The stochastic lot-sizing problem with quantity discounts

Jiao, Wen; Zhang, Juliang; Yan, Hong

doi:10.1016/j.cor.2016.11.014

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…This policy is particularly effective in dealing with unexpected demand realisations. Recent works involving this policy are Jiao, Zhang, and Yan (2017); Xiang et al (2018); Azoury and Miyaoka (2020). The static-dynamic uncertainty ((R, S) policy) fixes the replenishment moments at the beginning of the planning horizon and decides the size when placing the order.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic Dynamic Programming Heuristic for the (R, s, S) Policy Parameters Computation

Visentin¹,

Prestwich²,

Rossi³

et al. 2022

Preprint

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The (R, s, S) is a stochastic inventory control policy widely used by practitioners. In an inventory system managed according to this policy, the inventory is reviewed at instant R; if the observed inventory position is lower than the reorder level s an order is placed. The order's quantity is set to raise the inventory position to the order-upto-level S. This paper introduces a new stochastic dynamic program (SDP) based heuristic to compute the (R, s, S) policy parameters for the non-stationary stochastic lot-sizing problem with backlogging of the excessive demand, fixed order and review costs, and linear holding and penalty costs. In a recent work, Visentin et al. ( 2021) present an approach to compute optimal policy parameters under these assumptions. Our model combines a greedy relaxation of the problem with a modified version of Scarf's (s, S) SDP. A simple implementation of the model requires a prohibitive computational effort to compute the parameters. However, we can speed up the computations by using K-convexity property and memorisation techniques. The resulting algorithm is considerably faster than the state-of-the-art, extending its adoptability by practitioners. An extensive computational study compares our approach with the algorithms available in the literature.

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Section: Literature Reviewmentioning

confidence: 99%

Stochastic Dynamic Programming Heuristic for the (R, s, S) Policy Parameters Computation

Visentin¹,

Prestwich²,

Rossi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Purchasing process basically involves with inventory management in determining when and how many materials to purchase. Efficiency purchasing process normally order with the amount that minimize or balance between costs of inventory and purchasing (Jiao, et al 2017;Pereira and Costa 2017;Rezaeisaray, et al 2016). To determine the purchasing process, the problems related to purchasing must be clarified.…”

Section: Purchasing -Purchase Ordermentioning

confidence: 99%

“…One is to purchase under demand uncertainty or order with other constraints and the other is to purchase under demand certainty with no constraints. To solve the former kind of purchasing problems, the researchers apply mathematical modelling, deterministic or stochastic methodologies (Jiao, et al 2017;Somboonwiwat, et al 2018;Yu and Tsai 2008), while the latter kind of problem must be further distinguished. A company normally purchase many items from a supplier.…”

Section: Purchasing -Purchase Ordermentioning

confidence: 99%

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ECMS 2019 Proceedings edited by Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco

2019

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The 33rd edition of the ECMS conference, a widely consolidated interdisciplinary forum for simulation and modeling researchers and practitioners that keeps its reference position year after year, brings with it two novelties: a new name and a new form of publication. In fact, to better catch in the name the actual situation of participants, the European Council for Modelling and Simulation decided to rename the event as "ECMS International Conference on Modelling and Simulation". This year, we also start a new book series of annual volumes, namely "Communications of the ECMS", which replaces the name "Proceedings" in order to acknowledge the recognition and reputation of the scientific community of simulation. This year the chosen conference venue is in Caserta, Italy. The Università degli Studi della Campania "Luigi Vanvitelli", previously known as Seconda Università degli Studi di Napoli, a state-owned university founded in 1990 with the explicit mission of meeting the cultural demand of the population living in the northern part of Campania. Caserta, famous for its wonderful royal palace, whose facilities will host the conference, is a city with a long history, already inhabited in the 5th century BC. It flourished in the middle age and then in the XVI-XVII century AD, surrounded by a geographical area rich of historical relevance that was under Roman influence.The university is characterized by its diffusion over the territory of the province of Caserta, having its 16 departments located in Caserta, Aversa and Santa Maria Capua Vetere, and Napoli, in which its roots can be found: the most of the departments are located in historical landmarks, that have been saved and recovered by specific agreements and funding. More than 25000 students from Italy and abroad study at Vanvitelli University enrolled in bachelor, master and PhD. curricula, taught as well in Italian as in English, and covering different aspects of mathematics, physics, data analytics, engineering, law, economics, political sciences, medicine, nursery, biotechnology, pharmacy, environmental sciences, cultural heritage, literature, psychology, tourism, design and architecture.We are honored to offer, besides the scientific program of the conference, consisting in more than 65 high quality accepted papers, a rich and various series of social events, as usual for the spirit of this conference.

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“…[5,6,35,22,2]) extended quantity discounts to deteriorating and perishable products, and then construct several efficient inventory models. Other extensions of the EOQ/EPQ models with quantity discounts could be found in Lin et al [11], Mendoza and Ventura [33], Mansini et al [47], Lee et al [32], Meena and Sarmah [20], and Venegas and Ventura [51]. All of these researchers recognize that quantity discounts provided by the supplier may significantly influence the buyer s decision making.…”

mentioning

confidence: 99%

An optimal setup cost reduction and lot size for economic production quantity model with imperfect quality and quantity discounts

Lin¹,

Sarker²,

Lin³

2021

Journal of Industrial &Amp; Management Optimization

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The purpose of this paper concentrates on an economic production quantity model with the factors of imperfect quality and quantity discounts, in which the inspection action occurs during the production stage. There is specific consideration of there being a finite production rate, and the quantity discounts offered by the supplier serves the purpose of stimulating buying greater quantities. This is in contrast to EPQ models that do not take these added factors into consideration. The objective of this paper is to determine the setup cost reduction, which is a function of capital investment, and inventory lot size. An alternative solution procedure was developed that does not employ the Hessian Matrix concavity in the expected total profit. We develop an algorithm to determine the optimal solution for this model. Theoretical results are discussed and a numerical example is proposed. Managerial insights are also examined.

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The stochastic lot-sizing problem with quantity discounts

Cited by 12 publications

References 32 publications

Stochastic Dynamic Programming Heuristic for the (R, s, S) Policy Parameters Computation

Stochastic Dynamic Programming Heuristic for the (R, s, S) Policy Parameters Computation

ECMS 2019 Proceedings edited by Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco

An optimal setup cost reduction and lot size for economic production quantity model with imperfect quality and quantity discounts

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