A review of the joint replenishment problem literature: 1989–2005

Khouja, Moutaz; Goyal, S. K.

doi:10.1016/j.ejor.2007.03.007

Cited by 246 publications

(136 citation statements)

References 60 publications

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“…The idea is to coordinate replenishment decisions across multiple items. The associated planning task is known as (stochastic demand multi-item) joint replenishment problem (JRP) (Khouja and Goyal 2008). Two kinds of transportation costs are distinguished, major and minor costs.…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrated Inventory-Transportation System with Periodic Pick-Ups and Leveled Replenishment

Völling

Grünewald

Spengler

2013

Bus Res

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Section: Literature Reviewmentioning

confidence: 99%

An Integrated Inventory-Transportation System with Periodic Pick-Ups and Leveled Replenishment

Völling

Grünewald

Spengler

2013

Bus Res

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“…The JRP policy means that group items are ordered from the same supplier to achieve the target of sharing fixed ordering costs and saving procurement costs [2]. A corresponding review has been conducted by Khouja and Goyal [3]. Adopting a joint replenishment policy can result in cost savings, so managers are aware that using a joint replenishment and delivery (JRD) policy can obtain a scale effect of replenishment and transportation simultaneously, thereby further reducing total cost [4].…”

Section: Introductionmentioning

confidence: 99%

An Effective Hybrid Differential Evolution Algorithm Incorporating Simulated Annealing for Joint Replenishment and Delivery Problem with Trade Credit

Zeng¹,

Peng²,

Zhang³

et al. 2016

IJCIS

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In practice, suppliers often provide retailers with forward financing to increase demand or decrease inventory. This paper proposes a new and practical joint replenishment and delivery (JRD) model by considering trade credit. However, because of the complex mathematical properties of JRD, high-quality solutions to the problem have eluded researchers. We design an effective hybrid differential evolution algorithm based on simulated annealing (HDE-SA) that can resolve this non-deterministic polynomial hard problem in a robust and precise way. After determining the suitable parameters by a parameter-tuning test, we verify the performance of the HDE-SA through numerical JRD examples. Compared with the results of other popular evolutionary algorithms, results of randomly generated JRDs indicate that HDE-SA can always obtain slightly lower total costs than differential evolution algorithm (DE) and genetic algorithm (GA) under different situations. Moreover, the convergence rate of the HDE-SA is higher than that of DE and GA. Thus, the proposed HDE-SA is a potential tool for the JRD with trade credit.

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“…The available published literature provides a large variety of models and applications, with different methods, that seek lower computational time, optimal solutions, or, in their minority solving practical approaches. Furthermore, there has been a time gap since the last review about JRP models provided in Khouja & Goyal (2008), and the update of the state of art on JRP may help researchers and professionals on knowing the latest approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, recent studies have focused on finding more efficient and faster solution algorithms, also considering the practical context in where the problem is presented, such as metaheuristic methods, instead of only generating exact solutions, as it results in large computational time (Khouja & Goyal, 2008;Qu et al, 2015). Porras & Dekker (2008) assert that the joint replenishment strategy results in saving up to 13% on the total cost compared to Economic Order Quantity models for a single item approach considering a set of twenty products.…”

Section: Introductionmentioning

confidence: 99%

“…On a mathematical perspective, a large amount of solutions has been proposed by means of optimization models, with the objective of reducing costs and fulfilling the demand (Saracoglu et al, 2014). One of the main topics is the Joint Replenishment Problem (JRP), which consists in programming the replenishment of different types of items in the same order, generally provided by the same supplier, aiming total inventory costs reduction (Khouja & Goyal, 2008).…”

Section: Introductionmentioning

confidence: 99%

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A systematic literature review on the joint replenishment problem solutions: 2006-2015

Bastos

Mendes

Nunes

et al. 2017

Prod.

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Among all existing inventory replenishment models, this research was dedicated to the Joint Replenishment Problem (JRP), which consists in the replenishment of multiple items simultaneously, aiming total cost reduction. Literature has presented several optimal and approximated solutions to this problem, with different applications and techniques, which results in a large quantity of solution proposals. Therefore, this research aimed to map existing solutions to the problem in 2006-2015 in order to provide a guide for interested parts in JRP and to update previous reviews. Hence, systematic review was used to assess papers from that period interval. From a total of 128 papers, a general trend for seeking JRP extensions and practical applications was verified. Furthermore, the heuristic and metaheuristic methods were the most used and considered the most suitable due to their simplicity in understanding and application. KeywordsInventory management. Multi-product. Joint replenishment problem. Systematic review.

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A review of the joint replenishment problem literature: 1989–2005

Cited by 246 publications

References 60 publications

An Integrated Inventory-Transportation System with Periodic Pick-Ups and Leveled Replenishment

An Integrated Inventory-Transportation System with Periodic Pick-Ups and Leveled Replenishment

An Effective Hybrid Differential Evolution Algorithm Incorporating Simulated Annealing for Joint Replenishment and Delivery Problem with Trade Credit

A systematic literature review on the joint replenishment problem solutions: 2006-2015

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