Two-product inventory management with fixed costs and supply uncertainty

Firouzi, Fatemeh; Baglieri, Enzo; Jaber, Mohamad Y.

doi:10.1016/j.apm.2014.03.018

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…They used the concept to conclude the optimality of the (𝜎, S) policy, which, as in Johnson (1967), gave the optimum sequence of actions if the system begins with an initial x ≤ S. Recall that for the average cost problem, if the system starts with any x, it is optimal not to order until x ≤ S and then follow the (𝜎, S) policy. Firouzi et al (2014) determined the optimal ordering policy for a two-product, periodic-review, finite-horizon inventory problem with random demands and productspecific ordering costs K 1 and K 2 . The probability of supply availability is unknown and is updated using Bayesian learning.…”

Section: Production and Operations Managementmentioning

confidence: 99%

A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Discrete‐time case

Perera

Sethi

2023

Production and Operations Management

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Fixed costs of ordering items or setting up a production process arise in many real-life scenarios. In their presence, the most widely used ordering policy in the stochastic inventory literature is the (s, S) policy. Optimality of (s, S) policies and (s, S)-type policies have been examined for various inventory models, including those with the inventory level being reviewed in every period or continuously, finite and infinite horizons, discounted-cost and average-cost criteria, backlogging and lost-sales practices, standard and generalized demands and/or costs, deterministic and stochastic lead times, single-product and multi-product settings, and coordinated pricing-inventory decisions. We comprehensively survey the vast literature accumulated over seven decades in two papers. This paper is devoted to discrete-time models, and the companion paper, also published in this journal issue, reviews continuous-time models. We go over model specifications, proof techniques, specific results, and limitations of the articles published in the literature. We conclude each paper by providing corresponding suggestions for extensions and directions for future research. K E Y W O R D Sdiscounted-and average-cost criteria, discrete-time inventory models, K-convexity, Markovian demand, optimality of (s, S)-type policies CONTENTS

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Section: Production and Operations Managementmentioning

confidence: 99%

A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Discrete‐time case

Perera

Sethi

2023

Production and Operations Management

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“…Ciarallo and Niranjan (2014) took a capacity-constrained problem characterized as the all-or-nothing type into consideration for deriving the optimal order-up-to level. Firouzi et al (2014) considered an (s,S) periodic review inventory system under stochastic supply disruption for two products. The proposed model helps determine the optimal production quantity, where there are switching costs between these two products.…”

Section: Public Interest Statementmentioning

confidence: 99%

Derivation of closed-form expression for optimal base stock level considering partial backorder, deterministic demand, and stochastic supply disruption

Saithong¹,

Lekhavat

2020

Cogent Engineering

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In the presence of stochastic supply disruption, the optimal variables of an inventory policy must be determined appropriately. Considering a two-echelon system comprised of a supplier and a retailer, the objective of this research is to help the retailer derives the optimal base stock level that achieves the minimum costs per unit of time regarding the stochastic unavailability of the supplier. The expression of the optimal base stock level is determined in closed-form in consideration of a continuous random variable of a disruption length together with a partial backorder of shortage inventory. A solution method which facilitates the retailer to derive the correct expression for the optimal base stock level is proposed. The applicability of the proposed solution method is illustrated through numerical experiments.

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“…From the perspective of theory, issues about supply chain decisions under demand updating and loss-averse have been studied individually. For example, in the research of supply chain decisions under demand updating scholars use the two ordering opportunities strategy to analyze the supply chain strategy [9,10], and practical issues under complicated environments are explored [11][12][13][14]. Studies of strategies in supply chain with loss-averse mainly focus on supply chain coordination [15,16] and inventory management [17] in manufacturing companies.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…The other is two-stage ordering policies in supply chain under demand updating. The Bayesian updating method [12], conditional distribution method [25], and AR(1) process [10] are widely used to perform demand…”

Section: Supply Chain Coordination Under Demand Updatingmentioning

confidence: 99%

Service capacity procurement of logistics service supply chain with demand updating and loss-averse preference

Liu

Wang

Zhu

et al. 2019

Applied Mathematical Modelling

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This paper studies the impacts of loss-averse preference on the service capacity procurement decisions with demand updating in a logistics service supply chain, which consists of one logistics service integrator and one functional logistics service provider. It starts from a basic two-stage Stackelberg game model, then, extends to three scenarios where either the integrator or the provider or neither of them has loss-averse preference. The impact of loss-averse preference on the decisions of supply chain members is discussed by comparing the four models. Our results reveal, first, the loss-averse preferences do not always affect the decisions of supply chain members. If certain conditions are satisfied, the logistics service integrator can benefit from its loss-averse preference. Second, the increased service level can affect the logistics service integrator's procurement strategy and the functional logistics service provider's pricing strategy. This effect is only related to the loss-averse preference of the functional logistics service provider. Last, under certain conditions, the total service capacity decreases with the increased service level, regardless of whether or not the supply chain members have loss-averse preferences.

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Two-product inventory management with fixed costs and supply uncertainty

Cited by 6 publications

References 24 publications

A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Discrete‐time case

A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Discrete‐time case

Derivation of closed-form expression for optimal base stock level considering partial backorder, deterministic demand, and stochastic supply disruption

Service capacity procurement of logistics service supply chain with demand updating and loss-averse preference

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