Traditional Planning Approaches Research Articles

A mission planning method for deep space detectors using deep reinforcement learning

Mission planning for deep space detectors is a pivotal step in the successful execution of detection missions. Traditional planning approaches, which typically compartmentalize mission planning and data transmission scheduling, exhibit limitations in adapting to uncertainties and are often inadequate in responding to unforeseen opportunity targets. This paper introduces a mission planning method for deep space detectors designed to address these challenges. Firstly, a Markov Decision Process (MDP) model is formulated, integrating mission planning and data transmission scheduling, with a consideration for planning balance between detection missions and opportunity targets. Subsequently, a Planning Balance with Proximal Policy Optimization (PB-PPO) algorithm is proposed. The proposed algorithm, based in the Proximal Policy Optimization (PPO) algorithm, integrates an orthogonal initialization algorithm to afford improved control over parameter updates. Furthermore, a dynamic learning rate strategy is implemented to accelerate convergence speed. Experimental results show that, the PB-PPO achieves rewards that are 4.42%, 6.78%, 18.32%, and 26.81% higher than those obtained by compared algorithms. Additionally, the PB-PPO demonstrates the capability to address the planning balance between detection missions and opportunity targets, ensuring stable reward growth even when planning a significant number of opportunity targets. In summary, the PB-PPO integrates of MDP, deep reinforcement learning, and innovative strategies to make it a robust solution for detector mission planning in the complex and dynamic environment of deep space.

Urban Livability and Vertical Inclusion: Exploring Mixed-Use Development Strategies in Khulna, Bangladesh

As urban populations expand and future needs evolve amidst limited land availability, rethinking traditional planning and construction approaches becomes imperative. Architect Frank Schlesinger advocates for urban environments that embrace mixed- use complexes, conceptualizing urban land as dynamic, three-dimensional blocks accommodating diverse functions above, at, and below ground levels. Shopping facilities, pivotal in urban landscapes, reflect regional and temporal variations in consumer behavior. Integrating public and private amenities is essential for cultivating a thriving urban ecosystem. However, rigid block structures in current city master plan to constrain diversity and livability, are exacerbated by inadequate setbacks that challenge urban identity. In Khulna, bustling daytime cores contrast with deserted evenings and holidays, reflecting segregated land uses that hinder societal inclusivity. Despite demand, office and commercial spaces often remain vacant. This study employs a mixed-method approach, combining quantitative surveys with qualitative methods such as interviews and observational studies. It aims to address critical local issues including land scarcity, traffic management, vertical development challenges, and the provision of sophisticated services to enhance urban comfort. By exploring innovative configurations of civic facilities, both horizontally and vertically, the study seeks to demonstrate how integrating functions can create cohesive and vibrant urban environments. Ultimately, the adoption of vertically integrated spaces holds promise for fostering inclusivity and sustainability in Khulna’s urban fabric.

Research on Low-Carbon Design and Energy Efficiency by Harnessing Indigenous Resources through BIM-Ecotect Analysis in Hot Climates

In the face of contemporary challenges, such as economic instability, environmental degradation, and the urgent global warming crisis, the imperative of sustainability and energy efficiency has reached unparalleled significance. Sustainability encompasses not only the natural environment, but also extends to our immediate surroundings, including the built structures and the communities they serve. Embracing this comprehensive perspective, we embarked on a mission to conceive and construct a model house that harnesses state-of-the-art energy-efficient technologies. Our goal was to seamlessly integrate these features not only to meet our sustainability objectives, but also to mitigate environmental threats.This model embodies a harmonious fusion of indigenous resources, employing locally sourced stone and employing traditional construction techniques. Through this approach, we achieved significant reductions in carbon emissions and established a framework for passive cooling and heating systems. Moreover, the design is intrinsically attuned to its contextual surroundings, preserving the diverse tapestry of regional architectural styles. This study stands as a testament to the potential of innovative design and technology in shaping a sustainable future. The research employs a multi-dimensional approach, encompassing strategies of architectural design with a traditional planning approach, sustainable material selection, energy efficiency, and life cycle assessment across a diverse set of case studies. Building energy analysis is conducted through the application of BIM (Ecotect), providing insights into how BIM can adapt and thrive in various environments. Key findings underscore that thermal performance, minimizing energy loads, and reducing carbon emissions are pivotal aspects in designating a building as both green and energy efficient.

Effect of different optimization parameters in single isocenter multiple brain metastases radiosurgery

PurposeAutomated treatment planning for multiple brain metastases differs from traditional planning approaches. It is therefore helpful to understand which parameters for optimization are available and how they affect the plan quality. This study aims to provide a reference for designing multi-metastases treatment plans and to define quality endpoints for benchmarking the technique from a scientific perspective.MethodsIn all, 20 patients with a total of 183 lesions were retrospectively planned according to four optimization scenarios. Plan quality was evaluated using common plan quality parameters such as conformity index, gradient index and dose to normal tissue. Therefore, different scenarios with combinations of optimization parameters were evaluated, while taking into account dependence on the number of treated lesions as well as influence of different beams.ResultsDifferent scenarios resulted in minor differences in plan quality. With increasing number of lesions, the number of monitor units increased, so did the dose to healthy tissue and the number of interlesional dose bridging in adjacent metastases. Highly modulated cases resulted in 4–10% higher V10% compared to less complex cases, while monitor units did not increase. Changing the energy to a flattening filter free (FFF) beam resulted in lower local V12Gy (whole brain-PTV) and even though the number of monitor units increased by 13–15%, on average 46% shorter treatment times were achieved.ConclusionAlthough no clinically relevant differences in parameters where found, we identified some variation in the dose distributions of the different scenarios. Less complex scenarios generated visually more dose overlap; therefore, a more complex scenario may be preferred although differences in the quality metrics appear minor.

Distributed Maritime Operations and the Medical Challenge: How a Medical Common Operating Picture Can Help Bridge the Gap.

This work explores the challenges of delivering medical care in the geographically dispersed and resource-constrained environment of Distributed Maritime Operations (DMO) and Expeditionary Advanced Base Operations (EABO). Traditional medical planning approaches may struggle to adapt to the vast operational space, extended evacuation times, and limited medical force present in these scenarios. The concept of a Medical Common Operating Picture (COP) emerges as a potential solution. By providing a shared view of the medical situation across the theater, encompassing logistics, personnel, and patient data, a medical COP has the potential to facilitate medical command and control (MED C2) in DMO/EABO. The implementation of a medical COP has the potential to optimize resource allocation, enhance situational awareness, streamline medical evacuation, and reduce healthcare provider moral injury in large-scale combat operations. A medical COP will allow medical planners to make informed decisions on triage, resupply, and evacuation, ensuring the best use of limited medical resources. This is done by leveraging a comprehensive understanding of the medical landscape, enabling informed clinical and operational decision-making by humanitarian and combat personnel respectively. A fully realized medical COP system will enable a dynamic theater evacuation policy, balancing the conflicting needs of patient care at higher echelons with the operational expediency of returning servicemembers to their operational units, thereby maximizing evacuation effectiveness. It will further enable medical personnel to perform dynamic casualty triage based on operational realities, mitigating potential ethical dilemmas. Implementing such a medical COP system will require overcoming communication limitations to facilitate data exchange and potentially integrating clinical decision support tools for real-time data analysis and recommendations. It will also require the rapid adoption of modernized operational medicine documentation solutions by medical assets within the operational forces. Ultimately, this work suggests that a medical COP has the potential to bridge the gap between traditional medical planning and the unique demands of DMO/EABO, ultimately optimizing casualty care, maximizing resource efficiency, and preserving the fighting force.

Project management logics for agile public strategic management: Propositions from the literature and a research agenda

In this paper, we call for an integration of project management logics within the now mature field of public strategic management, to analyze the potential contribution of projects in terms of increased strategic agility, in a context where traditional strategic planning and management tools and approaches are increasingly seen at risk of not being responsive enough to rapidly changing external conditions. To pursue this objective, we carry out a problematizing literature review on the two streams, by incorporating journal and book contributions from the last 30 years on Web of Science Database. 509 contributions have been quantitatively and qualitatively analyzed to answer two interconnected research questions: What is the state of the art in the literature on the interactions between project management and public strategic management? And, how can project management logics be integrated within traditional strategic planning and management processes in the public sector in order to achieve strategic agility? We find that, until today, public management literature has only sporadically dealt with the potential influence of project management logics on strategic management and, more in detail, strategy implementation. Furthermore, the review enables a discussion of five organizational drivers fostering an agile approach in public strategy implementation. Using a narrative approach, they then lead to the formulation of five researchable propositions. We conclude by proposing an updated model of the strategic planning and management cycle in the public sector, factoring in potential agile practices and feedback mechanisms induced by incorporating project logics in the implementation of strategy.

Multi-resource dynamic coordinated planning of flexible distribution network

The flexible distribution network presents a promising architecture to accommodate highly integrated distributed generators and increasing loads in an efficient and cost-effective way. The distribution network is characterised by flexible interconnections and expansions based on soft open points, which enables it to dispatch power flow over the entire system with enhanced controllability and compatibility. Herein, we propose a multi-resource dynamic coordinated planning method of flexible distribution network that allows allocation strategies to be determined over a long-term planning period. Additionally, we establish a probabilistic framework to address source-load uncertainties, which mitigates the security risks of voltage violations and line overloads. A practical distribution network is adopted for flexible upgrading based on soft open points, and its cost benefits are evaluated and compared with that of traditional planning approaches. By adjusting the acceptable violation probability in chance constraints, a trade-off between investment efficiency and operational security can be realised.

Milwaukee’s Swing Park: Guerrilla Urbanism at the Intersection of Public Space, Community, and Urban Planning

Guerrilla urbanism involves citizen-led bottom-up unsanctioned temporary efforts. This kind of temporary urbanism may offer unique opportunities not possible through traditional planning approaches. This paper is a case study of the Swing Park in Milwaukee, Wisconsin, USA. It began as an effort to temporarily enliven an area at one end of a pedestrian walkway – the Marsupial Bridge – suspended under a viaduct across the Milwaukee River. What started as an illegal temporary installation of swings made of used tires was embraced physically and emotionally as part of the everyday life of the pedestrian bridge by the local community who wanted the swings to stay. The guerrilla urbanism group found itself in conflict with the City’s Department of Public Works over concerns about safety and legal liability, aside from budgetary constraints on permanently upgrading and maintaining the swings. This paper conceptualizes the case study within a three-part framework of public space, community, and urban planning. It examines how the Swing Park has been seen to have both succeeded and failed, while problematizing it within the context of the interdisciplinary literature on temporary urbanism.

Reimagining space layout design through deep reinforcement learning

Abstract Space layout design is a critical aspect of architectural design, influencing functionality and aesthetics. The inherent combinatorial nature of layout design poses challenges for traditional planning approaches; thus, it demands the exploration of novel methods. This paper presents a novel framework that leverages the potential of deep reinforcement learning (RL) algorithms to optimize space layouts. RL has demonstrated remarkable success in addressing complex decision-making problems, yet its application in the design process remains relatively unexplored. We argue that RL is particularly well-suited for the design process due to its ability to accommodate offline tasks and seamless integration with existing computer-aided design software, effectively acting as a simulator for design exploration. Framing space layout design as an RL problem and employing RL methods allows for the automated exploration of the expansive design space, thereby enhancing the discovery of innovative solutions. This paper also elucidates the synergy between the design process and the RL problem, which opens new avenues for exploring the potential of RL algorithms in design. We aim to foster experimentation and collaboration within the RL and architecture communities. To facilitate our research, we have developed SpaceLayoutGym, an environment specifically designed for space layout design tasks. SpaceLayoutGym serves as a customizable environment that encapsulates the essential elements of the layout design process within an RL framework. To showcase the effectiveness of SpaceLayoutGym and the capabilities of RL as an artificial space layout designer, we employ the Proximal Policy Optimization (PPO) algorithm to train the RL agent in selected design scenarios with both geometrical constraints and topological objectives. The study further extends to contrast the effectiveness of PPO agents with that of genetic algorithms, and also includes a comparative analysis with existing layouts. Our results demonstrate the potential of RL to optimize space layouts, offering a promising direction for the future of artificial intelligence-aided design.

The Time-Sensitive Networking Scheduling Algorithm Based on Q-learning

Abstract Time-Sensitive Networking (TSN) occupies a vital position in modern communication domains, with the 802.1Qbv standard being an important network technology designed to meet real-time requirements. This standard requires network traffic to be transmitted within strict time windows, presenting challenges in network planning, necessitating efficient resource allocation and scheduling strategies. This paper addresses the 802.1Qbv planning problem through the introduction of reinforcement learning algorithms, offering an automated and intelligent solution. We have designed a reinforcement learning agent capable of perceiving network status, learning optimal resource allocation strategies, and dynamically adjusting in real-time environments. Through simulation and experimentation, we have validated the effectiveness of our proposed method, comparing it with traditional planning approaches. The contribution of this study lies in introducing a novel solution to the 802.1Qbv planning problem for time-sensitive networks, enhancing network resource utilization and performance. This approach offers strong support for the development and enhancement of TSN-like networks, holding significant importance for meeting the growing demands of real-time applications.

Path Inference based on Voronoi Graph for Unmanned Maritime Vehicles

Design/methodology/approachWe propose a novel path inference approach based on Voronoi graph for unmanned maritime vehicles (UMV). We model the two-dimensional space with a flexible construction scheme of Voronoi graph, represent the inter-cell shortest distance with a delicate calculation of cell diameters, and infer the potential qualified navigation paths of UMVs w.r.t. the specified distance constraint based on a diameter path model with an efficient depth-first exploration algorithm. PurposeExisting path planning approaches are generally focused on finding a single optimal path. However, in many practical scenarios, such as UMV patrolling and search and rescue, it is the comprehensiveness rather than the optimality that matters for the UMV paths. Inspired by the above problem, we propose a path inference method based on Voronoi graph to infer all the possible UMV paths satisfying the distance constraint. FindingsExperimental results indicate that our path inference approach based on Voronoi graph is able to perform more delicately and efficiently than the traditional path planning approach in terms of both space modeling and path exploration. The paths identified by our path inference method were more precise in distance estimation as well as more comprehensive when compared with the grid-based path planning method. In the simulated scenario of UMV search & rescue, we obtained the coverage rates of 63.14% and 100% against those of 14.5% and 81.07% for the grid-based method. Originality/valueOur path inference approach is able to provide valuable insights in various practical UMV applications.

Assessment of Demski’s Model in House Construction Planning

The construction industry has witnessed significant advancements in recent years, particularly in the area of planning and project management. Efficient and effective planning plays a vital role in the successful completion of construction projects, ensuring timely delivery, cost-effectiveness, and quality. One prominent planning model that has gained attention is Demski's Model, which aims to optimize the house construction planning process by incorporating various factors and variables. This research paper presents an in-depth assessment of Demski's Model in the context of house construction planning. The objective is to evaluate the model's effectiveness, its applicability in realworld scenarios, and the potential benefits it offers to the construction industry. The assessment process involves a comprehensive literature review, analyzing existing studies, scholarly articles, and relevant industry reports. The gathered information provides a foundation for understanding the fundamental principles and components of Demski's Model. The research methodology consists of both quantitative and qualitative analysis. Quantitative analysis involves examining key performance indicators such as project duration, cost, and resource allocation, comparing projects that utilized Demski's Model with those that followed traditional planning approaches. Qualitative analysis focuses on gathering feedback from project stakeholders, including contractors, homebuilders, and house owners, to gauge their experiences and perceptions regarding the model's effectiveness and practicality. The findings of this research contribute to the existing knowledge base on house construction planning methodologies. The assessment of Demski's Model provides insights into its strengths, weaknesses, and potential areas for improvement. It also sheds light on the challenges and limitations that may arise during its implementation. By understanding these factors, construction professionals can make informed decisions when considering the adoption of Demski's Model in their planning processes. Ultimately, this research aims to provide a comprehensive assessment of Demski's Model in house construction planning, offering valuable insights for researchers, practitioners, and decision-makers in the construction industry. By evaluating the model's effectiveness and practicality, this study contributes to advancing planning methodologies and optimizing construction project outcomes in the pursuit of enhanced efficiency, reduced costs, and improved quality.

An intelligent planning method to optimize high-density residential layouts considering the influence of wind environments

There is a growing conflict between building density and the comfort of the external environment in residential construction, especially in high-density cities in China. To address this conflict, a sensible building layout has to take both aspects into account. However, it is difficult for traditional planning approaches to produce a sensible building layout. This is partly due to the fact that an architect's subjective experiences are unreliable. On the other hand, the wind environment simulations of professional software are often time-consuming so that they are difficult to apply efficiently in practice. This study therefore focuses on the automatic generation of optimized high-density residential building layouts as well as the fast and accurate calculation of the corresponding wind environments. By combining the automatic optimization function of a genetic algorithm and the prediction function of a fully convolutional neural network, an intelligent planning method is proposed for producing optimal high-density residential building layouts in consideration of the local wind environment. To further verify its practicality and significance, a case study was carried out in the Yangtze River Delta region, China, through the automatic generation of a residential building layout, wind environment simulation, and a scheme comparison for optimization.

Bale Collection Path Planning Using an Autonomous Vehicle with Neighborhood Collection Capabilities

This research was mainly focused on the evaluation of path planning approaches as a prerequisite for the automation of bale collection operations. A comparison between a traditional bale collection path planning approach using traditional vehicles such as tractors, and loaders with an optimized path planning approach using a new autonomous articulated concept vehicle with neighborhood reach capabilities (AVN) was carried out. Furthermore, the effects of carrying capacity on reduction in the working distance of the bale collection operation was also studied. It was concluded that the optimized path planning approach using AVN with increased carrying capacity significantly reduced the working distance for the bale collection operation and can thus improve agricultural sustainability, particularly within forage handling.

Improved A* algorithm and model predictive control- based path planning and tracking framework for hexapod robots

PurposePath planning is a fundamental and significant issue in robotics research, especially for the legged robots, since it is the core technology for robots to complete complex tasks such as autonomous navigation and exploration. The purpose of this paper is to propose a path planning and tracking framework for the autonomous navigation of hexapod robots.Design/methodology/approachFirst, a hexapod robot called Hexapod-Mini is briefly introduced. Then a path planning algorithm based on improved A* is proposed, which introduces the artificial potential field (APF) factor into the evaluation function to generate a safe and collision-free initial path. Then we apply a turning point optimization based on the greedy algorithm, which optimizes the number of turns of the path. And a fast-turning trajectory for hexapod robot is proposed, which is applied to path smoothing. Besides, a model predictive control-based motion tracking controller is used for path tracking.FindingsThe simulation and experiment results show that the framework can generate a safe, fast, collision-free and smooth path, and the author’s Hexapod robot can effectively track the path that demonstrates the performance of the framework.Originality/valueThe work presented a framework for autonomous path planning and tracking of hexapod robots. This new approach overcomes the disadvantages of the traditional path planning approach, such as lack of security, insufficient smoothness and an excessive number of turns. And the proposed method has been successfully applied to an actual hexapod robot.

Learning Minimum-Time Flight in Cluttered Environments

We tackle the problem of minimum-time flight for a quadrotor through a sequence of waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. Early works relied on simplified dynamics or polynomial trajectory representations that did not exploit the full actuator potential of the quadrotor, and, thus, resulted in suboptimal solutions. Recent works can plan minimum-time trajectories; yet, the trajectories are executed with control methods that do not account for obstacles. Thus, a successful execution of such trajectories is prone to errors due to model mismatch and in-flight disturbances. To this end, we leverage deep reinforcement learning and classical topological path planning to train robust neural-network controllers for minimum-time quadrotor flight in cluttered environments. The resulting neural network controller demonstrates substantially better performance of up to 19% over state-of-the-art methods. More importantly, the learned policy solves the planning and control problem simultaneously online to account for disturbances, thus achieving much higher robustness. As such, the presented method achieves 100% success rate of flying minimum-time policies without collision, while traditional planning and control approaches achieve only 40%. The proposed method is validated in both simulation and the real world, with quadrotor speeds of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$42 \,\mathrm{k}\mathrm{m}\mathrm{h}^{-1}$</tex-math></inline-formula> and accelerations of 3.6 g.

Fighting business uncertainty with Scenario Planning at Bologna Airport: An antifragile experience in times of COVID-19

The unprecedented impact of the COVID-19 pandemic on our lives during the last 18 months has been evident, as in the aviation sector. The level of uncertainty we all have to face, both at the personal and the managerial level is so high that traditional planning approaches have become useless. We are not in the position to understand or forecast how the future will evolve, because the number of variables is high and the interactions somehow unknown. Under these circumstances, we need to evaluate our business options under alternative ‘futures’ with different strategies to be implemented. This paper describes the approach undertaken by Aeroporto G. Marconi di Bologna to ‘fight business uncertainty’, applying a Scenario Planning approach, in the darkest hours of the COVID-19 pandemic.

Experimentation, planning, and structure in early‐stage ventures: Evidence from pitch decks

AbstractResearch SummaryAn important strategic choice for early‐stage ventures is about how to learn about the market. This choice often translates into focusing on either experimentation or planning. These strategies are best supported by different structures. Hence, the fit between strategy and structure should be considered by stakeholders evaluating the venture. We hypothesize that communicating coherent combinations—experimentation and informal structure or planning and formal structure—is positively associated with evaluation and that evaluators with entrepreneurial experience are more sensitive to this coherent choice. We test this argument combining data from a university‐based venture competition and an online experiment. We find a robust positive correlation between coherent choice and evaluation. We find no clear evidence that this pattern is driven by evaluators with entrepreneurial experience.Managerial SummaryThe Lean Startup suggests that entrepreneurs should rely on experimentation rather than the more traditional planning approach. However, the value of experimentation might depend on the structure the venture adopts. We ask whether the fit between strategy and structure plays a role in the context of venture evaluation. We analyze data from a university‐based venture competition and an online experiment. We find evidence that ventures communicating coherent choices—experimentation and informal structure or planning and formal structure—tend to be evaluated better than those communicating alternative choices. However, contrary to our expectation, this pattern does not appear to be driven by evaluators with entrepreneurial experience.

Interval valued intuitionistic fuzzy analytic hierarchy process-based green supply chain resilience evaluation methodology in post COVID-19 era.

Supply chain organizations should calmly and cautiously take the most accurate and sustainable decisions quickly and put them into practice. It is obvious that traditional time series–based demand and supply planning approaches are insufficient to meet current business needs due to factors such as sharp changes in market and commercial dynamics, pandemics, and natural disasters on the management of green supply chains, especially these days. In the near future, there will be a need for more resilient supply chains with a flexible business models that are not affected by sudden changes and that can make sustainable decisions dynamically. Additionally, all stakeholders must act with a green supply chain approach to conduct production and service activities in a way that causes the least damage to nature. Companies must build more resilient supply chains by considering environmental sensitivities to compete in the market and ensure their continuity. In this context, the green supply chains should be evaluated according to their resilience. For this purpose, Supply Chain Operations Reference (SCOR) model is extended with novel performance attributes to evaluate resilience of green supply chains in this study. The SCOR-embedded novel green supply chain resilience evaluation model is structured as a three-level performance attribute hierarchical structure. Then, the model is handled as a multi-criteria decision-making problem to determine importance of the performance attributes. Best Worst Method integrated Interval Valued Intuitionistic Fuzzy Analytic Hierarchy Process is used to determine the importance of performance attributes. Most important performance attributes are determined in each level of hierarchy. According to results, organizational factors play a key role to build more resilient supply chains. Especially, integrated systems are required for supply chain resilience.

РАЗВИТИЕ ПРАКТИЧЕСКИХ ПОДХОДОВ К ИСПОЛЬЗОВАНИЮ СТРАТЕГИЙ РОСТА В ДЕЯТЕЛЬНОСТИ КОМПАНИЙ В СТРАТЕГИЧЕСКОЙ ПЕРСПЕКТИВЕ

The willingness of market-leading companies to have sustainable growth over the past few decades has been significantly complicated by the increasing level of uncertainty of the overall impact of environmental factors in all spheres of economic activity. Macroeconomic instability, sharp fluctuations in the raw material markets, intense industries’ competition, political instability, the multidirectional dynamics of social processes, unpredictable natural and climatic cataclysms of global proportion - all these have significantly impact that complicate the possibilities of forming market development strategies, based on traditional planning approaches that take into account mainly retrospective dynamics. Moreover, the economic instability of the last decade, caused by environments, has shown to significant part of international companies the instability of the chosen strategies for the development of the firm. The dilemma of choosing the direction of strategy is relevant for most companies of Russian and foreign business – managers face the question of choosing the direction of the corporate strategy development of the company, taking into account all the components of the business portfolio. The author studies the evolutionary development of the theory of strategic growth, presents a variety of approaches to the classification of organizational growth strategies, identifies their similarities and contradictions, and give arguments for the traditional growth strategies classification. Strategies of intensive growth, vertical integration, and diversification are attractive for development, but at the same time require certain efforts and resources. In the course of the study, the author conducted research to identify and choose the most optimal growth strategy when the company reach a certain level of stability for dynamic development, identified the basic criteria for choosing the strategic vector of the company's development.