Firefly Algorithm for Supply Chain Optimization

Elkhechafi, Mariam; Benmamoun, Zoubida; Hachimi, Hanaâ; Amine, Aouatif; Elkettani, Youssfi

doi:10.1134/s1995080218030125

Cited by 13 publications

(10 citation statements)

References 31 publications

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“…M. Elkhechafi, Z. Benmamoun et al [13] This paper illuminates the firefly calculation all the more profoundly. The firefly calculation has been introduced as a standout amongst the best and most recent bio persuaded calculation.…”

mentioning

confidence: 72%

Inculcating Global Optimization in ZRP through Newfangled Firefly Algorithm

Sharma¹,

Batra²,

Zafar³

2019

IJCNIS

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Zone Routing Protocol (ZRP) has evolved as an efficient hybrid routing protocol with extremely high potentiality owing to the integration of two radically different schemes, proactive and reactive in such a way that a balance between control overhead and latency is achieved while maintaining routng and security concerns. The execution of ZRP in any case, is affected by different system conditions, for example, zone span, arrange measure, portability and so forth. The exploration work depicted in this paper centers around enhancing the execution of zone steering convention by lessening the measure of receptive traffic which is fundamentally in charge of corrupted system execution in the event of extensive systems. The methodology is structured to such an extent that the zone range of the system stays unaffected while accomplishing better QOS(Quality of Service) execution alongside productive memory utilization.This is actualized by utilizing two calculations. The principal calculation is intended to adjust the measure of proactive and receptive traffic without expanding the zone sweep dependent on the collection of courses in a focal overseer called Head.The utilization of Route Aggregation(RA) approach helps in decreasing the steering overhead and furthermore help accomplish execution optimization.The execution of proposed convention is evaluated under fluctuating hub size and versatility. The second calculation called the firefly streamlining calculation intends to accomplish worldwide enhancement which is very hard to accomplish due to non-linearity of capacities and multimodality of calculations. Different customary improvement procedures like angle based methods, tree based calculations need to manage such issues so this exploration based work uses the meta-heuristic calculation; it takes focal points of both course total and firefly calculations to upgrade QOS of Mobile Ad-hoc Network. For execution assessment a lot of benchmark capacities are being embraced like, parcel conveyance proportion and start to finish postponement to approve the proposed methodology. Recreation results delineate better execution of proposed brand new Firefly Algorithm (FRA) when contrasted with ZRP and RA-ZRP.

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mentioning

confidence: 72%

Inculcating Global Optimization in ZRP through Newfangled Firefly Algorithm

Sharma¹,

Batra²,

Zafar³

2019

IJCNIS

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“…Meta-heuristic approaches, in contrast with deterministic methods such as B&B, offer features that allow traversing any solution landscape despite the presence of numerous local optima and provide fewer dependencies or a priori knowledge about the problem. The classical FA, an efficient nature-inspired meta-heuristics approach, has been successfully applied to supply chain optimization problems (Elkhechafi et al 2018). However, the original FA is not applicable in this study due to the discrete nature of the SCND problem.…”

Section: Binary Firefly Algorithmmentioning

confidence: 99%

“…Venkatadri et al (2012) found their proposed meta-heuristic method to be a highly practical solution technique. In this study, a meta-heuristic approach using the binary firefly algorithm (BFA), a binary-coded variant of the firefly algorithm (FA) (Elkhechafi et al 2018), was also developed to solve the SCND model. BFA is known to be efficient in solving set covering problems (Crawford et al 2014), the knapsack problem (Bhattacharjee and Sarmah 2015), feature selection (Zhang et al 2010), and many more in literature (Fister et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Optimization Model of Supply Chain Network Design for Small-scale Banana Flour Production Using Deterministic and Metaheuristic Approach

Oguis¹,

Ligue²,

Mata³

et al. 2022

Philipp J Sci

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Problems concerning low-profit-generating production occur in many small producer enterprises, including banana flour production by small-scale Cavendish banana farmers. One usual difficulty is the inability of these small producers to adjust to market conditions due to limited resources. These farmers are unable to allocate adequate resources such as transport vehicles, equipment, and laborers to various activities in the production process. This paper formulated a mixed-integer linear programming model based on a supply chain network design for banana flour production. This model aimed to determine the optimal number of resources for a producer organization to maximize its total profit. The deterministic branch-and-bound technique and metaheuristic binary firefly algorithm were implemented to obtain the model solution. Based on a case study, both approaches consistently showed that to maximize the profit for small-scale banana flour production, they must operate one mill and one truck with a 4,000- kg maximum capacity and hire nine non-regular and 14 regular laborers. The methodology developed in this study can also be applied to other banana producer organizations with a similar supply chain network to explore alternative enterprises and improve profitability.

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“…The firefly algorithm (FA), proposed by Yang [35], is inspired by the ability to produce flashing light by fireflies. The main tasks of them can be expressed briefly in two steps: In the first step, they attract mating partners and in the second for a predominant predator of reminded the bitter taste of firefly [36]. To summarise the FA steps, we can introduce it through three phases [37]: Initialise: A lightning signal is assigned to each firefly according to the two fireflies' distance.…”

Section: Firefly Algorithmmentioning

confidence: 99%

Credit Risk Assessment Using Learning Algorithms for Feature Selection

Hassani

Meybodi

Hajihashemi³

2020

Fuzzy Information and Engineering

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Firefly algorithm is one of the latest outstanding bio-inspired algorithms, which could be manipulated in solving continuous or discrete optimisation problems. In this context, we have utilised the firefly algorithm accompanied by five well-known models of feature selection classifiers to have an accurate estimation of risk, and further to improve the interpret-ability of credit card prediction. One of the significant challenges in the real-world datasets is how to select features. As most of the datasets are unbalanced, the selection of features turns to the maximum class of data that is not fair. To overcome this issue, we have balanced the data using the SMOTE method. Our experimental results on four datasets show that balancing data has increased accuracy. In addition, using a hybrid firefly algorithm, the optimal combination of features that predicts the target class label is achieved. The selected features by the proposed method besides been reduced can represent both majority and minority classes.

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Firefly Algorithm for Supply Chain Optimization

Cited by 13 publications

References 31 publications

Inculcating Global Optimization in ZRP through Newfangled Firefly Algorithm

Inculcating Global Optimization in ZRP through Newfangled Firefly Algorithm

Optimization Model of Supply Chain Network Design for Small-scale Banana Flour Production Using Deterministic and Metaheuristic Approach

Credit Risk Assessment Using Learning Algorithms for Feature Selection

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