Develop Cost Models Research Articles

Expanding the horizons of metal additive manufacturing: A comprehensive multi-objective optimization model incorporating sustainability for SMEs

Metal Additive Manufacturing (MAM) has seen significant growth in recent years, with sub-processes like Metal Material Extrusion (MEX) reaching industrial readiness. MEX, known for its cost-effectiveness and ease of integration, targets a distinct market segment compared to established high-end MAM processes. However, despite technological improvements, its overall integration into the industry as a viable manufacturing technology remains incomplete. This paper investigates the competitiveness of MEX, specifically its integration into the supply chain and the implications on cost and carbon emissions. Utilizing real-world data, the research develops a multi-objective optimization (MOO) model for a four-echelon supply chain including suppliers, airports, production facilities, and customers. The optimization model is combined with a previously developed cost model for MEX to optimize facility location in Norway using the NSGA-II algorithm. Employing a case study approach, the paper examines the production of an industrial part using stainless steel 17-4PH, detailing concrete process costs and system-level costs across four different production scenarios: 10, 100, 1,000, and 10,000 parts. The findings indicate MEX’s potential for cost-effective production at low and diversified volumes, supporting the trend towards customization and manufacturing flexibility. However, the study also identifies significant challenges in maintaining competitiveness at higher production volumes. These challenges underline the necessity for further advancements in MEX technology and process optimization to enhance its applicability and efficiency in larger-scale production settings.

A bibliometric review of the statistical modelling techniques for cost estimation of infrastructure projects

PurposeCost overrun in infrastructure projects is a constant concern, with a need for a proper solution. The current estimation practice needs improvement to reduce cost overruns. This study aimed to find possible statistical modelling techniques that could be used to develop cost models to produce more reliable cost estimates.Design/methodology/approachA bibliographic literature review was conducted using a two-stage selection method to compile the relevant publications from Scopus. Then, Visualisation of Similarities (VOS)-Viewer was used to develop the visualisation maps for co-occurrence keyword analysis and yearly trends in research topics.FindingsThe study found seven primary techniques used as cost models in construction projects: regression analysis (RA), artificial neural network (ANN), case-based reasoning (CBR), fuzzy logic, Monte-Carlo simulation (MCS), support vector machine (SVM) and reference class forecasting (RCF). RA, ANN and CBR were the most researched techniques. Furthermore, it was observed that the model's performance could be improved by combining two or more techniques into one model.Research limitations/implicationsThe research was limited to the findings from the bibliometric literature review.Practical implicationsThe findings provided an assessment of statistical techniques that the industry can adopt to improve the traditional estimation practice of infrastructure projects.Originality/valueThis study mapped the research carried out on cost-modelling techniques and analysed the trends. It also reviewed the performance of the models developed for infrastructure projects. The findings could be used to further research to develop more reliable cost models using statistical modelling techniques with better performance.

Towards sustainable manufacturing by enabling optimum selection of life extension strategy for industrial equipment based on cost modelling

Sustainable manufacturing is of great importance in today’s world. In manufacturing, keep industrial equipment well-functioning is important because failure of equipment leads to significant financial and production losses. In addition, disposal of such failed equipment is both costly and environmentally unfriendly and does not recover any residual value. This raises the need to adopt methods and means that help extending the life of equipment and reduce waste of material. This paper presents a digital toolkit of cost model to estimate and understand the costs to be incurred when applying life extension strategy for industrial equipment. It is meant to be integrated with other tools and methodologies to enable end-users to perform optimal decision-making regarding which life extension strategy (e.g., remanufacturing, refurbishment, repair) to implement for large industrial equipment that is towards its end-of-life or needs maintenance, taking into account criteria such as cost, machine performance, and energy consumption. The cost model developed integrates a combination of parametric costing and activity-based costing methods to per form cost estimation. It has been implemented in an Excel-based Macro platform. A case study with application scenarios has been conducted to demonstrate the application of the cost model to optimize life extension strategies for industrial equipment. Finally, conclusions on the developed cost model have been reported.

THE ROLE OF THE COST AND QUALITY IN ADDITIVE MANUFACTURING

Global competition demands faster and more economically production process, products to be more personalized with modern design, sophisticated and of higher quality. Companies are looking for new solutions and production technologies to achieve these requirements. Although they have been on the market for almost 30 years, it is only in the last decade that additive manufacturing (AM) technologies have reached a certain level of maturity to be accepted in the wide industry. Currently additive manufacturing is in a period of transition from its initial purpose for the rapid prototyping and small series to the serial production of parts. On that path, there are still obstacles that slow it down in the form of costs associated with the production process (machines, materials), the lack of advance process control, traditional approach to R&D, education of engineers and the lack of software integration from design stage to post processing. Main focus of this paper is cost analysis in AM. In this regard paper will give brief overview of some developed cost models which brought significant novelties in the cost models considering the economics of AM and quality. Based on this analysis the paper will present suggestions what should be included in new cost model for production of end usable metal parts produced by PBF technology. Also, some quality inspection methods for AM will be briefly explained.

COMORP: Rapid prototyping for mathematical database cost models development

The database technology evolution trends had pushed the researchers to redesign and adapt the past mathematical database cost models with the consideration of additional aspects due to the emergence of this evolution. As a result, many scientists today propose database cost models by rethinking query processing and optimization with every change in hardware, workload, and applications. This requires a high level of domain expertise. However, assisting users to build database cost models by context-aware is difficult and an increasingly harder challenge. A new paradigm is needed to change cost model development methodology by designing it at a high level of abstraction to mitigate the gap between core database and conceptual modeling communities in order to shorten its long development cycle. Moreover, we need to support incrementally complex design of cost models and follow the evolution of database technologies that change rapidly and continuously to fit the new requirements. We investigate Model-Driven Engineering paradigms that enable database cost models, fast prototyping of modeling/analysis and optimize reusability of its integration components. This article presents a framework that aims to develop cost models as an extensible and customized kernel that provides metrics related to the most sensitive layers of database systems and assists the cost model composition process by combinations of fundamental primitives. We implement our framework to help designers/researchers create a successful cost model product semi-automatically correlate with their manifests. Experimental evaluations show that by using our framework, we can reduce the time spent by 60% on CM building, while being able to recommend useful components with up to 90%.

Cost Modelling to Support Optimum Selection of Life Extension Strategy for Industrial Equipment in Smart Manufacturing

Industrial equipment/machinery is an important element of manufacturing. They are used for producing objects that people need for everyday use. Therefore, there is a challenge to adopt effective maintenance strategies to keep them well-functioning and well-maintained in production lines. This will save energy and materials and contribute genuinely to the circular economy and creating value. Remanufacturing or refurbishment is one of the strategies to extend life of such industrial equipment. The paper presents an initial framework of cost estimation model based on combination of activity-based costing (ABC) and human expertise to assist the decision-making on best life extension strategy (e.g. remanufacturing, refurbishment, repair) for industrial equipment. Firstly, ABC cost model is developed to calculate cost of life extension strategy to be used as a benchmark strategy. Next, expert opinions are employed to modify data of benchmark strategy, which is then used to estimate costs of other life extension strategies. The developed cost model has been implemented in VBA-based Excel® platform. A case study with application examples has been used to demonstrate the results of the initial cost model developed and its applicability in estimating and analysing cost of applying life extension strategy for industrial equipment. Finally, conclusions on the developed cost model have been reported.

Comparative Study on the Attributes Analysis of Software Development Cost Model Based on Exponential-type Lifetime Distribution

In this paper, the development cost attributes were newly analyzed by applying the Exponential–type lifetime distributions (Burr-Hatke-exponential, ExponentialBasic, Exponential-exponential, Inverse-exponential) widely utilized in the field of software lifetime testing and quality evaluation to the software development cost model. Also, to verify the attributes of the analyzed development cost, after analyzing the future reliability, the optimal cost model was presented. For this study, an analysis algorithm using software failure time data was proposed to solve the research solution, the maximum likelihood estimation (MLE) was applied to solve the parameter values, and the nonlinear equation was calculated using the binary method. Simulations show that if the cost of removing one flaw found during the test phase increases, the development cost increases, but the release time does not change. However, if the cost of flaws correction discovered by operators increased, both development costs and release times increased. Therefore, we must remove all possible flaws at the testing stage to eliminate failures. In conclusion, First, the Exponential-exponential distribution model showed the best performance among the proposed models because it had the lowest software development cost and the highest future reliability. Second, the software development cost attributes of the Exponential-type lifetime distributions were newly analyzed. Third, through this data, it was able to help software developers to analyze the most economical development cost

Cost effectiveness of conventionally and solar powered monovalent selective electrodialysis for seawater desalination in greenhouses

Reverse osmosis is the most widely used desalination technology for treating irrigation water. Reverse osmosis removes both monovalent ions detrimental to crops (Na+,Cl−) and divalent ions beneficial for crops (Ca2+,Mg2+,SO42−). Fertilizer must then be added to the desalinated water to reintroduce these nutrients. Unlike reverse osmosis, monovalent selective electrodialysis selectively removes monovalent ions while retaining divalent ions in the desalinated water. This paper investigates the monovalent selectivity and cost effectiveness of the widely-used Neosepta and new Fujifilm monovalent selective electrodialysis membranes in treating seawater for irrigation. Membrane selectivity, limiting current, and resistance are experimentally characterized. These system parameters are inputs to the developed cost model, which determines fertilizer and water savings, as well as operating and capital costs, relative to reverse osmosis; the primary operating cost difference stems from reverse osmosis’s significantly lower energy consumption. Given prices of commercially available membranes, monovalent selective electrodialysis costs an average of 30% more than reverse osmosis. At the projected sales price of Fujifilm membranes, which are still under development, monovalent selective electrodialysis costs an average of 10% more than reverse osmosis; if electricity costs are less than 0.08 $/kWh, monovalent selective electrodialysis is on par with reverse osmosis. Regardless of membrane price and electricity cost, solar-powered desalination is only economical if photovoltaic capital costs are significantly reduced to 0.10–0.20 $/kWh. When monovalent selective electrodialysis exceeds reverse osmosis cost, the financial requirements for competitive monovalent selective electrodialysis (e.g., energy consumption, electricity cost, energy source, membrane cost) are evaluated.

Economic and environmental sustainability of liquid hydrogen fuel for hypersonic transportation systems

Based on recent research activities, the cost of the propellant may represent up to the 90% of the Direct Operating Cost for a hypersonic vehicle. Therefore, it can be considered the most relevant cost item of the overall Life Cycle Cost. In this context, the paper focuses on the estimation of the cost of liquid hydrogen, one of the most promising fuels for high-speed applications, considering its specific energy content. In particular, a methodology is here presented to guide engineers through the evaluation of the impact of the LH2 price on Direct Operating Cost and then onto the overall Life Cycle Cost for a long-haul point-to-point transportation system. Starting from an overview of the current H2 productive scenarios, future possible technological improvements allowing an increment of the production rate and a reduction of the related socio-economic impact are described. Then, a detailed cost-estimation model is presented for the so-called “green” hydrogen. Eventually, the developed cost model is applied to the LAPCAT A2 and LAPCAT MR2.4 vehicles and mission concepts, demonstrating that in a future scenario (2050) the LH2 cost can be lowered down to 2 €/kg. With this fuel price, the share of fuel cost onto Direct Operating Cost can be reduced down to 70%.

Continuum approximation models for joint delivery systems using trucks and drones

ABSTRACT This research focuses on exploring the economics related to the joint delivery system using trucks and drones. Transportation distances and costs are approximated as simple functions using continuum approximation (CA) methods, which can keep key issues and tradeoffs in focus. The main contribution is to develop cost models using the derived methods and gain a greater understanding of delivery activities by focusing on the tradeoffs between major components, which are valuable for decision-makers: choose economical delivery mode based on customer density, partition service region into optimal sub-regions, and obtain an optimal delivery ratio between trucks and drones.

QoSMOS: QoS metrics management tool suite

Researchers are unanimous that the database management system (DBMS) is one of the most promising candidates to be the backbone of quality-conscious information services. Usually, this quality is measured by subjective metrics that can be interpreted as the degree to which a DBMS possesses a given functionality that affects its quality. Note that the Quality of Service (QoS) concerns all phases of the designing database application life-cycle, with a particular interest to the physical design phase – considered as the funnel of other phases. The satisfaction of the QoS in the context of the physical design is ensured by metrics. A metric can be seen as a function returning a single numerical vale whose input parameters belong to (i) the database application (including its schema and queries), (ii) the DBMS hosting that database and (iii) the deployment platform. These metrics are usually expressed by analytical cost models whose development is time consuming and requires calibration to reflect the evolution of the database technology (that refers to both software and hardware). Face to this situation, the presence of management tools dedicated to the construction, exploitation and calibration of cost models becomes a necessity for researchers and students. In this paper, we first propose a domain specific language, called CostDL, to develop cost models for QoS-enabled database physical design. The development of such a language necessitates the explicitation of the different entries of a cost model thanks to meta-modeling techniques. Secondly, to increase their reuse, a persistent repository is proposed to store the new developed cost models. The two contributions are regrouped in a framework called QoSMOS providing functionalities related to the management of cost models.

Comparative Study between Genetic Algorithms and Iterative Optimization for Economic Dispatch of Practical Power System

This paper presents a genetic approach for solving the economic dispatch problem in power systems. The cost functions are newly and precisely developed for some power plants operating currently in Jordan using curve fitting. The developed cost models are introduced to a modern genetic algorithm tool for the purpose of generation scheduling. The feasibility of the proposed method is demonstrated and compared with the lambda iterative method in terms of the solution quality and computation efficiency. MATLAB software scripts in cooperative manner with graphical user interface (GUI) is used for the proposed methods. Different models of cost curve fitting of the thermal units are considered. The study is done initially for a three power plants and then it has been extended for four plants. The simulation results show that the proposed GA method was capable of obtaining accurate solutions in ED problems. It is finally deduced that the approach can be promising proposal for improvement of economy in energy sector in Jordan by fuel cost minimization with demand satisfaction and generation constraints.

A Levelized Cost of Energy (LCOE) model for wind farms that include Power Purchase Agreements (PPAs)

Abstract The Cost of Energy is a major concern for the electric power industry. Customers are sensitive to the cost of renewable energy, which is typically more expensive than conventional energy generation due to the variability and uncertainty associated with their sources. Power Purchase Agreements (PPAs) are developed to balance the energy price and associated risks with poewr gerneation and transmission. The energy delivery limits imposed by current PPAs impact the Levelized Cost of Energy (LCOE) in ways that are not accomodated by existing LCOE models. In this work, a new cost model is developed to evaluate the LCOE from a wind power source under a PPA contract. The application of the model to real wind farms demonstrates that the actual LCOE depends on the defined minimum/maximum energy purchase limitations within a PPA contract. The developed cost model can be used as a basis for setting appropriate PPA terms, such as a price schedule and performance metrics. Hence, it can help the Seller to negotiate penalties and energy price within their PPAs.

Developing cost model for preliminary estimate of road projects in Nigeria

This paper was aimed at developing cost models to predict the preliminary estimate of road projects in Nigeria at the early state using parametric estimating techniques. To achieve this, there is need to identify both the quantitative and qualitative factors that affects the construction cost of road projects. The used procedure resulted in the formulation of linear and multiple regression models which provide a means of determining the cost of road functional activities as a function of projects characteristics. The models developed were for seven major construction activities based on 50 sets of data collected from road projects executed within between 2010 and 2015 in South Western Nigeria. The addition of all elements will give the probable cost of the road project. The coefficient of determination r2 for the developed models ranged from 0.85 to 0.99 indicating that the predicted values from a forecast models fit with the real-life data.

Determining the Optimal Number of Interview Waves in a Panel Survey with Application to the National Crime Victimization Survey

Panel surveys need to balance the benefits of repeated measurements (e.g., bounded interview, reduced cost, increased response rates) with the drawbacks that may eventually occur (e.g., respondent fatigue, mode effect). The optimal number of interview waves for a panel survey needs to maximize the advantages while minimizing the potential for bias due to incorporating sampling units for too many interview waves. In this paper, we develop cost models for two potential constraints: (1) keeping the number of interviews constant across designs, and (2) keeping the cost constant across designs. These models are applied to the National Crime Victimization Survey (NCVS). The NCVS currently uses a seven-wave or time-in-sample (TIS) design. In an effort to maintain or reduce costs and improve data quality, the Bureau of Justice Statistics commissioned a Panel Design Study to evaluate the effects of changing the NCVS from a 7-TIS design to a 5-TIS, 4-TIS, 3-TIS, or 1-TIS design. The study used a set of simulations to mimic different panel designs. The simulation assumptions were constructed using NCVS data from 1999 to 2011, and included assumptions about sample sizes, costs, response rates, household replacement, type of interview, demographics, and victimization propensities. Samples were simulated with different panel designs and summary victimization propensities, and standard errors were computed for key estimates. Simulations considered both keeping the cost constant and keeping the number of interviews constant across the different panel design options. In this paper, we show the impact of changing the number of panel TISs on property and violent victimization rates in terms of point estimates, variability, sample sizes, and costs, by several population characteristics. Simulation results found that a 4-TIS design is optimal for the NCVS.

Minimum cost plastic design of steel beams using Eurocode 3

This paper presents a model for minimum cost plastic design of steel beams according to the Eurocode3 (EC3). The objective function comprises material cost and fabrication costs. The constraint functions are set to meet design requirements of Eurocode3 (EC3). They consist on plastic bending resistance constraints, plastic vertical shear resistance, design constraints derived from Eurocode3 (EC3), deflection due to both dead and live loads and current practices rules.The minimum cost plastic design process is developed through the use of the Generalized Reduced Gradient (GRG) algorithm. Two numerical examples are given to illustrate the applicability of the proposed model. The optimized results are compared to traditional design solutions from conventional design office methods to evaluate the performance of the developed cost model. Substantial savings were achieved through this approach. In addition, this study shows the difference between steel beams optimized for minimum cost and minimum weight. The proposed approach is practically simple, reliable and computationally effective compared to classical designs procedures used by designers and engineers.

A methodology for near net shape process feasibility assessment

Manufacturing engineers are frequently asked to select the best process for creating components but often the judgement is qualitative rather than quantitative. This paper presents a methodology (DCFA – Differential Cost and Feasibility Analysis) for assessing the technological and economic feasibility of using Near Net Shape (NNS) processes for the manufacturing of specific components. The methodology examines changes in raw material usage and finish processes (e.g. machining processes) that would result from adaption of a new manufacturing process. To illustrate the method, a case study that assesses the feasibility of using centrifugal casting for the production of valve cages is detailed. The case study concludes that the application of this process to the current manufacturing lines could result in significant cost reductions (particularly in machining time and reduction of scrappage). The feasibility methodology is generic and can potentially be used to investigate the application of a broad range of NNS processes in general manufacturing applications. Further, the developed cost models also allow the economic impact of a new process to be assessed, even at the early stages of product design.

A Note on Competitive Cost Analysis

This technical note provides a conceptual framework and specific technical skills for cost driver modeling. Excerpt UVA-OM-1190 Rev. Mar. 7, 2013 A NOTE ON COMPETITIVE COST ANALYSIS An understanding of the factors that drive the micro-economics of a business provides the insight needed for strategic and tactical improvements in operational effectiveness. Competitive cost analysis, a technique for analyzing the cost structure of two competing companies, brings the drivers of competitive differences into high resolution for managerial action. A rigorous competitive cost analysis facilitates a variety of decisions—ranging from product/service redesign to the global operations footprint, to business process outsourcing—by highlighting the key factors that drive operational costs. Unfortunately most accounting and financial data address the elements of cost rather than the factors that drive the cost (cost drivers). This note presents a conceptual framework to help focus competitive cost analysis into categories of cost drivers and explains various applications for competitive cost analysis. The latter part of the note offers a set of principles to consider when developing cost models for competitive analysis as well as some specific modeling techniques. The Role of Competitive Cost Analysis Competitive cost analysis represents a strategic application of cost modeling. At the highest level it allows for a direct comparison of a company and one of its products or services to a direct competitor or competitive offering. During the 1980s, many U.S. manufacturing concerns benchmarked their operations versus Japanese competitors in an attempt to understand how the Japanese were able to offer superior quality products at lower costs. Over time, initial assumptions of “price dumping,” wage-rate advantages, and superior technology were replaced with an appreciation for Lean manufacturing. Ultimately, this insight led to a dramatic renewal through much of the U.S. manufacturing sector. Current competitive benchmarking efforts focus on developing regions such as India and China in an attempt to understand the offsetting impact of transportation cost and time versus labor cost advantages. These insights now shape the footprint of manufacturing companies as well as the deployment of resources in “backroom” operations in a wide range of industries. . . .

Competitive-Cost Analysis: Cost-Driver Framework

The first in a three-part series on competitive-cost analysis, this note introduces cost drivers, presents a conceptual framework for organizing them, and explains various applications of competitive-cost analysis. It also offers a set of principles to consider when developing cost models for competitive analysis. Excerpt UVA-OM-1254 Rev. Aug. 9, 2018 Competitive-Cost Analysis: Cost-Driver Framework An understanding of the factors that drive the microeconomics of a business provides the insight needed for strategic and tactical improvements in operational effectiveness. Competitive-cost analysis, a technique for analyzing the cost structure of two competing companies, brings the drivers of competitive differences into high resolution for managerial action. A rigorous competitive-cost analysis facilitates a variety of decisions—ranging from product/service redesign to the global-operations “footprint” to business-process outsourcing—by highlighting the key factors that drive operational costs. Unfortunately, most accounting and financial data address the elements of cost rather than the factors that drive the cost (cost drivers). This first of three notes on competitive-cost analysis (see also UVA-OM-1255 and UVA-OM-1256) presents a conceptual framework for organizing cost drivers and explains various applications of competitive-cost analysis. It also offers a set of principles to consider when developing cost models for competitive analysis. The second note introduces four specific cost-modeling tools to analyze cost drivers. The final note provides more details on the mechanics of estimating scale curves and adjusting for utilization effects. The Role of Competitive-Cost Analysis Competitive-cost analysis represents a strategic application of cost modeling. At the highest level, it allows for a direct comparison of a company and one of its products or services with a direct competitor or competitive offering. During the 1980s, many US manufacturing concerns benchmarked their operations against Japanese competitors in an attempt to understand how the Japanese were able to offer superior products at lower prices. Over time, initial assumptions of “price dumping,” wage-rate advantages, and superior technology were replaced by an appreciation for what became known as “Lean manufacturing.” Ultimately, this insight led to a dramatic renewal throughout much of the US manufacturing sector. Today, competitive-benchmarking efforts often focus on such developing regions as India and China in an attempt to understand whether transportation costs and lead-time delays will offset the labor-cost advantages available in such developing regions. These insights now shape the footprint of manufacturing companies as well as the deployment of resources in “backroom” operations in a wide range of industries. . . .

어랑분포의 형상모수 변화에 따른 소프트웨어 개발 비용모형에 관한 비교 연구

소프트웨어 개발과정에서 소프트웨어 신뢰성은 매우 중요한 문제 중에 하나이다. 소프트웨어 고장현상을 분석하기 위하여 비동질적인 포아송과정에서 고장 발생 추이를 의미하는 위험함수가 고장시간에 독립적으로 일정하거나, 종속적인 경우, 즉 비-증가 또는, 비-감소하는 속성을 가질 수 있다. 본 연구에서는 소프트웨어 제품 테스팅 과정에서 고장 수명분포로서 어랑분포의 다양한 형상모수를 고려한 소프트웨어 개발 비용 분석에 대하여 연구되었다. 소프트웨어 고장현상을 분석하기 위하여 모수추정은 최우추정법이 사용되었다. 따라서 본 논문에서는 어랑분포의 형상모수를 고려한 소프트웨어 개발비용모형 분석을 위하여 소프트웨어 고장간격 시간자료를 이용하여 비교 및 평가하였다. 그 결과 형상모수에 따른 비용곡선을 비교 하였을 때 형상모형이 작을수록 비용이 많고 소프트웨어 최적 방출시간이 지연 됨을 알 수 있었다. 이 연구를 통하여 소프트웨어 개발자들에게 소프트웨어 형상모수에 따른 개발 비용을 탐색하는데, 기본적으로 도움을 줄 수 있는 사전정보의 역할을 할 수 있을 것으로 판단된다. Software Reliability implemented in software development is one of the most important issues. In finite failure NHPP software reliability models for software failure analysis, the hazard function that means a failure rate may have constant independently for failure time, non-increasing or non-decreasing pattern. In this study, software development cost analysis considering the variable shape parameter of Erlang distribution as the failure life distribution in the software product testing process was studied. The software failure model was applied finite failure Non-Homogeneous Poisson Procedure and the parameters approximation using maximum likelihood estimation was accompanied. Thus, this paper was presented comparative analysis by applying a software failure time data to the software, considering the shape parameter of Erlang distribution for development cost model analysis. When compared to the cost curve in accordance with the shape parameter, the model of smaller shape can be seen that the optimal software release time delay and more cost. Through this study, it is thought that it can serve as a preliminary information which can basically help the software developers to search for development cost according to software shape parameters.