Cost Optimization Model Research Articles

Multimethod simulation approach for capacity design of a truck parking area in city ports

The port induced freight can cause traffic congestion problems in city ports if road freight transportation is used. The secondary congestion problem arises from the pooling of the trucks at the port gates because of the delays of the port operations. The absence of the truck parking areas inside the port causes additional truck trips between the port and auxiliary truck servicing areas around the port. For reducing the impacts of the associated problems of the port induced truck traffic, truck parking areas can be used as buffer zones between the port and city. The purpose of this study is to develop a Decision Support System (DSS) with using multimethod simulation and cost optimization model for the capacity design of a truck parking area for a city port. The preliminary design of the parking area is used to estimate the development cost and outputs of the simulation model is coupled for the capacity optimization for truck arrival scenarios. The methodology is implemented for a case study of the İzmir city port in Turkey. The results of the study indicated that significantly different parking area capacities are required for different truck dwell times for the time restricted and unrestricted truck arrivals

Experimental Research on Mix Ratio of Construction Waste Cemented Filling Material Based on Response Surface Methodology

In order to solve the problem of insufficient supply of aggregate for cemented filling material, the experimental research on the mix ratio of cemented filling material using construction waste as recycled coarse aggregate was carried out. A ternary quadratic regression model was created using the Box-Behnken design (BBD) based on the response surface methodology (RSM), using slump, bleeding ratio, and 28 d uniaxial compressive strength of the filling material as response variables, and the model’s accuracy and reliability were confirmed. The findings of the regression model reveal that the response value is influenced by a single component as well as the interaction between the two factors. Finally, based on filling material cost optimization model, an optimal mix ratio is given, in which the wet fly ash/aggregate ratio, Talbol gradation index of recycled aggregate, and dosage of water reducing agent to cement are 0.507, 0.5, and 0.678%, respectively. The filling material which adopted the optimal mix ratio exhibited a good performance with slump of 215 mm and bleeding ratio of 4.00%, uniaxial compressive strength increased significantly within 3 d, and 28 d uniaxial compressive strength was 4.08 MPa, which meet the requirements of field construction.

The influence of increasing atmospheric CO2 , temperature, and vapor pressure deficit on seawater-induced tree mortality.

Increasing seawater exposure is killing coastal trees globally, with expectations of accelerating mortality with rising sea levels. However, the impact of concomitant changes in atmospheric CO2 concentration, temperature, and vapor pressure deficit (VPD) on seawater-induced tree mortality is uncertain. We examined the mechanisms of seawater-induced mortality under varying climate scenarios using a photosynthetic gain and hydraulic cost optimization model validated against observations in a mature stand of Sitka spruce (Picea sitchensis) trees in the Pacific Northwest, USA, that were dying from recent seawater exposure. The simulations matched well with observations of photosynthesis, transpiration, nonstructural carbohydrates concentrations, leaf water potential, the percentage loss of xylem conductivity, and stand-level mortality rates. The simulations suggest that seawater-induced mortality could decrease by c. 16.7% with increasing atmospheric CO2 levels due to reduced risk of carbon starvation. Conversely, rising VPD could increase mortality by c. 5.6% because of increasing risk of hydraulic failure. Across all scenarios, seawater-induced mortality was driven by hydraulic failure in the first 2 yr after seawater exposure began, with carbon starvation becoming more important in subsequent years. Changing CO2 and climate appear unlikely to have a significant impact on coastal tree mortality under rising sea levels.

Review Paper on an Innovative Time-Cost-Quality Tradeoff Modeling of Building Construction Project Based on Resource Allocation

The time, quality, and cost are three important but contradictive objectives in a building construction project. It is a tough challenge for project managers to optimize them since they are different parameters. Tradeoffs between project duration, total cost, quality and risk are extensively discussed in the project scheduling. This paper tries to develop a Time, Cost and Quality optimization model that enables managers to optimize multi objectives. The model is from the project break down structure method where task resources in a construction project are divided in to series of activities and further in to construction labors, materials, equipment and administration. Quality is an important parameter correlating highly with time and cost parameters .But it is not a quantitative in nature , practical time cost , quality tradeoff models are seldom developed from previous research works of the literature. Although the objectives of cost and time must be mentioned frequently by natural numbers. This paper will present a new solution for solving time, cost, and quality tradeoff problem based on project break down structure method and task resource allocation. The resource utilized in a construction activity would eventually determine its construction time, cost, quality tradeoff model is finally generated based on correlations between construction activities.

Investigation and improvement of intelligent evolutionary algorithms for the energy cost optimization of an industry crude oil pipeline system

Based on the simulation predication of an industry crude oil pipeline system, a novel energy cost optimization model combining discrete grid points and a penalty factor is proposed. Combined with the optimization model, the optimization performance of four representative intelligent evolutionary algorithms is compared and analysed. The comparative results indicate that the improved differential evolution (DE) algorithm obtains a lower energy cost and exhibits better optimization performance than the other three representative algorithms. Compared with the lowest energy cost of the actual field, an energy cost saving of 4.62% can be made. To further improve the performance of intelligent evolutionary algorithms for energy cost optimization, hybrid coding and selection schemes of the algorithms are researched. The hybrid coded DE algorithms can more easily obtain stable optimal energy costs. The modified genetic algorithm with a greedy selection scheme exhibits excellent optimization performance, and can obtain the optimal energy cost more quickly than DE.

Storage requirements in a 100% renewable electricity system: extreme events and inter-annual variability

In the context of 100% renewable electricity systems, prolonged periods with persistently scarce supply from wind and solar resources have received increasing academic and political attention. This article explores how such scarcity periods relate to energy storage requirements. To this end, we contrast results from a time series analysis with those from a system cost optimization model, based on a German 100% renewable case study using 35 years of hourly time series data. While our time series analysis supports previous findings that periods with persistently scarce supply last no longer than two weeks, we find that the maximum energy deficit occurs over a much longer period of nine weeks. This is because multiple scarce periods can closely follow each other. When considering storage losses and charging limitations, the period defining storage requirements extends over as much as 12 weeks. For this longer period, the cost-optimal storage needs to be large enough to supply 36 TWh of electricity, which is about three times larger than the energy deficit of the scarcest two weeks. Most of this storage is provided via hydrogen storage in salt caverns, of which the capacity is even larger due to electricity reconversion losses (55 TWh). Adding other sources of flexibility, for example with bioenergy, the duration of the period that defines storage requirements lengthens to more than one year. When optimizing system costs based on a single year rather than a multi-year time series, we find substantial inter-annual variation in the overall storage requirements, with the average year needing less than half as much storage as calculated for all 35 years together. We conclude that focusing on short-duration extreme events or single years can lead to an underestimation of storage requirements and costs of a 100% renewable system.

Can wind and solar replace coal in Texas?

Texas has seen a rapid decline in coal use in recent years, but still burns more coal and emits more carbon dioxide and sulfur dioxide than any other state. Coal’s share of power generation in the Electric Reliability Council of Texas (ERCOT) system that covers most of the state fell to 20 percent in 2019, while wind grew to 20 percent and solar to 2 percent. Here, we investigate the potential for new wind and solar projects already proposed in the ERCOT interconnection queue as of June 2020 to replace the coal power that remained in 2019. The Wind Integration National Data Set (WIND) Toolkit is used to simulate the output of each wind project, and the System Advisor Model to simulate solar output, for 3 years of meteorological conditions. Mixed integer cost-optimization modeling finds that a portfolio of just 72 of the 108 wind projects and 42 of the 262 solar projects in the queue would be sufficient to replace most coal generation in ERCOT, leaving 10 percent of coal output uncovered and generating larger surpluses at other times. The complementary timing of solar and wind in Texas, with sunshine peaking midday and winds peaking overnight in the west and on summer evenings near the coast, enables these high levels of displacement to be achieved. In fact, the wind and solar portfolio would outproduce retired coal on summer afternoons when demand peaks, leaving small gaps in evenings and shoulder seasons when demand is lower.

Spare Parts Stocking Decision Strategy and Service Logistics Cost Optimization of Two-Echelon Service Logistics System considering Multifailure Mode.

For the repair level and spare parts stocking decision problems, generally METRIC type methods and level of repair analysis (LORA) are used separately. In practical engineering, the repair level of large-scale systems is usually judged according to the failure modes. The method of judging the repair level by the maintenance success rate is no longer applicable. In the case of multiple failure modes of a large-scale system, considering the requirements of system availability, we build a spare parts stocking decision and service logistics cost optimization model of a two-echelon service logistics system. Aiming at the spare parts stocking allocation problem caused by multiple failure modes, we improve the iterative greedy heuristic algorithm to find the global optimal stocking allocation strategies. Finally, through the analysis of typical examples, the correctness and effectiveness of the model and algorithm are verified. The impact of multifailure mode spare parts stocking allocation strategies on availability and service logistics cost is analyzed. The research results are helpful to simplify the support engineering design process of system engineers and have certain theoretical and application value.

OEE improvement of mining shovels by survival analysis and linear optimisation as per sustainable development goals

ABSTRACT Sustainability is acknowledged as an emerging megatrend in business that significantly affects companies’ survival and competitiveness in the market-place. Environmental, global workforce and complex supply chain networks have created pressures to have a clear vision and improve sustainability due to geopolitical dimensions. As per United Nations (UN), Sustainable Development Goals (SDGs), responsible production and consumption (SDG 12), and industry, innovation and infrastructure (SDG 9), and attain higher economic scales of productivity (SDG 8) oblige to drive productivity improvement. The mining maintenance costs constitute around 30% to 40% of the direct mining costs in mining due to diverse operating conditions. First, this article aims to develop the Cox regression Machine Learning (ML) model to derive shovels’ Remaining Useful Life (RUL). Second, formulate and model the maintenance schedule optimisation of mining equipment. Third, test and validate the cost optimisation model by deploying Decision Optimisation (DO) ILOG CPLEX to combine maintenance schedules of Preventive Maintenance (PM) and Predictive Maintenance (PdM). Finally, the data-driven actions demonstrate operating cost reduction through the metrics of Overall Equipment Effectiveness (OEE), Overall Throughput Effectiveness (OTE) and Impact Factor (IF) computation. Further, this article demonstrates the benefits of IF improvements through a case study. The combined optimised maintenance reduced shovels’ maintenance by 2.27 hours per combined schedule, which led to the potential ‘OEE’: improvement between 2.7% and 7.2% of different shovels and which is a measure of equipment productivity. The computed IF improvement for mining shovels is 49% and is aligned as per the SDG of responsible production.

Learning based cost optimal energy management model for campus microgrid systems

The introduction of microgrids has enabled an efficient energy management in the system with installation of renewable energy sources. As one of the representative models of microgrid, various studies on campus microgrids (CMGs) have been conducted. In operation of CMG, various energy consumption resources and renewable energy are considered to minimize overall cost or peak power in the system. However, most of conventional researches only deal with performance analysis in terms of simulation by collecting data from the different environments. In this case, there are lack of consideration in power regulation or electricity cost which make it difficult to apply the researched energy operation technology to the actual power system. To solve the problem, this paper build a test-bed in an actual CMG environment and collect dataset through the various IoT sensors. In addition, uncertainties that occur through the various power resources are analyzed and used to derive net energy consumption scenarios. In this way, we propose a new cost optimal energy management model with the detailed analysis of power generation and consumption using various auxiliary IoT devices. Based on the real-world datasets from the implemented CMG, we show that the proposed analytical models and energy management model are feasible for actual environments. With satisfying the constraints, we show that the daily electricity cost could be reduced up to 2.16% and peak power is reduced up to 3% compared to the case without considering the uncertainties in CMG.

Long-term energy efficiency and decarbonization trajectories for the Swiss pulp and paper industry

The contribution of the industrial sector is essential to realize long-term energy and climate policy goals. The present research paper explores trajectories improving energy efficiency and reaching net-zero emissions in the Swiss pulp and paper industry by 2050. A techno-economic bottom-up cost optimization model is developed based on the Swiss TIMES Energy system Model (STEM) and applied for a scenario analysis. Establishing an advanced modeling methodology including material and product flows in addition to energy flows, allowed us to assess explicit process improvements and the impact of specific innovative production technologies. Furthermore, this paper demonstrates the value of dividing industrial heat demand into different temperature levels which enables a detailed assessment of high-temperature heat pumps and waste heat recovery as important decarbonization options in industry. The results of the scenario analysis performed with this advanced model show that an energy reduction of 23% and a reduction in the annual CO2 emissions of 71% until 2050 would result from a cost-optimal technology deployment even without major policy intervention, given the assumptions made on technology progress and costs. These improvements are achieved by fuel switching, improvements in the production processes and deployment of efficient technologies, in particular high-temperature heat pumps and efficient motors. Achieving a net-zero goal in the pulp and paper industry by 2050 requires increased amounts of biomass in the short term and additionally high-temperature heat pumps up to 200 °C in the long-term in case of biomass scarcity. On the other hand, this leads to 49% higher energy related costs compared to the baseline development, if all other sectors decarbonize simultaneously to reach net-zero CO2 emissions in Switzerland.

A Pathway for the German Energy Sector Compatible with a 1.5 °C Carbon Budget

We present an energy transition pathway constrained by a total CO2 budget of 7 Gt allocated to the German energy system after 2020, the Budget Scenario (BS). We apply a normative backcasting approach for scenario building based on historical data and assumptions from existing scenario studies. The modeling approach combines a comprehensive energy system model (ESM) with REMix—a cost optimization model for power and heat that explicitly incorporates sector coupling. To achieve the necessary CO2 reduction, the scenario focuses on electrifying all end use sectors until 2030, adding 1.5–2 million electric vehicles to the road per year. In buildings, 400,000–500,000 heat pumps would be installed annually by 2030, and the share of district heating would double until 2050. In the scenario, coal needs to be phased out by 2030. Wind and Photovoltaic (PV) capacities would need to more than double to 290 GW by 2030 and reach 500 GW by 2050. The BS results indicate that a significant acceleration of the energy transition is necessary before 2030 and that this higher pace must be maintained thereafter until 2050.

On the History and Future of 100% Renewable Energy Systems Research

Research on 100% renewable energy systems is a relatively recent phenomenon. It was initiated in the mid-1970s, catalyzed by skyrocketing oil prices. Since the mid-2000s, it has quickly evolved into a prominent research field encompassing an expansive and growing number of research groups and organizations across the world. The main conclusion of most of these studies is that 100% renewables is feasible worldwide at low cost. Advanced concepts and methods now enable the field to chart realistic as well as cost- or resource-optimized and efficient transition pathways to a future without the use of fossil fuels. Such proposed pathways in turn, have helped spur 100% renewable energy policy targets and actions, leading to more research. In most transition pathways, solar energy and wind power increasingly emerge as the central pillars of a sustainable energy system combined with energy efficiency measures. Cost-optimization modeling and greater resource availability tend to lead to higher solar photovoltaic shares, while emphasis on energy supply diversification tends to point to higher wind power contributions. Recent research has focused on the challenges and opportunities regarding grid congestion, energy storage, sector coupling, electrification of transport and industry implying power-to-X and hydrogen-to-X, and the inclusion of natural and technical carbon dioxide removal (CDR) approaches. The result is a holistic vision of the transition towards a net-negative greenhouse gas emissions economy that can limit global warming to 1.5°C with a clearly defined carbon budget in a sustainable and cost-effective manner based on 100% renewable energy-industry-CDR systems. Initially, the field encountered very strong skepticism. Therefore, this paper also includes a response to major critiques against 100% renewable energy systems, and also discusses the institutional inertia that hampers adoption by the International Energy Agency and the Intergovernmental Panel on Climate Change, as well as possible negative connections to community acceptance and energy justice. We conclude by discussing how this emergent research field can further progress to the benefit of society.

Алгоритм управління інтелектуальним капіталом

The aim of the article is to develop a new approach to the management of intellectual capital of the enterprise. Achieving this goal requires solving the following tasks: analysis of the theoretical foundations of intellectual capital management (IC); development of an algorithm and structural scheme of intellectual capital management based on the assessment of IC using the additive utility function; economic and mathematical formulation of the problem of optimizing the cost of resources in the course of management of intellectual capital; choice of model and method of resource cost optimization. Intellectual capital is a priority type of capital. Intellectual capital management is presented as a multilevel dynamic open system. This system includes human, organizational and consumer capital, which should be considered as subsystems of management. Systemic and program-targeted approaches were used to study the intellectual capital of the enterprise. The concept of goal management is used in the work. An additive utility function is proposed as a criterion for optimality in achieving the goal in the process of intellectual capital management. An algorithm and a structural scheme of intellectual capital management of the enterprise have been developed. The developed algorithm of intellectual capital management on the basis of its estimation with use of function of utility allows planning, controlling and regulating a state of this system. To optimize the cost of resources associated with achieving high efficiency of intellectual capital, the problem is set and a mathematical model of the «backpack problem» is proposed. The proposed approach to the allocation of funds for activities that bring the real state of the intellectual capital management system to the planned state, allows for optimal use.

Optimising truck arrival management and number of service gates at container terminals

Purpose Truck appointment systems have been applied in critical container ports in the United States due to their potential to improve handling operations. This paper aims to develop a truck appointment system to optimise the total cost experiencing at the entrance of container terminals by managing truck arrivals and the number of service gates satisfying a given level of service. Design/methodology/approach The approximation of Mt/G/nt queuing model is applied and integrated into a cost optimisation model to identify (1) the number of arrival trucks allowed at each time slot and (2) the number of service gates operating at each time slot that ensure the average waiting time is less than a designated time threshold. The optimisation model is solved by the Genetic Algorithm and tested with a case study. Its effectiveness is identified by comparing the model's outcomes with observed data and other recent studies. Findings The results indicate that the developed truck appointment system can provide more than threefold and twofold reductions of the total cost experiencing at the terminal entrance compared to the actual data and results from previous research, respectively. Originality/value The proposed approach provides applicably coordinated truck plans and operating service gates efficiently to decrease congestion, emission and expenses.

Life Cycle Climate Change Impact of a Cost-Optimal HVDC Connection to Import Solar Energy from Australia to Singapore

This paper aims to evaluate the life cycle greenhouse gas (GHG) emissions of importing electrical power into Singapore, generated from a large-scale solar photovoltaic (PV) power plant in Australia, through a long-distance subsea high-voltage direct current (HVDC) cable. A cost optimization model was developed to estimate the capacities of the system components. A comprehensive life cycle assessment model was built to estimate emissions of manufacturing and use of these components. Our evaluation shows that, for covering one fifth of Singapore’s electrical energy needs, a system with an installed capacity of 13GWPV, 17 GWh battery storage and 3.2GW subsea cable is required. The life cycle GHG emissions of such a system are estimated to be 110gCO2eq/kWh, with the majority coming from the manufacturing of solar PV panels. Cable manufacturing does not contribute largely toward GHG emissions. By varying full-load hours and cable lengths, it was assessed that sites closer to Singapore might provide the same energy at same/lower carbon footprint and reduced cost, despite the lower insolation as compared to Australia. However, these sites could cause greater emissions from land use changes than the deserts of Australia, offsetting the advantages of a shorter HVDC cable.

Assessment of plum rain\u2019s impact on power system emissions in Yangtze-Huaihe River basin of China

As a typical climate that occurs in the Yangtze-Huaihe River basin of China with a size of 500,000 km2, plum rain can reduce the photovoltaic (PV) potential by lowering the surface irradiance (SI) in the affected region. Based on hourly meteorological data from 1980 to 2020, we find that plum rain can lower the SI in the affected region with a weekly peak drop of more than 20% at the most affected locations. This SI drop, coupled with a large number of deployed PV systems, can cause incremental CO2 emissions (ICEs) of local power systems by increasing the additional thermal power. Using a cost optimization model, we demonstrate that the ICEs in 2020 already reached 1.22 megatons and could range from 2.21 to 4.73 megatons, 3.47 to 7.19 megatons, and 2.97 to 7.43 megatons in 2030, 2040, and 2050, respectively, considering a change trend interval of a ±25% fluctuation in power generation and demand in the different years. To offset these ICEs, we compare four pathways integrated with promising technologies. This analysis reveals that the advanced deployment of complementary technologies can improve the PV utilization level to address climate impacts.

Analysis and Optimization of Cooling Water System Operating Cost under Changes in Ambient Temperature and Working Medium Flow

The circulating cooling water system is widely used in various industrial production fields, and its operating cost largely depends on external factors, such as ambient temperature and working medium flow. Considering the relative elevation of the heat exchanger, this study establishes a total system operation cost analysis and optimization model based on the superstructure method. The model uses ambient dry bulb temperature, ambient wet bulb temperature, and working medium flow as random variables. Water supply temperature is adopted as the decision variable, and the minimum operating cost of the system is used as the objective function. An analysis of the effect of the three random variables on the operation cost shows that the effect of ambient dry bulb temperature on the operation cost is negligible, and the effect of ambient wet bulb temperature and working medium flow on the operation cost is significant. In addition, a control equation of water supply temperature is established to determine the “near optimal” operation, which is based on the correlation among ambient wet bulb temperature, working medium flow, and optimal water supply temperature. Then, the method is applied to a case system. The operating cost of the system is reduced by 22–31% at different times during the sampling day.

Comparative Study on the Attributes Analysis of Software Development Cost Model Based on Exponential-type Lifetime Distribution

In this paper, the development cost attributes were newly analyzed by applying the Exponential–type lifetime distributions (Burr-Hatke-exponential, ExponentialBasic, Exponential-exponential, Inverse-exponential) widely utilized in the field of software lifetime testing and quality evaluation to the software development cost model. Also, to verify the attributes of the analyzed development cost, after analyzing the future reliability, the optimal cost model was presented. For this study, an analysis algorithm using software failure time data was proposed to solve the research solution, the maximum likelihood estimation (MLE) was applied to solve the parameter values, and the nonlinear equation was calculated using the binary method. Simulations show that if the cost of removing one flaw found during the test phase increases, the development cost increases, but the release time does not change. However, if the cost of flaws correction discovered by operators increased, both development costs and release times increased. Therefore, we must remove all possible flaws at the testing stage to eliminate failures. In conclusion, First, the Exponential-exponential distribution model showed the best performance among the proposed models because it had the lowest software development cost and the highest future reliability. Second, the software development cost attributes of the Exponential-type lifetime distributions were newly analyzed. Third, through this data, it was able to help software developers to analyze the most economical development cost

Social discounting, social costs of carbon, and their use in energy system models

Discounting plays a large role in cost-optimization models, but is nevertheless often only covered in little detail in energy system models. The aim of this paper is to highlight the effects of varying discount rates and social costs of carbon in energy system models with the example of the Global Energy System (GENeSYS-MOD), propagating open debate and transparency about chosen parameters for model applications. In doing so, this paper adds to the academic discourse on socio-economic factors in energy system models and gives an outline to modelers in the field by providing example results. The results show that close-to-zero discount rates that factor in intergenerational equality, total emissions could be reduced by up to 41% until 2050 compared to the baseline discount rate of 5%. This effect is even increased when a carbon price akin to the actual social costs of carbon is chosen. This underlines the importance of the topic, which is, up to now, seldom covered in cost-optimizing energy system models.