فهرست نشریات علمی دسترسی آزاد ایران

Selecting investment projects is one of the most critical decisions managers must make. If it is not grounded in mathematical and economic principles, it may squander resources, fail to meet stakeholder expectations, and lead to excessive expenditures. Important strategic and operational decisions, the imprecise nature of parameters, project interdependencies, the uncertainty-handling approach based on Credibility-based Chance Constraint Programming (CCCP), and the implementation of Project Portfolio Selection (PPS) in waste management contexts are all issues that are not adequately addressed in the literature. Thus, this research provides a framework for selecting project portfolios that considers numerous strategic and operational considerations, including portfolio selection, material ordering, machinery, human resource management, transportation, and inventory management. Interdependencies between projects are considered in the model, which is built on the principles of mutual exclusivity and complementarity. To address uncertainties, the imprecision of key factors is accounted for, and a CCCP technique is used. To demonstrate the relevance of the suggested approach, a case study is presented that focuses on waste management projects in Shiraz, Iran. Considering seven distinct types of waste and 16 investment projects, establishing one organic waste composting center, one construction and demolition waste recycling center, and one advanced thermal treatment center would yield the highest profit in Shiraz at a confidence level of 0.9. Moreover, parameter uncertainty cannot be disregarded, as the overall profit of the chosen projects varies greatly when parameters are unknown. On average, the CCCP model's profit is 18.57% lower than the deterministic model's. However, the gained profit is better insulated against high levels of uncertainty, and investors can choose projects that are more resilient to unpredictability. Sensitivity analysis indicates that the fixed investment, machinery, and human resource costs are the most important parts of the model.