In this paper, we extend the multi-skilled resource-constrained multi-project scheduling problem (MSRCMPSP) by introducing the concept of multiple project types (MSRCMPMTSP). The study considers two categories of projects: investment projects, which generate positive net present values (NPVs), and mandatory projects, which result in negative NPVs but are required to be executed. To solve this problem, we employ a priority rule-based heuristic approach. Specifically, forward scheduling is applied to projects expected to yield positive NPVs, whether they are optional or mandatory. In contrast, backward scheduling is used for projects with negative NPVs, as this strategy minimizes the impact of excessive negative NPVs. The dataset for this study is constructed using the design of experiments (DoE) methodology, enabling a comprehensive evaluation of the proposed heuristic. We compare the performance of our approach against randomly generated schedules through extensive simulations. The results indicate that the heuristic is effective in addressing the MSRCMPMTSP.

A majority of companies are involved in the planning and execution of projects. The number of projects that companies need to evaluate has significantly increased in recent years. This trend has various causes, such as the digitalization of corporate processes, diversification, or strategic positioning in the face of ever-changing market conditions. The characterization of projects into mandatory and optional, as well as the evaluation of these projects, can be conducted based on various mixed criteria, which may include both cost and benefit criteria. The limited resources of companies necessitate a critical assessment of projects. They must be ranked based on realistic and plausible criteria regarding their benefits and objectives of the company. In the literature, various approaches to project evaluation exist. Examples include financial assessment, the utilization of evaluation models considering risks, or even multi-criteria models that incorporate different aspects of projects into the evaluation process. We propose a robust, scalable, and easily calibrated multi-criteria evaluation model for project evaluation and ranking, encompassing evaluation criteria such as financial criteria measured by Net Present Value (NPV), Risk, Classification, Priority, Strategy, and Sustainability. To achieve this goal, the Technique for Order of Preference by Similarity to Ideal Solution with multi type projects multi mixed cost and benefit criteria (TOPSIS-MTPMMCBC) is employed. The model is adapted to evaluate and rank optional and mandatory projects. An important feature of the projects in this study is that the criteria values of the projects can have negative or positive values. Particularly noteworthy is the increasing significance of sustainability as a key criterion for businesses, driven by political mandates. Consequently, a decision based on the criterion of sustainability will be important in the future and is implemented in the proposed model. The proposed research can be adapted to use a variety of Key Performance Indicators (KPIs) as multiple decision criteria. An objective calculation of the criteria weights for sample dataset was carried out using the CRITIC method, followed by a sensitivity analysis. Subsequently, the optimization of the project portfolio was carried out by combining an integer programming model with the proposed TOPSIS-MTPMMCBC method. An initial solution of project evaluation and ranking is conducted to demonstrate the applications.