Development of NPP decommissioning cost estimation algorithm based on the CANDU structure

Abstract The problem under consideration is the cost estimation of part manufacturing while taking into account processing alternatives. In order to determine overall costs for feasible process plans we take into account, in our Petri net model, costs caused by machine, setup and tool changing in addition to the single operation cost. We introduce a new Petri net model that allows the application of incremental cost analysis algorithms. This is a CPP-net (Compact Process Planning net) which represents manufacturing knowledge in the form of precedence constraints and incorporates machining cost, machine, setup and tool information in each transition. We show that the CPP-net allows the calculation of the optimum process plan without the need to first develop all possible solutions. We apply the developed methods and calculate the optimum process plan to an industrial case study of a mechanical workpiece of moderate complexity.

Download Full-text

DATA DRIVEN PERFORMANCE EVALUATION IN SHIPBUILDING

Brodogradnja ◽

10.21278/brod71403 ◽

2020 ◽

Vol 71 (4) ◽

pp. 39-51

Author(s):

Umran Bilen ◽

◽

Sebnem Helvacioglu

Keyword(s):

Cost Estimation ◽

Structural Design ◽

Data Science ◽

Rapid Development ◽

Estimation Algorithm ◽

Data Driven ◽

Medium Size ◽

Analysis Techniques ◽

Use Of Data ◽

Evaluation Of Performance

Rapid development in data science keeps paving the way for use of data for many purposes in shipbuilding, both for product development and production, such as Industry 4.0 have been developing many industries. Similar to other industries the evaluation of performance in shipbuilding is the key to success which is closely connected to productivity and lowered costs. Data mining and analysis techniques are used to create effective algorithms to evaluate the performance, also by means of cost estimation based on parametric methods. However, it is usually not very clear how data are collected, organised and prepared for analysing and deriving valuable knowledge as well as algorithms. In most of the cases, having this data requires either continuous investment in expensive software or expensive external expertise which are generally not available for small and medium size shipyards. In this study, considering the needs of the small and medium sized shipyards, a step-by-step methodology is proposed which could be easily applied with widely available low budget software. The application is demonstrated with a case to evaluate the performance of early phase structural design with a data driven cost estimation algorithm.

Download Full-text

A Databased Time and Cost Estimation Algorithm for Piece Part Design and Manufacturing

CAD/CAM Robotics and Factories of the Future ’90 ◽

10.1007/978-3-642-84338-9_64 ◽

1991 ◽

pp. 458-465

Author(s):

K. W.-N. Lau ◽

M. Ramulu

Keyword(s):

Cost Estimation ◽

Estimation Algorithm ◽

Design And Manufacturing

Download Full-text

Probabilistic green infrastructure cost calculations using a phased life cycle algorithm integrated with uncertainties

Journal of Hydroinformatics ◽

10.2166/hydro.2018.107 ◽

2018 ◽

Vol 20 (5) ◽

pp. 1201-1214

Author(s):

Ziwen Yu ◽

Franco Montalto ◽

Chris Behr

Keyword(s):

Life Cycle ◽

Interest Rates ◽

Green Infrastructure ◽

Cost Estimation ◽

Life Cycle Cost ◽

Net Present Value ◽

Rapid Assessment ◽

Low Impact Development ◽

Estimation Algorithm ◽

Present Value

Abstract Green infrastructure (GI) is often considered a cost-effective approach to urban stormwater management. Though various models have been created to simulate the life cycle cost (LCC) and present value (PV) of GI investments, decision-support tools are still few. This paper introduces a probabilistic GI cost estimation algorithm built into the Low Impact Development Rapid Assessment (LIDRA) model. This algorithm tracks annual and cumulative costs associated with the construction, operation and maintenance (O&M), and ultimate replacement of GI systems. In addition, the algorithm accounts for uncertainties in cost drivers, such as a GI's useful life (until replacement), capital and annual O&M costs, inflation, and interest rates. Net present value (NPV) is used to normalize future money flows and cumulative costs of different GI investment scenarios into a comparable current year cost equivalent. Demonstrated at the block scale, the results of the LIDRA algorithm are compared to an MS Excel-based computation of average costs. Variations of uncertainties are then integrated and further explored using an alternative implementation rate. This algorithm is a way to evaluate GI costs considering physical, socioeconomic and life cycle dimensions.

Download Full-text