Inventory management and pricing decisions based on quantitative models both in industrial practice and academic works often rely on minimizing expected cost, which refers to the concept of risk-neutrality of the decision maker. Although many useful insights in operational problems can be obtained by such an approach, it is well understood that incorporating attitudes toward risk is an important lever for building new theories in other fields such as economics and finance. In this work spectral risk measures are applied to the price-setting newsvendor problem and optimal policies are derived. This allows to unify results obtained so far in the literature under the common concept of spectral risk measures for the case of zero and non-zero shortage penalty cost.
Inventory management and pricing decisions based on quantitative models both in industrial practice and academic works often rely on minimizing expected cost, which refers to the concept of risk-neutrality of the decision maker. Although many useful insights in operational problems can be obtained by such an approach, it is well understood that incorporating attitudes toward risk is an important lever for building new theories in other fields such as economics and finance. In this work spectral risk measures are applied to the price-setting newsvendor problem and optimal policies are derived. This allows to unify results obtained so far in the literature under the common concept of spectral risk measures for the case of zero and non-zero shortage penalty cost.
As a fundamental problem in stochastic inventory control, the newsvendor problem has been studied since the 18th century in the economic literature, and has been widely used to analyze supply chains in fashion and seasonal product industries. Since the 1950s, the newsvendor problem has been extensively studied in operations research and extended to model a variety of real-life problems. The simplest and most elementary version of the newsvendor problem is an optimal stocking problem in which a newsvendor needs to decide how many newspapers to order for future demand, where the future demand is uncertain and follows a stationary distribution. Research in this area has greatly increased over the last few years, and now the Handbook of Newsvendor Problems: Models, Extensions and Applications captures the state of the art. The handbook consists of two sections -- Models and Extensions, and Applications. Each section includes many interesting works in the respective domain. Section I presents papers on topics like the multi-product newsvendor problems; the newsvendor problem with law invariant coherent measures of risk; a Copula approach to inventory pooling problems with newsvendor products; repeated newsvendor games with transshipments; cooperative newsvendor games; an economic interpretation for the price-setting newsvendor problem; newsvendor models with alternative risk preferences within expected utility theory and prospect theory frameworks; and newsvendor problems with VaR and CVaR consideration. Section II presents papers on such topics as a two-period newsvendor problem for closed-loop supply chain analysis; the remanufacturing newsvendor problem; inventory centralization in a newsvendor setting when shortage costs differ; production planning on an unreliable machine for multiple items; analysis of the newsvendor problem under carbon emissions policies; optimal decisions of the manufacturer and distributor in a fresh product supply chain involving long distance transportation; a newsvendor perspective on profit target setting for multiple divisions; and a portfolio approach to multi-product newsvendor problem with budget constraint. This well-balanced handbook presents a wealth of theoretical results from different perspectives. With contributions from many of the leading researchers in the field, the Handbook of Newsvendor Problems: Models, Extensions and Applications is a timely addition to the literature and consolidates all the new and exciting works related to the newsvendor problem into one high quality source.
Volume 20 of Applications of Management Science focuses on the application of management science methodologies, data envelopment analysis and multi-criteria decision making.
The risk-based approach to capital markets regulation is in crisis. Climate change, shifting demographics, geopolitical conflicts and other environmental discontinuities threaten established business models and shorten the life spans of listed companies. The current rules for periodic disclosure in the EU fail to inform market participants adequately. Unlike risks, uncertainties are unquantifiable or may only be quantified at great cost, causing them to be insufficiently reflected in periodic reports. This is unfortunate, given the pivotal role capital markets must play in the economy’s adaptation to environmental discontinuities. It is only with a reformed framework for periodic disclosure, that gradual and orderly adaptation to these discontinuities appears feasible. To ensure orderly market adaptation, a new reporting format is required: scenario analysis should be integrated into the European framework for periodic disclosure.
The thesis focuses on the analysis of various extensions of the classical multi-period single-item stochastic inventory problem. Specifically, we investigate two particular approaches of modeling risk in the context of inventory management: risk-averse models and robust formulations. We analyze the classical newsvendor problem utilizing a coherent risk measure as the objective function. Properties of coherent risk measures allow us to offer a unifying treatment of risk averse and min-max type formulations. We show that the structure of the optimal policy of the risk-averse model is similar to that of the classical expected value problem for both single and multi-period cases. The result carries over even when there is a fixed ordering cost. We expand our analysis to robust formulations of multi-period inventory problems. We consider both independent and dependent uncertainty sets and prove the optimality of base-stock policies for the general problem formulation. We focus on budget of uncertainty approach and develop a heuristic that can also be employed for a class of parametric dependency structures. We compare our proposed heuristic against alternative solution techniques.
The classic newspaper boy or single period inventory problem is reviewed, and its origins and development during the past 30 years are traced. The review reveals several variations for the classic problem, both in cost structure and in the decision principles involved. The critical role of costs and demand functions to solutions is examined. A large variety of potential applications for this model is summarized from the literature. Also, optimal solutions are derived for the constant surplus cost case and the exponential cost case. (Author).
The classic newspaper boy or single period inventory problem is reviewed, and its origins and development during the past 30 years are traced. The review reveals several variations for the classic problem, both in cost structure and in the decision principles involved. The critical role of costs and demand functions to solutions is examined. A large variety of potential applications for this model is summarized from the literature. Also, optimal solutions are derived for the constant surplus cost case and the exponential cost case. (Author).
Inspired by the Encyclopedia of Statistical Sciences, Second Edition, this volume presents the tools and techniques that are essential for carrying out best practices in the modern business world The collection and analysis of quantitative data drives some of the most important conclusions that are drawn in today's business world, such as the preferences of a customer base, the quality of manufactured products, the marketing of products, and the availability of financial resources. As a result, it is essential for individuals working in this environment to have the knowledge and skills to interpret and use statistical techniques in various scenarios. Addressing this need, Methods and Applications of Statistics in Business, Finance, and Management Science serves as a single, one-of-a-kind resource that guides readers through the use of common statistical practices by presenting real-world applications from the fields of business, economics, finance, operations research, and management science. Uniting established literature with the latest research, this volume features classic articles from the acclaimed Encyclopedia of Statistical Sciences, Second Edition along with brand-new contributions written by today's leading academics and practitioners. The result is a compilation that explores classic methodology and new topics, including: Analytical methods for risk management Statistical modeling for online auctions Ranking and selection in mutual funds Uses of Black-Scholes formula in finance Data mining in prediction markets From auditing and marketing to stock market price indices and banking, the presented literature sheds light on the use of quantitative methods in research relating to common financial applications. In addition, the book supplies insight on common uses of statistical techniques such as Bayesian methods, optimization, simulation, forecasting, mathematical modeling, financial time series, and data mining in modern research. Providing a blend of traditional methodology and the latest research, Methods and Applications of Statistics in Business, Finance, and Management Science is an excellent reference for researchers, managers, consultants, and students in the fields of business, management science, operations research, supply chain management, mathematical finance, and economics who must understand statistical literature and carry out quantitative practices to make smart business decisions in their everyday work.
From the Preface... The preparation of this book started in 2004, when George B. Dantzig and I, following a long-standing invitation by Fred Hillier to contribute a volume to his International Series in Operations Research and Management Science, decided finally to go ahead with editing a volume on stochastic programming. The field of stochastic programming (also referred to as optimization under uncertainty or planning under uncertainty) had advanced significantly in the last two decades, both theoretically and in practice. George Dantzig and I felt that it would be valuable to showcase some of these advances and to present what one might call the state-of- the-art of the field to a broader audience. We invited researchers whom we considered to be leading experts in various specialties of the field, including a few representatives of promising developments in the making, to write a chapter for the volume. Unfortunately, to the great loss of all of us, George Dantzig passed away on May 13, 2005. Encouraged by many colleagues, I decided to continue with the book and edit it as a volume dedicated to George Dantzig. Management Science published in 2005 a special volume featuring the “Ten most Influential Papers of the first 50 Years of Management Science.” George Dantzig’s original 1955 stochastic programming paper, “Linear Programming under Uncertainty,” was featured among these ten. Hearing about this, George Dantzig suggested that his 1955 paper be the first chapter of this book. The vision expressed in that paper gives an important scientific and historical perspective to the book. Gerd Infanger
