A mathematical model for project scheduling and material ordering problem with sustainability considerations: A case study in Iran

Abstract

In the traditional project planning approach, the manager first schedules the project activities and then plans the material ordering timetable accordingly. But in this approach, the tradeoff and association between the project implementation costs and ordering expenses are disregarded. This approach is also ill-suited for modern organizations that are under growing public pressure to support inter and intra generational justice and introduce environmental and social objectives to their competitive strategy and business mission. This paper provides an integrated framework for the Project Scheduling and Material Ordering (PSMO) problem with sustainability considerations. The proposed framework consists of two phases: (a) quantifying the environmental and social merits of the potential suppliers of the project resources, and (b) constructing and solving a mathematical model based on the acquired data. The model is able to determine the activities schedule, material ordering time and quantity, and the supplier selection that maximize the project NPV and the environmental and social benefits of its suppliers. The presented model falls within the class of NP-Hard problems, so two multi-objective metaheuristic algorithms, namely NSGA-II and MOPSO were modified to serve as solution methods for this model. For small problems, the performance of these methods was compared with that of second version of the augmented e-constraint (AUGMECON2) method, but for larger problems, where the exact method was unable to produce a solution within a reasonable time, these two algorithms were compared with each other. The results showed that regardless of problem size, NSGA-II outperforms MOPSO in the majority of evaluation metrics. The paper also includes a case study conducted on the trackbed construction project in Section 5 of Mianeh-Bostanabad-Tabriz railway in Iran, which demonstrates the applicability of the proposed model and provide an illustrative example of its implementation.