One of the most difficult problems in construction is taking objective decisions. A decision-making process is very complicated and time consuming (due to the complex nature of construction projects). Many experts with extensive knowledge of construction industry take subjective decisions related to verbal methods of decision-making. Difficulties are related mostly to the creation of a set of relevant criteria, providing answers to the decision-maker’s questions. A set of proper criteria and mathematical tools (such as computer calculation algorithms with multi-criteria analysis) could significantly improve objective decision-making. The paper presents ESORD – an informatics tool allowing to establish a hierarchy (ranking) of different types of solutions on the basis of mathematical calculation. The authors present a comparison of different methods used for multi-criteria decision-making.

Construction program, spatial, architectural, and structural decisions taken in the early stages of a project have a significant impact on meeting the goals and needs of the client. The use of principles and methods of value management allows for an in-depth analysis of the project assumptions from the investor’s perspective and leads to the best ratio of the project’s utility value/sustainability to the price of its implementation. However, analysis of literature sources allows to state that optimization of the economic value of the project takes place only in the preimplementation phase of the project. This paper presents the original concept of combining issues of construction project’s utility and economic value optimization. The model enables the maximization of the utility value of the subject of the project, taking into account its economic parameters. To support the implementation of the model, a schedule optimization procedure was developed using metaheuristic algorithm. The model was demonstrated on the basis of a case study. The presented proprietary approach to optimize the construction schedule taking into account the economic and sustainability of a construction project can be used in “design and build” projects, with particular emphasis on projects managed in the sustainable Project Management system.

Practical problems in construction can be easily qualified as NP-hard (non-deterministic, polynomial-time hard) problems. The time needed for solving these problems grows exponentially with the increase of the problem’s size – this is why mathematical and heuristic methods do not enable finding solutions to complicated construction problems within an acceptable period of time. In the view of many authors, metaheuristic algorithms seem to be the most appropriate measures for scheduling and task sequencing. However even metaheuristic approach does not guarantee finding the optimal solution and algorithms tend to get stuck around local optima of objective functions. This is why authors considered improving the metaheuristic approach by the use of neural networks. In the article, authors analyse possible benefits of using a hybrid approach with the use of metaheuristics and neural networks for solving the multi-mode, resource-constrained, project-scheduling problem (MRCPSP). The suggested approach is described and tested on a model construction project schedule. The results are promising for construction practitioners, the hybrid approach improved results in 87% of tests. Based on the research outcomes, authors suggest future research ideas.

The choice of material solutions and the appropriate technology for the execution of works have a significant impact on the success of construction projects. The earlier in the investment cycle of a project, the greater the possibility of improving the project’s success indicators. The currently used planning methods assume late integration of schedules with material and technological solutions. This limits the possibility of optimizing construction projects. The author proposed a new approach. The new method is based on the value engineering principles. The article presents a computational model supported by a case study—construction of an office building. Thanks to the use of artificial intelligence and metaheuristic algorithms, the economic results of construction projects have improved. This new method can help construction managers select materials and technologies in a way that will improve project parameters.

The article presents the profit optimization model for multi-unit construction projects. Such projects constitute a special case of repetitive projects and are common in residential, commercial, and industrial construction projects. Due to the specific character of construction works, schedules of such projects should take into account many different aspects, including durations and costs of construction works, the possibility of selecting alternative execution modes, and specific restrictions (e.g., deadlines for the completion of units imposed by the investor). To solve the NP-hard problem of choosing the order of units’ construction and the best variants of works, the authors used metaheuristic algorithms (simulated annealing and genetic search). The objective function in the presented optimization model was the total profit of the contractor determined on the basis of the mathematical programming model. This model takes into account monthly cash flows subject to direct and indirect costs, penalties for missing deadlines, costs of work group discontinuities, and borrowing losses. The presented problem is very important for maintaining a good financial condition of the enterprise carrying out construction projects. In the article, an experimental analysis of the proposed method of solving the optimization task was carried out in a model that showed high efficiency in obtaining suboptimal solutions. In addition, the operation of the proposed model has been presented on a calculation example. The results obtained in it are fully satisfying.

Investment and construction plans, architectural and construction decisions, and spatial and technology-related decisions made at the early stages of a project have a significant impact on meeting the investment goals and customer expectations. Decision making is a very time-consuming and complicated process (due to the complexity of construction processes). The whole difficulty comes to specifying the appropriate criteria for assessing the given activities, providing answers to the questions of the decision-making bodies. A set of appropriate criteria and mathematical tools (such as computer algorithms with multi-criteria analysis) can significantly improve and accelerate the decision-making process. This article combines ESORD (an IT tool that allows you to compare different types of solutions based on mathematical calculations) with the Monte Carlo method. The developed approach can help the investor to optimize their cash-flow schedule. The original method enables the client to select a construction project variant characterized by the best economical and sustainable parameters, while taking into account customers’ demands.