All projects require a structure to meet project requirements and achieve established goals. This framework is called project management. Therefore, project management plays an important role in national development and economic growth. Project management includes various knowledge areas such as project integration management, project scope management, project schedule management, etc. The article focuses on the resource-constrained project scheduling known as problem so- called the resource-constrained project scheduling problem (RCPSP). The RCPSP is a part of schedule management. The standard RCPSP has two important constraints, resource constraints and precedence relationships of activities during project scheduling. The objective of the problem is to optimize and minimize the project duration, subject to the above constraints. In this paper, we develop a convolutional neural network approach to solve the standard single mode RCPSP. The advantage of this algorithm over conventional methods such as metaheuristics is that it does not need to generate many solutions or populations. In this paper, the serial schedule generation scheme (SSGS) is used to schedule the project activities using an evolved convolutional neural network (CNN) as a tool to select an appropriate priority rule to filter out a candidate activity. The evolved CNN learns according to the eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, etc. The above parameters are the inputs of the network and are recalculated at each step of the project planning. Moreover, the developed network has priority rules which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter an activity from the eligible activities. In this way, the algorithm is able to schedule all project activities according to the given project constraints. Finally, the performance of the Convolutional Neural Network (CNN) approach is investigated using standard benchmark problems from PSPLIB in comparison to the MLFNN approach and standard metaheuristics.

Project management has a fundamental role in national development, industrial development, and economic growth. Schedule management is also one of the knowledge areas of project management, which includes the processes employed to manage the timely completion of the project. This paper deals with the Resource-Constrained Project Scheduling Problem (RCPSP), which is a part of schedule management. The objective of the problem is to optimize and minimize the project duration while constraining the resource quantities during project scheduling. There are two important constraints in this problem, namely resource constraints and precedence relationships of activities during project scheduling. Many methods such as exact, heuristic, and meta-heuristic have been developed by researchers to solve the problem, but there is a lack of investigation of the problem using methods such as neural networks and machine learning. In this article, we develop a multi-layer feed-forward neural network (MLFNN) to solve the standard single- mode RCPSP. The advantage of this method over evolutionary methods or metaheuristics is that it is not necessary to generate numerous solutions or populations. The developed MLFNN learns based on eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, percentage of remaining work, etc., which are calculated at each step of project scheduling, and identified priority rules, which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter out an unscheduled activity from the list of eligible activities and schedule all activities of the project according to the given project constraints. Finally, we investigate the performance of the presented approach using the standard benchmark problems from PSPLIB.

The Resource-Constrained Project Scheduling Problem (RCPSP) remains a critical area of study in project management, focusing on optimizing project schedules under constraints such as limited resources and task interdependencies. This review synthesizes advancements from 2016 to 2024, encompassing problem variants, optimization techniques, objectives, and real-world applications. Key developments include the evolution of hybrid metaheuristics, multi-objective optimization approaches, and the integration of stochastic models to enhance robustness against uncertainties. Furthermore, the application of machine learning and sustainability-driven models has expanded the practical scope of RCPSP in dynamic and complex environments. Challenges such as scalability, uncertainty management, and the need for practical implementations are addressed, with future directions emphasizing AI integration, decentralized scheduling, and real-time adaptive solutions. This study provides a comprehensive perspective on RCPSP, bridging theoretical research with practical implications for diverse industries.

Motivated by a practical situation in a digital transformation project, this paper considers a resource-constrained project scheduling problem with multiple modes, multiple skill types, and differentiated professional capabilities. In the proposed problem, each project activity has one or more alternative execution modes associated with a trade-off between processing time and resource consumption. In an execution mode, an activity requires a certain number of employees with specific skill types and required professional capabilities. A mixed integer programming model is developed to minimize the total project duration. Since this problem is NP-hard, an efficient immunoglobulin-based artificial immune system (EIAIS) algorithm with a new encoding and decoding scheme and novel components is proposed. The effectiveness of the proposed EIAIS algorithm is tested on randomly generated instances. Computational results show that the proposed EIAIS algorithm has better performance than the existing algorithms.

The time-cost optimization is amongst the most critical fields, which has an extensive range of implementation in project scheduling. Achieving a satisfactory balance between these factors can lead to an efficient construction project by reducing both the length of the project and costs at the same time. An effective balance can be achieved using various methods, depending on the situation. This study aims to incorporate the various algorithms used in the last 15 years to reach a satisfying balance between time and cost, including meta-heuristics, heuristics, and exact algorithms. A comprehensive view of the problems associated with time-cost optimization will be provided throughout this review to assist new and challenging researchers who are interested in this type of research. For this purpose, we have reviewed some objective functions and uncertainty techniques that could be employed in time-cost balancing problems. The literature review tables contain a variety of columns, including uncertainties such as fuzzy, probabilistic, interval, robust, and objective functions, along with cost and time, for the investigation of various types of balance issues. In the conclusion of this article, we will show the results of our literature review table using different types of graphic diagrams. For each main column of the table, we will show various types of diagrams to make the results easier to understand.

This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).

One subcategory of project scheduling is the resource constrained project scheduling problem (RCPSP). The present study proposes a differential evolution algorithm for solving the RCPSP making a small change in the method to comply with the model. The RCPSP is intended to program a group of activities of minimal duration while considering precedence and resource constraints. The present study introduces a differential evolution algorithm and local search was added to improve the performance of the algorithm. The problems were then solved to evaluate the performance of the algorithm and the results are compared with genetic algorithm. Computational results confirm that the differential evolution algorithm performs better than genetic algorithm.

The objective of this paper is to evaluate factors affecting the progress of completing construction of irrigation projects using state budget funds. To achieve the goal, to research and conduct surveys of investors, contractors, managers of irrigation construction investment projects. Analysis results from 269 valid questionnaires using SmartPLS 3.6 software showed that all 6 factors: Investor, Peripheral, Capital, Contractors, Consultant, Convenience, all factors have a positive impact on progress of completing irrigation construction projects using state budget funds in Thai Binh province, Vietnam.

Resource-Constrained Project Scheduling Problem (RCPSP) is considered as an important project scheduling problem. However, increasing dimensions of a project, whether in number of activities or resource availability, cause unused resources through the planning horizon. Such phenomena may increase makespan of a project and also decline resource-usage efficiency. To solve this problem, many methods have been proposed before. In this article, an effective backward-forward search method (BFSM) is proposed using Greedy algorithm that is employed as a part of a hybrid with a two-stage genetic algorithm (BFSM-GA). The proposed method is explained using some related examples from literature and the results are then compared with a forward serial programming method. In addition, the performance of the proposed method is measured using a mathematical metric. Our findings show that the proposed approach can provide schedules with good quality for both small and large scale problems.

Resource-Constrained Project and Multi-Project Scheduling Problems (RCPSPs and RCMPSPs) have been essential topics of study over the last three decades. Both prob-lems consist of activities that must be scheduled subject to precedence and resource constraints. This paper surveys studies of RCPSPs and RCMPSPs under consideration of four categories of project activities, simply recorded as categories A, B, C, and D. Category A refers to activities can be performed using fixed resources along The Y-axis over fixed durations along The X-axis, and cannot be interrupted. Category B applies to activities that can be performed using the same type of resource in category A but can be interrupted. Category C refers to activities that can be performed using flexible resources over flexible durations and cannot be interrupted. Category D refers to activities can be performed using flexible resources over flexible durations and can be interrupted. Many algorithms have been developed to solve the RCPSPs and RCMPSPs when activities are classified individually under category A, B, or C. However, in practice, welding, cutting or assembly activities in a manufacturing projects for an oil cargo can be under a new category so-called D. The project manager can speed up or slow down these activities by allocating or removing more resources, and these activities can be interrupted or can be resumed at any time. From the perspective of activity categories, we intend to review the literature on RCPSPs and RCMPSPs and to obtain the new research directions for solving the problems.