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

{"title":"Probabilistic Model for Population Dynamics With Uniform Catastrophes","authors":"Helder Rojas, K. Fernandez","doi":"10.2139/ssrn.3797940","DOIUrl":"https://doi.org/10.2139/ssrn.3797940","url":null,"abstract":"In the paper we study the stochastic process which corresponds to the random population dynamics with linear growth and uniform catastrophes, where an eliminating portion of the population is chosen uniformly. The law of large numbers (LLN), central limit theorem (CLT) and large deviations (LD) are proved for our model with uniform catastrophes.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133862703","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":"Solution of the Compromise Optimization Problem of Network Graphics on the Criteria of Uniform Personnel Loading and Distribution of Funds","authors":"Olena Domina","doi":"10.15587/2706-5448.2021.225527","DOIUrl":"https://doi.org/10.15587/2706-5448.2021.225527","url":null,"abstract":"The object of research is a model network schedule for performing a complex of operations. One of the most problematic areas is the lack of a unified procedure that allows finding a solution to the problem of compromise optimization, for which the optimization criteria can have a different nature of the influence of input variables on them. In this study, such criteria are the criteria for the uniformity of the workload of personnel and the distribution of funds. Two alternative cases are considered: with monthly planning and with quarterly planning of allocation of funds and staff load.\u0000The methods of mathematical planning of the experiment and the ridge analysis of the response surface are used.\u0000The peculiarities of the proposed procedure for solving the problem of compromise optimization are its versatility and the possibility of visualization in one-dimensional form – the dependence of each of the alternative criteria on one parameter describing the constraints. The solution itself is found as the point of intersection of equally labeled ridge lines, which are curves that describe the locally optimal values of the output variables.\u0000The proposed procedure, despite the fact that it is performed only on a model network diagram, can be used to solve the trade-off optimization problem on arbitrary network graphs. This is due to the fact that the combination of locally optimal solutions in a parametric form on one graph allows visualizing all solutions to the problem. The results obtained at the same time make it possible to select early dates for the start of operations in such a way that, as much as possible, take into account possible difficulties due to the formation of bottlenecks at certain stages of the project. The latter may be due to the fact that for the timely execution of some operation, it may be necessary to combine two criteria, despite the fact that the possible costs may turn out to be more calculated and estimated as optimal.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"os-18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127768415","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":"On the Hardness of Learning from Censored Demand","authors":"G. Lugosi, Mihalis G. Markakis, Gergely Neu","doi":"10.2139/ssrn.3509255","DOIUrl":"https://doi.org/10.2139/ssrn.3509255","url":null,"abstract":"Problem definition: We consider a repeated newsvendor problem where the inventory manager has no prior information about the demand, and can access only censored data. The manager needs to simultaneously \"explore\" and \"exploit\" with her inventory decisions, in order to minimize the cumulative cost that the firm incurs. We study the hardness of the problem disentangled from any probabilistic assumptions on the demand, and we develop inventory control policies with guaranteed performance.<br><br>Academic/practical relevance: The problem is motivated by multi-period inventory management of perishable goods, such as newspapers, fresh food, or certain pharmaceutical products, where demand needs to be \"learned\" only through sales. Demand for many goods is non-stationary, e.g., exhibiting trends and/or seasonalities, yet existing literature offers policies that are tailored to, or facilitated by time stationarity. Methodology: We adopt the regret criterion for performance evaluation purposes. By combining concepts and results from partial monitoring, we couple a carefully designed cost estimator to the well-known ExponentiallyWeighted Forecaster.<br><br>Results: We develop a simple and easy-to-interpret policy that achieves optimal scaling of the expected regret (up to logarithmic factors) with respect to both the number of time periods and available actions. We demonstrate the flexibility of our approach by extending these performance guarantees to: (i) tracking regret, a powerful notion of regret that uses a large class of non-stationary action sequences as benchmark; (ii) single-warehouse multi-retailer inventory management of a perishable product.<br><br>Managerial implications: Our results lead to two important insights: the benefit from “information stalking” as well as the cost of censoring are insignificant in this setting; paving the way for the design of applicable heuristic policies. Further supported by numerical experiments, our findings illustrate the performance loss that can be incurred when policies that are designed under stationarity assumptions are applied to non-stationary environments.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126330244","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":"Offering Free Upgrades Even before Stocks Run Out: The Value of Proactive Upgrades","authors":"David Chen, Christopher S. Tang, Huihui Wang, Rowan Wang, Yimin Yu","doi":"10.2139/ssrn.3777846","DOIUrl":"https://doi.org/10.2139/ssrn.3777846","url":null,"abstract":"Problem definition: When selling multiple products with different feature combinations over a short selling season, a seller often adopts a “reactive” upgrade policy by offering a free upgrade to the next-price-level product only after a customer’s preferred product is out of stock. However, when customers’ preferences are heterogeneous for different feature combinations, some unyielding customers may reject free upgrades. In this paper, we consider a new “proactive” upgrade policy under which the seller may offer free upgrades even before a product is out of stock. Academic/practical relevance: The proactive upgrade policy enables the seller to strategically keep some units of a product in reserve to secure future sales of this product for those unyielding customers. However, the value of the proactive upgrade policy over the traditional reactive upgrade policy remains unclear. Methodology: Given the product choice probability the “upgrade acceptance probability” of each arriving customer, we formulate the problem of how to offer proactive upgrades as a finite horizon dynamic program with an embedded Markov decision process, and we determine the optimal proactive upgrade policy. Results: By exploiting the underlying mathematical structure, we prove that the optimal value function possesses the “anti-multimodularity” property such that the optimal upgrade strategy under the proactive upgrade policy is governed by two state-dependent thresholds: one threshold dictates when to offer proactive upgrades, and the other threshold dictates when to offer reactive upgrades. We also show that the proactive upgrade policy can create significant value over the reactive upgrade policy when the next-price-level product has similar consumer utility or when the price sensitivity is intermediate. Managerial implications: We identify the conditions under which the proactive upgrade policy provides significant value over the traditional reactive upgrade policy. These results can be useful for sellers who sell variants of similar products with different feature combinations to customers with heterogeneous feature preferences.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"527 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123087348","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":"Development of an Automated Industrial Dynamics System","authors":"G. Solodovnik, Kateryna Kovalenko","doi":"10.15587/2706-5448.2020.217079","DOIUrl":"https://doi.org/10.15587/2706-5448.2020.217079","url":null,"abstract":"The object of research is the process of determining the main indicators of the functioning of a manufacturing enterprise using the method of system dynamics. Any enterprise for the production and sale of products is a complex socio-economic system that is closely related to the external environment through input and output channels. The external environment determines the conditions for the functioning of the enterprise and can be described by a set of a large number of different parameters, the values of which will dynamically change and are fundamentally indeterminate.<br><br>Coordination and control over material and financial flows at a manufacturing enterprise is often a separate problem. The interaction of financial resources and material flows, which are selected by the enterprise as the main ones in accordance with market requirements and the specifics of the activity, must be coordinated accordingly to achieve a more efficient operation of the enterprise. Therefore, the task of the presented study is to develop a model of material and financial flows of a production enterprise with its further software implementation. The purpose of the software implementation is to further conduct experiments with the model to determine the main indicators of the production enterprise, depending on changes in the functioning parameters due to the external environment.<br><br>All the variety of modeling methods considered in modeling theory can be conditionally divided into two groups: analytical and simulation modeling. To solve the problem of this study, simulation modeling was used, which provides for the construction of a model with characteristics adequate to the original on the basis of a certain information principle.<br><br>In the course of the research, a model of material and financial flows of a production enterprise was built. The mathematical model of flows was developed using the system dynamics method by J. Forrester. An automated system was also developed, which is a software implementation of the proposed model.<br><br>The automated system of industrial dynamics of a production enterprise developed in the study will significantly increase the efficiency and scientific validity of decisions regarding the management of material and financial resources.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128430737","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":"Inventory Timing: How to Serve a Stochastic Season","authors":"Jochen Schlapp, M. Fleischmann, Danja Sonntag","doi":"10.2139/ssrn.3747789","DOIUrl":"https://doi.org/10.2139/ssrn.3747789","url":null,"abstract":"Problem Definition. Firms that sell products over a limited selling season often have only imperfect information about (a) the exact timing of that season, (b) the demand volume to expect, and (c) the temporal distribution of demand over the selling season. Given these uncertainties, firms must determine not only how much inventory to stock but also when to make that inventory available to customers. We ask: What is a firm’s optimal inventory quantity and timing for products sold during a stochastic selling season? <br><br>Academic and Practical Relevance. Managers are frequently confronted with challenging inventory timing decisions, especially when the products they manage exhibit high inventory holding costs and substantial uncertainty concerning the pattern of customer demand. Although the newsvendor literature has developed a thorough understanding of the firm’s optimal inventory quantity, it has failed to inform decision makers about choosing the optimal inventory timing. <br><br>Methodology. We develop a theoretical model of a firm that sells a product over a stochastic selling season, and we study how this firm should choose its inventory timing and inventory quantity so as to maximize expected profits. <br><br>Results. We derive the firm’s optimal inventory policy—which comprises inventory timing and inventory quantity—and discuss the interaction effects between these two decisions. We also identify the effects of optimal inventory timing on a firm’s ability to satisfy customer demand and show how early inventory timing can be detrimental to customer service. <br><br>Managerial Implications. Our core insights imply two immediate recommendations for managers. First, optimal inventory timing is an effective weapon for combatting both high inventory holding costs and high levels of uncertainty in the firm’s customer demand pattern. Second, naive decision rules (e.g., “earlier is better”) may reduce not only the firm’s profits but also its capacity to serve customer demand.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116181112","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":"Forecasting the Olympic Medal Distribution during a Pandemic: A Socio-Economic Machine Learning Model","authors":"C. Schlembach, Sascha L. Schmidt, Dominik Schreyer, Linus Wunderlich","doi":"10.2139/ssrn.3745595","DOIUrl":"https://doi.org/10.2139/ssrn.3745595","url":null,"abstract":"Forecasting the number of Olympic medals for each nation is highly relevant for different stakeholders: Ex ante, sports betting companies can determine the odds while sponsors and media companies can allocate their resources to promising teams. Ex post, sports politicians and managers can benchmark the performance of their teams and evaluate the drivers of success. To significantly increase the Olympic medal forecasting accuracy, we apply machine learning, more specifically a two-staged Random Forest, thus outperforming more traditional na\"ive forecast for three previous Olympics held between 2008 and 2016 for the first time. Regarding the Tokyo 2020 Games in 2021, our model suggests that the United States will lead the Olympic medal table, winning 120 medals, followed by China (87) and Great Britain (74). Intriguingly, we predict that the current COVID-19 pandemic will not significantly alter the medal count as all countries suffer from the pandemic to some extent (data inherent) and limited historical data points on comparable diseases (model inherent).","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122924677","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 Analytics to Detect Panic Buying and Improve Products Distribution Amid Pandemic","authors":"Y. Adulyasak, Omar Benomar, Ahmed Chaouachi, Maxime C. Cohen, Warut Khern-am-nuai","doi":"10.2139/ssrn.3742121","DOIUrl":"https://doi.org/10.2139/ssrn.3742121","url":null,"abstract":"The COVID-19 pandemic has triggered a panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Unfortunately, such a hoarding behavior disproportionately puts vulnerable groups of people at risk as they cannot \"compete\" with the demand surge, hence creating a critical societal issue. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this research is to develop a data-driven framework that can systematically alleviate this issue by leveraging statistical models and machine learning techniques. We leverage both internal and external data sources and show that using external data enhances the predictability and interpretability of our model. Our proposed framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential products distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help the retailer increase access to essential products by 56.74%.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130633441","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":"Keynes Rejected Kalecki’s Theory of Investment Because There Is No Major Difference Between Kalecki’s and Tinbergen’s Theories of Investment: Both Kalecki and Tinbergen Accepted Precise Theories of Probability Because They Were Frequentists","authors":"M. E. Brady","doi":"10.2139/ssrn.3730811","DOIUrl":"https://doi.org/10.2139/ssrn.3730811","url":null,"abstract":"It is quite impossible for Kalecki’s Theory of Effective Demand to have anything to do with Keynes’s Theory of Effective Demand because Kalecki, like Tinbergen, was a frequentist who accepted only precise, exact, additive numerical probability as the general case. For Keynes, probability was generally imprecise, inexact, non additive and non numerical (interval).<br><br>The belief that there is some kind of connection between Kalecki’s frequentist theory of investment and Keynes’s non frequentist theory is due to the false claims made by Joan Robinson, a mathematically and statistically illiterate economist, who did not realize that Kalecki’s theory of investment is in all major respects, just another version of Tinbergen’s theory based on frequentist, precise probability.<br><br>Lopez and Mott (1999) and Mott (2009) do not seem to have any knowledge of the fact that Kalecki’s theory of investment and Tinbergen’s theory of investment are, in all major respects, identical:<br><br>“Investment, and the level of employment in capitalism, are highly volatile. In part, investment volatility stemmed from psychological factors, such as 'animal spirits,’ expectations and conventions. But it was due also to an assumption that the 'decision period’ for capitalists (i.e. a period long enough for capitalists to take new decisions) was a very short one. Capitalists were viewed as taking decisions almost on a day-to-day basis.<br><br>Kalecki never denied that psychological factors do influence investment decisions or that investment might be volatile. In fact, in several works he actually made reference to a 'crisis of confidence’. But in his theory the weight is given entirely to 'objective’ factors. He insisted that capitalists did not react solely, or mainly, to their expectations, but rather to the 'hard fact’ of realized profits; and he assumed the investment function to be relatively stable in the sense that investment will not fall or rise due to events with a very short life.” (Lopez and Mott, 1999, pp.293-294; boldface and underline added).<br><br>The boldfaced and underlined sentences above are identical to the position that Tinbergen defended against Keynes in the Economic Journal in the period 1938-1940.<br><br>Lopez and Mott (1999),as well as Mott (2009), appear to be completely unaware that their second paragraph would be an excellent summary of Tinbergen’s critique of Keynes that he made in 1940. It should not be as surprising, then, that Tinbergen and Kalecki, who are both Frequentists and both advocates of precise probability, have theories of investment completely different from Keynes’s theory, which is built on imprecise probability, inexact measurement, and approximation, which allows for evidence to be incorporated in probability assessments in the form of propositions that allows a decision maker to consider evidence which is infrequent and nonfrequent, as well as frequent, evidence as opposed to Kalecki and Tinbergen, where the only evid","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"19 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116687588","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":"Non-Excludable Dynamic Mechanism Design","authors":"S. Balseiro, V. Mirrokni, R. Leme, Song Zuo","doi":"10.2139/ssrn.3716352","DOIUrl":"https://doi.org/10.2139/ssrn.3716352","url":null,"abstract":"Dynamic mechanism design expands the scope of allocations that can be implemented and the performance that can be attained compared to static mechanisms. Even under stringent participation constraints and restrictions on transfers, recent work demonstrated that it is possible for a designer to extract the surplus of all players as revenue when players have quasilinear utilities and the number of interactions is large. Much of the analysis has focused on excludable environments (i.e., any player can be excluded from trade without affecting the utilities of others). The mechanisms presented in the literature, however, do not extend to non-excludable environments. Two prototypical examples of such environments are: (i) public projects, where all players must have the same allocation; and (ii) non-disposable goods, where each item must be allocated to some player. We show a general mechanism that can asymptotically extract full surplus as revenue in such environments. Moreover, we provide a tight characterization for general environments, and identify necessary and sufficient conditions on the possibility of asymptotic full surplus extraction. Our characterization is based on the geometry of achievable utility sets -- convex sets that delineate the expected utilities that can be implemented by static mechanisms. Our results provide a reduction from dynamic to static mechanism design: the geometry of the achievable utility set of static mechanisms completely determines whether it is possible to fully extract surplus in the limit.","PeriodicalId":200007,"journal":{"name":"ERN: Statistical Decision Theory; Operations Research (Topic)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122122691","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}