Frequent Flyer Cost Estimation

The marketing activity of a modern airline is quite diverse. Under the conditions of an oligopoly, airlines develop their business in competition for a passenger.In modern conditions in Russia, the use of dumping methods of the early 1990s is very ineffective. There are no weak airlines left on the long-distance air transportation market, and in regional markets large companies lose to small regional carriers due to the high cost of performing flights on large-capacity aircrafts of the airline’s fleet.Generally, non-price methods of competition come out on top. Flexible tariff policy, advertising, and high service in servicing passengers remain leading traditional methods of competing for a passenger.This article is devoted to the study of another marketing method for attracting passengers, or rather retaining passengers on the airline’s flights, which is development of bonus programs, frequent flyer programs. PJSC Russian Airlines (Aeroflot) was chosen as the object for the study. The subject of the research is the «Aeroflot Bonus» program.The objective of the study is to study the cost of the program. For research purposes, this is the value of the frequent flyer program point. The problem proposed to be solved is to determine the amount of expenses for implementation of the bonus program of frequent flyers. When solving the problem in its staging part, the assessment is not limited to direct costs associated with the costs of marketing efforts in the form of costs for organising a special advertising department, issuing bonus cards, software, and wages. The estimation refers also to indirect costs in the form of unreceived proceeds from free bonus tickets. Besides, a rough estimate has been made of the airline’s hidden costs due to an unpaid seat on the plane. The study conclusions indicate that hidden costs will be taken into account in calculating the cost of a flight and the bonus program has a right to exist.

An Effectiveness and Cost-Estimation Model for Deploying Assistive Technology Solutions in Elderly Care

Background Deployment of modern assistive technologies is one of the major trends contained in the strategies of developed countries. However, the use of technology in households is not yet a common practice. The aim of this paper is present a model for assessment of selected smart device solutions in elderly care and the evaluation of overall care costs. The model provides the optimal set of devices for different target groups in terms of financial savings. Methods The model uses demographic projections taken from Eurostat for EU countries and the disability incidence from the annual report of the Ministry of Labour and Social Affairs of the Czech Republic as an input. The model was implemented in the software Stella Professional dedicated to system dynamics modelling including a web interface and is accessible online. Results In relation to the combination of five assistive devices for the elderly, the optimal solution, the cost savings are 37.8% or182 billion CZK), cumulatively in the simulated time period 2021-2060. Out of the five available assistive devices, up to three - UpWalker, Jaco robotic arm and Poseidon - were employed by the model for different target groups. Conclusion According to the performed analysis the assistive technologies proved a significant potential to maintain the quality of life of elderly and lessen the burden on public budgets. With respect to the ongoing demographic transition, the need to employ smart device solutions should further increase and their price could decline with increasing scale of production and overall advancement in technology.

Expediting the Cost Estimation Process for Aged-Housing Renovation Projects Using a Probabilistic Deep Learning Approach

Since the early 1980s, the Korean government has rapidly boosted residential buildings to cope with substantial housing shortages. However, as buildings have been aging simultaneously, the performance of a large number of residential buildings has deteriorated. A government plan to upgrade poor housing performance through renovation is being adopted. However, the difficulty of accurate construction cost prediction in the early stages has a negative effect on the renovation process. Specifically, the relationship between renovation design elements and construction work items has not been clearly revealed. Thus, construction experts use premature intuition to predict renovation costs, giving rise to a large difference between planned and actual costs. In this study, a new approach links the renovation design elements with construction work items. Specifically, it effectively quantifies design factors and applies data-driven estimation using the simulation-based deep learning (DL) approach. This research contributes the following. First, it improves the reliability of cost prediction for a data-scarce renovation project. Moreover, applying this novel approach greatly reduces the time and effort required for cost estimation. Second, several design alternatives were effectively examined in an earlier stage of construction, leading to prompt decision-making for homeowners. Third, rapid decision-making can provide a more sustainable living environment for residents. With this novel approach, stakeholders can avoid a prolonged economic evaluation by selecting a better design alternative, and thus can maintain their property holdings in a smarter way.

Large-Scale Transport Projects: Accuracy in Cost Estimation

Currently, several studies have been conducted about overspending of funds in large-scale projects, including transport. International studies show that overspending by 20–100 % is not an exception, but an established fact. The article presents the results of comparing the estimated cost with the actual costs for several large-scale transport projects that were implemented on the territory of Russia. The conclusion is made about the need for a systematic analysis when evaluating the effectiveness of projects of this class.

LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model

One of the most important and critical factors in software projects is the proper cost estimation. This activity, which has to be done prior to the beginning of a project in the initial stage, always encounters several challenges and problems. However, due to the high significance and impact of the proper cost estimation, several approaches and methods have been proposed regarding how to perform cost estimation, in which the analogy-based approach is one of the most popular ones. In recent years, many attempts have been made to employ suitable techniques and methods in this approach in order to improve estimation accuracy. However, achieving improved estimation accuracy in these techniques is still an appropriate research topic. To improve software development cost estimation, the current study has investigated the effect of the LEM algorithm on optimization of features weighting and proposed a new method as well. In this research, the effectiveness of this algorithm has been examined on two datasets, Desharnais and Maxwell. Then, MMRE, PRED (0.25), and MdMRE criteria have been used to evaluate and compare the proposed method against other evolutionary algorithms. Employing the proposed method showed considerable improvement in estimating software cost estimation.

An Improved Model for House Price/Land Price Prediction using Deep Learning

House price predictions are a crucial reflection of the economy; sometimes house prices include the land prices and demand of the place and location. The house price and land price are two different things, but both are important for both buyers and sellers. This chapter introduced the combination of ML and DL approaches to predict the house price with the updated regression algorithm. The algorithm named as ‘Mopuri algorithm' reads the 14 attributes like crime rate, population density, rooms, etc. and produces the cost estimation result as a prediction. The proposed model accurately estimates the worth of the house as per the given features. The results of the model tested with the different datasets existing in the Kaggle data source using Python libraries with the Jupyter platform and continuation of the model using the Android OS to develop the smart home web-based application.