Environmental Pollutions Assessment by a New Project Scheduling Model under a Fuzzy Environment

Document Type : Research Article

Authors

1 Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran

2 Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran

3 Faculty of Arts, Tarbiat Modares University, Tehran, Iran

Abstract

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 non-polynomial 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 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.

Keywords

References

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Environmental Energy and Economic Research
Volume 6, Issue 2
May 2022
Pages 1-19

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