Current Cost Models Research Articles

Adaptive consensus reaching process with dynamic weights and minimum adjustments for group interactive portfolio optimization

The mean–variance model (MV) can help companies in an economic downturn identify portfolios that increase revenue while reducing risk. However, the MV optimization model derives a set of optimal portfolios. The limited rationality and experience of the company's manager, as well as the conflict of interest with the shareholders, require several experts to help the manager make investment decisions. Moreover, experts' unique preferences require negotiation through interaction to reach a consensus, which is not considered in existing group interactive optimization algorithms. The social network group decision-making (SNGDM) method is an effective approach for reaching consensus. Notably, in addition to the trust relationships, experts' intrinsic risk preferences and rational levels affect investment decisions and change with the decision-making process. Another important problem is reaching consensus with finite interaction costs. Although dynamic opinion is considered by current minimum cost models, the impact of dynamic trust on consensus is neglected. This research aims to develop a group interactive portfolio optimization method based on SNGDM (SNGDM-PO) to address the mentioned limitations. First, we introduce consensus into group interactive optimization algorithms and develop an adaptive learning model that simulates group preferences. Then, we combine dynamic intrinsic influence and extrinsic trust to generate experts' dynamic weights. To reduce interaction consensus costs, we propose an adaptive consensus feedback mechanism that minimally adjusts for dynamic opinion and trust with a time constraint. Finally, empirical study and comparison findings demonstrate that the SNGDM-PO can efficiently obtain consensus with minimum adjustments and low interaction costs and produce high group satisfaction.

Agent-Based Mobility Simulation for Potential Cost and Benefits of Subscription Services with Associated Cost Structures

This study aims to present a method to analyze the problems that arise from the increasing use of shared mobility systems to solve the current problems of congestion. The use of shared and/or autonomous vehicles (SAVs) is beneficial since it could reduce the number of vehicles on the roads. On the other hand, the increased usage of SAVs to fulfill the same transportation needs would affect the total life of those vehicles. Current cost models are based on average values and life cycles of 15 years; they may not be useful in the case of increased daily travel miles of vehicles when subscription-based shared mobility systems become prevalent. In this paper, we present a comprehensive framework which considers the interactions of new cost structures and the system performance under new mobility schemes. This paper envisions the scenarios that will exemplify the importance of considering the changing costs because of the increased mileage of vehicles. Cost functions are properly designed to assess the new scenarios, a pricing scheme is introduced from the service provider’s point of view to adjust the demand for the services and the profits, an agent-based simulation platform is presented, and a framework is demonstrated to consider the interactions and optimize the system.

SRGM Decision Model Considering Cost-Reliability

Aiming at the current software cost model and optimal release research, which does not fully consider the actual faults in the testing phase, a cost-reliability SRGM evaluation and selection algorithm SESABCRC is proposed. From the perspective of incomplete debugging, introducing new faults, and considering testing effort, the imperfect debugging SRGM is established. The proposed SRGM can be used to describe the testing process of the software through the actual failure data set verification, and is superior to other models. Based on the proposed SRGM, the corresponding cost function is given, which explicitly considers the impact of imperfect debugging on the cost. Furthermore, an optimal release strategy is proposed when given restricted reliability target requirements and when considering the uncertainty that the actual cost may exceed the expected cost. Finally, an experimental example is given to illustrate and verify the optimal publishing problem, and parameter sensitivity analysis is carried out.

Aircraft Cost Modelling, Integrated in a Multidisciplinary Design Context

Most of the current cost models focus on a particular manufacturing process or a specific maintenance aspect, therefore not providing the whole picture. The main challenge in modelling the manufacturing cost, associated to a new aircraft at the initial design stage, is to examine all the cost features and the way to link them into the decision making process. It is important to understand the cost related to different competing designs, and this can be tackled by including cost estimation in the design process. Estimating the cost at the early design stage is paramount to reduce the life cycle cost of the aircraft. This paper presents the development of a new methodology for the generation of a cost estimation approach for preliminary aircraft design in a multidisciplinary environment. The framework is able to capture the design attributes that drive the cost allowing a designer to assess cost changes with respect to different design configurations. The cost model is built in Excel using a Visual Basic interface and it is integrated within Model Centre platform, where it can be treated as a component of a computational design process. The paper concludes by presenting the results from a real wing trade-off study that includes all the components of a complete design system.

Optimization of data flow execution in a parallel environment

Although the modern data flows are executed in parallel and distributed environments, e.g. on a multi-core machine or on the cloud, current cost models, e.g., those considered by state-of-the-art data flow optimization techniques, do not accurately reflect the response time of real data flow execution in these execution environments. This is mainly due to the fact that the impact of parallelism, and more specifically, the impact of concurrent task execution on the running time is not adequately modeled in current cost models. The contribution of this work is twofold. Firstly, we propose an advanced cost model that aims to reflect the response time of a data flow that is executed in parallel more accurately. Secondly, we show that existing optimization solutions are inadequate and develop new optimization techniques targeting the proposed cost model. We focus on the single multi-core machine environment provided by modern business intelligence tools, such as Pentaho Kettle, but our approach can be extended to massively parallel and distributed settings. The distinctive features of our proposal is that we model both time overlaps and the impact of concurrency on task running times in a combined manner; the latter is appropriately quantified and its significance is exemplified. Furthermore, we propose extensions to current optimizers that decide on the exact ordering of flow tasks taking into account the new optimization metric. Finally, we evaluate the new optimization algorithms and show up to 59% response time improvement over state-of-the-art task ordering techniques.

Dynamic optimization of maintenance and improvement planning for water main system: Periodic replacement approach

This paper proposes a Markov decision process (MDP) based approach to derive an optimal schedule of maintenance, rehabilitation and replacement of the water main system. The scheduling problem utilizes auxiliary information of a pipe such as the current state, cost, and deterioration model. The objective function and detailed algorithm of dynamic programming are modified to solve the periodic replacement problem. The optimal policy evaluated by the proposed algorithm is compared to several existing policies via Monte Carlo simulations. The proposed decision framework provides a systematic way to obtain an optimal policy.

Vendor Managed Inventory: Why you need to talk to your supplier

Purpose The purpose of this paper is to investigate the concept of Vendor Managed Inventory (VMI) from an inter-organisational perspective. Extant literature on VMI tends to investigate the concept from a focal perspective, even though VMI has originally been born as a collaborative arrangement. Design/methodology/approach The paper is based on a literature review and an empirical study. It provides a comprehensive literature review on VMI and an illustrative case study of a supplier and a buyer jointly implementing VMI. Findings The findings of this paper are twofold. First, a literature review uncovers that contemporary research has delimited the analysis of VMI to a focal company perspective as current VMI cost models tend not to capture the picture of the complete supply chain. Second, it demonstrates through an illustrative case study that adoption of an inter-organisational approach to VMI is vital if companies are to optimize their buyer-supplier relationships. Research limitations/implications Future research should test the implications proposed in the empirical section, as this piece of research can be seen as exploratory case study research with the aim of analytical generalizations. Practical implications The inter-organisational VMI cost perspective in supply chains should be emphasized in purchasing departments since such a perspective significantly raises the awareness of the costs incurred in a supply chain. Originality/value Existing research has not explicitly focused on inter-organisational costs incurred by companies implementing VMI. This study seeks to bridge this research gap.

An automated approach for an optimised least cost solution of reinforced concrete reservoirs using site parameters

This paper presents design, development and application of a finite-element based least cost optimisation model (named ResOp) for reservoirs using a Genetic Algorithm. The model makes use of site specific parameters not normally considered at outline design but which are usually available; such as site plan limits, maximum height above ground level and geotechnical conditions.The results show that such site based parameters have a significant effect on cost which can be easily incorporated at outline design stage without making expensive changes at the detailed design stage of a project. This would also be suitable when considering a selection of sites. Current cost models in the industry are too basic and should become more site specific.The design of a reservoir constructed in Cornwall was compared to an optimised reservoir design using ResOp. The results show a potential for substantial savings to be made. The aspect ratio and shape found reasonable correlation to best practice, but the developed model suggests a more refined optimisation approach which includes site variables.

Corporate-owned power plant under the conditions of supply to determine the cost of transmission

Conditions for captive power plant, the establishment of the DC current cost model changes, the transmission cost of captive power plant analysis, the text node using a numerical example of the model is validated, the results show that the model and the physical concepts clear and balancing machines node position independent of the choice, with strong operational and practical.

Improving Space Project Cost Estimating With Engineering Management Variables

:Current aerospace parametric cost models predict space project costs using regression equations. The equations relate the historical cost of projects to technical variables such as weight and power. While improvements in parametric cost modeling have been made, there is still significant variability in the estimates. This is particularly true when estimating the costs for complex systems. Part of this variability may be due to the models' inability to capture the impacts of the engineering management considerations required to develop these systems. This article lays the groundwork for the introduction of engineering management variables into current cost models to improve their predictive capabilities.

A revised model for the cost of quality

Traditionally, process improvement is considered a defect prevention effort. Current cost models consider the coupled effect of both prevention and appraisal costs on the cost of failure. This paper proposes a new model for the cost of quality, which captures the value of continuous process improvement in achieving economic operation. The model is developed to incorporate two cost functions. The first accounts for quality related costs incurred while maintaining a stable level of operation, while the second accounts for the cost of process improvement. Using incremental economics, the two cost functions are assembled and an economic criterion for evaluating improvement alternatives is developed. Numerical examples are used to illustrate potential applications and performance of the model.

A framework for integrating data alignment, distribution, and redistribution in distributed memory multiprocessors

Parallel architectures with physically distributed memory provide a cost-effective scalability to solve many large scale scientific problems. However, these systems are very difficult to program and tune. In these systems, the choice of a good data mapping and parallelization strategy can dramatically improve the efficiency of the resulting program. In this paper, we present a framework for automatic data mapping in the context of distributed memory multiprocessor systems. The framework is based on a new approach that allows the alignment, distribution, and redistribution problems to be solved together using a single graph representation. The Communication Parallelism Graph (CPG) is the structure that holds symbolic information about the potential data movement and parallelism inherent to the whole program. The CPG is then particularized for a given problem size and target system and used to find a minimal cost path through the graph using a general purpose linear 0-1 integer programming solver. The data layout strategy generated is optimal according to our current cost and compilation models.

Monitoring and measuring the change-prediction process at different granularity levels: an empirical study

Monitoring and measuring industrial software processes are necessary for understanding processes and establishing baselines for process improvement. In this paper we monitor and measure the capability of experienced software developers to predict software change caused by new requirements to an existing software system (i.e. impact analysis) at different levels of granularity. The study shows a general underprediction and that the accuracy of the prediction deteriorated with increasing level of detail. The results were non-intuitive for the practitioners participating in the study and therefore important to consider when revising current processes and cost models based on such estimates. The study is a good example of how software industry can co-operate with university researchers in monitoring, measuring and evaluation of current practices. The results will be used by the organization as a baseline for further studies of subsequent releases of the product. Copyright © 1998 John Wiley & Sons, Ltd.

Discussion of The Analytical Derivation and Empirical Test of a Tax-Adjusted Fundamental Value Model

This paper considers the valuation of used fixed assets. The authors claim that the tax-adjusted fundamental value (FV) model they develop yields a superior measure of value relative to the historical cost (HC) model or the current cost (CC) model, which is used by over 90% of firms in the preparation of their SEAS No. 33 disclosures. Conference participants raised questions regarding both theoretical and empirical aspects of the paper. Section 2 discusses the theoretical issues, and section 3 discusses the empirical tests.

Software parametric cost estimation: Wave of the future

The cost of computer software is comprising an increasingly significant portion of the life cycle costs of modern systems. Computers and software are being used in all types of systems from military weapon systems to automobile engines and televisions, so that software costs must almost always be addressed. Furthermore, the amount and complexity of software being used in all types of systems is steadily increasing: These costs cannot be ignored. The objective of this paper is to provide the reader with an overview of software parametric cost estimating in order that he or she can better understand how software costs can be estimated, and what can be expected from currently-available software parametric cost estimating techniques. First, the use of parametrics in software cost estimating are explained. Next, some of the current popular software parametric cost models are briefly described. Third, some of the issues related to cost estimation, such as software size estimation and software reliability, are briefly discussed. The paper concludes with the writer's assessment of the current status of parametric estimating, including a discussion of what quality and accuracy can be expected from currently-available techniques. Also, an evaluation of what should be accomplished in the future in the field of software parametric cost estimation is included.

Time-Sensitive Cost Models in the Commercial MIS Environment

Current time-sensitive cost models suggest a significant impact on project effort if elapsed time compression or expansion is implemented. This paper reports an empirical study into the applicability of these models in the management information systems environment. It is found that elapsed time variation does not consistently affect project effort. This result is analyzed in terms of the theory supporting such a relationship, and an alternate relationship is suggested.

A cos-benefit analysis of accounting for inflation

This research develops a methodology to assess the net social benefits associated with three alternative measurement models: general price-level adjusted historical cost (GPLA), current cost (CC), and current cost general price-level adjusted (CC-GPLA). Net benefits were estimated in six basic steps: 1) computer programs were used to restate historical cost financial information covering 16 years for 21 companies for specific and general price-level changes; 2) three multiple regression models were developed for each company and each measurement model that were used to develop forecasts of the societal rate of return (income before deducting interest and income taxes divided by total assets net of accumulated depreciation, depletion, and amortization) for each year of a three-year test period; 3) the total societal resources available to each industry were allocated to the companies with forecasted average rates of return in the higher percentiles of the distribution of rates of return as measured by each mode; 4) gross benefits were estimated by comparing the total societal income (using CC-GPLA as a surrogate) under GPLA, CC, and CC-GPLA with the total societal income under the historical cost model; 5) estimated costs of applying each model were obtained by surveying company executives; 6) net benefits of each measurement model were estimated by discounting the differences between estimated gross benefits and the estimated costs. The results indicated that the CC-GPLA model almost always was superior to GPLA and CC. The CC model was inferior to GPLA for the chemical industry, but whether it was inferior for the steel industry depended on the assumptions made.