Designing a Strategic Planning Model for Project Management in Uncertainty Situations (Case Study: Crude Oil Storage Tank Maintenance Project)

Considering the key role of storage tank capacity in increasing and sustainability of oil production and preventing daily fluctuations due to production-related operational problems as well as maintaining storage capacity in oil exports, minimizing all costs and being included in the service of tanks is the main strategy at the Ministry of Petroleum.
The resource-constrained project scheduling problem (RCPSP) is one of the most important problems in the field of project management that has attracted a lot of researchers in recent years. Combining this with other project management problems has recently become very common.
Classical approaches such as the critical chain assume that project resources are unlimited. However, this assumption does not make sense in most projects. Subsequently, the resource constraint assumption was added to the model. Such problems are called resource-constrained project scheduling problems (RCPSP).
Most studies assume that activities are performed in an ideal setting and that the proposed schedule can be executed exactly according to plan. The existence of uncontrollable factors such as lack of access to resources, the addition of unforeseen activities to the project, and bad weather conditions practically lead to the failure to achieve the project objectives in the desired time. This can bring significant costs to the project. Therefore, one of the main challenges facing construction projects is the existence of uncontrollable factors. The effect of these factors on the project can be greatly reduced if different scenarios are predicted in the planning done for the project and the planning is done based on these scenarios. A robust optimization is a new approach that has been proposed in recent years to deal with data uncertainty in various scenarios. In this approach, near-optimal solutions are considered that are highly feasible and resistant to change. In other words, the feasibility of the solution obtained in different scenarios is guaranteed by slightly deviating from the value of the objective function. Accordingly, in this study, a robust optimization approach is used to deal with changes in different scenarios to minimize the effect of different modes of events in the project on the accuracy of the plans made. In real conditions, ignoring some limitations and realities in modeling can reduce the applicability of the proposed model. Therefore, in this study, several important issues in modeling are considered. These issues include non-renewability, perishability, discounts on project resources, uncertainty, and lag times. The following is a brief description of these concepts for more familiarity with them. In this study, a robust planning model for the problem of robust integrated project scheduling and material ordering under uncertainty, lag times, non-renewability, perishability, and various project implementation scenarios was provided. Simultaneous consideration of the above in the model brought the model closer to real-world conditions, made the results more practical, and improved the quality of results. The model had the objective function to minimize the total cost, including ordering, maintenance, purchasing, and penalties for delay minus the bonus for hastening the project delivery. Model robustification was then performed with a possibilistic approach. After providing the model, numerical problems of different sizes were solved using a hybrid solution method of genetic algorithm and GAMS software. Moreover, a sample problem concerning an oil resource repair project and several numerical problems were designed and solved using the desired approach. The results indicated a significant reduction in solution time using the proposed hybrid approach.