Increased Capacity Utilization Research Articles

The economic impact of E-commerce on the U.S. airports: Cost savings and productivity growth

The rise of e-commerce, fueled by digitalization and shifting in consumer demand, has revolutionized air cargo. While all e-commerce stakeholders are engaged in fierce competition to increase their market share, airports as integral elements of the air cargo supply chain could not be apathetic to this competition. This study examines the economic implication of e-commerce on high cargo volume airports, focusing on cost savings and productivity growth. Utilizing the Farrell measure of input-oriented technical efficiency, we estimate scope economies for the U.S. airports between 2009 and 2019. The results reveal higher scope economies for high cargo volume airports, indicating greater cost savings resulting from e-commerce activities. In addition, the Malmquist index is employed to assess productivity growth. The index is decomposed into technical change, efficiency change, and scale change. The findings show a 3.3% overall productivity growth in the U.S. airport industry, primarily driven by technical change. Besides, high cargo volume airports exhibit higher productivity growth compared to low cargo volume airports, with 4.1% and 2.9% growth rates, respectively. The gap between high cargo volume and low cargo volume airports widened in 2017, aligning with the pre-pandemic peak in e-commerce activity. Furthermore, high cargo volume airports experience higher efficiency gains and technical advancements. While efficiency gains are attributed to improved performance and increased capacity utilization, the technical advancements can be explained by the digitalization and customized operations associated with e-commerce. Overall, the results highlight the significant contribution of e-commerce to cost savings and productivity growth in high-cargo volume airports.

A study of the impact of de-capacity policies on industry capacity utilization paths: Evidence from the Chinese steel industry

The issue of overcapacity has become an unavoidable challenge in the rapid development of nations, constraining economic progress, particularly within industries like steel, coal, and cement. This study, using the example of the Chinese steel industry in the context of supply-side structural reform, employs data envelopment analysis (DEA) models to measure capacity utilization, and ordinary least squares (OLS) models to investigate the impact of capacity reduction policies on the steel industry’s capacity utilization pathways. The research findings indicate that capacity reduction policies have a significantly positive impact on the capacity utilization in the steel industry. They enhance capacity utilization through four pathways: “equipment optimization and upgrade”, “enterprise mergers and restructuring”, “technology innovation-driven”, and “environmental protection regulations”. Among these, “technology innovation-driven” and “environmental protection regulations” play predominant roles, while the effect of “international market expansion” on increasing capacity utilization in the steel industry is not significant. To ensure the sustained effectiveness of capacity reduction policies, the nation should continue to strengthen the “technology innovation-driven” and “environmental protection regulations” pathways. Additionally, it should activate the “national market expansion” pathway, fully exploring the potential for international cooperation to achieve improved capacity utilization in the steel industry.

Optimal power dispatch capability and reliability analysis of hybrid renewable energy system

These days, using solar and wind energy resources is much more cost-effective and appealing. The rapid depletion and growing expense of non-renewable energy supplies resulted in the development of alternative energy sources that utilize renewable energy sources. In recent times, hybrid renewable energy algorithms have often been connected to wind and solar power systems. The primary objective of this work (PWBHPS) is to determine the power dispatch capacity of a PV-wind-battery hybrid power system. In the modern electric power market, the PWBHPS's power dispatch capacity needs to be explicitly specified at the beginning of every dispatching time interval in order to collaborate with the transmission system operator (TSO). Energy storage is a workable option for efficiently integrating renewable energy sources into the system. A recently developed storage device called the vanadium redox flow battery (VRFB) may have certain advantages in large-scale grid-connected applications. State-of-charge monitoring provides extra insights into battery performance by displaying increased capacity utilization and longer cycle times, both of which extend the life of the cells. This study looks at the minimum-max technique of individual solar energy, wind energy, and a combined PV wind battery hybrid power system to size a VRB battery for application in renewable energy systems. Individual wind and solar systems need larger battery capacities for consistent, constant power output; hybrid systems require batteries with lower ratings. An optimal charging mechanism is proposed and explored through MATLAB simulations.

Percolation investigation of organic radical battery electrodes and its crucial impact on capacity utilization

Organic battery electrodes are usually composites consisting of an active material, a conductive additive, and a binder. The present work investigates the role of the conductive additive SuperP® (SP) in a standard composite electrode based on poly(2,2,6,6-tetramethyl-4-piperinidyl-N-oxylmethacrylate) (PTMA) and analyzes the relevance of percolation theory for the electrode’s capacity utilization. It is demonstrated by impedance spectroscopy (IS) and dielectric spectroscopy that at values between 5 wt.-% and 10 wt.-% of the conductive additive the composite shows a phase transition from dielectric behavior into a conducting state. This is caused by the creation of an interconnected network of conducting SP particles penetrating the dielectric polymer matrix as indicated by the measured Nyquist spectra. This percolation mechanism generates a 1,000-fold increase of conductivity from 10-7 to 10-4Sm-1. Evaluations of different composite parameters such as permittivity, dielectric loss, and conductivity as well as scanning electron microscopy (SEM) investigations verify the formation of conducting percolation paths in the organic electrode. Additionally, the first distribution of relaxation time (DRT) analysis of an electrode of an organic radical battery is presented to unravel conduction mechanisms in the dielectric non-percolated and conducting percolated electrodes. Subsequently, galvanostatic cycling with potentiostatic holding times revealed an increased capacity utilization with increasing amount of conductive additive. Electrodes below the percolation threshold do not show charge storage characteristics due to high ohmic overpotentials. Thus, the formation of electron conducting percolation paths by the additive is a crucial mechanism for enabling efficient accessibility of redox centers in the battery electrodes. Hence, IS is presented as a preliminary electrode characterization step prior to the cell assembly to ensure a high capacity utilization of the electrode within the battery.

Optimizing Sugar Manufacturing: A Hybrid Simulation-Based Approach and MCDM for Efficient Decision Making

Efficient truck flow is essential for the efficient operation of a factory and the distribution of its products. This study demonstrated methods to improve truck loading times and overall efficiency at a major sugar manufacturing facility in the Middle East. The objective was to reduce truck waiting times at loading units and increase capacity. The data were collected through questionnaires, observations, and interviews with stakeholders. A simulation software was employed to analyze truck activity at loading stations at the factory. Multi-criteria decision making (MCDM) tools, AHP and TOPSIS, addressed five primary criteria and nine sub-criteria to assist in identifying, evaluating, and ranking feasible solutions. The study suggested different utilization of the various factory loading platforms at different times of the day. The findings from this study emphasize the importance of simulation-based approaches supplemented with decision-making processes to improve efficiency in sugar manufacturing facilities that may have broader applications in the factories of other industries. The study highlights remarkable improvements in operational efficiency, as seen in Alternative 4 substantial 27.9% enhancement, resulting in cost savings and time efficiency. By implementing these findings, factories can enhance their truck flow management system, reduce waiting times, increase capacity utilization, optimize resource allocation, and improve overall efficiency.

Imprecise WareHouse Space in Aggregate Production Planning Using Fuzzy Goal Programming

Considering the fluctuating market demands with variable storage capacity and available production capacity, this study examines a number of workable techniques for modeling multiproduct aggregate production planning problems with fuzzy numbers. The suggested method makes use of factors including; inventory levels, labor levels, overtime, backordering levels, workforce capacity, machine capacity, and fuzzy warehouse capacity in an effort to reduce operating costs, reduce production waste, and increase capacity utilization rate. With the aid of this formulation and interpretation, a fuzzy multiproduct aggregate production planning model is developed. Finally, the study's conclusions were arrived at using information provided by Rich Pharmaceuticals Ltd. using Lingo version 18 software (RPL).and it uses parametric programming, best balancing, and interactive techniques to give solutions that can be adjusted to fit a variety of decision-making circumstances.

The impact of a short-term digital marketing campaign on improving the sales of hotel capacities

The subject of the research is the influence of a specific online marketing campaign through selected social networks on the improvement of the sales of accommodation capacities with the example of the hotel Putnik on Kopaonik. The aplication of the campaign should find a quick way to potential users and to encourage their interest. It is of great importance to plan and direct the marketing campaign because the wide distribution does not directly affect the results. Online marketing has shown to be a useful tool for fast group targeting to which the offer will be directed. Therefore, this paper aims to present an example of an active approach to the use of social networks as a part of marketing activities, to stimulate short-term capacity sales in specific terms. Also, the aim was to present preliminary short-term results of social media marketing activities combined with incentive pricing strategies on increasing capacity utilization and achieving higher business results. The data used in the paper are unpublished, acquired from the official business report of the hotel software "PROTEL". The marketing campaign gave good results and therefore hotels can carry out similar activities in order to achieve efficient hotel capacity filling.

Optimizing vehicle utilization in the cold chain: Literature review

Optimizing vehicle routes in the cold chain is a process to improve transport efficiency with the aim of increasing the profitability and sustainability of the transport company, considering minimizing costs, ensuring the quality and safety of products, reducing environmental impact and service user satisfaction. Given the fragility of the transport substrate, it is important to improve transport efficiency. This includes optimizing routes and using advanced mathematical models and algorithms to increase capacity utilization, reduce vehicle turnaround time and minimize costs. The most common method for optimizing the routes of vehicles in the cold chain is to use technology to maintain and monitor product quality and safety during transport. Such an approach ensures efficient transport of the substrate in the cold chain with the fastest transport time and the shortest route, which is necessary to minimize the harmful effects that may occur during transport. Service user satisfaction and environmental impact play an increasingly important role and will play a major role in optimizing vehicle routes in the cold chain in the future.

Deep Reinforcement Learning-based Trajectory Pricing on Ride-hailing Platforms

Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and the satisfaction of both passengers and drivers. Many proposed dynamic pricing technologies focus on short-term optimization and face poor scalability in modeling long-term goals for the limitations of solution optimality and prohibitive computation. In this article, a deep reinforcement learning framework is proposed to tackle the dynamic pricing problem for ride-hailing platforms. A soft actor-critic (SAC) algorithm is adopted in the reinforcement learning framework. First, the dynamic pricing problem is translated into a Markov Decision Process (MDP) and is set up in continuous action spaces, which is no need for the discretization of action space. Then, a new reward function is obtained by the order response rate and the KL-divergence between supply distribution and demand distribution. Experiments and case studies demonstrate that the proposed method outperforms the baselines in terms of order response rate and total revenue.

Stabilising transient disturbances with utility‐scale inverter‐based resources

This paper presents a trajectory tracking control strategy that modulates the active power injected by geographically distributed inverter-based resources to support transient stability. Each resource is independently controlled, and its response drives the local bus voltage angle toward a trajectory that tracks the angle of the center of inertia. The center-of-inertia angle is estimated in real time from wide-area measurements. The main objectives are to stabilize transient disturbances and increase the amount of power that can be safely transferred over key transmission paths without loss of synchronism. Here we envision the actuators as utility-scale energy storage systems; however, equivalent examples could be developed for partially-curtailed photovoltaic generation and/or Type 4 wind turbine generators. The strategy stems from a time-varying linearization of the equations of motion for a synchronous machine. The control action produces synchronizing torque in a special reference frame that accounts for the motion of the center of inertia. This drives the system states toward the desired trajectory and promotes rotor angle stability. For testing we employ a reduced-order dynamic model of the North American Western Interconnection. The results show that this approach improves system reliability and can increase capacity utilization on stability-limited transmission corridors.

Examining the relationships between innovation, quality, productivity, and customer satisfaction in pure service companies

PurposeThis study investigates the relationships between innovation, quality, productivity and customer satisfaction in pure service companies. Previous studies have shown a negative relationship between quality and productivity in services. However, we argue the two can be positively related when innovation is present.Design/methodology/approachThis paper develops and tests hypotheses using secondary data from COMPUSTAT, KLD STAT and the American Customer Satisfaction Index (ACSI). We test these hypotheses using ordinary least squares regression and conduct additional testing using path analysis.FindingsThe findings show that quality and productivity are positively related when innovation is present in pure service companies. The findings also reveal find that innovation is antecedent to both service quality and productivity which in turn positively affect customer satisfaction.Practical implicationsBoth companies and customers can increase their outcomes including higher levels of service quality, productivity and customer satisfaction. Managers should therefore design innovative systems that enable customers to participate in service production. Other innovative systems may help to increase capacity utilization by smoothing high and low demand times, thus increasing both service quality and productivity.Originality/valueThis study contributes to service research by identifying innovation as the key to simultaneously increasing service quality and productivity. The authors find empirical support for a model in which service quality and productivity have a complementary relationship leading to customer satisfaction with innovation as an antecedent, and we do so using a sample of pure service firms.

Development and Testing of a 4-Columns Periodic Counter-Current Chromatography System Based on Membrane Adsorbers

Continuous chromatography can surmount the disadvantages of batch chromatography like low productivities and extensive usage of consumables. In this work, a 4-column continuous chromatographic system based on the principle of periodic counter-current chromatography (PCCC) was developed and tested with a model protein mixture of BSA and lysozyme. The PCCC system was specially designed for membrane adsorbers as an alternative to conventional columns to facilitate the use of disposable process units and to further increase the productivity due to higher convective mass transport in the membrane adsorber. Membrane adsorber Sartobind® Q was used to continuously purify BSA from the protein mixture. The usage of PCCC led to an increased capacity utilization (here 20%) and higher space–time-yields, and thus to a remarkable productivity increase and cost savings.

Dynamic pricing and matching in ride‐hailing platforms

AbstractRide‐hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride‐hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride‐hailing platforms. We provide a review of matching and DP techniques in ride‐hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool‐matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.

Long-term energy system planning considering short-term operational constraints

The intermittent nature of renewable energy sources (RESs) brings formidable challenges in the operation of power system. Long-term energy system planning models overlook the impact of renewable intermittency on system operations due to the computational burden associated with large model size and long planning horizon. Hence, strategies such as soft-linking multiple models are developed, but they do not assure the convergence and optimality of such incoherent modeling framework. In this context, this paper utilizes unit commitment (UC) extension of TIMES modeling framework to integrate operational constraints directly in a long-term power system planning model. This strategy eliminates the complexity of handling multiple models. Results indicate that incorporation of UC constraints improve the performance of conventional generators in terms of increased capacity utilization, and help to assess flexibility requirements with high RESs. Energy storage provides the balancing and flexibility needs with stringent generator constraints. Sensitivity analysis shows that improved flexibility of thermal generators enables increased renewable penetrations.

A Novel Dynamic Performance Analysis and Evaluation Model of Series-Parallel Connected Battery Pack for Electric Vehicles

Due to the low voltage and small capacity of a Li-ion battery cell, large numbers of cells are connected to construct a battery pack to satisfy the voltage and capacity requirement of the power system of an electric vehicle. Focusing on parallel and series connection mode of battery packs, the main contributions include the following. First, in order to increase the utilization rate of cells and enhance the performance of the battery pack, a method that makes the battery pack achieve their maximum initial capacity has been proposed. Second, a dynamic modeling and analysis method for the battery pack based on the equivalent circuit model has also been proposed. The results show that the battery pack in parallel and then in series has a better performance on charge/discharge capacity, efficiency, and utilization rate of cells. Third, the performance analysis and evaluation platform of the battery pack has been built, and the battery pack test results of different groups supported the conclusion of the simulation. The battery packs in parallel and then in series can achieve superior performance and greatly increase capacity and energy utilization.

Periodic railway timetabling with sequential decomposition in the PESP model

With continuously increasing capacity utilization of railway networks as well as growing requirements on service quality and reliability, railway timetabling is becoming increasingly difficult. Although most timetables are still constructed manually in practice, the demand for advanced automatic timetabling techniques is evident. Long computation times, however, are a major impediment for the use of optimization-based timetabling tools within today's planning process. Focusing on the construction of periodic timetables via the periodic event scheduling problem (PESP), the paper introduces a new decomposition technique to speed up automatic timetabling. The approach is based on solving a sequence of smaller subproblems and can be parameterized to reach a suitable compromise between the two extremes of either simultaneous or sequential planning. Computational results on large timetabling instances for Switzerland's railway network show very promising results. In particular, finding feasible as well as near optimal timetables can be considerably accelerated compared to solving the PESP using the standard MILP formulation.

Tool allocation to smooth work-in-process for cycle time reduction and an empirical study

As semiconductor devices are increasingly employed in consumer electronics and industrial applications, cycle time reduction is critical for semiconductor companies to maintain competitive advantages. Semiconductor manufacturing is capital intensive, in which fab capacity is configured by the interchangeable tools that can be allocated for different steps of reentrant processing. Focusing on realistic needs, this study aims to propose a novel approach that integrates data mining approach to forecast arrival rates and determining the allocation of interchangeable tool sets to reduce the work in process (WIP) bubbles for cycle time reduction. In particular, a hybrid approach of decision tree and back-propagation neural network (BPNN) was developed to forecast the arrival rates of individual tool sets and thus predict the WIP levels of individual tool sets. Therefore, the tool allocation decisions can be generated to minimize the total WIP of interchangeable tool sets given the same throughput level. An empirical study was conducted in a leading semiconductor company in Taiwan for validation. The results have shown practical viability of the proposed approach that can effectively reduce WIP and increase capacity utilization in real settings.

Optimal operation of a photovoltaic generation-powered hydrogen production system at a hydrogen refueling station

As the popularity of fuel cell vehicles continues to rise in the global market, production and supply of low-carbon hydrogen are important to mitigate CO2 emissions. We propose a design for a hydrogen refueling station with a proton exchange membrane electrolyzer (PEM-EL)-based electrolysis system (EL-System) and photovoltaic generation (PV) to supply low-carbon hydrogen. Hydrogen is produced by the EL-System using electricity from PV and the power grid. The system was formulated as a mixed integer linear programming (MILP) model to allow analysis of optimal operational strategies. Case studies with different objective functions, CO2 emission targets, and capacity utilization of the EL-System were evaluated. Efficiency characteristics of the EL-System were obtained through measurements. The optimized operational strategies were evaluated with reference to three evaluation indices: CO2 emissions, capacity utilization, and operational cost of the system. The results were as follows: 1) Regardless of the objective function, the EL-System generally operated in highest efficiency state, and optimal operation depended on the efficiency characteristics of the EL-System; 2) mitigation of CO2 emissions and increase in capacity utilization of the EL-System required trade-offs; and 3) increased capacity utilization of the EL-System showed two opposing effects on hydrogen retail price.

Technical and Economic Efficiency of Laying Hen Farms in Konya, Turkey

ABSTRACT This study was carried out to determine the technical and economic efficiency levels of laying hen farms and factors affecting the efficiency scores. For this purpose, a personal interview was carried out. Technical and economic data of 39 laying hen farms consisting their inputs and outputs over the period 2013-2015 was formed the material of this study. Efficiency levels were estimated by using Data Envelopment Analysis (DEA). Mean technical efficiency score of the enterprises were determined as 98.6%. Also 48.7% of the enterprises were determined fully technical efficient. On the other hand, mean economic efficiency of the enterprises was determined as 88.8%, while only 17.9% of the enterprises were fully economic efficient. In order to determine the factors affecting efficiency scores, Tobit regression analysis was performed. According to the analysis results, chick mortality rate, hen mortality rate and feed conversion ratio had negative effects on technical efficiency scores, while education level of the farmer and capacity utilization ratio had positive effects. Egg cost, chick mortality rate, feed conversion rate and production length had negative effect while egg yield had positive effect on economic efficiency. As a result, enterprises would increase their technical and economic efficiency scores by decreasing the mortality rate, feed conversion rate, production length. Furthermore, increasing capacity utilization ratio, education level of the owner and egg yield/hen would help to increase efficiency levels of the farms.

Dynamic Pricing and Matching in Ride-Hailing Platforms

Ride-hailing platforms such as Uber, Lyft and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing algorithms -- the two key levers in ride-hailing -- have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride-hailing platforms. We provide a review of matching and dynamic pricing techniques in ride-hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool-matching mechanism called dynamic waiting that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing dynamic pricing and dynamic waiting, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.