynamically adjust the workforce in response to unexpected variations in process durations at different stations, a common scenario in the automotive industry. We model the dynamic workforce assignment decision as a Markov Decision Process (MDP), and this MDP accounts for uncertainties in process times, and it incorporates dynamic task assignment and workers' movements. This MDP is subsequently translated into a linear program that we integrate into a higher-level Mixed-Integer Linear Programming model responsible for dimensioning the workforce and selecting equipment in the station. This approach results in the creation of assembly lines designed to be resilient in the face of unexpected variations in task process durations. To deal with scalability issues, we employ the Benders decomposition algorithm. The paper also presents a validation with data from a car manufacturer that reinforces the practical applicability of our methodology. Additionally, we provide managerial insights on effectively managing process time uncertainty in automotive production systems, empowering decision-makers with optimization strategies, cost-reduction approaches, and resilience-building techniques to enhance the performance and reliability of Mixed-Model Assembly Lines.European Union (EU) European Commission Joint Research Centr

The impact of COVID-19 pandemic on tourism employees: Was it the last straw?

Tourism, as one of the most vulnerable industries, has survived numerous global crises with substantial negative impact on economies, communities, businesses, and individuals. Despite the circumvention of the industry after those experiences of mild and severe crises, COVID-19 pandemic has been the most serious case with deep global impact in every corner of the world leading to the explosion of academic research on a plethora of pandemic aspects. However, research offering insights on tourism and hospitality employees' experiences, is scarce in the relevant literature in spite of the chronic problems of employee retention, qualified and long-term labor force. Therefore, the aim of this study addresses at examining the experiences of hotel employees in T & uuml;rkiye during and after COVID-19, which caused sudden and deep changes in the lives following the severe decline in tourism employment and economic problems it ushered in. The data was collected through in-depth interviews with 21 individuals who formerly worked in city or resort hotels at various positions and departments. Two sensemaking perspectives were integrated to find out the consequences of the pandemic leading to the causes and factors to end working in the industry. Study findings offer important insights into pandemic-related dynamics and could support the development of effective tourism policy and practices leading to improve crisis management efforts in the tourism and hospitality industry

Optimizing election logistics: A multi-period routing problem embedding time-dependent reward functions

With the 2024 US Presidential Election now concluded, the growing complexity of designing effective election campaigns has become clearer. Motivated by the logistical challenges associated with US election campaigns, we introduce the Reward-driven Multi-period Politician Routing Problem. It involves diverse politicians planning their campaigns over multiple days, considering constraints such as clustered locations, time-and location-dependent rewards, budget limits, mandatory rest days, and flexible daily routes that can be either open or closed, with starting and ending locations not known in advance. We model the problem as a mixed-integer linear program, complemented with several valid inequalities, and innovate by designing new subtour elimination techniques that jointly deal with open and closed paths. We developed 36 new benchmark instances tailored to the US presidential elections. To tackle large-sized instances, we develop a Sequential Route Construction Matheuristic that exploits the multi-period structure of the problem to provide efficient and effective solutions. We incorporate time-dependent reward profiles (concave, convex, linearly decreasing, linearly increasing, and periodic) into the objective function to capture diverse decision-making perspectives. Experimental results show interesting computational issues on the different tested models and the impact of the chosen reward profile on their performance.Publisher versio

Elucidating the role of processing parameters on microstructure, grain refinement, and mechanical features of Al/Ti laminated composite fabricated by accumulative roll bonding

Multilayered composites are widely used in automobile components. The properties of multilayered composites mainly depend on processing parameters. In this investigation, Al/Ti composites were fabricated by accumulative roll bonding (ARB). The influence of rolling parameters including rolling velocity (10 < v < 20 r/min), rolling cycle (7 cycles), and friction coefficient (0.08 < < 0.14) on the evolution of microstructure, grain refinement, variations of hardness, and tensile features were examined. The results of microstructural characterization showed that the layers became more discontinuous and the broken pieces of Ti were better distributed in the Al matrix when the rolling cycle and rolling friction increased and the rolling velocity decreased. In addition, the characterization of grain structure revealed grain refinement in Ti and Al layers after the 7(th) cycle. The grain structure was better refined when the friction coefficient was 0.14 and rolling velocity was 10 r/min. The hardness and strength values increased as the rolling cycle and friction coefficient increased and the rolling velocity decreased. When the friction coefficient was 0.14 and the rolling velocity was 20 r/min, the highest strength of 520 MPa was obtained and the maximum hardness of Ti and Al were 76 HV and 292 HV, respectively. Although the fracture surfaces showed delamination and different sizes of dimples, a larger number of dimples were observed at higher rolling velocities and lower friction coefficients

Development of the usage possibilities of adobe with computational design

In addition to improving the physical material properties of adobe, the ability to use it with today's design approach also plays an important role in this material being considered a contemporary building material. The development of computer-aided design technology not only changes the architectural design concept but also improves the usage possibilities of traditional building materials. The parametric structures created with computational design allow the use of traditional materials in different ways, leading to the emergence of innovative construction methods. With its easily accessible, economical, and sustainable features, adobe is a preferred material for contemporary designs, and it meets today's building production needs. It is a necessity of our age to investigate the adobe material, which increases the indoor air quality and creates healthy spaces, as a building material of today, as well as a material of the future. This study aims to consider how the usage possibilities and production methods of Adobe material can be improved by examining the innovations brought by computational design to Adobe material via parametrically designed Adobe building projects and structural elements. It is also important to do a benchmark in this study by examining the usage of other building materials used in computational design projects and establishing a relationship between these techniques and adobe

Clustering-based negative sampling approaches for protein-protein interaction prediction

The lack of confirmed negative interactions poses a major challenge to the prediction of protein-protein interactions. The reliable selection of these negative samples within a dataset is crucial for a better understanding of the underlying patterns and dynamics. The random sampling method is the most widely used negative sampling method, where negative pairs are randomly selected from unlabelled samples (i.e., samples not experimentally confirmed as positive interactions). However, they tend to introduce inaccurately labelled negative samples, resulting in less reliable predictions, which may affect the efficiency of the learning process. Our study aims to assess the reliability of clustering-based negative sampling methods and highlight their fundamental differences from the widely used random sampling method. To achieve this goal, we propose a hierarchical clustering-based algorithm that uses different mechanisms to select negative instances from unlabelled instances. We investigated the effectiveness of our proposed approach compared to existing clustering-based negative sampling methods and random sampling on four different datasets. The results indicate that clustering-based methods surpass the commonly used random sampling method.TÜBİTA

Asymptotically optimal energy consumption and inventory control in a make-to-stock manufacturing system

We study a make-to-stock manufacturing system in which a single server makes the production. The server consumes energy, and its power consumption depends on the server state: a busy server consumes more power than an idle server, and an idle server consumes more power than a turned-off server. When a server is turned on, it completes a costly set-up process that lasts a while. We jointly control the finished goods inventory and the server's energy consumption. The objective is to minimize the long-run average inventory holding, backorder, and energy consumption costs by deciding when to produce, when to idle or turn off the server, and when to turn on a turned-off server. Because the exact analysis of the problem is challenging, we consider the asymptotic regime in which the server is in the conventional heavy-traffic regime. We formulate a Brownian control problem (BCP) with impulse and singular controls. In the BCP, the impulse control appears due to server shutdowns, and the singular control appears due to server idling. Depending on the system parameters, the optimal BCP solution is either a control-band or barrier policy. We propose a simple heuristic control policy from the optimal BCP solution that can easily be implemented in the original (non-asymptotic) system. Furthermore, we prove the asymptotic optimality of the proposed control policy in a Markovian setting. Finally, we show that our proposed policy performs close to optimal in numerical experiments.TÜBİTAK ; European Union's Horizon 2020 ; Türkiye Bilimler Akademis

Neuro-symbolic ai for supporting chronic disease diagnosis and monitoring

In remote areas or regions with limited access to medical specialists, there is often a high reliance on telemedicine and Artificial Intelligence (AI)-based diagnostic tools. However, misdiagnoses or inadequate care may occur if the AI system lacks domain knowledge, failing to adhere to medical protocols. Despite the incredible research efforts applying AI in medicine, only a few models have been routinely adopted in medicine, due to issues related to trustworthiness. To address these concerns, Symbolic Knowledge Injection (SKI) has been proposed as a solution: it integrates domain-specific expertise into Machine Learning (ML) models, to improve their predictive capabilities. Despite their promising results in other fields, applicability of SKI in healthcare scenarios has not been thoroughly investigated, yet. Accordingly, in this study, we explore the applicability of a SKI method on medical datasets to evaluate: (i) how the predictive capabilities of ML models changes, (ii) their adherence to the medical protocols, and (iii) their robustness w.r.t. data degradation. Results demonstrate the potential of integrating data-driven models with established medical guidelines by improving different clinically relevant metrics

Digital transformation and cybersecurity risks

This study analyzes the impact of integrating cybersecurity measures on the financial performance of banks. Utilizing regression analysis with data from 100 financial institutions, the findings reveal that banks prioritizing cybersecurity perform better financially. This study demonstrates that it is the quality and strategic integration of cybersecurity measures, as revealed through disclosures, that significantly influence financial outcomes, rather than the sheer scale of investment. A subsample analysis suggests that larger banks appear more resilient to cybersecurity threats due to scale-related advantages, while smaller banks can also improve their financial performance by adopting proportionate, strategically aligned cybersecurity measures. Effective cybersecurity integration correlates with improved financial metrics such as return on assets and equity. Furthermore, the severity of cybersecurity incidents negatively impacts financial performance, emphasizing the importance of proactive risk management. This study underscores the critical role of cybersecurity in financial strategy, enabling banks to navigate digital transformation challenges effectively

Resistance under confinement: resilience of protests and their limits in authoritarian Turkey

In this paper, we examine the relationship between the process of autocratisation and protests, and argue that scholarship on electoral autocracies should not only focus on major protest cycles but also examine 'ordinary' protests to understand how social and political actors resist and push back against autocratisation. Using an original dataset of protest events from 2015 to 2021, we analyse the transformation of protests in Turkey as it experienced gradual but significant autocratisation. We discuss two mechanisms through which autocratisation might affect levels, actors and repertoires of protesting: first, via increasing repression; and, second, via the policy choices of the authoritarian regime. Our findings indicate that protests continued even under the state of emergency in Turkey, but with significant changes in levels and repertoires of protesting. The protest scene was dominated by protests using tactics that rely on a small number of individuals and are contained in their spatial reach and disruptiveness. This research underlines the importance of examining ordinary protests to analyse how autocratisation transforms protests, using original data from local sources.Bogazici Universit