Constrained Stochastic Joint Replenishment Problem with Option Contracts in Spare Parts Remanufacturing Supply Chain

Liang, Y.; Qiao, P. L.; Luo, Z. Y.

doi:10.2507/ijsimm15(3)co13

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…Braglia et al (2017) investigated the periodic review SJRP with backorders and lost sales. Liang et al (2016) studied the SJRP through the use of option contracts. Cui et al (2020b) investigated the SJRP in the presence of a random number of imperfect items and devised the bare-bones differential evolutionary (BBDE) method to solve it.…”

Section: The Jrps Under Stochastic Demandmentioning

confidence: 99%

A review of the joint replenishment problem from 2006 to 2022

Peng

Wang

2022

MSE

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In the increasingly competitive market, supply chain decision-makers are making efforts to improve operational efficiency and reduce costs by joint replenishment approach. Recognizing the value of joint replenishment strategy in the supply chain, we are motivated to write a review on the importance of joint replenishment strategy. Despite the vast literature on the joint replenishment problem (JRP), a comprehensive study survey for recent years is lacking. The goal of this study is to review and synthesize research on JRP from 2006 to 2022. Details of JRP are introduced first. Literature selection and an overview of the extant literature are then discussed. Recent research on JRP with relaxed assumptions is summarized, including stochastic demand, dynamic demand, and resource constraints. In addition, recent research on other JRPs and the joint replenishment and delivery (JRD) problem is summarized. The observations and insights of these studies can guide academics and practitioners to implement joint replenishment strategies in different aspects of supply chain management.

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Section: The Jrps Under Stochastic Demandmentioning

confidence: 99%

A review of the joint replenishment problem from 2006 to 2022

Peng

Wang

2022

MSE

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“…Authors emphasize that the changing business environment, in which supply chain is acting, needs a more effective production process planning and control method that is able to deal with uncertain product demand and forecast from downstream supply chain participant. Similar considerations are presented for remanufacturing supply chain [5]. A fixed cycle joint replenishment policy is proposed, based on the option contract.…”

Section: Literature Review On Inventory Management In a Supply Chainmentioning

confidence: 99%

Simulation of Bullwhip Effect in a Supply Chain for Lean Learning Factory Purposes

Gjeldum¹,

Crnjac²,

Bilić³

2016

Int. j. simul. model.

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A mission of the Learning Factory (LF) is a development of practice-based engineering curriculum through the simulation of a real factory by specialized equipment. In this paper, supply chain network simulator is developed, as a replacement for the traditional board Beer Game. The board Beer Game could be misused by participants in order to avoid significant bullwhip effect, which occurrence is the Beer Game main aim. The disadvantage of the board Beer Game, that participants have insight in inventory levels and placed orders and thus strategically act upon their knowledge or even activate a sort of decentralized information sharing policy, is avoided. Further considerations prove the mandatory occurrence of bullwhip effect without decentralized information sharing policy on three case studies. Optimal mathematical model for placing orders within a supply chain was defined according to multi-criteria optimization process using spreadsheet simulation. As even optimal model results with the bullwhip effect, newly developed system will always lead to the tremendous bullwhip effect during the learning process.

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“…Civil aviation emergency decision is a prospective issue, but return and replacement of emergency supplies is insignificant, except for commercial products [15]. In [16], authors came up with a novel reverse logistics system for post-disaster debris capable of systematically minimizing total reverse logistical costs, risk-induced cost and psychological cost.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Simulation for Airport Emergency Inventory with Replacement

Meng¹,

Guo²,

Zhao³

2017

Int. j. simul. model.

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The paper assumes that the accident occurrence time is stochastic and emergency supplies is perishable, then two replacement stochastic models based on remaining lifetime and remaining quantity are first proposed. In order to identify the effectiveness of replacement strategy, two replacement-based stochastic models are compared with the general stochastic model that is non-replacement, measured by inventory level and total costs. A discrete-event simulation model is developed to demonstrate effects of occurrence time uncertainty, replacement ratios and distributed functions in occurrence time and demand. Sensitive analysis shows that the optimal decision is more sensitive to remaining quantity ratio as compared to remaining lifetime ratio. The paper shows that when decision-makers ignore occurrence time uncertainty and limited warehousing time, they may significantly miss better decisions. Further, simulation results demonstrate that different distributed functions in both occurrence time and demand lead to different inventory strategies.

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Constrained Stochastic Joint Replenishment Problem with Option Contracts in Spare Parts Remanufacturing Supply Chain

Cited by 5 publications

References 38 publications

A review of the joint replenishment problem from 2006 to 2022

A review of the joint replenishment problem from 2006 to 2022

Simulation of Bullwhip Effect in a Supply Chain for Lean Learning Factory Purposes

Optimization and Simulation for Airport Emergency Inventory with Replacement

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