Rough-Cut Cost Estimation in a Capacitated Environment

2010 ◽  
pp. 1935-1953
Author(s):  
Mark Eklin ◽  
Yohanan Arzi ◽  
Avraham Shtub
Keyword(s):  
Cost Estimation ◽  
Capacity Planning ◽  
Deterministic Model ◽  
Unit Cost ◽  
Greedy Heuristic ◽  
Shop Floor ◽  
Machine Loading ◽  
Proposed Model ◽  
Traditional Costing ◽  
Rough Cut

This article presents a deterministic model for rough-cut cost estimation in a capacitated madeto- order environment. We assume that a firm can execute each job either at its own shop or by outsourcing it. The model calculates the unit cost of each product while taking into account the shop floor rough-cut capacity planning, and by determining what to produce in the firm’s shop and what to outsource. In order to reduce run times, a greedy heuristic algorithm was developed. Comparison of the proposed model with a model that takes into account precedence between operations and with a traditional costing approach was conducted. The article gives insight on the affect of shop workload, machine loading, and outsourcing decisions on the product unit cost estimation.

Cost Estimation in a Capacitated Environment

2010 ◽  
pp. 16-88
Author(s):  
Mark Eklin ◽  
Yohanan Arzi ◽  
Avraham Shtub
Keyword(s):  
Cost Estimation ◽  
Unit Cost ◽  
Setup Time ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Estimation Model ◽  
Process Characteristics ◽  
Machine Loading ◽  
Cost Estimation Model ◽  
Rough Cut

In this chapter we discuss rough-cut cost estimation in a capacitated made-to-order environment. We develop models that analyze the effects of shop workload, machine loading, and outsourcing decisions on product unit cost estimation. A comparative study of five alternative rough-cut cost estimation methods is presented. An activity based cost estimation model, which takes into account stochastic process characteristics as well as setup time, machine failures and product yields, was developed. The activity based cost estimation was found to perform better than the traditional cost estimation. We found that by taking into account the capacity and stochastic nature of the parameters, the cost estimation accuracy is improved significantly.

A Rough-Cut Cost Estimation in a Finite-Capacity Stochastic Environment Based on Forced Idle Time Prediction

2008 ◽  
Author(s):  
Mark Eklin ◽  
Yohanan Arzi ◽  
Avraham Shtub
Keyword(s):  
Cost Estimation ◽  
Estimation Method ◽  
Idle Time ◽  
Estimation Methods ◽  
Shop Floor ◽  
Limited Capacity ◽  
Time Prediction ◽  
The Cost ◽  
Rough Cut ◽  
Cost Estimation Models

In recent years several researchers suggested cost estimation models that consider the limited capacity of the shop. In these studies, the stochastic nature of the shop floor is modeled by a time-consuming simulation. This paper proposes five alternative rough-cut cost estimation methods that can replace the simulation. Three of five methods based on forced idle time prediction. The study compares the cost estimations derived from these methods. A cost estimation method, based on the forced idle time of the bottleneck workstation, was found to be outperform the others. As the best method, the bottleneck-based method was compared to the actual order’s cost and was found as a replacement to simulation.

scholarly journals CONCEPTUAL COST ESTIMATIONS USING NEURO-FUZZY AND MULTI-FACTOR EVALUATION METHODS FOR BUILDING PROJECTS

2017 ◽  
Vol 23 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Wei-Chih WANG ◽  
Tymur BILOZEROV ◽  
Ren-Jye DZENG ◽  
Fan-Yi HSIAO ◽  
Kun-Chi WANG
Keyword(s):  
Adaptive Learning ◽  
Cost Estimation ◽  
Unit Cost ◽  
Cost Component ◽  
Building Projects ◽  
Regression Methods ◽  
Proposed Model ◽  
Average Unit ◽  
Factor Evaluation ◽  
Mathematical Techniques

During the conceptual phase of a construction project, numerous uncertainties make accurate cost estimation challenging. This work develops a new model to calculate conceptual costs of building projects for effective cost control. The proposed model integrates four mathematical techniques (sub-models), namely, (1) the component ratios sub-model, (2) fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm (fmGA) based sub-model, (3) regression sub-model, and (4) multi-factor evaluation sub-model. While the FALCON- and fmGA-based sub-model trains the historical cost data, three other sub-models assess the inputs systematically to estimate the cost of a new pro­ject. This study also closely examines the behavior of the proposed model by evaluating two modified models without considering fmGA and undertaking sensitivity analysis. Evaluation results indicate that, with the ability to more thor­oughly respond to the project characteristics, the proposed model has a high probability of increasing estimation accura­cies more than the three conventional methods, i.e., average unit cost, component ratios, and linear regression methods.

A noble extended stochastic logistic model for cell proliferation with density-dependent parameters

2021 ◽  
Author(s):  
Trina Roy ◽  
Sinchan Ghosh ◽  
Bapi Saha ◽  
Sabyasachi Bhattacharya
Keyword(s):  
Cell Proliferation ◽  
Cell Density ◽  
Negative Feedback ◽  
Deterministic Model ◽  
Culture Media ◽  
Logistic Growth ◽  
Density Dependent ◽  
Proposed Model ◽  
Dependent Cell ◽  
Growth Inhibiting

Abstract Cell proliferation often experiences a density-dependent intrinsic proliferation rate (IPR) and negative feedback from growth-inhibiting molecules in culture media. The lack of flexible models with explanatory parameters fails to capture such a proliferation mechanism. We propose an extended logistic growth law with the density-dependent IPR and additional negative feedback. The extended parameters of the proposed model can be interpreted as density-dependent cell-cell cooperation and negative feedback on cell proliferation. Moreover, we incorporate further density regulation for flexibility in the model through environmental resistance on cells. The proposed growth law has similarities with the strong Allee model and harvesting phenomenon. We also develop the stochastic analog of the deterministic model by representing possible heterogeneity in growth-inhibiting molecules and environmental perturbation of the culture setup as correlated multiplicative and additive noises. The model provides a maximum sustainable stable cell density (MSSCD) and a new fitness measure for proliferative cells. The proposed model shows superiority to the logistic law after fitting to real cell culture datasets. We illustrate both MSSCD and the new cell fitness for a range of parameters. The cell density distributions reveal the chance of overproliferation, underproliferation, or decay for different parameter sets under the deterministic and stochastic setups.

scholarly journals A Simple Trajectory Model for Climatological Study of Tropical Cyclones

2020 ◽  
Vol 33 (18) ◽  
pp. 7777-7786
Author(s):  
Kaiyue Shan ◽  
Xiping Yu
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Deterministic Model ◽  
North Atlantic Ocean ◽  
North Pacific Ocean ◽  
Computational Cost ◽  
Ocean Basin ◽  
Trajectory Model ◽  
Proposed Model

AbstractThe establishment of a tropical cyclone (TC) trajectory model that can represent the basic physics and is practically advantageous considering both accuracy and computational cost is essential to the climatological studies of various global TC activities. In this study, a simple deterministic model is proposed based on a newly developed semiempirical formula for the beta drift under known conditions of the environmental steering flow. To verify the proposed model, all historical TC tracks in the western North Pacific and the North Atlantic Ocean basins during the period 1979–2018 are simulated and statistically compared with the relevant results derived from observed data. The proposed model is shown to well capture the spatial distribution patterns of the TC occurrence frequency in the two ocean basins. Prevailing TC tracks as well as the latitudinal distribution of the landfall TC number in the western North Pacific Ocean basin are also shown to agree better with the results derived from observed data, as compared to the existing models that took different strategies to include the effect of the beta drift. It is then concluded that the present model is advantageous in terms of not only the accuracy but also the capacity to accommodate the varying climate. It is thus believed that the proposed TC trajectory model has the potential to be used for assessing possible impacts of climate change on tropical cyclone activities.

scholarly journals ANALISA PERENCANAAN KAPASITAS PRODUKSI PADA PT. MUNCUL ABADI DENGAN METODE ROUGH CUT CAPACITY PLANNING

2018 ◽  
Vol 8 (2) ◽  
pp. 91
Author(s):  
Iksan Iksan
Keyword(s):  
Capacity Planning ◽  
Production Capacity ◽  
Washing Machine ◽  
Production Forecast ◽  
Production Index ◽  
Forecast Result ◽  
One Year ◽  
Production Target ◽  
Rough Cut ◽  
Planning Production

Production capacity planning this company have obstacle in meeting its production target, then company oftentimes unable to fulfill consumer demands. It can be inflict the company loose. The problems is how planning production capacity based on Rough Cut Capacity Planning (RCCP) Method in order to make consumers demand be able to supplied? “ The researcher attempt to resolving problem in PT Muncul Abadi by aiming calculate product capacity plan based on Rough Cut Capacity Planning Method and determine required product. To be useful as consideration for the company in planning production. Forecast done within the coming one year term. Capacity planning base on Rough Cut Capacity Planning (RCCP) Method. From forecast result toward previous demand quantity period within 12 periods we could be make production index schedule and order Bill of Resources with standard time that is 0,000316 hours/kg. Able to know machine capacity need in 1 workday = 8 hours day, with 3 shifts per day, 1 week = 6 workdays, 1 month = 25 effective workdays then : available time per month = 25 effective workdays x 8 hours day x 3 shift per day = 600 hours/month. Available capacity for Washing machine = 1.394 hours/month. Available capacity for Crushing machine = 1.394 hours/month. Available capacity for Pelletizing machine = 1.859 hours/month.

Modelling and Optimization of Integrated Planning of Production and Maintenance with Subcontract Constraint

2018 ◽  
Vol 40 ◽  
pp. 184-203 ◽  
Author(s):  
Nouhayla Hafidi ◽  
Abdellah El Barkany ◽  
Morad Mahmoudi
Keyword(s):  
Production System ◽  
Unit Cost ◽  
Production Capacity ◽  
Threshold Level ◽  
Research Direction ◽  
Optimal Choice ◽  
Proposed Model ◽  
Joint Policy ◽  
Two Parameters ◽  
New Research

This article addresses the problem of the joint policy of production and maintenance under constraint of outsourcing. The production system considered brings together two companies; the principal represented by a machine Md, while the subcontractor represented by a machine Ms. Our production system aims to satisfy a constant and continuous demand for a single product type. Indeed, outsourcing is justified by the lack of production capacity. However, the main objective is to determine simultaneously for each period, the age of preventive maintenance, the optimal stock threshold level, the maximum capacity of subcontractor and its unit cost of production, to better satisfy the customer's need. The last two parameters encourage an optimal choice of subcontractor, while minimizing the total cost generated by the contractor, including the costs of maintenance, production, storage and shortage. The results show that the proposed model performs quite well and opens new research direction for future improvements.

Mathematical Programming for Modelling Green Supply Chains Under Randomness and Fuzziness

2017 ◽  
Vol 6 (1) ◽  
pp. 56-85
Author(s):  
Javad Nematian ◽  
Seyed Salar Ghotb
Keyword(s):  
Supply Chains ◽  
Deterministic Model ◽  
Negative Impact ◽  
Fuzzy Theory ◽  
Mixed Integer ◽  
Chance Constrained Programming ◽  
Proposed Model ◽  
Constrained Programming ◽  
Green Supply Chains ◽  
Fuzzy Random

Nowadays by growing concerns about environmental problems, businesses and industries are under pressure to decrease their negative impact on environment, consequently firms and industries have to reconsider about their activities and make their business compatible with environment. So industries should green their supply chains to optimize economic and environmental concerns, but because of uncertainty in the real world like inconsistency of world economy, the process of greening supply chains can be more complex. To optimize total costs and the unfavourable sides of supply chains simultaneously in an uncertain situation, this paper presents a multi-objective mixed integer programming with fuzzy random variables (FRVs) and by using fuzzy theory and fuzzy random chance-constrained programming (FRCCP), the proposed model is converted to deterministic model. This paper can be also suitable for decision making with optimistic, pessimistic and realistic notion. Finally, a numerical example is presented to illustrate the model.

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