An improved fruit fly optimization algorithm and its application to joint replenishment problems

Wang, Lin; Shi, Yafei; Liu, Shan

doi:10.1016/j.eswa.2015.01.048

Cited by 128 publications

(41 citation statements)

References 39 publications

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“…The objective is to optimize the total replenishment cost by reducing the inventory ordering and holding costs (Wang et al, 2012c;Salameh et al, 2014;Qu et al, 2015;Wang et al, 2015). Hence, the joint replenishment presents two advantages: it is possible to earn discounts from the supplier when ordering large batches of multiple items; and the (ordering) fixed costs per item can be reduced as well as the transportation costs (Salameh et al, 2014).…”

Section: The Joint Replenishment Problem In Inventory Managementmentioning

confidence: 99%

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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Section: The Joint Replenishment Problem In Inventory Managementmentioning

confidence: 99%

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

Bastos

Mendes

Nunes

et al. 2017

Prod.

View full text Add to dashboard Cite

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“…Wang et al developed a new mathematical model for the JRP based on fuzzy costs of minor replenishment and inventory holding, proposing a differential evolution algorithm based on traditional fuzzy simulation [11]. Wang et al proposed an effectively improved fruit fly optimization algorithm (IFOA) to resolve the optimization model for the JRP [12].…”

Section: Review Of Previous Literaturementioning

confidence: 99%

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

Liang¹,

Qiao²,

Luo³

2016

Int. j. simul. model.

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In a real business environment, the stochastic joint replenishment with resource restrictions commonly adopts the wholesale sales mechanism. The mechanism causes conflicts of interest between the supplier and the service provider. In order to effectively compete in an uncertain business environment, the option contract is a viable mechanism for coordination between the supplier and the service provider. This paper proposes a fixed-cycle joint replenishment policy based on the option contracts. Stochastic joint replenishment problem of spare parts considers the forward supply process and the reverse recycling process in the remanufacturing supply chain. The optimization model of stochastic joint replenishment problem is developed with a fixed-cycle joint replenishment policy based on the option contracts. According to characteristics of the optimization model, the adaptive immune genetic algorithm is established for solving the optimization problem. Finally, the validity of the optimization model and algorithm are illustrated by data.

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“…On the other hand, excessive fly distance range may lead to slow convergence rate of the iteration process. In [23], an improved FOA was presented to solve the joint replenishment problems. In order to avoid local optimal solution, swarm collaboration and random perturbation were added into original FOA.…”

Section: Fruit Fly Optimization Algorithmmentioning

confidence: 99%

Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

Wang

Tan

et al. 2016

Applied Sciences

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Abstract:As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA) is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT) to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA) was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

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An improved fruit fly optimization algorithm and its application to joint replenishment problems

Cited by 128 publications

References 39 publications

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

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

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

Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

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