ERN: Statistical Decision Theory; Operations Research (Topic)最新文献

{"title":"Optimal Dynamic Nonlinear Pricing for Airline Networks","authors":"Weipeng Zhang","doi":"10.2139/ssrn.3833013","DOIUrl":"https://doi.org/10.2139/ssrn.3833013","url":null,"abstract":"Airfares vary across the booking horizon according to intertemporal price discrimination and adjustment of fares in response to stochastic demand shocks. Previous works studying these economic forces abstract away from the ramifications of pricing an itinerary has on revenue and consumer welfare in a larger network containing itineraries subjected to common interconnected capacity constraints. I estimate a dynamic structural model of airline network pricing with a novel high frequency data set on flight prices and seat availability while relaxing the assumption of optimality. By a method of simulated moments, I resolve the classic econometric issues of endogeneity, censoring, and truncation in order to recover the airline’s beliefs on its stochastic demand process. I perform several counterfactual experiments to compare revenue and consumer welfare under several different pricing strategies and network configurations to elucidate the channels through which pricing externalities are transmitted in the network. I show that when the airline strategically prices itineraries jointly in their network according to a network-perfect pricing policy, it can increase revenue by a lower bound of 2.3% relative to the existing industry standard of network-oblivious pricing policies that independently set seemingly optimal prices for individual itineraries, a significant gain given the industry.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128894310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conditional Quantiles: An Operator-Theoretical Approach","authors":"Luciano I. de Castro, B. Costa, A. Galvao, J. Zubelli","doi":"10.2139/ssrn.3924597","DOIUrl":"https://doi.org/10.2139/ssrn.3924597","url":null,"abstract":"This paper derives several novel properties of conditional quantiles viewed as nonlinear operators. The results are organized in parallel to the usual properties of the expectation operator. We first define a τ-conditional quantile random set, relative to any sigma-algebra, as a set of solutions of an optimization problem. Then, well-known properties of unconditional quantiles, as translation invariance, comonotonicity, and equivariance to monotone transformations, are generalized to the conditional case. Moreover, a simple proof for Jensen’s inequality for conditional quantiles is provided. We also investigate continuity of conditional quantiles as operators with respect to different topologies and obtain a novel Fatou’s lemma for quantiles. Conditions for continuity in Lp and weak continuity are also provided. We also investigate differentiability properties of quantiles. We demonstrate the validity of the Leibniz’s rule for conditional quantiles for the cases of monotone, as well as separable functions. Finally, although the law of iterated quantiles does not hold in general, we characterize the maximum set of random variables for which this law holds, and investigate its consequences for the infinite composition of conditional quantiles.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125813119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scientific and Methodological Approaches to Modeling the Optimal Strategy for Increasing the Competitiveness of Pharmacy Chains of Different Sizes","authors":"I. Bondarieva, V. Malyi, O. Posilkina, Zhanna Mala, M. Nessonova","doi":"10.15587/2519-4852.2021.239389","DOIUrl":"https://doi.org/10.15587/2519-4852.2021.239389","url":null,"abstract":"The aim of the work is to develop scientific and methodological approaches to modelling the optimal strategy to increase the competitiveness of pharmacy chains (PC), which belong to different clusters. \u0000Materials and methods. The algorithm for determining the optimal strategy for increasing the competitiveness of PC for different clusters using the method of constructing a decision tree and cluster analysis is proposed. To solve this problem, an expert survey of more than 400 pharmacy managers, who were part of the PC of different sizes, was previously conducted. According to the results of an expert survey using hierarchical clustering methods based on the values of 13 input variables - scores of the strengths of the competitiveness of the PC, three clusters of networks were identified, each of which proposed its own algorithm for modelling the optimal strategy of competitiveness. \u0000Results. Using modern economic and mathematical tools, the distribution of PC depending on their size into clusters for modelling the dynamics of competitiveness is substantiated. Indicators are identified, which show a significant difference between clusters, which was taken into account in the process of modelling and selection of the optimal strategy to increase the competitiveness of PC. It is established that the biggest negative impact on the strategy of increasing the competitiveness of small networks has a slow response to changes in market conditions, the biggest positive impact – the availability of additional services in the networks; for medium PC the most important factors influencing the level of competitiveness are the location of pharmacies and competent management; for large PC – the use of modern automated management programs, the level of efficiency of the marketing complex and location features. \u0000The algorithm of the generalized model of “decision tree” for a choice of optimum strategy of increase of competitiveness depending on the size of PC is constructed. It was found that the following factors are of the greatest importance: the size of the PC, the use of the discount card system, and the least - the speed of response to market changes and the stability of the financial condition. \u0000Conclusions. The proposed generalized mathematical model of the “decision tree” allows a reasonable approach to choosing the optimal strategy to increase the competitiveness of PC depending on its size. The assessment of the importance of predictor variables for each cluster of PC allows determining the priority factors in the implementation of measures aimed at implementing the chosen strategy to increase competitiveness","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"28 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123582844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vaccine Appointment Scheduling: The Second Dose Challenge","authors":"Chaitanya Bandi, S. Y. Gao, Rajeeva Moorthy, C. Teo, K. Toh","doi":"10.2139/ssrn.3909792","DOIUrl":"https://doi.org/10.2139/ssrn.3909792","url":null,"abstract":"In many countries, the COVID-19 vaccination program faces great challenges, from the management of limited and irregular supply of vaccines, uncertain vaccine take-up rate from the population, and appropriate appointment booking management to reduce congestion etc. In addition, the feature of a two-dose regimen of most COVID-19 vaccines poses a unique operational challenge, the \"blocking phenomenon,\" where the need to reserve vaccines for the second-dose appointment may \"block\" the take-up rate for the first-dose appointment. Determining the appropriate volume of vaccines to be kept in reserve in case of disruption to the supply schedule is an important operational problem for vaccine rollout. In this paper, we use the concept of \"booking curve\" (from the revenue management literature) to develop a practical tool that jointly determines the vaccine appointment booking limits that control the administration of the first and second doses, followed by an invitation schedule that decision-makers can use to regulate the appointment bookings (demand). The optimization framework aims to design the vaccine rollout policy to maximize the vaccination rate, while ensuring that the aggregate appointment waiting time in the system remains minimal, with sufficient cushion to account for supply disruption and uncertain take-up of appointments. We show that the optimization problems can be efficiently solved by linear and conic programs. A vaccine rollout numerical study based on the Singapore vaccination program is presented to demonstrate the novelty and advantage of the optimization framework. The optimization framework has been developed into an open-source tool to assist the policymakers in designing an effective and adaptive COVID-19 vaccine rollout policy facing the evolving challenges in fighting against the pandemic.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127619957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph Theory, Scheduling Problems, and Its Modern Applications","authors":"Gavriel Owens, J. Khusanov, Adilet Zholdoshov, Azamat Dzholbunov","doi":"10.2139/ssrn.3911654","DOIUrl":"https://doi.org/10.2139/ssrn.3911654","url":null,"abstract":"Graph theory is one of the most significant mathematics branches that can be utilised for various implementations, such as solving scheduling problems. This study aims to determine what graph theory is and how it can solve scheduling problems. In order to come up with our conclusion, we gathered information through numerous amount of research hours and information analyses. Therefore, the result of our study shows that graph theory can be utilised to resolve scheduling problems, as well as different types of other applications that can be applied.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"15 24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125471034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Demand Information for Price-Setting Newsvendors: A Monotone Comparative Statics Approach","authors":"Junjie Zhou, Ying‐ju Chen","doi":"10.2139/ssrn.3910155","DOIUrl":"https://doi.org/10.2139/ssrn.3910155","url":null,"abstract":"In this paper, we examine the role of demand forecast information for price-setting newsvendors. In our model, the newsvendor faces stochastic demand that is price-sensitive, has the discretion of setting the retail price, and receives some informative signal that helps the quantity decision. Focusing on a specific family of signal structure which satisfies the scale-location property, we find that when the newsvendor's demand forecast gets improved, the optimal retail price can either increase, decrease, or stay the same, and we provide concrete sufficient conditions on primitives under which each sort of monotonicity prevails. The analysis utilizes the monotone comparative statics approach, which does not require the solution uniqueness and largely weakens the regularity conditions commonly adopted in the literature. We offer a comprehensive investigation of a full spectrum of information structures, whereas the extant literature focuses exclusively on the comparison between the full-information and null-information cases. We also provide several commonly adopted distribution functions as applications of this approach.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122705062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systemic Risk in Supply Chains: A Vector Autoregressive Measurement Approach Based on the Example of Automotive and Semiconductor Supply Chains","authors":"Dirk Laschat, T. Ehrmann","doi":"10.2139/ssrn.3882809","DOIUrl":"https://doi.org/10.2139/ssrn.3882809","url":null,"abstract":"Supply chain failures and supply shortages have always been a matter of high risk. Especially when considering the scope and velocity of modern supply chains, small disturbances can cause immense damage. However, a framework for quantifying supply chain systemic risk is still missing.<br><br>To address this, we use the principles of the Diebold and Yilmaz connectedness approach, which is based on assessing the decomposition of the forecast error variance of a vector autoregressive (VAR) model, and adjust it to supply chains. By doing so, we seek to establish a systemic risk measurement of individual supply chains on different aggregation levels. In detail, we examine the automotive and semiconductor supply chains. Looking at specific firms, we identify vulnerable nodes and hubs of these supply chains and, thus, can measure the risk exposure originated by a certain region or supply chain level.<br><br>Our results show that for both supply chains, risk spillovers were at their highest levels during the COVID-19 pandemic, and firms facing U.S. trade restrictions experienced particularly strong effects during our sample period. In general, our approach provides convincing results, since companies identified as particularly risky are in line with specific company news that indicate risky spillovers during the study period.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129152764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Age-Based Maintenance under Population Heterogeneity: Optimal Exploration and Exploitation","authors":"Ipek Dursun, A. Akçay, G. Houtum","doi":"10.2139/ssrn.3871676","DOIUrl":"https://doi.org/10.2139/ssrn.3871676","url":null,"abstract":"We consider a system with a finite lifespan and a single critical component that is subject to random failures. An age-based replacement policy is applied to preventively replace the component before its failure. The components used for replacement come from either a weak population or a strong population, referred to as population heterogeneity. However, the true type of the population is unknown to the decision maker. By considering that the decision maker has a belief on the probability of having a weak population, we build a partially observable Markov decision process model that optimizes the age-based replacement decisions with the objective of minimizing the total cost over the lifespan of the system. The resulting optimal policy updates the belief variable in a Bayesian fashion by using the data obtained over the course of the system lifespan. It optimally balances the trade-off between the cost of learning the true population type (via deliberately delaying the preventive replacement time to better learn the population type) and the cost of maintenance activities. By addressing this so-called exploration-exploitation trade-off, we generate insights on the optimal policy and compare its performance with existing heuristic approaches from the literature. We also characterize a lower bound to the optimal cost, allowing us to determine the value of resolving the uncertainty on the population type.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115040806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Pricing for a New Product","authors":"Mengzhenyu Zhang, Hyun-Soo Ahn, J. Uichanco","doi":"10.2139/ssrn.3545574","DOIUrl":"https://doi.org/10.2139/ssrn.3545574","url":null,"abstract":"Pricing a New Product with Data Before a product launch (or even after a launch), firms often have little demand information and often do not know critical information such as the market size, the willingness-to-pay distribution, or the adoption speed. The lack of information makes pricing a new product challenging, and insufficient data makes demand forecasting very difficult. This is particularly costly for new products because the current price not only affects the current revenue, but also the number of adopters who can influence future demand. In this paper, we consider a setting in which a firm can learn by observing early sales data at (different) prices over time. We propose a simple and computationally tractable pricing policy that guides price changes after introducing the product. Using mathematical proofs and computational study, we show that our method substantially outperforms existing methods even with a very few price changes during a selling season.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122463142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Persistent Increase in Cost of Electricity on Performance of Small and Medium Enterprises in Uganda at Oyam Town Board","authors":"olango gerald","doi":"10.2139/ssrn.3853814","DOIUrl":"https://doi.org/10.2139/ssrn.3853814","url":null,"abstract":"The research explores the impact of persistent increase in the cost of electricity on the performance of SMEs. The study used primary data and secondary data was also used to backup primary data. The study talked about the relationship between per unit cost of electricity an prices of goods and services, impacts of electricity cost on SMEs profits and output, cost of electricity of SMEs. The study also talked about policy recomendations to be undertaken to control persistent increase in cost of electricity and other areas for further studies.<br>","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116061086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}