Search

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Infrastructure projects are generally implemented in less developed areas. These areas usually have a pristine and pollution-free environment. Environmental pollution during project implementation is considered in various countries under strict regulations. Therefore, it has become necessary to consider environmental factors during scheduling a project in recent decades. The project schedule is one of the primary and widely used planning fields. Applying theories in practice and extensive studies in this field indicate its importance more than before. These problems have various kinds regarding the limitations and conditions of the financial aspects of the contract. Resource-constrained project scheduling problems (RCPSP) are nonpolynomial problems - hard (NP-Hard), and usually, meta-heuristic methods were used for solving them. This paper developed a new model for RCPSP called fuzzy green multi-objective multi-mode resource-constrained project scheduling problems (GFMMRCPSP). This model considers three objectives: minimum environmental pollution, minimum Cmax, and maximum NPV. Because of the uncertainty in the real world, all model parameters are assumed fuzzy. The initial feasible solution algorithm was introduced to increase the speed of problem-solving algorithms (NSICA, NSGA-II, and MOPSO), which removed the unfeasible search space. The proposed model and algorithms were tested using standard PSPLIB problems. The test results of different methods for solving the GFMMRCPSP model were compared using eight different criteria: Number of Pareto Solutions (NPS), Mean Ideal Distance (MID), Mean of Coefficient of Closeness (MCC), Mean of Similarity (MS), Quality Metric (QM), Spacing, Diversity, and Running time. The results show that NSICA is the most efficient and effective algorithm, and the NSGA-II algorithm is more suitable than the MOPSO algorithm to solve the research problems. [ABSTRACT FROM AUTHOR]