Optimal ordering policy for inventory mechanism with a stochastic short-term price discount

Wang, Yiju; Xing, Wei; Gao, Hengxia

doi:10.3934/jimo.2018199

Cited by 5 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Paraschos et al (2020) and Kara and Dogan (2018) propose an inventory management system that allows to optimally evaluate the tradeoff between cost (associated with equipment failures) and benefit. Wang et al (2020) develop an order generation system based on price discount strategies. Giannoccaro and Pontrandolfo (2002) develop an inventory management system that allows making decisions in relation to supply, production, and distribution.…”

Section: Comparison With Other Workmentioning

confidence: 99%

“…Giannoccaro and Pontrandolfo (2002) develop an inventory management system that allows making decisions in relation to supply, production, and distribution. Wang et al (2020) develop an optimal replenishment and stocking strat- On the other hand, there are still important works that present the inventory management problem as an optimization problem, seeking to minimize different aspects, such as storage costs, among others (Abdelhalim et al, 2021;Thürer et al, 2022). Other works have mixed them with machine learning techniques to, for example, predict the behavior of certain variables (Ran, 2021;Aguilar et al, 2022).…”

Section: Comparison With Other Workmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid algorithm based on reinforcement learning for smart inventory management

Cuartas

Aguilar

2022

J Intell Manuf

View full text Add to dashboard Cite

This article proposes a hybrid algorithm based on reinforcement learning and the inventory management methodology called DDMRP (Demand Driven Material Requirement Planning) to determine the optimal time to buy a certain product, and how much quantity should be requested. For this, the inventory management problem is formulated as a Markov Decision Process where the environment with which the system interacts is designed from the concepts raised in the DDMRP methodology, and through the reinforcement learning algorithm—specifically, Q-Learning. The optimal policy is determined for making decisions about when and how much to buy. To determine the optimal policy, three approaches are proposed for the reward function: the first one is based on inventory levels; the second is an optimization function based on the distance of the inventory to its optimal level, and the third is a shaping function based on levels and distances to the optimal inventory. The results show that the proposed algorithm has promising results in scenarios with different characteristics, performing adequately in difficult case studies, with a diversity of situations such as scenarios with discontinuous or continuous demand, seasonal and non-seasonal behavior, and with high demand peaks, among others.

show abstract

Section: Comparison With Other Workmentioning

confidence: 99%

Section: Comparison With Other Workmentioning

confidence: 99%

Hybrid algorithm based on reinforcement learning for smart inventory management

Cuartas

Aguilar

2022

J Intell Manuf

View full text Add to dashboard Cite

show abstract

“…Adilov [10] considered the portfolio procurement from the perspective of the supplier. Inderfurth et al [11] considered the multiperiod portfolio procurement with the short-term procurement of the spot market and the capacity reservation of the long-time contract, and so on [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Optimal Ordering Policy for Supply Option Contract with Spot Market

Hou

Chen

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper considers the procurement mechanism with two supply channels, namely, an option contract purchase and a spot market. For the mechanism, under the stochastic demand and the stochastic spot price, we consider the portfolio procurement with the spot trading liquidity and the option speculation respectively. To maximize the buyer’s profit, we establish two optimal portfolio procurement strategy models for those two scenarios. Based on the buyer’s cost-benefit analysis, we present a solution method to each model and provide an optimal ordering policy to the buyer. By the obtained results, we analyze the role of the spot trading liquidity and option speculation in a buyer’s expected profit. Some numerical experiments are presented to show the validity of the formulated models.

show abstract

“…Specifically, according to the price-demand relationship, the literature on the optimal lot-sizing problem under TPR can be classified into two streams. One stream of literature assumes a constant demand rate and determines the optimal special order quantity; see [62,47,56,13].…”

mentioning

confidence: 99%

Optimal pricing and ordering policy for defective items under temporary price reduction with inspection errors and price sensitive demand

Zhu¹

2022

JIMO

View full text Add to dashboard Cite

This paper studies the retailer's optimal promotional pricing, special order quantity and screening rate for defective items when a temporary price reduction (i.e., TPR) is offered. Although previous studies have examined the similar issue, they assume a constant demand and an error-free screening process. A subversion of these two assumptions differentiates our paper. First, using a price-sensitive demand, we analyze that the original screening rate may be insufficient, and propose the CPD (i.e., control the promotional demand) and the ISR (i.e., increase the screening rate through investment) strategy to handle it. Second, we incorporate both Type I and Type II inspection errors into our model. Then we establish an inventory model aiming to maximize the retailer's profit under CPD and ISR, respectively. Finally, numerical examples are conducted and several results are obtained: (1) a higher portion of defects makes ISR more profitable; (2) both a higher probability of a Type I error and a Type II error decrease the profit under CPD and ISR, but a Type I error has a more pronounced negative impact; and (3) comparing with the existing studies with a constant demand, our model generates a higher profit especially in markets with a higher price sensitivity.

show abstract

Optimal ordering policy for inventory mechanism with a stochastic short-term price discount

Cited by 5 publications

References 27 publications

Hybrid algorithm based on reinforcement learning for smart inventory management

Hybrid algorithm based on reinforcement learning for smart inventory management

Optimal Ordering Policy for Supply Option Contract with Spot Market

Optimal pricing and ordering policy for defective items under temporary price reduction with inspection errors and price sensitive demand

Contact Info

Product

Resources

About