Sensitivity of shortest distance search in the ant colony algorithm with varying normalized distance formulas

Syah, Rahmad; Nasution, Mahyuddin K. M.; Nababan, Erna Budhiarti; Efendi, Syahril

doi:10.12928/telkomnika.v19i4.18872

Cited by 6 publications

(6 citation statements)

References 26 publications

(29 reference statements)

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“…Root mean square error (RMSE) measures the accuracy of the regression model, measuring the difference between the predicted value and the actual value of the dependent variable (y), or the square root of the average squared difference between the predicted and actual value of the dependent variable. Mean absolute percentage error (MAPE) measures the accuracy of a forecasting model, measuring the average absolute percentage difference between the actual value and the predicted value of the time series [32]- [34].…”

Section: Assessment Of Modelsmentioning

confidence: 99%

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

Y. Syah,

Muliono,

Akbar Siregar

et al. 2024

IJ-AI

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Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer churn in the market business. Particle swam optimization (PSO) was used in this study as a metaheuristic method to provide a strategy to guide the search process for new customers and obtain parameters for processing by support vector regression (SVR). SVR predicts the value of a continuous variable by determining the best decision line to find the best value. The number of transactions, the number of periods, and the conversion value are the parameters that are visible. Efficiency models are added to improve prediction results through two optimizations: prediction flexibility and risk minimization. The findings demonstrate the effectiveness of prediction in reducing customer churn.

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Section: Assessment Of Modelsmentioning

confidence: 99%

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

Y. Syah,

Muliono,

Akbar Siregar

et al. 2024

IJ-AI

View full text Add to dashboard Cite

show abstract

“…This stage consists of four steps: data reduction, feature selection, data transformation, and data normalization. The date of the last purchase, calculating customer purchases, total income from customers for one year, and calculating product items purchased by customers are all features [26], [27]. The final feature is based on the opinion of an expert.…”

Section: Data Preparing Customers Lifetime Valuementioning

confidence: 99%

An boosting business intelligent to customer lifetime value with robust M-estimation

Elveny,

Y. Syah,

M. Nasution

2024

IJ-AI

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When a business concentrates too much on acquiring new clients rather than retaining old ones, mistakes are sometimes made. Each customer has a different value. Customer lifetime value (CLV) is a metric used to assess longterm customer value. Customer value is a key concern in any commercial endeavor. When there are variations in customer behavior, CLV forecasts the value of total customer income when the data distribution is not normal, and outliers are present. Robust M-estimation, a maximum likelihood type estimator, is used in this study to enhance CLV data. Through the minimization of the regression parameter from the residual value, robust Mestimation eliminates data outliers in customer metric data. With an accuracy of 94.15%, R-square is used to gauge model performance. This research shows that CLV optimization can be used as a marketing and sales strategy by companies.

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“…Lv et al established a Nash negotiation revenue sharing contract model starting from the price subsidy income under the premise that the product price is affected by the demand; under the constraints of onboard capacity and mixing time window, the simultaneous optimization of single-cycle inventory and routing is studied [4]. Syah et al put forward the corresponding control strategy for the bullwhip effect and control problem under VMI mode through the construction of the model and the comparative analysis of the total cost before and after the implementation of supplier managed inventory strategy [5]. Salviano et al studied the application of VMI in lithium battery manufacturers.…”

Section: Literature Reviewmentioning

confidence: 99%

Inventory Path Optimization of VMI Large Logistics Enterprises Based on Ant Colony Algorithm

Wang

2022

Mobile Information Systems

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In order to better manage the supplier’s inventory and formulate a reasonable distribution plan, based on the existing research, taking the two-level supply chain system composed of one supplier and multiple retailers as the research object and VMI mode as the background, this paper studies the inventory path optimization problem with the goal of minimizing the total cost in the planning period. Taking the retailer’s inventory capacity and vehicle capacity constraints as constraints, this method constructs a mixed integer programming model with random demand in multiple cycles and the goal of minimizing the total cost in the planning period. When building the model, the distribution cost is refined. In addition to identifying shipping-related start-up costs and travel costs, processing costs are associated with additional delivery time, which is close to reality. In the algorithm design, the genetic algorithm is combined with the C–W algorithm, a similar hybrid genetic algorithm is used to solve the model, and the sample model is used for estimation; that is, the expected value of the random sample is taken, which is used as the target value for each chromosome. In addition, when optimizing roads, the C–W algorithm is used to divide vehicle capacity according to truck capacity as much as possible, thereby reducing the number of vehicles used and saving the overall total cost of preparation time for certain projects. Facts have proved that the optimized and improved inventory path is more conducive to help enterprises reduce logistics costs and provide theoretical support for enterprise management decisions.

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Sensitivity of shortest distance search in the ant colony algorithm with varying normalized distance formulas

Cited by 6 publications

References 26 publications

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

An boosting business intelligent to customer lifetime value with robust M-estimation

Inventory Path Optimization of VMI Large Logistics Enterprises Based on Ant Colony Algorithm

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