Unit Cost Function Research Articles

A bargaining perspective on vertical integration

AbstractWe analyze vertical integration incentives in a bilaterally duopolistic industry with bargaining in the input market. Vertical integration incentives are a combination of horizontal integration incentives upstream and downstream and depend on the strength of substitutability and complementarity and the shape of the unit cost function. Under particular circumstances, vertical integration can convey more bargaining power to the merged entity than a horizontal merger to monopoly. In a bidding game for an exogenously determined target firm, a vertical merger can dominate a horizontal one, while pre‐emption does not occur.

Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling.

In recent years, the world has seen incremental growth in online activities owing to which the volume of data in cloud servers has also been increasing exponentially. With rapidly increasing data, load on cloud servers has increased in the cloud computing environment. With rapidly evolving technology, various cloud-based systems were developed to enhance the user experience. But, the increased online activities around the globe have also increased data load on the cloud-based systems. To maintain the efficiency and performance of the applications hosted in cloud servers, task scheduling has become very important. The task scheduling process helps in reducing the makespan time and average cost by scheduling the tasks to virtual machines (VMs). The task scheduling depends on assigning tasks to VMs to process the incoming tasks. The task scheduling should follow some algorithm for assigning tasks to VMs. Many researchers have proposed different scheduling algorithms for task scheduling in the cloud computing environment. In this article, an advanced form of the shuffled frog optimization algorithm, which works on the nature and behavior of frogs searching for food, has been proposed. The authors have introduced a new algorithm to shuffle the position of frogs in memeplex to obtain the best result. By using this optimization technique, the cost function of the central processing unit, makespan, and fitness function were calculated. The fitness function is the sum of the budget cost function and the makespan time. The proposed method helps in reducing the makespan time as well as the average cost by scheduling the tasks to VMs effectively. Finally, the performance of the proposed advanced shuffled frog optimization method is compared with existing task scheduling methods such as whale optimization-based scheduler (W-Scheduler), sliced particle swarm optimization (SPSO-SA), inverted ant colony optimization algorithm, and static learning particle swarm optimization (SLPSO-SA) in terms of average cost and metric makespan. Experimentally, it was concluded that the proposed advanced frog optimization algorithm can schedule tasks to the VMs more effectively as compared with other scheduling methods with a makespan of 6, average cost of 4, and fitness of 10.

BSEE decommissioning cost estimates in the deepwater US Gulf of Mexico

ABSTRACT Since 2016, the Bureau of Safety and Environmental Enforcement (BSEE) has collected decommissioning expenditure data from operators in the deepwater US Gulf of Mexico, defined as water depth greater than 122 m, to estimate the decommissioning liability of leases. BSEE uses both statistical tools and work decomposition methods to estimate the decommissioning cost of all active wells, structures, and pipelines on deepwater leases. The purpose of this paper is to evaluate BSEE cost estimates in the deepwater US Gulf of Mexico across four decommissioning stages and to infer unit cost statistics and cost functions in the region. Fixed platforms in 250–500 m water depth are expected to cost between $10 and $80 million to decommission, spars about $55 million, ship-shape floaters around $30 million, and tension leg platforms and their smaller (mini) versions between $18 and $25 million. The deepwater decommissioning market value is estimated at $24.3 billion circa 2022.

Operational and Environmental Comparison of Two Felling and Piling Alternatives for Whole Tree Harvesting in Quercus Coppices for Bioenergy Use

Coppices are a major potential source of forest biomass in Spain, where they occupy around 4M ha. Quercus coppices are mostly neglected because of their high harvesting costs and the small size of their products. This makes them very interesting to test and compare alternative means for utilizing their resources in an optimized way. Hence, a comparative study of motor-manual and mechanized felling and bunching was conducted when thinning dense coppice stands of the two most important oak species in Spain to obtain biomass for bioenergy use. In particular, the study matched chainsaw felling and manual piling against the work of a drive-to-tree feller-buncher previously analyzed in the very same sites. Productivity functions for motor-manual felling and piling were fitted for each species. The derived unit cost functions show that the felling-bunching costs are lower for the motor-manual option in stands of both species, particularly for the smaller tree sizes. Nevertheless, when the strongly reduced loading times in forwarding associated to the mechanization are taken into account, the total harvesting cost is often lower for the mechanized option. That is true for all tree sizes of Q. ilex, and for trees larger than 13 cm diameter at breast height (DBH) for Q. pyrenaica. Residual stand damage was low to moderate, but always significantly greater for the mechanized option compared with the motormanual one. Soil damage was very low for both alternatives. The stumps experimented significantly greater damages in the mechanized felling and bunching, but further research is needed to determine if those damages have any impact on stump mortality, sprouting capability and future plants vigor. The greater productivity and level of tree damages found in Q. ilex when compared to Q. pyrenaica are likely due to the narrower and lighter crown of the latter.

BSEE decommissioning cost estimates in the shallow water US Gulf of Mexico

ABSTRACT The Bureau of Safety and Environmental Enforcement (BSEE) has collected decommissioning expenditure data from operators in the US Gulf of Mexico since 2016 to estimate the decommissioning liability of federal leases. BSEE uses the collected data to estimate the expected decommissioning cost for every active wellbore, structure, and pipeline segment in federal waters but does not otherwise disclose the statistical results of their data collection efforts or methods employed in cost estimation. The task of this paper is to summarise BSEE decommissioning cost estimates in the shallow water US Gulf of Mexico for each of the main decommissioning stages and to infer unit cost statistics and cost functions in the region. Fixed platform removals are expected to cost on average $977,000 in water depth less than 30 m and up to $3.6 million in water depth from 61 to 122 m, about three times more expensive than caisson removals in similar water depth.

Optimal strategy of electricity and natural gas aggregators in the energy and balance markets

This paper presents a stochastic two-stage model for energy aggregators (EAs) in the energy and balancing markets to supply electricity and natural gas to end-users equipped with combined heat and power (CHP) units. The suggested model takes into account the battery energy storage (BES) as a self-generating unit of EA. The upper and lower subproblems determine the optimal energy supply strategy of EA and consumption of consumers, respectively. In the lower subproblem, the McCormick relaxation is used to linearize the cost function of the CHP unit. To solve the proposed model, the two-stage problem is transformed into a linear single-stage problem using the KKT conditions of the lower subproblem, the Big M method, and the strong duality theory. The performance and efficiency of the proposed model are evaluated using a case study and three scenarios. According to the simulation results, adding CHP units to the energy-scheduling problem of BES-owned aggregators increases the profit of EA by 5.96% and decreases the cost of consumers by 1.57%.

Bounded risk per unit cost index constraint for sequential estimation of the mean in a two-parameter exponential distribution

In this article, the two-stage sequential sampling procedure is proposed to estimate the mean of an exponential distribution under the modified square error loss function. The main aim of the article is to consider the associated risk per unit cost function by bounding it from above with a fixed preassigned positive number, ω. We provide the exact distribution of the total sample size, explicit formulas for the mathematical expectation of the rth power of the total sample size, the expected value and mean squared error of the maximum likelihood estimate of the scale, and mean parameters of the exponential distribution under the two-stage sequential procedure. The performances of the proposed methodologies are investigated with the help of simulations. In the end, we supplement with extensive sets of data analysis via computer simulations, validating that the two-stage sequential method performs very well. Finally, using a real data set, the procedure is clearly illustrated.

COMPARATIVE COST ANALYSIS OF DIFFERENT THERMAL POWER PLANT PROJECTS

Realistic budgeting for complex power plant projects is important for informed investment decisions and avoiding frequent cost overruns. Comparative cost analysis thus provides significant insights for future projects’ investment decisions. This study analyzed the project costs using a regression analysis and Monte Carlo Simulation (MCS) guided by the error analysis for Combined Cycle Power Plants (CCPP) and Natural Gas (NG)-based power plants. The regression analysis shows the cost trend lines with the prediction equation and its R2 value against production capacity in Mega-Watts (MW) of power plant projects in Bangladesh. The MCS presents the cumulative probability function (CDF) and probability density function (PDF) of unit cost in Million U.S. Dollars (MUSD)/MW of both types of projects. The results show that the NG project costs are higher than CCPP power plant projects, where the most frequent project development cost is in a range of 1.01-1.25 MUSD/MW, comparing 0.95-1.15 MUSD/MW of CCPP projects. Among the models, the MCS performs better than the regression model predicting project costs. Finally, MCS is demonstrated in predicting the cost of both types of power plant projects. The study outcomes will assist decision-makers and policy planners in further investment decisions in the power plant and similar infrastructure projects in Bangladesh and economically similar countries.

COMPARATIVE COST ANALYSIS OF DIFFERENT THERMAL POWER PLANT PROJECTS

Realistic budgeting for complex power plant projects is important for informed investment decisions and avoiding frequent cost overruns. Comparative cost analysis thus provides significant insights for future projects’ investment decisions. This study analyzed the project costs using a regression analysis and Monte Carlo Simulation (MCS) guided by the error analysis for Combined Cycle Power Plants (CCPP) and Natural Gas (NG)-based power plants. The regression analysis shows the cost trend lines with the prediction equation and its R2 value against production capacity in Mega-Watts (MW) of power plant projects in Bangladesh. The MCS presents the cumulative probability function (CDF) and probability density function (PDF) of unit cost in Million U.S. Dollars (MUSD)/MW of both types of projects. The results show that the NG project costs are higher than CCPP power plant projects, where the most frequent project development cost is in a range of 1.01-1.25 MUSD/MW, comparing 0.95-1.15 MUSD/MW of CCPP projects. Among the models, the MCS performs better than the regression model predicting project costs. Finally, MCS is demonstrated in predicting the cost of both types of power plant projects. The study outcomes will assist decision-makers and policy planners in further investment decisions in the power plant and similar infrastructure projects in Bangladesh and economically similar countries.

Accuracy study of linearization methods for quadratic cost curves of thermal units in unit commitment problems

To solve the unit commitment (UC) problems with efficient mixed-integer linear programming solvers, the quadratic cost functions (QCFs) of thermal units are always approximated to piecewise linear (PWL) functions. This study examines the accuracies of different approximation methods for piecewise linearizing the QCFs of units in UC problems. We use five piecewise linearization methods—evenly spaced PWL interpolation, evenly spaced PWL tangent, evenly spaced PWL–ɛmax/2 shifted interpolation, tighter PWL interpolation, and evenly spaced PWL least-squares fit—to approximate the QCFs of units. The authors first perform a series of reproductivity studies to verify the program. Then, numerical tests are conducted using different methods on the systems with 10, 100, and 800 units. The results show that different approximation methods lead to considerable differences in operating costs and the tighter PWL interpolation, compared with the other methods, is preferred in terms of approximation accuracy.

Solution of Combined Heat and Power Economic Dispatch Problem Using Genetic Algorithm

This research proposes a synergistic meta-heuristic algorithm for solving the extreme operational complications of combined heat and power economic dispatch problem towards the advantageous economic outcomes on the cost of generation. The combined heat and power (CHP) is a system that provides electricity and thermal energy concurrently. For its extraordinary efficiency and significant emission reduction, it is considered a promising energy prospect. The broad application of combined heat and power units requires the joint dispatch of power and heating systems, in which the modelling of combined heat and power units plays a vital role. The present research employs the genetic optimization algorithm to evaluate the cost function, heat and power dispatch values encountered in a system with simple cycle cogeneration unit and quadratic cost function. The system was first modeled to determine the various parameters of combined heat and power units towards solving its economic dispatch problem directly. In order for modelling to be done, a general structure of combined heat and power must be defined. The test system considered consists of four units: two conventional power units, one combined heat and power unit and one heat-only unit. The algorithm was applied to test system while taking into account the power and heat units, bounds of the units and feasible operation region of cogeneration unit. Output decision variables of 4-unit test systems plus cost function from Genetic Algorithm (GA), was determined using appropriate codes. The proposed algorithm produced a well spread and diverse optimal solution and also converged reasonably to the actual optimal solution in 51 iterations. The result obtained compared favourably with that obtained with the direct solution algorithm discussed in a previous paper. We conclude that the genetic algorithm is quite efficient in dealing with non-convex and constrained combined heat and power economic dispatch problem.

GEOMETRICAL SIMULATION OF THE OPTIMAL TRAJECTORY OF THE PIPELINE OF EFFECTIVE WATER SUPPLY SYSTEMS

In the article describes the research of geometric modeling of the optimal trajectory of pipelines of an external water supply network. A model for the distribution of the values of unit cost of land plots was constructed. It based on a preliminary analysis of the building area in terms of economic value of land plots. Modeling of the optimized geometric shape of the discrete image of the network of pipelines of the water supply system is performed with using the principles of the static-geometric method of discrete geometry. The coefficients characterizing the force of interaction between the nodes of the pipeline link (section of the pipeline) are taken equal to the value of the unit cost of pipelines to the length of the links of a water supply system. The functional dependencies of the optimal placement of pipeline nodes are determined on basis of the proposed radial-basic distribution function of unit cost of land plots. The position of the branching nodes of the pipeline water system is determined by solving a system of nonlinear equations.The approach considered in the article demonstrated good convergence of the iterative calculation. Also, an analysis according to the obtained calculation results was carried out on the indicators of material savings and labor costs, which are the result of a reduction in the total length of the links of the simulated pipeline system. A sufficiently high economic effect was demonstrated with minor adjustments to the original system. In turn, a practical solution to the problem posed in this article will significantly reduce the further construction and operational costs of the water supply network even at the stage of design work.

Productivity propagation with networks transformation

We model sectoral production by cascading binary compounding processes. The sequence of processes is discovered in a self-similar hierarchical structure stylized in the economy-wide networks of production. All substitution elasticities and Hicks-neutral productivity growth are calibrated so that the general equilibrium feedbacks between all sectoral unit cost functions replicate the transformation of networks observed as a set of two temporally distant input-output coefficient matrices. This system of unit cost functions is then examined to study how idiosyncratic sectoral productivity shocks propagate into aggregate macroeconomic fluctuations in light of potential networks transformation. Additionally, we study how sectoral productivity increments propagate into the dynamic general equilibrium, thereby allowing networks transformation and ultimately producing social benefits.

Estimating the Unit Costs of Healthcare Service Delivery in India: Addressing Information Gaps for Price Setting and Health Technology Assessment.

BackgroundIndia’s flagship National Health insurance programme (AB-PMJAY) requires accurate cost information for evidence-based decision-making, strategic purchasing of health services and setting reimbursement rates. To address the challenge of limited health service cost data, this study used econometric methods to identify determinants of cost and estimate unit costs for each Indian state.MethodsUsing data from 81 facilities in six states, models were developed for inpatient and outpatient services at primary and secondary level public health facilities. A best-fit unit cost function was identified using guided stepwise regression and combined with data on health service infrastructure and utilisation to predict state-level unit costs.ResultsHealth service utilisation had the greatest influence on unit cost, while number of beds, facility level and the state were also good predictors. For district hospitals, predicted cost per inpatient admission ranged from 1028 (313–3429) Indian Rupees (INR) to 4499 (1451–14,159) INR and cost per outpatient visit ranged from 91 (44–196) INR to 657 (339–1337) INR, across the states. For community healthcare centres and primary healthcare centres, cost per admission ranged from 412 (148–1151) INR to 3677 (1359–10,055) INR and cost per outpatient visit ranged from 96 (50–187) INR to 429 (217–844) INR.ConclusionThis is the first time cost estimates for inpatient admissions and outpatient visits for all states have been estimated using standardised data. The model demonstrates the usefulness of such an approach in the Indian context to help inform health technology assessment, budgeting and forecasting, as well as differential pricing, and could be applied to similar country contexts where cost data are limited.Electronic supplementary materialThe online version of this article (10.1007/s40258-020-00566-9) contains supplementary material, which is available to authorized users.

A Two-Stage Mathematical Programming Approach for the Solution of Combined Heat and Power Economic Dispatch

The optimal generation scheduling of combined heat and power units aims to minimize total cost of generation units satisfying power and heat demands.The combined heat and power economic dispatch problem should be solved considering a series of electrical and operational constraints, which will be more complicated considering valve-point effects of power-only units and nonconvex feasible operating regions of cogeneration units. In such a case, the problem has nonconvex and nondifferential characteristic. In this article, a two-stage model is proposed to handle the nonconvexity and nondifferentiability of valve-point effects in cost functions of power-only units. The proposed model obtains a convex feasible operating region in the first stage using a linear approximation model. In addition, an equivalent formulation is proposed in the second stage to handle the nondifferentiable term of valve-point effects using the obtained convex feasible operating regions. The proposed model is implemented on two case studies to evaluate performance of the proposed model.

Pointers in Recursion: Exploring the Tropics

We translate the usual class of partial/primitive recursive functions to a pointer recursion framework, accessing actual input values via a pointer reading unit-cost function. These pointer recursive functions classes are proven equivalent to the usual partial/primitive recursive functions. Complexity-wise, this framework captures in a streamlined way most of the relevant sub-polynomial classes. Pointer recursion with the safe/normal tiering discipline of Bellantoni and Cook corresponds to polylogtime computation. We introduce a new, non-size increasing tiering discipline, called tropical tiering. Tropical tiering and pointer recursion, used with some of the most common recursion schemes, capture the classes logspace, logspace/polylogtime, ptime, and NC. Finally, in a fashion reminiscent of the safe recursive functions, tropical tiering is expressed directly in the syntax of the function algebras, yielding the tropical recursive function algebras.

Ant Lion Optimizer for Optimum Economic Dispatch Considering Demand Response as a Visual Power Plant

Demand response is the reduction in power consumption of electric utility customers from their normal consumption to better match the demand for power with the supply. It comprises changes in consumption when the power system operators need it in order to balance the electrical grid. These changes are obtained by offering price incentives or any other way to encourage consumers to shift their demand peaks to higher energy production hours. The reduction in electric energy due to large consumers’ response can be considered as virtual power plants which can be added to the system capacity as a demand resource. In this paper, demand response is included in the optimal economic load dispatch problem as a demand resource. The demand response cost function is proposed in a similar way as the cost functions of conventional generation units. A new nature-inspired optimization algorithm called ant lion optimizer is used to calculate the optimum combination between both generations; conventional generators and demand response as a demand resource. The proposed approach takes into account two objectives; minimization of generation cost, and the emissions from the thermal power plants. The ant lion optimizer algorithm is coded and implemented in MATLAB environment. The proposed method is validated by applying it to two test systems; 6-bus system and IEEE-30 bus test system. The obtained results prove that the proposed method can significantly minimize both the operation and emissions costs of power systems while satisfying the specified constraints.

Supplier selection problem in IoT solutions

PurposeThe use of Internet of Things (IoT) and networks has built a potential impact on the product cost and time in a company’s manufacturing process. These IoT solutions provide end-to-end visibility and faster introduction of merchandise and supplier in the market. The main aim of this research paper is to supply products with improved quality and cheaper price, whereas the rising response and quality of the client service.Design/methodology/approachThis paper designs and develops two cases for selecting the most efficient vendor while keeping in mind the profit and cost constraints in optimization.FindingsOutsourcing is a vital parameter to cut back the price and maximize the profit of the manufacturer. Therefore, the integration of supply chain with IoT can provide a solution to the cost optimization and supplier/vendor selection problems in supply chain management.Research limitations/implicationsThe results show that the models are quite realistic and can help the IoT-based manufacturing units to make strategic decisions regarding product manufacturing and distribution.Practical implicationsThe authors can further extend the model to derive the retailer’s profit function and develop the end product cost to the consumers and hence make it a n-level multi-vendor selection model for IoT-based systems.Originality/valueThe right choice of vendor for IoT-enabled business is a crucial concern. In this paper, the authors designed and developed multi-vendor models with in-house production and outsourcing decisions to meet the demand along with the vendor selection. The variable demands and designed variable unit cost function and batch order are set to make vendor selection more realistic.

W-Scheduler: whale optimization for task scheduling in cloud computing

One of the important steps in cloud computing is the task scheduling. The task scheduling process needs to schedule the tasks to the virtual machines while reducing the makespan and the cost. Number of scheduling algorithms are proposed by various researchers for scheduling the tasks in cloud computing environments. This paper proposes the task scheduling algorithm called W-Scheduler based on the multi-objective model and the whale optimization algorithm (WOA). Initially, the multi-objective model calculates the fitness value by calculating the cost function of the central processing unit (CPU) and the memory. The fitness value is calculated by adding the makespan and the budget cost function. The proposed task scheduling algorithm with the whale optimization algorithm can optimally schedule the tasks to the virtual machines while maintaining the minimum makespan and cost. Finally, we analyze the performance of the proposed W-Scheduler with the existing methods, such as PBACO, SLPSO-SA, and SPSO-SA for the evaluation metrics makespan and cost. From the experimental results, we conclude that the proposed W-Scheduler can optimally schedule the tasks to the virtual machines while having the minimum makespan of 7 and minimum average cost of 5.8.

Multifactor CES general equilibrium: Models and applications

Sector-specific multifactor CES elasticities of substitution and the corresponding productivity growth are jointly measured by regressing the growth of per-factor cost shares against the growth of factor prices. We use linked input-output tables for Japan and the Republic of Korea as the data source for factor price and cost shares in two temporally distant states. We then construct a multisectoral general equilibrium model using the system of estimated CES unit cost functions and evaluate the economy-wide propagation of an exogenous productivity stimulus in terms of welfare. Further, we examine the differences between models based on a priori elasticities such as Leontief and Cobb-Douglas.