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Author: Qi Feng
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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Book Description
Inventory-based dynamic pricing has become a common operations strategy in practice and has received considerable attention from the research community. From an implementation perspective, it is desirable to design a simple policy like a base stock list price (BSLP) policy. The existing research on this problem often imposes restrictive conditions to ensure the optimality of a BSLP, which limits its applicability in practice. In this paper, we analyze the dynamic inventory and pricing control problem in which the demand follows a generalized additive model (GAM). The GAM overcomes the limitations of several demand models commonly used in the literature, but introduces analytical challenges in analyzing the dynamic program. Via a variable transformation approach, we identify a new set of technical conditions under which a BSLP policy is optimal. These conditions are easy to verify because they depend only on the location and scale parameters of demand as functions of price and are independent of the cost parameters or the distribution of the random demand component. Moreover, while a BSLP policy is optimal under these conditions, the optimal price may not be monotone decreasing in the inventory level. We further demonstrate our results by applying a constrained maximum likelihood estimation procedure to simultaneously estimate the demand function and verify the optimality of a BSLP policy on a retail dataset.
Author: Qi Feng
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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Book Description
Inventory-based dynamic pricing has become a common operations strategy in practice and has received considerable attention from the research community. From an implementation perspective, it is desirable to design a simple policy like a base stock list price (BSLP) policy. The existing research on this problem often imposes restrictive conditions to ensure the optimality of a BSLP, which limits its applicability in practice. In this paper, we analyze the dynamic inventory and pricing control problem in which the demand follows a generalized additive model (GAM). The GAM overcomes the limitations of several demand models commonly used in the literature, but introduces analytical challenges in analyzing the dynamic program. Via a variable transformation approach, we identify a new set of technical conditions under which a BSLP policy is optimal. These conditions are easy to verify because they depend only on the location and scale parameters of demand as functions of price and are independent of the cost parameters or the distribution of the random demand component. Moreover, while a BSLP policy is optimal under these conditions, the optimal price may not be monotone decreasing in the inventory level. We further demonstrate our results by applying a constrained maximum likelihood estimation procedure to simultaneously estimate the demand function and verify the optimality of a BSLP policy on a retail dataset.
Author: Elodie Adida
Publisher: VDM Publishing
ISBN: 9783836421430
Category : Business & Economics
Languages : en
Pages : 288
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Book Description
(cont.) We introduce and study a solution method that enables to compute the optimal solution on a finite time horizon in a monopoly setting. Our results illustrate the role of capacity and the effects of the dynamic nature of demand. We then introduce an additive model of demand uncertainty. We use a robust optimization approach to protect the solution against data uncertainty in a tractable manner, and without imposing stringent assumptions on available information. We show that the robust formulation is of the same order of complexity as the deterministic problem and demonstrate how to adapt solution method. Finally, we consider a duopoly setting and use a more general model of additive and multiplicative demand uncertainty. We formulate the robust problem as a coupled constraint differential game. Using a quasi-variational inequality reformulation, we prove the existence of Nash equilibria in continuous time and study issues of uniqueness. Finally, we introduce a relaxation-type algorithm and prove its convergence to a particular Nash equilibrium (normalized Nash equilibrium) in discrete time.
Author: Jing-Sheng J. Song
Publisher: Edward Elgar Publishing
ISBN: 180037710X
Category : Technology & Engineering
Languages : en
Pages : 565
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Book Description
This comprehensive Handbook provides an overview of state-of-the-art research on quantitative models for inventory management. Despite over half a century’s progress, inventory management remains a challenge, as evidenced by the recent Covid-19 pandemic. With an expanse of world-renowned inventory scholars from major international research universities, this Handbook explores key areas including mathematical modelling, the interplay of inventory decisions and other business decisions and the unique challenges posed to multiple industries.
Author: Sreekumar R. Bhaskaran
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
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Book Description
We consider a dynamic inventory (production) model with general order (production) costs and excess demand that can be backordered or refused by the firm. A unit backordered incurs a backorder cost, a unit refused incurs a lost sales charge. Endogenizing the sales decision is necessary in the presence of general convex order costs so that the firm is not forced to backorder a unit whose subsequent procurement would reduce total profits. In each period, the firm must determine the optimal order/production and sales strategy. We show that the optimal policy is characterized by an optimal buy up to level that increases with the initial inventory level and an order quantity that decreases with the initial inventory level. More importantly, we show the optimal sales strategy is characterized by a critical threshold, a backorder limit, that dictates when to stop selling. This threshold is independent of the initial inventory level and the amount purchased. We investigate various properties of this new policy. As demand stochastically increases, the amount purchased increases but the amount backordered decreases, reflecting a shift in the way excess demand is managed. We develop two regularity conditions, one that ensures some backorders are allowed in each period, and another that ensures the amount backordered is nondecreasing in the length of the planning horizon. We bound the buy up to levels in our model using buy up to levels from the pure lost sales and pure backlogging models. We illustrate our findings and results using several numerical examples.
Author: Francisco Saldanha-da-Gama
Publisher: Springer Nature
ISBN: 3031559274
Category :
Languages : en
Pages : 535
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Book Description
Author: Rainer Kleber
Publisher: Springer Science & Business Media
ISBN: 3540332308
Category : Business & Economics
Languages : en
Pages : 191
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Book Description
The integration of product recovery into regular production processes enables new opportunities for cost savings. In case of a dynamic planning situation, for instance when dealing with seasonality or the product life cycle, new motives for keeping stock arise. The work aims to identify those motives and to describe their effects by using methods of optimal control theory.
Author: Aravind Govindarajan
Publisher:
ISBN:
Category :
Languages : en
Pages : 20
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Book Description
We consider the problem of joint inventory and pricing control in a finite-horizon setting with Poisson demand, positive lead time, and lost sales. We study the performance of a lead time independent heuristic control, which we call Dynamic Batch Pricing (DBP), and show that it is asymptotically optimal when the annual expected demand is large. Moreover, with a proper tuning of the control parameters, we also show that DBP has a robust performance with respect to the magnitude of lost sales cost, which is important in applications such as retails where the targeted service level is very high (equivalently, the lost sales cost is very large). Our result for the lost sales system complements the recent result of Chen et al. (2019), who show that a lead time independent heuristic can be near optimal in the backorder system with a large lead time. Together, both our result and Chen et al. (2019) reinforce the message that simple lead time independent heuristics can perform sufficiently well, at least in some practically relevant cases if not in all cases. This insight is important given that the task of solving the joint inventory and pricing problem (under either backorder or lost sales system) with positive lead time for the most general model remains very challenging.
Author: Steffen Christ
Publisher: Springer Science & Business Media
ISBN: 3834961841
Category : Business & Economics
Languages : en
Pages : 363
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Book Description
Steffen Christ shows how theoretic optimization models can be operationalized by employing self-learning strategies to construct relevant input variables, such as latent demand and customer price sensitivity.
Author: Guillermo Gallego
Publisher: Springer
ISBN: 1493996061
Category : Business & Economics
Languages : en
Pages : 336
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Book Description
“There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available.” Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft. “The book by Gallego and Topaloglu provides a fresh, up-to-date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in-depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it." Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. “This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field. It covers traditional revenue management as well as assortment optimization and dynamic pricing. The comprehensive treatment of choice models in each application is particularly welcome. It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter. This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field.” Robert L. Phillips, Director of Pricing Research at Amazon “At last, a serious and comprehensive treatment of modern revenue management and assortment optimization integrated with choice modeling. In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems. For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference.” Richard Ratliff, Research Scientist at Sabre “This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics. New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers.” Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK.
Author: Hawking Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this article, we investigate a joint pricing and inventory problem for a retailer selling fresh-agri products (FAPs) with two-period shelf lifetime in a dynamic stochastic setting, where new and old FAPs are on sale simultaneously. At the beginning of each period, the retailer makes ordering decision for new FAP and sets regular and discount prices for new and old inventories, respectively. After demand realisation, the expired leftover is disposed and unexpired inventory is carried to the next period, for continuing selling. Unmet demand of all FAPs is backordered. The objective is to maximise the total expected discount profit over the whole planning horizon. We present a price dependent, stochastic dynamic programming model taking into account zero lead-time, linear ordering costs, inventory holding and backlogging costs, as well as disposal cost. As the influence of the perishability, each customer selects his preferred choice based on the utility of product price and quality. By the way of constructing demand rate vector, the original formulation can be transferred to be jointly concave and tractable. Finally, we characterise the optimal policy and develop effective methods to solve the problem. We also conduct numerical studies to further characterise the optimal policy, and to evaluate the loss of efficiency under static policies when compared to the optimal dynamic policy.
