Mixed-model Sequencing with Reinsertion of Failed Vehicles: A Case Study for Automobile Industry

In the automotive industry, some vehicles, failed vehicles, cannot be produced according to the planned schedule due to some reasons such as material shortage, paint failure, etc. These vehicles are pulled out of the sequence, potentially resulting in an increased work overload. On the other hand, the reinsertion of failed vehicles is executed dynamically as suitable positions occur. In case such positions do not occur enough, either the vehicles waiting for reinsertion accumulate or reinsertions are made to worse positions by sacrificing production efficiency. This study proposes a bi-objective two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. Moreover, an evolutionary optimization algorithm, a two-stage local search algorithm, and a hybrid approach are developed. Numerical experiments over a case study show that while the hybrid algorithm better explores the Pareto front representation, the local search algorithm provides more reliable solutions regarding work overload objective. Finally, the results of the dynamic reinsertion simulations show that we can decrease the work overload by ~20\% while significantly decreasing the waiting time of the failed vehicles by considering vehicle failures and integrating the reinsertion process into the mixed-model sequencing problem.Comment: 26 pages, 6 figures, 5 table

Optimal Pricing in Networks with Externalities

We study the optimal pricing strategies of a monopolist selling a divisible good (service) to consumers who are embedded in a social network. A key feature of our model is that consumers experience a (positive) local network effect. In particular, each consumer's usage level depends directly on the usage of her neighbors in the social network structure. Thus, the monopolist's optimal pricing strategy may involve offering discounts to certain agents who have a central position in the underlying network. Our results can be summarized as follows. First, we consider a setting where the monopolist can offer individualized prices and derive a characterization of the optimal price for each consumer as a function of her network position. In particular, we show that it is optimal for the monopolist to charge each agent a price that consists of three components: (i) a nominal term that is independent of the network structure, (ii) a discount term proportional to the influence that this agent exerts over the rest of the social network (quantified by the agent's Bonacich centrality), and (iii) a markup term proportional to the influence that the network exerts on the agent. In the second part of the paper, we discuss the optimal strategy of a monopolist who can only choose a single uniform price for the good and derive an algorithm polynomial in the number of agents to compute such a price. Third, we assume that the monopolist can offer the good in two prices, full and discounted, and we study the problem of determining which set of consumers should be given the discount. We show that the problem is NP-hard; however, we provide an explicit characterization of the set of agents who should be offered the discounted price. Next, we describe an approximation algorithm for finding the optimal set of agents. We show that if the profit is nonnegative under any feasible price allocation, the algorithm guarantees at least 88% of the optimal profit. Finally, we highlight the value of network information by comparing the profits of a monopolist who does not take into account the network effects when choosing her pricing policy to those of a monopolist who uses this information optimally

Mixed-model Sequencing with Stochastic Failures: A Case Study for Automobile Industry

In the automotive industry, the sequence of vehicles to be produced is determined ahead of the production day. However, there are some vehicles, failed vehicles, that cannot be produced due to some reasons such as material shortage or paint failure. These vehicles are pulled out of the sequence, and the vehicles in the succeeding positions are moved forward, potentially resulting in challenges for logistics or other scheduling concerns. This paper proposes a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provides improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. Moreover, we provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances.Comment: 30 pages, 9 figure

Dynamics in near-potential games

We consider discrete-time learning dynamics in finite strategic form games, and show that games that are close to a potential game inherit many of the dynamical properties of potential games. We first study the evolution of the sequence of pure strategy profiles under better/best response dynamics. We show that this sequence converges to a (pure) approximate equilibrium set whose size is a function of the “distance” to a given nearby potential game. We then focus on logit response dynamics, and provide a characterization of the limiting outcome in terms of the distance of the game to a given potential game and the corresponding potential function. Finally, we turn attention to fictitious play, and establish that in near-potential games the sequence of empirical frequencies of player actions converges to a neighborhood of (mixed) equilibria, where the size of the neighborhood increases according to the distance to the set of potential games

Genetic analysis of the complete G gene of viral hemorrhagic septicemia virus (VHSV) genotype Ie isolates from Turkey

Viral hemorrhagic septicemia virus (VHSV) is an enveloped non-segmented, single-stranded, negative-sense RNA virus that belongs to the Novirhabdovirus genus of the family Rhabdoviridae. This virus causes economically significant diseases in farmed rainbow trout, in Turkey, which is often associated with the transmission of pathogens from European resources. In this study, moribund rainbow trout (Oncorhynchus mykiss) samples were collected during an outbreak of VHSV in a rainbow trout fish farm in Bolu Province of Turkey in 2006. In addition, two VHSV strains were isolated from wild turbot (Scophthalmus maximus) in Trabzon Province of the Black Sea region of Turkey during a field survey. We have sequenced the full-length glycoprotein (G) gene of three VHSV isolates and compared them with 25 previously published gene sequences. Based on a complete gene nucleotide sequence, Turkish VHSV isolates were classified into class Ie of genotype I, which is closely related to GE-1.2 isolate (97.1-97.5% nucleotide identity and 98.2-98.4% amino acid identity) found in Georgia more than 30 years ago. These isolates could be an indigenous type of VHSV distributed in the Black Sea. On the other hand, Turkish isolates have 97.5-97.6% nucleotide identity and 98.8-99% amino acid identity with Finnish, Danish, and Norwegian isolates which are classified under Ib and Id. These results suggest that Turkish VHSV isolates may have orginated from Europe and co-circulated with indigenous strains which can threaten the aquaculture industry in Turkey

Visual snow syndrome after start of citalopram-novel insights into underlying pathophysiology

Purpose!#!Chronic pain is common in the older population and a significant public health concern. However, comprehensive studies on analgesics use in this age group from Germany are scarce. This study aims to give a comprehensive overview on the use of the most common therapeutic groups of analgesics in community-dwelling older adults from Germany.!##!Methods!#!A cross-sectional study was carried out using data from a German cohort of 2038 community-dwelling adults aged 63-89 years. Descriptive statistics and logistic regression models were applied to assess the utilization of analgesics by age, sex, pain severity, pain duration, and locations.!##!Results!#!One out of four study participants was suffering from high-intensity or disabling pain. Approximately half of those taking analgesics still reported to suffer from high-intensity or disabling pain. Among analgesics users, occasional non-steroidal anti-inflammatory drugs (NSAIDs) use was the most frequent pain therapy (in 43.6% of users), followed by metamizole (dipyrone) use (16.1%), regular NSAIDs use (12.9%), strong opioids use (12.7%), and weak opioids use (12.0%). In multivariate logistic regression models, higher age, higher pain severity, longer pain duration, abdominal pain, and back pain were statistically significantly associated with opioids use. Metamizole use was also statistically significantly associated with higher pain severity but inversely associated with pain duration.!##!Conclusions!#!A significant number of older German adults are affected by high-intensity and disabling chronic pain despite receiving analgesics. Long-term studies are needed to compare the effectiveness and safety of different treatments for chronic pain in older adults

A unique MRI-pattern in alcohol-associated Wernicke encephalopathy

There have been concerns about high rates of thus far undiagnosed SARS-CoV-2 infections in the health-care system. The COVID-19 Contact (CoCo) Study follows 217 frontline health-care professionals at a university hospital with weekly SARS-CoV-2-specific serology (IgA/IgG). Study participants estimated their personal likelihood of having had a SARS-CoV-2 infection with a mean of 21% [median 15%, interquartile range (IQR) 5-30%]. In contrast, anti-SARS-CoV-2 IgG prevalence was about 1-2% at baseline. Regular anti-SARS-CoV-2 IgG testing of health-care professionals may aid in directing resources for protective measures and care of COVID-19 patients in the long run

Near-Optimal Power Control in Wireless Networks: A Potential Game Approach

We study power control in a multi-cell CDMA wireless system whereby self-interested users share a common spectrum and interfere with each other. Our objective is to design a power control scheme that achieves a (near) optimal power allocation with respect to any predetermined network objective (such as the maximization of sum-rate, or some fairness criterion). To obtain this, we introduce the potential-game approach that relies on approximating the underlying noncooperative game with a "close" potential game, for which prices that induce an optimal power allocation can be derived. We use the proximity of the original game with the approximate game to establish through Lyapunov-based analysis that natural user-update schemes (applied to the original game) converge within a neighborhood of the desired operating point, thereby inducing near-optimal performance in a dynamical sense. Additionally, we demonstrate through simulations that the actual performance can in practice be very close to optimal, even when the approximation is inaccurate. As a concrete example, we focus on the sum-rate objective, and evaluate our approach both theoretically and empirically.National Science Foundation (U.S.) (DMI-05459100)National Science Foundation (U.S.) (DMI-0545910)United States. Defense Advanced Research Projects Agency (ITMANET program)7th European Community Framework Programme (Marie Curie International Fellowship

In the Words of the Medical Tourist: An Analysis of Internet Narratives by Health Travelers to Turkey

Background: Patients regularly travel to the West for advanced medical care, but now the trend is also shifting in the opposite direction. Many people from Western countries now seek care outside of their country. This phenomenon has been labeled medical tourism or health travel. Information regarding health travelers’ actual outcomes, experiences, and perceptions is lacking or insufficient. However, advanced Internet technology and apps provide information on medical tourism and are a vehicle for patients to share their experiences. Turkey has a large number of internationally accredited hospitals, is a top tourism destination, and is positioning itself to attract international patients. Objective: The objective of this research was to identify the important individual characteristics of health travelers, outline the push and pull factors for seeking health care in Turkey, identify satisfaction with the outcomes and the results of these individuals’ treatments, and note positive and negative factors influencing their perceptions and overall experiences about patients’ health travel. Methods: This research uses qualitative data from Internet narratives of medical tourists to Turkey. Ethical considerations of using Internet narratives were reviewed. Narratives for analysis were obtained by using the Google search engine and using multiple search terms to obtain publicly posted blogs and discussion board postings of health travelers via purposeful sampling. Narratives were included if they were written in English, described travel to Turkey for health care, and were publicly accessible. Exclusion criteria included narratives that were on medical tourism facilitator or provider promotional websites, not in English, and did not describe an experience of a medical tourist. Medical tourists’ written words were analyzed in an iterative analytic process using narrative analysis theory principles. Three stages of coding (open, axial, and selective) were conducted to identify characteristics and themes using qualitative analysis software. Results: The narrative posts of 36 individuals undergoing 47 procedures who traveled to Turkey for medical care between 2007 and 2012 were analyzed. The narratives came from 13 countries, not including the narratives for which patient origin could not be determined. Travelers were predominantly from Europe (16/36, 44%) and North America (10/36, 28%). Factors driving travelers away from their home country (push factors) were cost and lack of treatment options or insufficient insurance coverage in their home country. Leading factors attracting patients to destination (pull factors) were lower costs, physician’s expertise and responsiveness, and familiarity or interest in Turkey. Health travelers to Turkey were generally satisfied with the outcomes of their procedures and care provided by their physicians, many noting intent to return. Communication challenges, food, transportation, and gaps in customer service emerged as key areas for improvement. Conclusions: This analysis provides an understanding of the insights of medical tourists through the words of actual health travelers. This nonintrusive methodology provides candid insights of common themes of health travelers and may be applied to study other patient experiences. The findings of this research expands the body of knowledge in medical tourism and serves as a platform for further qualitative and quantitative research on health travelers’ experiences