Cost Deviation Research Articles

Cost factors and statistical evaluation of gas transmission pipeline construction and compressor-station cost in the USA, 2014-2019

The costs of constructing pipelines will vary widely from area to area depending on the length and diameter of the pipeline, population density, the terrain, number and type of crossings, right of way requirements, regulatory conditions, time of construction and the market conditions of equipment and materials. From 2014-2019, 2,063 miles of gas transmission pipeline and 1.9 million horsepower compression regulated by the Federal Energy Regulatory Commission were installed in the USA at a unit cost of $5.72 million per mile and $3,030 per horsepower. In this paper, pipeline construction cost factors are described and gas transmission construction and compressor-station cost statistics and regression models are evaluated. Aggregate metrics are the most stable and reliable statistic and probably the most representative, but for small samples significant restrictions apply. There are large cost deviations in pipeline construction compared to compressor-stations reflecting differences in material and service cost. Route length and line diameter are the only variables needed to develop robust linear models of pipeline construction cost. [Received: January 13, 2020; Accepted: January 13, 2020]

Effect of direct oral feeding following minimally invasive esophagectomy on costs and quality of life

Aims Following (minimally invasive) esophagectomy, patients often rely on tube feeding, since oral intake is often delayed. Consequently, additional support by a dietician and home care is needed until oral intake is commenced. In this study, the effects of direct start of oral feeding compared with tube feeding following an esophagectomy was evaluated on treatment costs and health-related quality of life (QoL). Methods Patients undergoing a minimally invasive esophagectomy were randomized in the NUTRIENT II study between controls (nil-per-mouth during 5 days and subsequent tube feeding) and a group in whom oral feeding was started directly postoperatively. Total hospital costs (including readmission and outpatient costs) and home care data for a period of 6 months after surgery were analyzed. QoL (measured using EORTC-QLQ-C30 and EORTC OG-25) was assessed preoperatively and 6 weeks, 12 weeks, and 6 months postoperatively. Results A total 132 patients were included (n = 65 direct oral feeding group and n = 67 control group). Mean patient hospital costs were €26,014 in the intervention group over a 6-month period compared to €26,989 in the control group (p = .825). Furthermore, people with direct oral feeding required significantly less home care assistance; i.e. 23 (48.9%) intervention patients versus 37 (77.1%) control patients (p = .004). Also, QoL in patients with direct oral feeding progressed more quickly when compared to the control group. Limitations Hospital costs were derived from a single hospital unit whereas costs from all the participating units may be a better reflection of the cost deviation. Availability of homecare data was limited, leading to difficulty in detecting differences in costs. Conclusion This study suggests that direct oral feeding leads to similar total costs and a significantly reduced need for home care assistance. Furthermore, QoL in intervention group increased more quickly when compared to the control group.

Organizational and methodological basis of implementation and improvement of internal economic control of accounts receivable for non-commodity transactions

The subject of the study is the organizational and methodological aspects of accounting and internal economic control of accounts receivable for non-commodity transactions. The purpose of the article is to improve them regarding to the internal economic control. The results of the study are in establishing the features of accounts receivable for non-commodity transactions and their accounting. This allowed us to identify the main problems, functions, directions and tasks of their internal economic control. The main measures for its improvement at the organizational (comprehensive audits) and methodological level (based on factual and documentary inspections) with the involvement of property survey boards and auditing commissions are proposed. The content of stages and sections of control inspections final report is developed. Field of application of the results: enterprises, educational institutions training the specialists in accounting and taxation, analysis, control. Conclusions. The effectiveness of the final stage – the stage of generalization and implementation of control results – should ensure the analytical character of control data set in the previous stage. It means, first of all, the distribution of deviations of production costs by reasons of their occurrence, grouping and coding of irregularities detected by their scale and impact on the economic process, the systematic connection of deviations of production costs with the centres responsible for irregularities, reference to the document containing investigative and legal substantiation of the specific performers guilt. This approach allows the enterprises management to eliminate the irregularities effective and timely and to prevent them in the future.

A supervised machine learning approach for the optimisation of the assembly line feeding mode selection

The Line Feeding Problem (LFP) involves the delivery of components to the production area. Previous models minimise the delivery costs and optimally assign each component to a line feeding mode between line stocking, kitting, and sequencing but cannot provide easily comprehensible guidelines. We use the Classification And Regression Tree (CART) algorithm to develop, in a supervised way, a decision tree based on problems that are solved with a Mixed Integer Programming (MIP) model for training purposes. Based on selected attributes of the components and the manufacturing environment, the decision tree suggests a line feeding mode for every component. For a synthetically determined training and evaluation data set, we find that the classification tree can predict the line feeding mode with an average classification accuracy of 78.49%. After the decision tree is implemented and a line feeding mode is selected for each component, an infeasible solution might occur. We develop a repair approach that solves this problem with an average cost deviation from the optimal solution of 0.38%.

Incorporating pavement deterioration uncertainty into pavement management optimization

ABSTRACT Pavement management systems can be used to efficiently allocate limited maintenance budgets to better align with pavement deterioration. However, pavement deterioration is subject to uncertain factors that complicate the prediction of future pavement conditions accurately, entailing differences in the optimum maintenance strategy. This paper addresses this challenge by introducing a method to aid local engineers in optimising the scheduling of maintenance activities under uncertain pavement deterioration conditions. Markov chains are used to simulate the variability of life-cycle performance. Moreover, a multi-objective optimisation of an urban network is carried out to find the maintenance programme that minimises the mean life-cycle cost, maximises the mean user benefit, and minimises the standard deviation of life-cycle cost. This third objective enables the optimisation routine to minimise the possibility of unintentionally increasing the life-cycle cost due to system variability. This approach results in a reduction of the life-cycle cost variability by up to 62%, provides pavement strategies that benefit road users as a result of better pavement conditions, and reduces the risk of resorting to costly future maintenance activities.

Schedule-Allocate and Robust Sequencing in Three-Machine Robotic Cell under Breakdowns

The purpose of this paper is to model two problems comprising schedule-allocate (in case of producing identical parts) and sequencing of parts (in case of producing different parts). The first model is used for minimizing the cycle time and operational cost, and the second one for minimizing both the mean and standard deviation of the total production cost as well the cycle time, in an unreliable three-machine robotic cell which confronted with many uncertainty factors. In the current article, mathematical modelling and simulation-based optimization method have been presented to schedule-allocate similar parts and trace the optimal sequence of different parts. Several solution procedures, including epsilon-constraint method and multiobjective particle swarm optimization algorithm, for identical parts case and response surface methodology for different parts case are applied. The results derived from solving numerical examples revealed some advantages in terms of time to attain the optimal solution.

Innovative strategy to design a mixed resilient-sustainable electricity supply chain network under uncertainty

Motivated by a real-world case, this paper deals with uncertainty issues in the resilient and sustainable electricity supply chain network design. Uncertainty is always found in electricity demand prediction and perfect foresight is not possible. The resilience of electricity networks in face of uncertainty can prevent catastrophic impacts. Besides, due to the growing concerns about social impacts, considering social measures along with the other measures is becoming critical when analyzing the network costs. In this paper, a multi-objective optimization model is developed, which consists of the total cost in the first objective, resiliency measures in the second objective, and some aspects of corporate social responsibility in the third objective. The resiliency measures minimize de-resiliency, including successive establishment, distributed generators inadequacy, congestion through electrical lines, and energy dissatisfaction level. A novel robust approach is also proposed to deal with the uncertainty of electricity demand based on the light robust programming and possibilistic theory in the fuzzy logic. By investigating a real case in Iran, the contributions of the considered measures, and the effects of uncertainty are analyzed. The analyses reveal that by applying the proposed model the decision-makers can enhance corporate social responsibility and resiliency by 50% and 20%, respectively, although it increases the total cost by 50%. Moreover, the results of applying the proposed fuzzy-robust approach show that how the decision-makers can effectively allocate the uncertainty budget and protection level to attain more robust solutions in terms of standard deviation and mean value of the total cost.

A Binary-Optimization-Based Coordinated Cyber-Physical Attack for Disrupting Electricity Market Operation

A reliable smart grid should possess the necessary security mechanism against any attack aiming at disrupting the electricity market operation. The operation is performed to allocate the final nodal price as per the states estimated in each node. The state estimator can be misdirected by changing the network configuration both at physical and/or information layer, leading to disruption in the market operation. As compared to the physical attack or cyber attack carried out independently, a coordinated cyber-physical attack has a larger impact on the market operation. This article proposes a coordinated cyber-physical attack to maximize the electricity market disruption in terms of deviation in the generation cost between the day-ahead market and real-time market operations. The identification of physical attack causing maximum unintended deviation in the electricity market operation has been formulated as a binary optimization problem. The second stage of the proposed attack involves injecting a state preserving attack vector into the communication channel. The false data injection hides the impact of the physical attack, thereby, refraining the control center from taking necessary actions. The impact of the attack has been quantified in terms of variation in the generation cost and nodal prices between the preattack and postattack scenarios for IEEE 9, 14, and 39-bus power systems.

Cost and Time Performance Analysis with Concept of Earned Value (Case Study Jakarta - Cikampek II Elevated Project)

The research procedure starts with conducting a literature study, collecting data, and summarizing the results of gathering existing data. The purpose of this study is to evaluate the deviation of costs and time, calculate the estimated cost and time needed to complete the project work, calculate the estimated profit or loss that may occur in the completion of the project. Variants emphasized here are to investigate deviations in costs or planned schedules that have been planned or determined. The results of the analysis in Jakarta-Cikampek II Elevated Project stated that in July 2019 the SPI value was 0.96 <1 which could be interpreted that the project progress had been delayed and the CPI value was 1.44> 1 which could be interpreted that the actual the project cost is still smaller than the total budget cost. Estimated total costs up to the end of the project in the amount of Rp.2,694,413,965,781 of the total budget cost of Rp. 3,872,143,387,752 can save Rp.1,177,729,421,971. As for the estimated time to finish until the end of the project which is for 918 days from the time of the 915 day contract, there will be a delay of 3 days.

Optimal production and inventory control of multi-class mixed backorder and lost sales demand class models

Manufacturers usually face customers with different service requirements. The challenge they face is how to determine which customers to serve when the inventory supply is limited. We consider the problem of a manufacturer serving two types of customers: those with long term commitment, divided into several backorder classes; and those without commitment consisting of a single lost sales class. We treat the problem within a continuous time integrated production and inventory control framework. We propose a two-step strategy to fully characterize the optimal policy. In a first step, we use the concept of L-natural convexity to partially describe the optimal policy. Based on these results, in a second step, we reformulate the problem and fully characterize the optimal policy. We show that the latter is characterized by state-dependent multidimensional thresholds. Due to the computational complexity of the problem, we propose three heuristic policies: The first uses linear thresholds that mimic the optimal policy. These thresholds are computed through a decomposition of the original problem into a series of single-lost sales, single-backorder class problems. The second heuristic treats all demand classes equally. The third heuristic uses static thresholds to control production and inventory rationing among demand classes. Extensive numerical results show that, for problems with one lost sales and two or three backorder classes, the first heuristic outperforms the other two with a cost deviation less than 1.25%, from the optimal. Furthermore, computing the thresholds of this heuristic is orders of magnitude faster than computing the optimal policy.

Identification of Factors Affecting the Performance of Rural Road Projects in Colombia

Rural roads play an indispensable role in economic and social well-being, especially in developing countries, contributing to achieving the Sustainable Development Goals. For this reason, it is necessary to plan these projects properly to guarantee their success. In this line, the objective of this research is to identify significant variables generating overruns in time and cost using empirical data of 535 rural road projects in Colombia from 2015 to 2018. Bivariate analysis, with statistical tools like Spearman’s Rho and Kruskal–Wallis, allowed identifying that higher values of variables like budget and project intensity are related to higher deviations in cost and time. Additionally, it was found that projects with shorter durations are reporting higher time overruns. The worst performers are projects executed in the year that council mayors start their terms, those developed in municipalities with more resources, and those awarded using a competitive bidding process. Multivariate analysis, through Random Forest, assessed the effect of considering all variables interacting simultaneously and ranking them in order of importance. The results demonstrated a relationship between cost and time performance, and that numerical variables are more significant than the categorical ones. This study contributes to a better understanding of the causes of delays and cost overruns on rural roads, providing useful insight for researchers and industry practitioners.

Correction to: Is it still worth it? Applying expectancy-value theory to investigate the intraindividual motivational process of forming intentions to drop out from university

The intraindividual process of study dropout, from forming dropout intention to deregistration, is of motivational nature. Yet typical studies investigate interindividual differences, which do not inform about intraindividual processes. Our study focused on the intraindividual process of forming dropout intention, and applied expectancy-value theory to analyze its motivational underpinnings. To expand research, we considered associations of intraindividual deviations in expectancy, intrinsic value, attainment value, utility value, and cost to intraindividual deviations in dropout intention. A total of 326 undergraduate students of law and mathematics rated motivational variables and dropout intention three times from semester start to the final exam period. Multilevel regression analyses revealed that intraindividual changes in intrinsic value, attainment, and cost, but not in expectancy and utility, related to intraindividual changes in dropout intention. Further, we considered students’ demographics as moderators. Only age moderated the association between intrinsic value and dropout intention. Our results stress the crucial role of certain value components, including cost, for emerging dropout intention.

Comparative study of optimum cost design of reinforced concrete retaining wall via metaheuristics

Design engineers may find various options of metaheuristic method in optimization of their problems. Because of the randomization nature of metaheuristic methods, solutions may trap to non-optimum solutions which are just optimums in a limited part of the selected range of the design variables. Generally, metaheuristics use several options to prevent this situation, but the same optimization process may solve different performances in every run of the process. Due to that, a comparative study by using ten different algorithms was done in this study. The optimization problem is the cost minimization of an L-shaped reinforced concrete (RC) retaining wall. The evaluation is done by conducting 30 multiple cycles of optimization, and comparing minimum cost, average cost and standard deviation values.

The near-optimal feasible space of a renewable power system model

Models for long-term investment planning of the power system typically return a single optimal solution per set of cost assumptions. However, typically there are many near-optimal alternatives that stand out due to other attractive properties like social acceptance. Understanding features that persist across many cost-efficient alternatives enhances policy advice and acknowledges structural model uncertainties. We apply the modeling-to-generate-alternatives (MGA) methodology to systematically explore the near-optimal feasible space of a completely renewable European electricity system model. While accounting for complex spatio-temporal patterns, we allow simultaneous capacity expansion of generation, storage and transmission infrastructure subject to linearized multi-period optimal power flow. Many similarly costly, but technologically diverse solutions exist. Already a cost deviation of 0.5% offers a large range of possible investments. However, either offshore or onshore wind energy along with some hydrogen storage and transmission network reinforcement appear essential to keep costs within 10% of the optimum.

Public management and cost overruns in public projects

There is a growing concern in the field of public administration regarding cost overruns, which are often perceived to be a sign of project failure. Past studies have identified potential causes and explanatory factors for the occurrence of cost deviations. This article expands the existing body of knowledge by explicitly considering exogenous determinants (political, governance, and economic) to analyze how public management affects cost deviations and overruns. The article provides an empirical analysis of 4,305 projects developed in Portugal from 1980 to 2014. The study found that electoral periods; institutional, legal, and regulatory frameworks; and economic cycles all influence cost overruns.

Development of maintenance cost correlation for high head run of river small hydro power plant

Small hydropower is a mature and cost-competitive energy technology. The maintenance cost of small hydropower plant is one of the critical factors to analyse the net benefits of the plant. In this study, an attempt has been made to develop the maintenance cost correlation for the high head run of river small hydropower plants based on capacity and head as cost-influencing parameters by linear regression approach in MATLAB. The results revealed that the maximum deviation is ±12.60%, the maximum average deviation cost obtained for turbine is ±11.35% and minimum average deviation cost for thrust bearing is ±3.5%. Overall average deviation in maintenance cost of all the studied components is ±7.35%, which shows that the developed correlations are in good agreement. Therefore, these correlations can be used to determine the maintenance cost of high head run of river small hydropower projects.

Note on the time complexity of resource constrained scheduling with general truncated job-dependent learning effect

In a recent paper (He et al. in J Comb Optim 33(2):626–644, 2017), the authors considered single machine resource allocation scheduling with general truncated job-dependent learning effect. For the convex resource consumption function and limited resource cost, the problem is to minimize the weighted sum of makespan, total completion time, the total absolute deviation in completion time and resource consumption cost. They conjectured that this problem is NP-hard. In this note we show that this problem can be solved in $$O(n^3)$$ time. It is also shown that Lemma 4 in He et al. (2017) is incorrect by a counter-example.

Актуализация подходов к распределению косвенных затрат в бухгалтерском учете субъектов малого предпринимательства сферы оптовой торговли

Цель: анализ затрат субъектов малого предпринимательства выступает важнейшим инструментом управления затратами, позволяет оценить тенденции изменения их уровня, установить причины отклонения фактических затрат от плановых (стандартных), выявить резервы их снижения. Для субъектов малого предпринимательства сферы оптовой торговли на первое место выступают аспекты поиска объективных методик распределения косвенных затрат в бухгалтерском учете для предотвращения искажения конечной себестоимости товарных групп. Обсуждения: эффективность системы управления затратами зависит от организации их учета и анализа, что определяется формой и методами учета затрат, степенью автоматизации учетных процессов торговой организации, уровнем планирования и нормирования затрат, наличием внутренней отчетности, содержащей информацию о выявленных отклонениях, причинах их образования, принимаемых мерах по их устранению. Наиболее актуализируется вопрос о существовании множества баз распределения косвенных затрат, относящихся к различным учетным моделям, которые имеют свои преимущества и недостатки. В работе поднимается вопрос об исследовании различных методов распределения: «котловой метод», распределение пропорционально выручки от продажи, функционально-стоимостное калькулирование. Перед руководством торговой организации поднимается ряд вопросов, решение которых способствует поиску объективных методик распределения затрат, а именно зачем использовать методики распределения затрат, какие положительные аспекты в области управления затратами получит экономический субъект, какие недостатки имеют существующие методики распределения. В настоящем исследовании осуществлялся поиск ответов на эти вопросы в ходе внедрения ABC-модели в сферу оптовой торговли. Результаты: в исследовании продемонстрирован процесс реализации функциональностоимостного калькулирования в учетной практике субъектов малого предпринимательства. Акцентировано внимание на решении недостатков и проблем внедрения модели, продемонстрирована возможность расчета рентабельности клиентов и товарных групп, предложена схема построения аналитического учета на счете 44 «Расходы на продажу».

Travel cost budget based user equilibrium in a bottleneck model with stochastic capacity

This paper studies a bottleneck model in which the capacity of the bottleneck is constant within a day but changes stochastically from day-to-day between a designed value (good condition) and a degraded one (bad condition). The study relates the travel cost variability due to stochastic capacity with commuters' departure time choice behaviors. We postulate that commuters acquire the variability of travel cost based on past experiences and factor such variability into their departure time choice consideration by minimizing their travel cost budget (TCB), defined as a weighted average of mean travel cost and standard deviation of travel cost. We show that the consideration of TCB yields seven possible equilibrium patterns. Closed form solutions to all possible equilibrium patterns and their corresponding parameter ranges are derived. The rationality of the patterns has been investigated. Dependence of travel cost and the duration of peak hours on the commuters' risk attitude has also been derived in each equilibrium pattern. Finally, numerical studies have been conducted to illustrate the properties.

Approximation algorithms for the min-max regret identical parallel machine scheduling problem with outsourcing and uncertain processing time

We consider the robust (min-max regret) version of identical parallel machine scheduling problem, in which jobs may be outsourced to balance total cost against production efficiency. The total cost is measured in terms of the total completion time of jobs processed in-house and the cost of outsourcing the rest. Processing times of in-house jobs are uncertain and they are described as two types of scenarios: discrete and interval. The objective is to obtain a robust (min-max regret) decision that minimises the absolute deviation of total cost from the optimal solution under the worst-case scenario. We first prove the worst-case scenario for any feasible solution. For the interval scenario, we further prove that the maximum regret value can be obtained in polynomial time for any feasible schedule. We also prove that for any discrete scenario, the minimum total cost can be obtained in polynomial time. Since the problem with the interval scenario is strongly NP-hard, we then transform the problem into an equivalent robust single machine scheduling problem. Finally, we develop 2-approximation algorithms for the problem with discrete and interval scenarios, respectively. These results are helpful for bridging the scheduling theory and practice in identical parallel machining environments with outsourcing and uncertain processing times.