A Sustainable Resource-Constrained Project Selection and Scheduling Model using Scenario-Based Stochastic Programming: A case study of industrial projects

Abstract

Abstract Due to a wide variety of real-world constraints, proper project portfolio selection is a critical issue for project-oriented organizations. In this paper, a bi-objective stochastic mixed-integer linear program is developed to cope with the resource-constrained project selection and scheduling problem in the presence of greenhouse gas emissions, and non-hazardous and hazardous wastes regulatory restrictions. Moreover, reinvesting proceeds of projects as well as loans are allowed to finance projects over the planning horizon. The proposed model maximizes the net present value of the expected project portfolio's terminal wealth under uncertain conditions, as well as the sustainability score of the project portfolio, simultaneously. The sustainability score is calculated by one of the recent multi-criteria decision-making methods, SECA, based on seven qualitative sustainability indicators and by solving a non-linear optimization model. To assess the performance of the proposed model, a case study of eighteen industrial projects is applied. Since the duration of industrial projects is usually uncertain, the proposed model is reformulated as a scenario-based stochastic programming model. Furthermore, the CPLEX solver and Branch and Benders algorithm are used to solve the problem. Results show that the Branch and Benders algorithm is much more efficient than the Cplex solver. In the end, according to the performed sensitivity analysis, in some cases, increasing the carbon and landfill tax rates, does not significantly reduce the negative environmental effects, while making the projects less attractive for investment. This highlights the importance of coping with the problem under consideration for managers, legislators, and policymakers.