Using Genetic Algorithms to Optimize the Selection of Cost Drivers in Activity-based Costing

Cost is an important factor in ensuring the company win in the competition on the market. Consumers will choose a manufacturer that is able to produce products and services that have high quality with low prices. Costs of Management Systems Contemporary emphasis on search than the allocation. And management based activities are at the heart of contemporary operating control system. At least two major factors that must be considered in the selection of cost driver (cost driver) are: the cost of measurement and the degree of correlation between the consumption cost driver with the actual overhead. Cost driver is divided into two categories, namely the structural cost driver and executional cost driver. Cost driver is the basis used to charge collected on cost pool to the product. So that the calculation of the cost through Time Driven activity-based costing system, the company obtain more precise information and accurate.

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Selection of Controller Parameters using Genetic Algorithms

Engineering Systems with Intelligence ◽

10.1007/978-94-011-2560-4_49 ◽

1991 ◽

pp. 431-438 ◽

Cited By ~ 14

Author(s):

Paulo Oliveira ◽

João Sequeira ◽

João Sentieiro

Keyword(s):

Genetic Algorithms ◽

Selection Of

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Activity-Based Costing System for a Small Manufacturing Company

Encyclopedia of Decision Making and Decision Support Technologies ◽

10.4018/978-1-59904-843-7.ch001 ◽

2011 ◽

pp. 1-19 ◽

Cited By ~ 1

Author(s):

Arkadiusz Januszewski

Keyword(s):

Integrated Circuits ◽

Production Costs ◽

Cost Calculation ◽

Activity Based Costing ◽

False Information ◽

Direct Production ◽

Costing System ◽

The Right ◽

The Cost ◽

Selection Of

The selection of the right cost calculation method is of critical importance when it comes to determining the real product profitability (as well as clients and other calculation objects). Traditional cost calculation methods often provide false information. The literature offers many examples of big companies that have given up traditional methods and applied a new method: activity-based costing (ABC). They discovered that many products that are manufactured generate losses and not profits. Managers, based on incorrect calculations, mistakenly believed in the profitability of each product. Turney (1991) reports on an example of an American manufacturer of over 4,000 different integrated circuits. The cost calculation with the allocation of direct production costs as machinery-hour markup demonstrated a profit margin of over 26% for each product. Implementing ABC showed that the production of more than half of the products was not profitable, and having factored in additional sales and management costs (which accounted for about 40% of the total costs), it was as much as over 75%.

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Ensemble-Based Modeling of Chemical Compounds with Antimalarial Activity

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190510100313 ◽

2019 ◽

Vol 19 (11) ◽

pp. 957-969 ◽

Cited By ~ 2

Author(s):

Ana Yisel Caballero-Alfonso ◽

Maykel Cruz-Monteagudo ◽

Eduardo Tejera ◽

Emilio Benfenati ◽

Fernanda Borges ◽

...

Keyword(s):

Genetic Algorithms ◽

Computational Models ◽

Antimalarial Activity ◽

Majority Vote ◽

Chemical Compounds ◽

Quantitative Structure Activity Relationship ◽

Qsar Modeling ◽

Design Work ◽

Data Set ◽

Selection Of

Background: Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure- Activity Relationship studies have been widely used in drug design work flows. Objective: The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds. Materials and Methods: For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance. Results: Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds. Conclusion: It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method.

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