Capacity Expansion Research Articles

A multi-period vaccines supply chain network design with capacity expansion and different replenishment cycles under uncertain demand

The World Health Organization (WHO) proposes guidelines for vaccine distribution networks that incorporate economic order quantity and risk pooling principles, specifically through implementing different replenishment cycles between different levels of the distribution network. The substantial impact of different replenishment cycles on inventory levels is demonstrated in the existing literature. However, current optimization network design research has paid limited attention to this aspect. This article aims to address this research gap by investigating a two-stage stochastic programming for a multi-period perishable vaccine network design problem, taking into account the different replenishment cycles characteristic and capacity expansion under uncertain demand conditions. Given the inherent difficulty of solving the proposed problem, a matheuristic approach based on Variable Neighborhood Search (VNS) is developed. To illustrate the practical application and analyze the impact of uncertain demand, we apply the proposed model and its solution method using the Indonesia dataset in the COVID-19 situation. The computational results indicate that lower replenishment cycles leads to higher inventory levels, total costs, and capacity requirements for the top-level distribution center. Optimum replenishment cycles and further managerial implications and future potential research are also presented.

Regionalization of primary health care units: An iterated greedy algorithm for large-scale instances

In this paper, we study the problem of multi-institutional regionalization of primary health care units. The problem consists of deciding where to place new facilities, capacity expansions for existing facilities, and demand allocation in a multi-institutional system to minimize the total travel distance from demand points to health care units. It is known that traditional exact methods as branch-and-bound are limited to solving small- to medium-size instances of the problem. Given that real world-instances can be large, in this paper we propose an iterated greedy algorithm with variable neighborhood descent search for handling large-scale instances. Within this solution framework, several methods are developed. A greedy constructive method and two deconstruction strategies are developed. Another interesting component is the exact optimization of a demand allocation subproblem that is obtained when the location of facilities is previously fixed. An empirical assessment using real-world data from the State of Mexico’s Public Health Care System is carried out. The results demonstrate the effectiveness of the proposed metaheuristic in handling large-scale instances.

The Role of Air Transport Infrastructure Towards Breaking Seasonality in Emerging Tourism Destinations: A Qualitative Study of Akureyri, North Iceland

This paper examines the impact of air infrastructure development on emerging insular destinations by focusing on Akureyri, located in North Iceland. It examines the intricate relationship between airport infrastructure capacity expansion, the building of human capacity, hospitality infrastructure growth, and seasonality challenges. Through extensive qualitative interviews and literature reviews from stakeholders, policymakers, and academics, the research synthesises how an airport expansion offers democratisation of tourism mobilities and connectedness with the outside world for remotely located communities in the region. The analysis found that despite the stakeholders’ increased awareness of the complexities in accommodating year-round tourism, there is a readiness to increase the tourism enterprise flows. Nevertheless, the study shows a high deficit in hospitality infrastructure along with regional investment imbalances, revealing the lack of a comprehensive capacity building strategy. Ultimately, the authors advocate for an ‘extended capacity’ approach to tackle the challenges presented in the process of overcoming seasonality issues. The paper stresses the need for developed human skill capacity and diversified services and products alongside infrastructure capacity upgrades. The gained insights are gathered in two figures. The first identifies and maps the contextual landscape encompassing North Iceland’s tourism industry and its stakeholders, while the other showcases the complexity of the process the destination undergoes for breaking seasonality, while exposing the interconnections of potential outcomes, stakeholders’ needs, and the existing and in process capacities.

Unraveling how intermediary‐beneficiary interaction shapes policy implementation

AbstractAs a result of policy growth, implementing agencies often face new mandates without the necessary capacity expansion to comply with, thus resorting to intermediaries. However, intermediaries are not innocuous to the implementation process, especially when they are expected to play the double role of target and intermediary, responsible for translating/interpreting regulation for beneficiaries. How does the interaction between beneficiaries and intermediaries‐target shape policy implementation? I argue that such interaction is not only determined by the role the intermediary adopts, and their relation with the beneficiary, but also by the motivations beneficiaries have for engaging in the regulatory process, and their capacity to do so. I develop a theoretical framework for understanding their interaction and apply it to a new regulatory policy in Mexico to provide social security for paid domestic workers. I explore the mechanisms by which the interaction between intermediaries and beneficiaries affects the outcome of the regulatory process.

Environmental Health Predictions for Fire Resistant and Corrosion Protection Measures for Community Health Tourism Building Facilities Due to Climate Change

The recent expansion of green tourism building facilities and warehouse capacities entails the installation of integrated community health tourism facility for cultural events, safe mobility, sport activities, agricultural and veterinary activities at old heritage buildings for public health, environmental health, green circular economy promotion within safe structures, which operations increase the occurrence of compartment fires involving flammable dangerous substances i.e biogas from sanitary biomass treatment for renewable resources, electricity and heating. The aim of this research is to compare and analyze the fire resistant materials of structural members and design economic solutions made of different structural materials for natural hazards and climate change. Useful results are presented for sustainable design solutions at integrated community health tourism renew facilities at old heritage buildings, applying proper utilities. Then, are examined the possibility of reinforcing the beams with carbon fiber lamellae and proposed additional fire protection requirements. The examining results present useful solutions for stakeholders. The study outputs enable warehouse designers, operators, stakeholders, investors and safety experts to ensure safe building facilities. Finally, sanitary digital drawing utilities, monitoring schemes are demonstrated for safe green designs so as to protect the examining building facilities at foundations or roof top levels from flood events and high temperatures due to climate change and extreme weather events.

Rapid transformation in aquatic food value chains in three Nigerian states

IntroductionNigeria is Africa’s most populous country, third largest fish producer, and second largest aquaculture producer. There have been numerous studies of aquaculture and fisheries in Nigeria, but no study to date has analyzed aquaculture and capture fisheries value chains together, while giving equal weight to all value chain segments and systematically capturing information on temporal trends in value chain structure, conduct, and performance. This paper does so.MethodsWe conducted a structured, qualitative, rapid reconnaissance survey of 180 actors from eight segments of the aquatic food value chain in three Nigerian states with contrasting geographies, Ebonyi, Kebbi, and Oyo.ResultsResults particularly important for future research and policy are as follows. (1) Partial vertical integration is a common feature of the organization of firms in all value chain segments, as a risk reduction strategy to overcome market imperfections and frictions. Scarcity and high cost of imported intermediate inputs is stimulating local innovation and import substitution manufacturing of feeds and fabricated items. (2) Inter-sectoral spillovers from the poultry industry have hastened the development of fish value chains, while improvements in power supply have enabled the expansion of cold storage capacity, facilitating the geographical lengthening of frozen fish value chains. (3) Rapid technological and/or institutional change is occurring in all value chain segments, including those usually viewed as traditional, such as processing and fishing. Logistics services are becoming more specialized and sophisticated, and digital communications technologies including social media play an increasingly important role in value chain coordination and marketing. (4) Increasing opportunity costs of time drive demand for convenience foods such as smoked and fried fish, which require little further preparation and can be stored at room temperature. Demand for farmed catfish is linked to growing consumption of food away from home at bars and restaurants. (5) Significant opportunities exist to improve the performance of value chains in terms of gender equity, environmental impacts, and food safety.

Insights from a Comprehensive Capacity Expansion Planning Modeling on the Operation and Value of Hydropower Plants under High Renewable Penetrations

This paper presents a quantitative assessment of the value of hydroelectric power plants (HPPs) in power systems with a significant penetration of variable renewable energy sources (VRESs). Through a capacity expansion planning (CEP) model that incorporates a detailed representation of HPP operating principles, the study investigates the construction and application of HPP rule curves essential for seasonal operation. A comparative analysis is also conducted between the proposed rule curve formulation and alternative modeling techniques from the literature. The CEP model optimizes installed capacities per technology to achieve predefined VRES penetration targets, considering hourly granularity and separate rule curves for each HPP. A case study involving twelve reservoir hydropower stations and two open-loop pumped hydro stations is examined, accounting for standalone plants and cascaded hydro systems across six river basins. The study evaluates the additional generation and storage required to replace the hydropower fleet under high VRES penetration levels, assessing the resulting increases in total system cost emanating from introducing such new investments. Furthermore, the study approximates the storage capabilities of HPPs and investigates the impact of simplified HPP modeling on system operation and investment decisions. Overall, the findings underscore the importance of reevaluating hydro rule curves for future high VRES penetration conditions and highlight the significance of HPPs in the energy transition towards carbon neutrality.

The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.

Telehealth Transformation: A Mixed-Methods Study on Organizational Change Processes and Outcomes in a Private Medical Practice.

This mixed-methods case study investigated the impacts, costs, barriers, and facilitators associated with implementing telehealth services across a private pediatric clinic system. The research examined the effects of telehealth on provider engagement and financial performance. Twenty-three clinicians, administrators, and staff across the pediatric clinics were interviewed before and after enterprise-level telehealth adoption to examine change processes amid this innovation. Twelve months of pre- and post-implementation financial records underwent statistical analysis to assess revenue and cost dynamics. Quantitative outcome measures encompassed expenses, revenues, and telehealth visit utilization rates, while qualitative analysis of interviews and focus groups revealed key implementation themes through rigorous inductive coding of participant narratives. Results showed significantly increased costs (44%) and revenues (47%) at clinics following virtual care expansion. Monthly telehealth visits per provider exponentially rose over 450%. Qualitatively, 83% of providers appreciated scheduling flexibility benefits, but 68% of staff cited workflow disruptions. Interpretatively, findings demonstrated catalyzed financial and productivity transformations and nuanced perceived disruption amid pronounced appointment capacity expansions. Recommendations encompass updated care coordination protocols, enhanced training and support resources, incentivizing provider usage, and modulating implementation pacing responding to user feedback during large-scale organizational innovation.

Measurement and comparison of transient thermal boundary layer of a Local-Heated Mini-Channel under sub- and super-critical conditions

This study is focused on experimentally measuring the transient thermal boundary layer following a sudden heat addition to turbulent flow boundary in supercritical mini-channels. Interferometric diagnostics were performed using a visualization channel to capture the time-evolution boundary layer. The measured pressure drop shows good agreement with numerical iterations according to the friction laws. Transient scenarios have been extracted from the raw interferograms to facilitate quantitative comparison of boundary conditions, such as bulk temperature and heat flux: (1) Localized heat accumulation and a slowly thermal response have been observed in subcritical state; (2) Relatively high density of 1.2 kg/m3 and low temperature changes of 0.008 K are observed in cases with relatively shorter critical distances to the critical point, attributed to the sharp increase in heat capacity and thermal expansion coefficients. (3) A relatively fast thermal convective field is established at larger heat fluxes due to the rapid density shifts of 1.2 kg/m3 and associated flow acceleration in the boundary layer.

Renewable energy expansion under taxes and subsidies: A transmission operator’s perspective

This paper investigates the role of a transmission system operator within a carbon footprint reduction strategy incorporating carbon taxes and renewable energy generation subsidies in the decentralised energy market. This is achieved via an optimisation bi-level model in which a welfare-maximising transmission system operator makes investments in transmission lines at the upper level while considering power market dynamics at the lower level. To account for the deregulated energy market structure, this paper assumes that the generation companies at the lower level make capacity investments as price-takers in perfect competition. Considering alternative transmission infrastructure expansion budgets, carbon emission taxes and monetary incentives for renewable energy generation capacity expansion, the impact of alternative compositions of these factors is analysed against three output factors: the share of renewable energy in the generation mix, total generation amount, and social welfare. The proposed modelling assessment is applied to an illustrative three-node instance and a case study considering a simplified representation of the energy system of the Nordic and Baltic countries. The results highlight that, under certain circumstances, renewable energy generation subsidies may lead to an increase of renewable energy in the generation mix followed by a simultaneous fall in the total generation amount. Nevertheless, when applied together, these three measures demonstrated a positive impact on all output factors within Nordics’ and Baltics’ energy systems. The experiments additionally suggest that considering the high value of the carbon tax does not have an impact on the output factors while the composition of high values of renewable energy generation subsidies and budget for transmission infrastructure expansion has the strongest effect.

Climate sustainability through a dynamic duo: Green hydrogen and crypto driving energy transition and decarbonization

Climate change persists as a pressing global issue due to high greenhouse gas emissions from fossil fuel-based energy sources. A transition to a greener energy matrix combined with carbon offsetting is imperative to mitigate the rate at which global temperature ascends. While countries have deployed faith in green hydrogen to accelerate worldwide decarbonization efforts, the concurrent rise of blockchain-operated crypto-applications, such as bitcoin, has exacerbated climate change concerns. In this study, we propose technological solutions that combine the green hydrogen infrastructure with bitcoin mining operations to catalyze environmental and socioeconomic sustainability in climate change mitigation strategies. Since the present state of crypto-operations undeniably contributes to worldwide carbon emissions, it becomes vital to explore opportunities for harnessing the widespread enthusiasm for bitcoin as an aid toward a sustainable and climate-friendly future. Our findings reveal that green hydrogen production, paired with crypto-operations, can accelerate the deployment of solar and wind power capacities to boost conventional mitigation frameworks. Specifically, leveraging the economic potential derived from green hydrogen and bitcoin for incremental investment in renewable energy penetration, this dynamic duo can enable capacity expansions of up to 25.5% and 73.2% for solar and wind power installations. Therefore, the proposed technological solutions that leverage green hydrogen and bitcoin mining, bolstered with appropriate policy interventions, can not only strengthen renewable power generation and carbon offsetting capacities but also contribute significantly to achieving climate sustainability.

Least-cost or sustainable? Exploring power sector transition pathways

The 2022 energy crisis augmented the European ambition for RES capacity expansion. However, beyond the issue of “how many” renewables, the research question that remains, is which capacity mixes achieve the emissions reduction, renewable integration, and energy autarky targets in a cost-effective way. This paper tries to answer this inquiry supported by two energy models and one decision support model. The analysis focuses on Greece, as an example country which opted for lignite phase out, turning to temporary heavy reliance on natural gas for power generation. The results indicate that even though from the energy and environmental targets’ perspective, more wind turbines in the RES capacity mix yield better results, when incorporating the economic point of view, balanced wind and solar mixes enable the achievement of targets in a cost-effective way. The most cost-efficient mixes consist of 50–55% wind and 45–50% solar capacity, with cost per additional percentage of RES share in the generation mix, equal to 40 million €. Nevertheless, in order to materialize them in a timely manner, and limit the short-term dependency on imported gas, permitting simplification, tenders for large-scale projects and incentives for small-scale projects are needed to facilitate accelerated RES deployment.

StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography

Image hiding aims to conceal one or more secret images within a cover image of the same resolution. Due to strict capacity requirements, image hiding is commonly called large-capacity steganography. In this paper, we propose StegFormer, a novel autoencoder-based image-hiding model. StegFormer can conceal one or multiple secret images within a cover image of the same resolution while preserving the high visual quality of the stego image. In addition, to mitigate the limitations of current steganographic models in real-world scenarios, we propose a normalizing training strategy and a restrict loss to improve the reliability of the steganographic models under realistic conditions. Furthermore, we propose an efficient steganographic capacity expansion method to increase the capacity of steganography and enhance the efficiency of secret communication. Through this approach, we can increase the relative payload of StegFormer to 96 bits per pixel without any training strategy modifications. Experiments demonstrate that our StegFormer outperforms existing state-of-the-art (SOTA) models. In the case of single-image steganography, there is an improvement of more than 3 dB and 5 dB in PSNR for secret/recovery image pairs and cover/stego image pairs.

Achieving a high share of non-hydro renewable integration in Brazil through wind power: Regional growth and employment effects

Brazil is a case of the achievement of a high share of wind energy integration to an interconnected power system in an emerging market. A series of successful energy supply programmes and a strong renewable-oriented finance framework implemented since 2002, have promoted alternative renewables – wind installed capacity increased over 22-fold from 927 MW in 2011 to 21 GW in 2022, reaching 11.77% of total procured power. From this, about 90% are concentrated in Brazil's least developed region, the Northeast, where wind investment should create jobs and propel socioeconomic development. Despite that, there is no studies specific assessing the regional and economy-wide effects of those programmes, gap that this paper aims to fill by applying the regional Computable General Equilibrium (CGE) model TERM-BR10. We assess that by modelling an alternative scenario for the period 2010-19 based on an official electricity matrix built in 2010, that projected 3.4 additional GW of wind power while not considering all the success of renewable policies, against the observed installed capacity expansion, where wind power was 3.7 times higher than what had been planned. Our results show that in the absence of renewable policies, Brazil would have faced economic losses such as GDP (−0.27%) and investment (−4.76%). The Northeast region would have been the most impacted in terms of GDP (−1.3%), real wage (−2.99%) and employment creation (−0.1%). We propose that such socioeconomic co-benefits are incorporated in energy planning, which could promote regional equality through renewable energy expansion.

Generation and Transmission Expansion Planning Using a Nested Decomposition Algorithm

This work presents an implementation of a Nested Decomposition Algorithm (NDA) applied to co-optimizing generation and transmission capacity expansion planning problems in power systems, including operational flexibility constraints. The proposed methodology has been gaining relevance in recent years, as it can efficiently solve large mixed-integer problems faster than the conventional extensive formulation (mixed-integer linear programming). Three case studies are conducted on two IEEE test power systems to evaluate the algorithm’s performance and cut configuration. The first case study compares the performance between the NDA and the extensive formulation. The second case study compares the performance of each cut type, analyzing differences in simulation times and algorithm convergence. The third case study proposes a set of cut patterns based on the prior outcomes, whose performance and convergence are tested. Based on the simulation results, conclusions are drawn about the capability and performance of the NDA applied to the capacity expansion planning problem. The study shows that obtaining results with reasonable convergence in less simulation time is possible using a particular pattern.

Evaluating emergency response at a hospital near the Gaza border within 24 h of increased conflict

BackgroundFrontline hospitals near active hostilities face unique challenges in delivering emergency care amid threats to infrastructure and personnel safety. Existing literature focuses on individual aspects like mass casualty protocols or medical neutrality, with limited analysis of operating acute services directly under fire.ObjectivesTo describe the experience of a hospital situated meters from hostilities and analyze strategies implemented for triage, expanding surge capacity, and maintaining continuity of care during attacks with limited medical staff availability due to hazardous conditions. A focus will be placed on assessing how the hospital functioned and adapted care delivery models in the event of staffing limitations preventing all teams from arriving on site.MethodsA retrospective case study was conducted of patient records from Barzilai University Medical Center at Ashkelon (BUMCA) Medical Center in Israel within the first 24 h after escalated conflict began on October 7, 2023. Data on 232 admissions were analyzed regarding demographics, treatment protocols, time to disposition, and mortality. Missile alert data correlated patient surges to attacks. Statistical and geospatial analyses were performed.ResultsPatients predominantly male soldiers exhibited blast/multisystem trauma. Patient surges at the hospital were found to be correlated with the detection of incoming missile attacks from Gaza within 60 min of launch. While 131 (56%) patients were discharged and 55 (24%) transferred within 24 h, probabilities of survival declined over time reflecting injury severity limitations. 31 deaths occurred from severe presentation.ConclusionInsights gleaned provide a compelling case study on managing mass casualties at the true frontlines. By disseminating BUMCA's trauma response experience, strategies can strengthen frontline hospital protocols optimizing emergency care delivery during hazardous armed conflicts through dynamic surge capacity expansion, early intervention prioritization, and infrastructure/personnel protection measures informed by risks.

Investigation on the operating capacity expansion through parallel schemes of reciprocating-switcher energy recovery device for desalination: Simulation, verification and parameterization

Parallel operation is a significant and feasible method to compensate the insufficient capacity of single reciprocating-switcher energy recovery device (RS-ERD) in membrane desalination system. To grasp the operating characteristics in capacity expansion, three parallel schemes of RS-ERD, single-operation scheme, two-parallel scheme and three-parallel scheme, are investigated through the numerical simulation, experimental verification and theoretical parameterization. Results indicate that, the two-parallel scheme and three-parallel scheme achieve the flowrate continuity of low-pressure seawater and the flowrate fluctuation amplitudes in parallel RS-ERDs decrease by 50 % and 83 % respectively when compared with single-operation scheme. Parallel operation schemes also broaden the flowrate range with high efficiency for RS-ERD. To ensure the 95 % energy recovery efficiency, two-parallel and three-parallel schemes expand the maximum flowrates to 140 m3/h and 210 m3/h in contrast with the 70 m3/h of single-operation scheme. In addition, the two-parallel and three-parallel schemes of RS-ERD realize the much lower specific energy consumption (SEC) of 2.11 kWh/m3 and 2.09 kWh/m3 than the 2.23 kWh/m3 in single-operation scheme which contributes to the operating costs savings about 63,830 CNY and 75,778 CNY in one year. This research provides a new perspective in capacity expansion through the parallel operation of energy recovery devices.

Financial impact of incorporating deep learning reconstruction into magnetic resonance imaging routine

PurposeArtificial intelligence and deep learning solutions are increasingly utilized in healthcare and radiology. The number of studies addressing their enhancement of productivity and monetary impact is, however, still limited. Our hospital has faced a need to enhance MRI scanner throughput, and we investigate the utility of new commercial deep learning reconstruction (DLR) algorithm for this purpose. In this work, a multidisciplinary team evaluated the impact of the widespread deployment of a new commercial deep learning reconstruction (DLR) algorithm for our magnetic resonance imaging scanner fleet. MethodsOur analysis centers on the DLR algorithm's effects on patient throughput and investment costs, contrasting these with alternative strategies for capacity expansion—namely, acquiring additional MRI scanners and increasing device utilization on weekends. We provide a framework for assessing the financial implications of new technologies in a trial phase, aiding in informed decision-making for healthcare investments. ResultsWe demonstrate substantial reductions in total operating costs compared to other capacity-enhancing methods. Specifically, the cost of adopting the deep learning technology for our entire scanner fleet is only 11 % compared to procuring an additional scanner and 20 % compared to the weekend utilization costs of existing devices. ConclusionsProcuring DLR for our existing five-scanner fleet allows us to sustain our current MRI service levels without the need for an additional scanner, thereby achieving considerable cost savings. These reductions highlight the efficiency and economic viability of DLR in optimizing MRI service delivery.

Competition under demand uncertainty: The roles of technology and capacity strategy

To uncover the secrets of creating competitive advantage for firms under demand uncertainty, we study the roles of technology level and capacity investment strategies. Specifically, we analyze the Nash equilibrium of two competing firms at different technology levels under two capacity investment strategies, namely flexible or inflexible. We examine both symmetrical competition where the two firms adopt a same capacity type (flexible or inflexible), and asymmetrical competition where the two firms invest different capacity types. We find that an advanced technology level incentivises capacity expansion regardless of the capacity strategy adopted. In addition, the technology-capacity relationship significantly depends on demand distribution when both firms adopt inflexible capacity investment, but is independent of demand distribution when both firms invest in flexible capacity. No overwhelming superiority is observed from either side when both firms adopt the same capacity investment, and firms always co-exist in a profitable market, despite significant differences in technology. However, overwhelming superiority emerges from one side when both firms have different capacity investments. A firm can squeeze out its competitor and capture the entire market by upgrading its technology. When the two firms adopt the same capacity strategy, each firm may increase or decrease its technology level and capacity volume proportionally in equilibrium, regardless of the competition scenarios. We further explore the endogenous capacity investment type and technology flexibility in competition. We show that the competition effect plays an important role when the flexible capacity cost is moderate.