Pricing and Inventory Management

Chen, Xin; Simchi‐Levi, David

doi:10.1093/oxfordhb/9780199543175.013.0030

Cited by 109 publications

(51 citation statements)

References 122 publications

(139 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their model has been extended to various settings; for comprehensive surveys, see Elmaghraby and Keskinocak (2003), Chan et al (2004), Yano and Gilbert (2005), Chen and Simchi-Levi (2012), and their references.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These reasons explain why there are hardly any papers that consider joint pricing and production for multiple items, given the complexity that arises when considering just a single product under uncertainty (Chen and Simchi-Levi 2012). Of course, few firms manufacture only a single product of uniform style and size; nearly all firms produce multiple versions of their products.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Pricing and Production: A Fusion of Machine Learning and Robust Optimization

2018

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Pricing and Production: A Fusion of Machine Learning and Robust Optimization

2018

View full text Add to dashboard Cite

“…In a recent model by Lu et al (), the firm adopts a dual‐pricing strategy with which quantity discounts can be utilized to increase sales. Interested readers may refer to the book chapters by Chan, Max Shen, Simchi‐Levi, and Swann (), Yano and Gilbert (), and Chen and Simchi‐Levi (), who provide excellent reviews on the pricing and inventory management literature. As we have remarked in Section 1, inventory replenishment and pricing decisions in the above literature are only made at the beginning of each period, while in our model, the replenishment effort and selling price are made in a real‐time manner.…”

Section: Relevant Literaturementioning

confidence: 99%

Dynamic pricing and replenishment: Optimality, bounds, and asymptotics

Xiao

2018

Naval Research Logistics

View full text Add to dashboard Cite

In many applications, managers face the problem of replenishing and selling products during a finite time horizon. We investigate the problem of making dynamic and joint decisions on product replenishment and selling in order to improve profit. We consider a backlog scenario in which penalty cost (resulting from fulfillment delay) and accommodation cost (resulting from shortage at the end of the selling horizon) are incurred. Based on continuous‐time and discrete‐state dynamic programming, we study the optimal joint decisions and characterize their structural properties. We establish an upper bound for the optimal expected profit and develop a fluid policy by resorting to the deterministic version of the problem (ie, the fluid problem). The fluid policy is shown to be asymptotically optimal for the original stochastic problem when the problem size is sufficiently large. The static nature of the fluid policy and its lack of flexibility in matching supply with demand motivate us to develop a “target‐inventory” heuristic, which is shown, numerically, to be a significant improvement over the fluid policy. Scenarios with discrete feasible sets and lost‐sales are also discussed in this article.

show abstract

“…Indeed, both academics and practitioners have recognized that substantial benefits can be obtained from coordinating operational and pricing decisions. As a result, the research literature on joint pricing and inventory decisions has rapidly grown in recent years, see, e.g., the survey papers by Petruzzi and Dada (1999), Elmaghraby and Keskinocak (2003), Yano and Gilbert (2003), and Chen and Simchi- Levi (2012).…”

Section: Introductionmentioning

confidence: 99%

Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning

2015

View full text Add to dashboard Cite

We consider a firm (e.g., retailer) selling a single nonperishable product over a finite-period planning horizon. Demand in each period is stochastic and price-dependent, and unsatisfied demands are backlogged. At the beginning of each period, the firm determines its selling price and inventory replenishment quantity, but it knows neither the form of demand dependency on selling price nor the distribution of demand uncertainty a priori, hence it has to make pricing and ordering decisions based on historical demand data. We propose a nonparametric data-driven policy that learns about the demand on the fly and, concurrently, applies learned information to determine replenishment and pricing decisions. The policy integrates learning and action in a sense that the firm actively experiments on pricing and inventory levels to collect demand information with the least possible profit loss. Besides convergence of optimal policies, we show that the regret, defined as the average profit loss compared with that of the optimal solution when the firm has complete information about the underlying demand, vanishes at the fastest possible rate as the planning horizon increases.

show abstract

Pricing and Inventory Management

Cited by 109 publications

References 122 publications

Joint Pricing and Production: A Fusion of Machine Learning and Robust Optimization

Joint Pricing and Production: A Fusion of Machine Learning and Robust Optimization

Dynamic pricing and replenishment: Optimality, bounds, and asymptotics

Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning

Contact Info

Product

Resources

About