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In recent years, airline practitioners and academics have started to explore new ways to model airline passenger demand using discrete choice methods. This book provides an introduction to discrete choice models and uses extensive examples to illustrate how these models have been used in the airline industry. These examples span network planning, revenue management, and pricing applications. Numerous examples of fundamental logit modeling concepts are covered in the text, including probability calculations, value of time calculations, elasticity calculations, nested and non-nested likelihood ratio tests, etc. The core chapters of the book are written at a level appropriate for airline practitioners and graduate students with operations research or travel demand modeling backgrounds. Given the majority of discrete choice modeling advancements in transportation evolved from urban travel demand studies, the introduction first orients readers from different backgrounds by highlighting major distinctions between aviation and urban travel demand studies. This is followed by an in-depth treatment of two of the most common discrete choice models, namely the multinomial and nested logit models. More advanced discrete choice models are covered, including mixed logit models and generalized extreme value models that belong to the generalized nested logit class and/or the network generalized extreme value class. An emphasis is placed on highlighting open research questions associated with these models that will be of particular interest to operations research students. Practical modeling issues related to data and estimation software are also addressed, and an extensive modeling exercise focused on the interpretation and application of statistical tests used to guide the selection of a preferred model specification is included; the modeling exercise uses itinerary choice data from a major airline. The text concludes with a discussion of on-going customer modeling research i

Against the backdrop of higher education expansion, studentification refers to a particular type of urban sociospatial restructuring resulting from university students' concentration in certain residential areas. Over the last decade, studentification has evolved into different forms and has spread to different locales. This study aims to provide a contextualised understanding of this distinct phenomenon in China so as to decode the complex dynamics of urban sociospatial transformation in the Chinese city. In this paper, I present a line of empirical evidence based on fieldwork in Xiadu Village and Nanting Village, two studentified villages close to university campuses in Guangzhou. These two villages exemplify different consumption and spatial outcomes of studentifcation, owing to different institutional arrangements, types of studentifiers and roles of villagers. Yet, in both villages, studentification has profoundly transformed the economic, physical, social and cultural landscapes. Notably, rather than the spatialisation of compromised and marginalised residential choices by higher education students, studentification in China is better interpreted as the spatial result of students' conscious residential, entrepreneurial and consumption choices to escape from the rigid control of university dorms, to accumulate cultural and economic capital, as well as to actualise their cultural identity. In the Chinese context, studentification provides a useful prism to understand a unique trajectory of urbanisation: re-urbanising the 'villages in the city' through bringing in urban living/urban consumptions. In the long run, studentification could provide a potential solution to sustain and upgrade the villages in the city.

Demand variability directly affects inventory management. The variability of intermittent demand causes high lost sales or holding costs. While lost sales reduce customer satisfaction, keeping excessive stock also creates high costs for companies. This situation can be prevented with an appropriate inventory policy. In this study, a Markov-based proactive inventory management approach supported by metaheuristic methods is proposed in the inventory management of intermittent demands. The main contribution of the proposed approach is to find a lower and upper limit for stock by modeling the intermittent demands in the past period with the Markov process. With these optimized limits, it is aimed to balance the largest costs caused by intermittent demands, namely stock and lost sales costs. The intermittent demands used were randomly generated in 4 different sizes from small to large. The proposed approach contributes to inventory management by minimizing the negativities caused by demand variability through the Markov process. A mathematical model has been proposed for stock level optimization, but no feasible solution has been found. The mathematical model was transformed into a fitness function and a solution was provided with the Tabu Search Algorithm and Simulated Annealing. The inventory management process of intermittent demand was first evaluated without the Markov approach, and then the Markov approach was included in the process. The results showed that the Markov approach was a good tool for inventory management of intermittent demand. When the results were examined, the stock limits computed with the Markov process balanced the increased inventory cost and lost sales costs due to intermittent demand.

Thompson sampling is a randomized Bayesian machine learning method, whose original motivation was to sequentially evaluate treatments in clinical trials. In recent years, this method has drawn wide attention, as Internet companies have successfully implemented it for online ad display. In “Online network revenue management using Thompson sampling,” K. Ferreira, D. Simchi-Levi, and H. Wang propose using Thompson sampling for a revenue management problem where the demand function is unknown. A main challenge to adopt Thompson sampling for revenue management is that the original method does not incorporate inventory constraints. However, the authors show that Thompson sampling can be naturally combined with a linear program formulation to include inventory constraints. The result is a dynamic pricing algorithm that incorporates domain knowledge and has strong theoretical performance guarantees as well as promising numerical performance results. Interestingly, the authors demonstrate that Thompson sampling achieves poor performance when it does not take into account domain knowledge. Finally, the proposed dynamic pricing algorithm is highly flexible and is applicable in a range of industries, from airlines and internet advertising all the way to online retailing. We consider a price-based network revenue management problem in which a retailer aims to maximize revenue from multiple products with limited inventory over a finite selling season. As is common in practice, we assume the demand function contains unknown parameters that must be learned from sales data. In the presence of these unknown demand parameters, the retailer faces a trade-off commonly referred to as the “exploration-exploitation trade-off.” Toward the beginning of the selling season, the retailer may offer several different prices to try to learn demand at each price (“exploration” objective). Over time, the retailer can use this knowledge to set a price that maximizes revenue throughout the remainder of the selling season (“exploitation” objective). We propose a class of dynamic pricing algorithms that builds on the simple, yet powerful, machine learning technique known as “Thompson sampling” to address the challenge of balancing the exploration-exploitation trade-off under the presence of inventory constraints. Our algorithms have both strong theoretical performance guarantees and promising numerical performance results when compared with other algorithms developed for similar settings. Moreover, we show how our algorithms can be extended for use in general multiarmed bandit problems with resource constraints as well as in applications in other revenue management settings and beyond. The online appendix is available at https://doi.org/10.1287/opre.2018.1755 .

Between now and 2030, every country will experience population ageing—a trend that is both pronounced and historically unprecedented. Over the past six decades, countries of the world had experienced only a slight increase in the share of people aged 60 years and older, from 8% to 10%. But in the next four decades, this group is expected to rise to 22% of the total population—a jump from 800 million to 2 billion people. Evidence suggests that cohorts entering older age now are healthier than previous ones. However, progress has been very uneven, as indicated by the wide gaps in population health (measured by life expectancy) between the worst (Sierra Leone) and best (Japan) performing countries, now standing at a difference of 36 years for life expectancy at birth and 15 years for life expectancy at age 60 years. Population ageing poses challenges for countries' economies, and the health of older populations is of concern. Older people have greater health and long-term care needs than younger people, leading to increased expenditure. They are also less likely to work if they are unhealthy, and could impose an economic burden on families and society. Like everyone else, older people need both physical and economic security, but the burden of providing these securities will be falling on a smaller portion of the population. Pension systems will be stressed and will need reassessment along with retirement policies. Health systems, which have not in the past been oriented toward the myriad health problems and long-term care needs of older people and have not sufficiently emphasised disease prevention, can respond in different ways to the new demographic reality and the associated changes in population health. Along with behavioural adaptations by individuals and businesses, the nature of such policy responses will establish whether population ageing will lead to major macroeconomic difficulties.

Background Revision THA and TKA are growing and important clinical and economic challenges. Healthcare systems tend to combine revision joint replacement procedures into a single service line, and differences between revision THA and revision TKA remain incompletely characterized. These differences carry implications for guiding care and resource allocation. We therefore evaluated epidemiologic trends associated with revision THAs and TKAs. Questions/purposes We sought to determine differences in (1) the number of patients undergoing revision TKA and THA and respective demographic trends; (2) differences in the indications for and types of revision TKA and THA; (3) differences in patient severity of illness scoring between THA and TKA; and (4) differences in resource utilization (including cost and length of stay [LOS]) between revision THA and TKA. Methods The Nationwide Inpatient Sample (NIS) was used to evaluate 235,857 revision THAs and 301,718 revision TKAs between October 1, 2005 and December 31, 2010. Patient characteristics, procedure information, and resource utilization were compared across revision THAs and TKAs. A revision burden (ratio of number of revisions to total number of revision and primary surgeries) was calculated for hip and knee procedures. Severity of illness scoring and cost calculations were derived from the NIS. As our study was principally descriptive, statistical analyses generally were not performed; however, owing to the large sample size available to us through this NIS analysis, even small observed differences presented are likely to be highly statistically significant. Results Revision TKAs increased by 39% (revision burden, 9.1%–9.6%) and THAs increased by 23% (revision burden, 15.4%–14.6%). Revision THAs were performed more often in older patients compared with revision TKAs. Periprosthetic joint infection (25%) and mechanical loosening (19%) were the most common reasons for revision TKA compared with dislocation (22%) and mechanical loosening (20%) for revision THA. Full (all-component) revision was more common in revision THAs (43%) than in TKAs (37%). Patients who underwent revision THA generally were sicker (> 50% major severity of illness score) than patients who underwent revision TKA (65% moderate severity of illness score). Mean LOS was longer for revision THAs than for TKAs. Mean hospitalization costs were slightly higher for revision THA (USD 24,697 +/− USD 40,489 [SD]) than revision TKA (USD 23,130 +/− USD 36,643 [SD]). Periprosthetic joint infection and periprosthetic fracture were associated with the greatest LOS and costs for revision THAs and TKAs. Conclusions These data could prove important for healthcare systems to appropriately allocate resources to hip and knee procedures: the revision burden for THA is 52% greater than for TKA, but revision TKAs are increasing at a faster rate. Likewise, the treating clinician should understand that while both revision THAs and TKAs bear significant clinical and economic costs, patients undergoing revision THA tend to be older, sicker, and have greater costs of care.

Children are traveling longer distances to school, and the share traveling by car is increasing. This paper examines the effects of school attributes on school choice, which in turn gives rise to travel distance and mode choice. It is well known that school quality is capitalized into residential land values. Households willing and able to pay price premiums may choose to live closer to good-quality schools. In contrast, households with less ability to pay are likely to live in places with schools of lower quality. The California public school system has an open enrollment policy, which allows students to transfer out of their neighbourhood school when places are available. When this option is exercised, students may travel longer distances to school compared with students who attend their neighbourhood schools. We used travel diary data from the 2001 Post Census Regional Household Travel Survey to model school destination choices for K-12 students in the Los Angeles region, California. Parents may choose to send their children to neighbourhood schools, other schools within their home district, or out-of-district schools. We find that location, school quality, and other school features influence the probability of a school being chosen, and the extent to which these factors influence choice varies depending on the characteristics of the residential district and the attributes of the household.

The prevalence of diabetes is increasing rapidly in low- and middle-income countries (LMICs), urgently requiring detailed evidence to guide the response of health systems to this epidemic. In an effort to understand at what step in the diabetes care continuum individuals are lost to care, and how this varies between countries and population groups, this study examined health system performance for diabetes among adults in 28 LMICs using a cascade of care approach. We pooled individual participant data from nationally representative surveys done between 2008 and 2016 in 28 LMICs. Diabetes was defined as fasting plasma glucose ≥ 7.0 mmol/l (126 mg/dl), random plasma glucose ≥ 11.1 mmol/l (200 mg/dl), HbA1c ≥ 6.5%, or reporting to be taking medication for diabetes. Stages of the care cascade were as follows: tested, diagnosed, lifestyle advice and/or medication given (\"treated\"), and controlled (HbA1c < 8.0% or equivalent). We stratified cascades of care by country, geographic region, World Bank income group, and individual-level characteristics (age, sex, educational attainment, household wealth quintile, and body mass index [BMI]). We then used logistic regression models with country-level fixed effects to evaluate predictors of (1) testing, (2) treatment, and (3) control. The final sample included 847,413 adults in 28 LMICs (8 low income, 9 lower-middle income, 11 upper-middle income). Survey sample size ranged from 824 in Guyana to 750,451 in India. The prevalence of diabetes was 8.8% (95% CI: 8.2%-9.5%), and the prevalence of undiagnosed diabetes was 4.8% (95% CI: 4.5%-5.2%). Health system performance for management of diabetes showed large losses to care at the stage of being tested, and low rates of diabetes control. Total unmet need for diabetes care (defined as the sum of those not tested, tested but undiagnosed, diagnosed but untreated, and treated but with diabetes not controlled) was 77.0% (95% CI: 74.9%-78.9%). Performance along the care cascade was significantly better in upper-middle income countries, but across all World Bank income groups, only half of participants with diabetes who were tested achieved diabetes control. Greater age, educational attainment, and BMI were associated with higher odds of being tested, being treated, and achieving control. The limitations of this study included the use of a single glucose measurement to assess diabetes, differences in the approach to wealth measurement across surveys, and variation in the date of the surveys. The study uncovered poor management of diabetes along the care cascade, indicating large unmet need for diabetes care across 28 LMICs. Performance across the care cascade varied by World Bank income group and individual-level characteristics, particularly age, educational attainment, and BMI. This policy-relevant analysis can inform country-specific interventions and offers a baseline by which future progress can be measured.

We build on the intuitive (static) modeling framework of Rosen (1974) and specify a simple, forward-looking model of location choice. We use this model, along with a series of graphs, to describe the potential biases associated with the static model and relate these biases to the time series of the amenity of interest. We then derive an adjustment factor that allows the potentially biased static estimates to be converted into forward-looking estimates. Finally, we illustrate these concepts with two empirical applications: the marginal willingness to pay to avoid violent crime and the marginal willingness to pay to avoid air pollution.