Multi-project Environment Research Articles

Integrating lean production and agile project management: a planning and control model for multi-project environments in construction

The Last Planner System (LPS) and Scrum are established planning and control methods that have produced positive results in managing complex projects. They are based on two different but overlapping theoretical frameworks: Lean Production and Agile Project Management. However, there is a lack of academic studies on how to combine those methods and whether their theoretical underpinnings are compatible. Furthermore, previous studies about those methods have not addressed the context of multiple projects developed simultaneously, and the connections between project management from the perspective of client organisations and production management from the perspective of suppliers. This research work proposes a planning and control model for this context. Using Design Science Research (DSR), an empirical study was carried out at a department store company in Brazil. The practical contributions of this study are related to adapting and combining LPS and Scrum elements to the context investigated. The model includes innovative practices, such as integrated look-ahead planning meetings between the project and the production management levels and using Scrum practices to remove certain constraints. This study also sheds light on the commonalities and differences between LPS and Scrum based on how they support the application of important production principles in construction.

Importance-Performance Analysis (IPA) in Researching the Satisfaction of Simulation

Students are the customers of higher education, and their satisfaction directly influences service quality in the tertiary sector. It is essential, therefore, to research students' views. This can be done by applying important-performance analysis (IPA), an effective tool for achieving satisfaction. The study described in this article focuses on data obtained from research into the Programme and Project Management major which is a Master's program in the United Kingdom. The course, Managing the multi-project and programme environment (MMPE), combines theory with practice through offline-business simulation. The program focuses on imparting skills to graduate students in the management field. The performance and importance of thirteen items were connected to motivation and teamwork. The results revealed that not all student respondents are satisfied with the simulation. Utilizing IPA, the research suggests that tutors should prioritize improving communication and relationships among both the programme and project teams, specifying accountability and work allocations, motivating students to achieve goals, and refining feedback methods. These recommendations could be applied to other business simulations that aim to improve student satisfaction.

DIGITALIZATION AS A MEANS OF OVERCOMING CHALLENGES AND THREATS OF CONSTRUCTION PARTICIPANTS IN A MULTIPROJECT ENVIRONMENT

This article examines the potential of digitalization as a strategic solution to address the challenges and threats faced by construction actors in a multi-project environment.Construction activities are inherently complex, often characterized by the simultaneous management of several projects, each with its own characteristics and challenges. In a multi-project environment, participants such as designers, developers, and contractors face significant obstacles, including resource allocation conflicts, communication barriers, inefficiencies, and increased risk of delays and cost overruns. The advent of digital technology offers a transformative approach to these challenges, enabling more efficient management and coordination across multiple projects. The main objective of this paper is to explore how digital tools and technologies can mitigate the specific challenges faced by different actors during the construction of multiple projects. Analyzing key issues such as scheduling conflicts, miscommunication, data management complexity and risk management difficulties, the article highlights critical areas where digitization can have a significant impact.One of the most urgent problems in a multi-project environment is the effective allocation and planning of resources. Traditional management approaches often fail to cope with overlapping schedules and competing priorities, resulting in delays and inefficiencies. Digital tools such as Building Information Modeling (BIM) and Project Management Information Systems (PMIS) offer integrated platforms that facilitate seamless coordination and real-time updates. These systems provide centralized data stores and sophisticated scheduling algorithms that improve decision-making and reduce the likelihood of conflicts.Communication barriers are another major problem in multi-project environments that can lead to misunderstandings, errors, and delays that negatively impact project timelines and outcomes. Digital communication platforms, including instant messaging, video conferencing, and collaboration software, enhance real-time communication and ensure alignment among all stakeholders. These platforms enable the rapid exchange of critical information and facilitate the collaborative environment necessary to manage multiple projects simultaneously.Ensuring that all stakeholders have access to accurate and up-to-date information is critical to effective decision-making. Digitization solves this problem by providing advanced data management and analytics capabilities. Big data analytics and artificial intelligence can analyze large data sets to predict potential problems and optimize resource allocation. These technologies help identify trends, predict project outcomes, and facilitate informed decision-making, ultimately increasing project efficiency and effectiveness.Risk management is particularly challenging in a multi-project environment due to increased complexity and interdependence. Digital risk management tools offer a framework for identifying, assessing and mitigating risks across projects. These tools provide scenario analysis, risk tracking and automatic alerts, enabling proactive risk management and reducing the likelihood of project failure.

Climate and geopolitical risk correlations and response decisions for semiconductor global supply chains

Threats from climate risk (CR) and geopolitical risk (GR) undermine the resilience of the Semiconductor Global Supply Chain (SGSC). Plenty of company profit has plummeted due to the chip supply disruptions and delays. Particularly, the complex risk correlations (RCs) in a multi-project environment, and the inter-entity relationships, render the risk analysis and risk response decisions (RRDs) within CR and GR challenging. To address the problem, this study proposes a novel three-stage approach to explore the impact of CR and GR and obtain RRDs. First, based on the actual operation of SGSC, a system dynamics model of the RCs is conducted to analyze the effects of CR and GR. Second, a Stackelberg game model is formed based on the project interdependencies to find the optimal response resources and profit considering the inter-entity relationships. Third, a binary optimization model constrained in the optimal response resources is developed to determine RRDs. From the simulation results and the numerical example, the proposed approach is a powerful tool for risk managers of SGSCs. The actionable recommendations on managing and responding to CR and GR are summarized. This study provides theoretical and managerial insights for resilient supply chain management.

A study of comprehensive resource scheduling under public health events: An improved heuristic quantum algorithm

This study explores the impact of public health events, multi-modal projects, multi-project environments, and multi-capacity resource constraints on project scheduling. It describes the comprehensive resource-constrained project scheduling problem (MCMRCMPSP) specifically for public health events, and proposes two approaches for modelling and solving the problem. The objective is to enhance the practical relevance of project scheduling and enrich the problem itself. To improve efficiency and the algorithm for scheduling problems, an enhanced quantum algorithm based on the quantum particle swarm algorithm (QPSO) is proposed. The enhancements include Gaussian variation and a tournament selection strategy. Furthermore, the article integrates multiple heuristic rules with the algorithm to minimize illogical computations, improve computational efficiency, and enhance solution quality. The proposed algorithm’s effectiveness is validated through performance tests and practical application experiments. The results show that the algorithm has superior convergence performance and solution accuracy compared with the traditional QPSO, particle swarm algorithm (PSO), genetic algorithm, ant colony algorithm, and cuckoo algorithm. Thus, the algorithm provides a targeted resource scheduling plan for real-world cases. This research contributes to the field of project scheduling problems and proposes a new solution.

Multi-objective planning for time-cost trade-offs in multi-project parallel environment

PurposeProject managers bear the responsibility of selecting and developing resource scheduling methods that align with project requirements and organizational circumstances. This study focuses on resource-constrained project scheduling in multi-project environments. The research simplifies the problem by adopting a single-project perspective using gain coefficients.Design/methodology/approachIt employs uncertainty theory and multi-objective programming to construct a model. The optimal solution is identified using Matlab, while LINGO determines satisfactory alternatives. By combining these methods and considering actual construction project situations, a compromise solution closely approximating the optimal one is derived.FindingsThe study provides fresh insights into modeling and resolving resource-constrained project scheduling issues, supported by real-world examples that effectively illustrate its practical significance.Originality/valueThe research highlights three main contributions: effective resource utilization, project prioritization and conflict management, and addressing uncertainty. It offers decision support for project managers to balance resource allocation, resolve conflicts, and adapt to changing project demands.

The performance of priority rules for the dynamic stochastic resource-constrained multi-project scheduling problem: an experimental investigation

We consider the problem of a project manager of a matrix organization responsible for the timely completion of projects arriving over time and requiring the usage of a set of resources under his/her purview. A project is defined by a set of activities, precedence relations between activities, resource requirements, a customer due date, and a priority (a weight). Arriving projects are released to a flow control system that monitors the progress of activities and schedules the set of available activities, i.e., activities that are ready to be processed, as appropriate. A critical feature of such control systems is the decision process for choosing the next activity to seize a given resource. This is the focus of this paper. In the past several decades, various heuristic priority rules have been proposed in the literature to support this type of decision in differing settings such as the job shop problem and the deterministic resource-constrained project scheduling problem. A gap exists with respect to testing the various rules all together in the more realistic dynamic-stochastic multi-project environment when the objective is to minimize weighted project tardiness. The purpose of this paper is to fill this gap. Results show that the priority rule “Weighted Critical Ratio and Shortest Processing Time” (W(CR+SPT))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\hbox {W}(\\hbox {CR}+\\hbox {SPT}))$$\\end{document} is the best performing rule with respect to minimizing weighted project tardiness. W(CR+SPT)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {W}(\\hbox {CR}+\\hbox {SPT})$$\\end{document} is shown to be a variant of the family of “Modified Due Date” rules first introduced by Baker and Bertrand (J Oper Manage 1(3):37–42, 1982). Repeated application of Duncan’s Multiple Range test demonstrates the robustness of our findings. For the environmental parameters (due date tightness, variation of expected activity durations, and utilization of resource), the W(CR+SPT)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {W}(\\hbox {CR}+\\hbox {SPT})$$\\end{document} rule is dominant with respect to weighted project tardiness among the eleven priority rules tested. Only when the number of resources is very modest (either 1 or 3 resources) or under a purely parallel resource network is W(CR+SPT)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {W}(\\hbox {CR}+\\hbox {SPT})$$\\end{document} not the dominant rule. In those cases, its variant W(CR+GSPT)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {W}(\\hbox {CR}+\\hbox {GSPT})$$\\end{document} is the best performing rule with respect to weighted project tardiness.

Research on the Application of MA Algorithm in the Optimization of Human Resource Allocation in Enterprises

Abstract MA algorithms have received extensive attention from scholars soon after they were proposed, and are rapidly developing in various fields such as the problem of project schedule and the optimal allocation of cloud resources. In this paper, firstly, to address the lack of performance of the ordinary MA algorithm, Kent chaotic mapping is used in the initialization position to improve the initial traversal ability, and Sigmoid function is introduced to optimize the step-size settings in different stages of the crawling process. Different test functions are chosen to compare the efficiency of the solution and verify the performance of the improved MA algorithm. Finally, the improved MA algorithm is applied to allocate human resources in a city called China Mobile and a multi-project environment. Searching for the optimal solution and solving for the optimal value is the strongest performance of the improved MA algorithm compared to the other three algorithms. The human resource balance metrics of a city China Mobile are optimized and the peak human resource demand reaches the project below 120, reducing the waste of resources by about 20 people. In a multi-project environment, where 4 employees perform four different tasks, the optimal solution was found, resulting in an improvement in profitability.

Framework for multi-project scheduling in product development based on available data

The amount of development projects in companies rises, increasing the difficulty to plan and schedule new projects since the complexity increases when the same human resources are used in a larger amount of projects. This can lead to more interdependencies between the projects and sometimes overload of the existing resources. With more interdependencies, changes in one project also affect other projects leading to more development projects missing their project objectives such as cost and/or time. Multi project management and scheduling addresses this circumstances by scheduling the resources with regard to the overall multi project environment. Although most scheduling approaches in the industry only consider resource availability on a multi-project view, while cost and time are optimized within the projects. The Resourceconstrained multi-project scheduling problem (RCMPSP) is in the scientific literature is widely researched and is concerned with the overall optimization of resource scheduling. These existing solutions for a mathematical optimization approach in resource scheduling, are rarely utilized in the industry. This paper focuses on building a framework to reduce the gap between industry and research. On the one hand, the paper analyses the requirements for project scheduling in corporate practice. On the other hand, a structured overview of the RCMPSP literature is provided from which requirements for the needed data is derived. The framework then matches the scheduling needs and existing data within a company to the research landscape of existing optimization solutions in order to support the use of resource scheduling in multiple projects.

Project Portfolio Management in a Multi-project Environment

Creating a portfolio of projects is an important scientific and practical task that applies to all areas of human activity, and the field of information technology is no exception. Research has shown that there are multiple approaches to solving this task, each characterized by certain features. Known methods of resource allocation have been analyzed, classified, and shown to be primarily related to linear programming problems, resource distribution matrices, or based on empirical knowledge. The project portfolio formation model was presented as a fuzzy problem of integer linear programming. A structural approach was used to depict the functioning principles of the system and information flows. An analysis was conducted, and development tools were selected. The outcome of the work resulted in a prototype of the software. Further research will focus on verifying the system's performance and constructing related modules.

METHODOLOGICAL SUPPORT FOR AGILE RESOURCE REALLOCATION IN A MULTI-PROJECT HEALTHCARE ENVIRONMENT

The organization of medical assistance in de-occupied territories and territories close to combat activities necessitates reorganizing the existing medical system, making the task of developing methodological support for Agile-redistribution of resources in a multi-project medical environment relevant. The article aims to develop a methodological framework for Agile redistribution of resources in the medical environment. Research results & conclusions. In the paper, the issue of resource reallocation in a multi-project healthcare environment is examined. A methodological framework for Agile redistribution of human resources in a healthcare setting has been developed, which is based on the use of donor-acceptor interaction. An Agile method of resource redistribution in a multi-project healthcare environment has been developed, enabling healthcare facilities to be provided with resources, taking into account the principles of resilience, adaptability, and functional reservation. A model of the Agile resource redistribution process (IDEF0) has been constructed. The use of a scenario approach to ensure Agile resource redistribution is considered. The main scenarios for redistribution have been identified. An IDEF3 model of the resource redistribution process (scenario approach) has been developed. An example of applying the developed methodological framework for conducting Agile redistribution of human resources in a healthcare environment is considered. The use of the developed support for the selected example allowed for the selection of a redistribution option that meets the defined reservation requirements, taking into account the chosen scenario.

A two‐layer approach for solving robust decentralized multiproject scheduling problem with multi‐skilled staff

AbstractUncertain activity durations and multi‐skilled staff allocation are two practical factors that are difficult to cope with in real project management. This paper studies a new decentralized resource‐constrained multiproject scheduling problem with uncertain activity durations and multi‐skilled staff allocation. The robust project scheduling is employed to tackle uncertainty. We name it the robust decentralized resource‐constrained multiproject scheduling problem with multi‐skilled staff (RDMPSP‐MS). To formulate this problem, a two‐layer model containing local scheduling and global coordination decision‐making is established based on the multiagent system. To solve this model, a two‐layer approach (TLA) is developed. In the TLA, a starting time criticality heuristic method is adopted to handle the local scheduling problem, and a global resource allocation heuristic algorithm is designed to allocate multi‐skilled staff. The computational experiments are conducted on the adapted Multi‐Project Scheduling Problem LIBrary (MPSPLIB) data set. The results show that the proposed TLA outperforms seven heuristic algorithms based on priority rules regarding solution quality and efficiency on most problem subsets. It is further verified that the robustness of the baseline schedule deteriorates with the increase in problem size and activity duration variability level. Additionally, the results of this research provide practical guidance for managers who expect to determine reasonable project deadlines in a decentralized multiproject environment.

MULTI-RESOURCE ALLOCATION AND LEVELING IN MULTI-PROJECT SCHEDULING PROBLEM WITH HYBRID-CHROMOSOME NON-DOMINATED SORTING GENETIC ALGORITHM II

Multi-resource allocation and leveling in multi-project (MR-AL-MP) scheduling refers to the attempt of producing a project schedule with minimum project duration and maximum resource utilization while complying with all precedence and resource availability constraints in a multi-project environment involving multiple resources. This study proposes a model that integrates both resource allocation and leveling models into a unified framework. This study develops a modified version of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), called Hybrid-Chromosome NSGA-II, as the optimization algorithm. For validation purposes, the performance of Hybrid-Chromosome NSGA-II is compared with two benchmark metaheuristic algorithms which are Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Symbiotic Organisms Search (MOSOS) in optimizing a case study. It is shown that the proposed model and algorithm are able to produce a set of non-dominated solutions that represent the feasible trade-off relationships between the objectives. Furthermore, the Hybrid-Chromosome NSGA-II is superior to MOPSO and MOSOS in terms of the quality, spread, and diversity of the solutions.

Modeling the Transformation of Configuration Management Processes in a Multi-Project Environment

Human resource management during project implementation in a multi-project environment requires addressing the resource-constrained project scheduling problem. Agile methodologies allow for greater management flexibility, necessitating an agile transformation of human resource management processes. Changes occurring in human resource management lead to modifications in the initial project team and alterations in the state of the resource pool in a multi-project environment. To ensure controllable changes in the project team and address the task of allocating (reallocating) limited resources among project tasks in a multi-project environment with subsequent optimization based on a selected criterion, it is proposed to use configuration management of human resources. Depending on the chosen level of detail, project specifics, and the implementation environment, configuration elements can be an executor, project team, or intact team. Types of equivalence applied to the set of configuration elements have been classified. A model of the configuration management process for human resources has been considered. Using the proposed model will allow for formalizing the process of implementing human resource configuration management in a multi-project environment. Constructive enumeration of configuration elements in a multi-project environment has been examined. Identifying a typical representative of the configuration and considering the given equivalence, followed by selecting a resource allocation/reallocation option that meets the specified constraints, enhances team adaptability. An example of configuration management in addressing team composition management tasks has been discussed. The proposed approach can be applied in managing human resources for agile transformation projects of critical infrastructure, particularly in the healthcare sector, during the establishment of hospital clusters and supercluster medical institutions. This is because implementing such projects necessitates continuous monitoring of changes and requirements for resource provisioning.

نموذج مقترح لإدارة المخاطر في الشركات ذات المشروعات المتعددة

Recently, the importance of risk management has increased in the field of project management, especially software projects. The importance of risk management is because it has direct effects on the success of the project. Therefore, software organizations face a major challenge in achieving effective and efficient risk management. Therefore, measuring risks in software development is a key activity to be undertaken in risk management. Risk measurement is also one of the important topics that need more effort, especially in a work environment with multiple software projects. A multi-project environment involves multiple projects sharing the same available resources. This research aims to conduct a literature review to discover patterns or trends in software development to measure project risks and to reveal aspects of the measurement process, risk indicators, and various metrics for software projects. This research also aims to present a proposed framework for assessing risks in a multi-project work environment with shared resources for different levels in organizations with different capabilities.

A multidimensional approach to the resource allocation problem (RAP) through the prism of industrial information integration (III)

The problem of resource allocation has been studied by applying diversified models and methods. There are efficient approaches to solving this task based on mathematical modeling, experts’ assessment, optimization methods, and others. However, the development of the modern world emphasizes the need of applying a new paradigm to deal with traditional problems. Industrial Informational Integration (III) as a new subject claims a multidimensional approach to achieve the company's goals in a volatile, uncertain, complex, and ambiguous (VUCA) environment. Today managers have to make faster decisions and revise them as quickly as possible. With the rise of sustainability considerations and digital transformation, classical approaches to industrial management should be transformed. In this article, we assume III as a driving tool for increasing the accuracy of business process management (BPM) on the example of the resource allocation problem (RAP). Our research considers the dissonance problem of local optimum different from overall system target goals within a multi-project industrial environment. This paper seeks to bring the discussion forward by analyzing the existing approaches and designing the integrated methodological framework for the resource assignment process relevant to a centralized resource management system.We have a broad view of resource allocation to the applicants through the prism of information integration, including sustainable development, systems performance improvement, and strategic goals achievement. The research article integrates several management, planning, and control tools with resource allocation methods. In the first step, we investigated diversified approaches towards the RAP for the multi-project economic environment and then proposed revised solutions combined with advanced managerial techniques. To meet the requirements of digital transformation, some computer-based conceptual solutions for RAP are proposed.The purpose of the present study is to contribute to the research field by claiming that the RAP can be solved within the information-integrated framework containing alternatives and limitations of each method to achieve many combinations reflecting to particular system requirements and target goals. To this extent, the RAP relies on efficient integration opportunities within an industrial multi-project system enabling transparent strategic goals transfer, cooperation, and operational excellence with a high service level provided.

Maximizing projects’ profitability, environmental score, and quality: a multi-project scheduling and material ordering problem

The proper trade-off between various project costs is often disregarded when planning projects. This leads to several detrimental effects, such as inaccurate planning and higher total cost, far more significant in a multi-project environment. To overcome this limitation, this study proposes a combined approach for the multi-project scheduling and material ordering problem (MPSMOP), which maintains the proper trade-off among various costs. Moreover, the environmental impact and project quality objectives are optimized alongside the economic criterion. The proposed methodology involves three stages: (a) quantifying the environmental performance of suppliers; (b) measuring the activities’ quality through the Construction Quality Assessment System approach; and (c) building and solving the mathematical model of the MPSMOP. The MPSMOP is modeled as a tri-objective optimization approach aiming to determine project scheduling and material ordering decisions so that the net present value, environmental score, and total quality of implemented projects are maximized simultaneously. As the proposed model comes into the nondeterministic polynomial optimization problem category, two powerful metaheuristics are customized and used to solve the problem. The efficiency of both algorithms was assessed on several datasets. The proposed framework is applied to railway construction projects in Iran as a case study, which presents the validity of the model and the decision-making options provided to managers.

An Integrated Framework for Dynamic Resource Allocation in Multi-project Environment

This paper proposes an integrated machine learning (ML) framework for dynamic resource allocation in a multi-project environment. The framework utilizes machine learning algorithms to predict future resource demands and identify potential resource shortages. The proposed framework considers various factors such as project priorities, resource availability, and project deadlines to optimize resource allocation decisions. The framework is designed to continuously learn from past resource allocation decisions and improve future resource allocation strategies. The effectiveness of the proposed framework is evaluated through a case study in a real-world multi-project environment. The results show that the framework can significantly improve resource utilization and project completion times while reducing resource waste and cost. Overall, the proposed framework provides a practical solution for dynamic resource allocation in complex multi-project environments.

Regret‐Minimizing Project Choice

An agent observes the set of available projects and proposes some, but not necessarily all, of them. A principal chooses one or none from the proposed set. We solve for a mechanism that minimizes the principal's worst‐case regret. We compare the single‐project environment in which the agent can propose only one project with the multiproject environment in which he can propose many. In both environments, if the agent proposes one project, it is chosen for sure if the principal's payoff is sufficiently high; otherwise, the probability that it is chosen decreases in the agent's payoff. In the multiproject environment, the agent's payoff from proposing multiple projects equals his maximal payoff from proposing each project alone. The multiproject environment outperforms the single‐project one by providing better fallback options than rejection and by delivering this payoff to the agent more efficiently.

Update of construction organization models in the context of their adaptability to modern management and digital technologies

The construction industry has a significantly higher degree of fragmentation of process characteristics compared to other types of activity, which are determined by an extremely high level of dynamism of the economic environment, where enterprises are constantly faced with the need to adapt to market transformations, an integral component of which are organizational and structural changes (transformation of the structure, regimes functioning, connections and relations between the components of the economic and production system of the enterprise as an object of management). The formation of development as an innovative investment infrastructure of a construction project has a dual purpose: on the one hand, it acts as an objective indicator of the level of competitiveness of the enterprise, which allows the investor to have a clear idea of the level of investment attractiveness of the enterprise as an object of external investment; on the other hand, as a tool for diagnosing the state of implementation of the investment program and making relevant functional and investment decisions. An innovative process-oriented toolkit for evaluating and selecting options for a portfolio of construction projects is proposed, the content of which is adapted: to the content of the operational activities of the developer as one of the leading subjects of the construction and investment process and to the nature of the structure of the developer company operating in a multi-project business environment as administrator, coordinator and regulator of the processes of initiation, preparation and implementation of construction investment projects. The scientific-methodical and analytical results of the study provide the construction stakeholder enterprise with the proper economic balance of the processes of functioning in the environment of the development project and in the digitally adapted space of formalized strategic coordinates, provide project implementing enterprises with a scientific rationale for the essential growth of adaptogenic characteristics and the choice of an option to substantially improve the state of competitiveness of the enterprise : the results of the work collectively update the scientific and methodological basis of economics and management of construction enterprises.