Minimizing conditional value‐at‐risk under a modified basestock policy

Li, Bo; Arreola‐Risa, Antonio

doi:10.1111/poms.13649

Cited by 6 publications

(2 citation statements)

References 58 publications

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“…Many SaaS providers evolve from building private capacity, to incorporating cloud computing infrastructure to form a hybrid system, and to completely relying on public cloud vendors. Another trend is moving from treating capacity as a constraint to making it endogenous (e.g., Li & Arreola‐Risa, 2022). The studies in this subsection follow these trends and are summarized in Table EC.5 in the Supporting Information.…”

Section: Saas Operationsmentioning

confidence: 99%

Managing Software‐as‐a‐Service: Pricing and operations

Kumar

2022

Production and Operations Management

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Software‐as‐a‐service (SaaS) applications have experienced a decade of explosive growth, eliminating barriers in reaching users and enabling real‐time interchanges and intelligence. Using business analytics, SaaS applications are increasingly embedded in the day‐to‐day activities of businesses and consumers with competition and innovative pricing. Due to the evolution in cloud business models, new issues are surfacing to challenge practitioners and scholars. A number of issues encountered in the practice have not been properly addressed or even recognized. In this paper, we attempt to fill this important gap. We propose a framework of recent business research on SaaS in light of wide adoption of the SaaS business model. This framework broadly classifies SaaS research into two basic themes. For each theme, we review past work that has been instrumental in setting the direction of this line of research and discuss how emerging research opportunities can be addressed. For each research opportunity, we also propose an initial model and the applicable methodology. Further, in order to aid researchers, we identify the data sources wherever applicable, and even present some of the initial results. We conclude by describing promising directions on a roadmap for future research and explain why an integrative perspective of operations, marketing, and information systems is critical to SaaS. In this paper, we bridge the gap between research and practice by identifying the relevant industry problems that would help researchers who are interested in working in this area both to get a starting point and to address important theoretical and practical challenges.

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Section: Saas Operationsmentioning

confidence: 99%

Managing Software‐as‐a‐Service: Pricing and operations

Kumar

2022

Production and Operations Management

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“…Conditional value at risk (CVaR), also called average VaR (value at risk) or expected shortfall, is a widely used risk metric, built upon the VaR and variance related risk metrics (Rockafellar & Uryasev, 2000). Recently, it has also been used in other engineering fields, such as energy systems (Asensio & Contreras, 2016; Li et al., 2018), manufacturing and supply chains (Dixit et al., 2020; Li & ArreolaRisa, 2022; Xie et al., 2018), and so on. Suppose X is a continuous random variable representing a stochastic loss and its distribution function is denoted as

F_X(x)

.…”

Section: Introductionmentioning

confidence: 99%

Risk‐sensitive Markov decision processes with long‐run CVaR criterion

Xia,

Zhang,

Glynn

2023

Production and Operations Management

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CVaR (Conditional value at risk) is a risk metric widely used in finance. However, dynamically optimizing CVaR is difficult, because it is not a standard Markov decision process (MDP) and the principle of dynamic programming fails. In this paper, we study the infinite‐horizon discrete‐time MDP with a long‐run CVaR criterion, from the view of sensitivity‐based optimization. By introducing a pseudo‐CVaR metric, we reformulate the problem as a bilevel MDP model and derive a CVaR difference formula that quantifies the difference of long‐run CVaR under any two policies. The optimality of deterministic policies is derived. We obtain a so‐called Bellman local optimality equation for CVaR, which is a necessary and sufficient condition for locally optimal policies and only necessary for globally optimal policies. A CVaR derivative formula is also derived for providing more sensitivity information. Then we develop a policy iteration type algorithm to efficiently optimize CVaR, which is shown to converge to a local optimum in mixed policy space. Furthermore, based on the sensitivity analysis of our bilevel MDP formulation and critical points, we develop a globally optimal algorithm. The piecewise linearity and segment convexity of the optimal pseudo‐CVaR function are also established. Our main results and algorithms are further extended to optimize the mean and CVaR simultaneously. Finally, we conduct numerical experiments relating to portfolio management to demonstrate the main results. Our work sheds light on dynamically optimizing CVaR from a sensitivity viewpoint.

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Managing customer waiting times in an inventory system using Conditional Value-at-Risk measure

Ahmadi,

Hesaraki,

Mahmoodi

et al. 2024

Ann Oper Res

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In today’s fast-paced world, delays or prolonged customer waiting times pose a threat to the firm’s profitability. This study utilizes the mean-CVaR metric to incorporate the risk associated with prolonged customer waiting times into the optimal trade-off decisions. For this purpose, we consider a single inventory system that faces Poisson demand and utilizes a base-stock policy to replenish its inventory, which takes a fixed amount of time. The firm implements a preorder strategy, encouraging customers to place their orders a fixed amount of time in advance of their actual needs, a period referred to as the commitment lead time. The firm rewards customers with a bonus termed the commitment cost, which increases with the length of the commitment lead time. We aim to determine the optimal control policy, including the optimal base-stock level and optimal commitment lead time, that minimizes the long-run average cost. The cost includes inventory holding, commitment, and customer waiting costs, with the latter adjusted for the firm’s degree of risk aversion. The optimal policy depends on the interdependence of the decisions, with the optimal commitment lead time following a “bang-bang” pattern, and the corresponding optimal base-stock level taking an “all-or-nothing” form. For linear commitment costs with a cost factor per time unit, we identify a threshold that increases with the firm’s risk aversion degree. Firms with greater risk aversion typically favor the buy-to-order strategy, while those with lower risk aversion may opt for either buy-to-stock or buy-to-order depending on their assessment of waiting costs.

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Minimizing conditional value‐at‐risk under a modified basestock policy

Cited by 6 publications

References 58 publications

Managing Software‐as‐a‐Service: Pricing and operations

Managing Software‐as‐a‐Service: Pricing and operations

Risk‐sensitive Markov decision processes with long‐run CVaR criterion

Managing customer waiting times in an inventory system using Conditional Value-at-Risk measure

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