## 6 Conclusions  
This paper shows a comprehensive review of multi-project scheduling approaches, with a focus on uncertainty and resource flexibility methodologies. This is a topic of growing interest for academics and users. From the SLR [19], the authors initially selected a limited number of studies (52 papers) from a pool of 108 papers. Additional relevant works and newly published papers from 2024 were subsequently incorporated, resulting in a final set of 76 papers being curated for review. The paper presented a review of RCMPSP evolution by analyzing related variants in terms of the objective functions, proposed solution methods, benchmarks, and connection to practice. The following text will report the achievements in light of the research questions. **RQ1. **_What are the objectives in multi-project scheduling problems?_  
The objective functions in multi-project scheduling typically include time-based, cost-based, cost-and-time-based, and resource leveling objectives. Among these, time-based and cost-based objectives are the most extensively studied. Specifically, minimization of the makespan, weighted project delay, and total project cost are the predominant goals within these categories. **RQ2. **_What are the different types of solving methods in multi-project scheduling problems?_  
There are two primary approaches for solving multi-project scheduling problems: exact and approximate methods. Among these, approximate algorithms are the most frequently used for addressing RCMPSP. Notably, genetic algorithms and PR-based algorithms are particularly prominent in the resolution of these issues. **RQ3. **_What types of instance sets are used in multi-project scheduling problems?_  
Regarding benchmarks, various instance sets are utilized, including generated instances, problem examples, real-world instances, MPSPLIB, PSPLIB, PostgreSQL, MMLIB, MPLIB1, MPLIB2, MISTA 2013, and iMOPSE. Among these, generated sets and the PSPLIB are the most frequently used, representing the two highest percentages of instance sets employed in research and practical applications. **RQ4. **_What are the potential future research directions in multi-project scheduling problems?_  
It is hoped that this work will encourage future research to explore this area further, fostering the development of more effective strategies and methodologies. A summary of the identified problems, objectives, and proposed future research directions is presented in Table 10.  
This research brings the user a researcher database for existing methodologies to improve investigation in the future to solve the problem using the available information on these topics and approaches. For example, consider the following:  
* Integrating concepts such as activity flexibility and activity priority into project scheduling. * Different types of uncertain factors should be considered, for example, dynamic project arrival, stochastic resource availability, etc., and their impact on the resource usage deviation levels of the projects should be measured. * Deviation in customer demand. * Different scenarios for project duration (pessimistic, most probable, and optimistic). * Design different PRs for the stochastic environment and compare them to the performance of the previous PRs. * Use the exact method along with a heuristic or meta-heuristic method to compare the results. * Analyze whether the company accepts delays and late penalty fees or outsourcing.