Inventory Planning and Activity Scheduling in The Supply Chain of Reconstruction Projects (Case Study: Sina Drilling Rig 1)

Document Type : Original Article

Authors

1 Assistant Professor, Department of Industrial Engineering, Faculty of Engineering and Research Institute, Imam Hossein University, Tehran, Iran.

2 Ph.D. Student, Department of Industrial Engineering, Faculty of Engineering and Research Institute, Imam Hossein University, Tehran, Iran.

3 Master of Science, Department of Industrial Engineering, Faculty of Engineering and Research Institute, Imam Hossein University, Tehran, Iran.

10.48308/jimp.15.1.9

Abstract

Introduction and objectives: Refurbishing an oil rig is one of the best alternatives to renting this capital-intensive equipment since its rental cost amounts to tens of thousands of dollars per day. This research focuses on the scheduling of activities and inventory planning in the supply chain of the Sina 1 drilling rig reconstruction project. Since no prior record of such an endeavor exists in the country, project scheduling is conducted under conditions of activity uncertainty. In addition, uncertainty in the timing of activities improves project owners' understanding of their schedule and creates a broader view of the project and future activities. In this model, contractor and supplier costs are considered simultaneously, and uncertain activity scheduling and order planning are carried out in a way that minimizes the overall cost of the chain.
Method: Given the large number of variables and constraints in the mathematical model of supply chain activity scheduling, the problem under study is classified as NP-hard. For this reason, meta-heuristic methods are used to solve such problems, which provide near-optimal answers in less time compared to exact methods. In this study, the electromagnetic algorithm has been used to solve this problem. This algorithm has been applied to a real project (the foundation section of the Sina 1 drilling rig).
Findings: The mathematical model proposed in this research has been coded using the electromagnetic algorithm within the MATLAB software environment. The input parameters include general parameters and control parameters of the electromagnetic meta-heuristic algorithm. General parameters pertain to the specifications of the contractor, suppliers, activities, resources, and consumables. To validate the efficiency and effectiveness of the designed electromagnetic algorithm, three case problems were selected. The first problem consists of five activities; the second problem involves the same five activities but with two suppliers, while the third problem features a larger-scale scenario. First, the exact solution for each problem was obtained using AIMMS software, and then the solutions and computational times of the electromagnetic algorithm were compared with those of AIMMS. Notably, as problem size increases, the solution time in AIMMS grows significantly (exponentially). The proposed electromagnetic algorithm demonstrates acceptable performance in terms of computational time. The meta-heuristic electromagnetic algorithm solves the sample problem in 115 seconds, whereas AIMMS requires approximately 747 seconds to find the exact solution. Additionally, the relative deviation of the electromagnetic meta-heuristic algorithm from AIMMS is approximately one percent. Finally, a sensitivity analysis was conducted to examine the impact of different payment methods on the project's supply chain costs.
Conclusion: This research presents the modeling and solution approach for a two-level project supply chain, encompassing both the contractor and suppliers, with the objective of minimizing project and inventory costs for both entities.By comparing the results obtained from the exact method and the electromagnetic algorithm, it was found that the solution time using the electromagnetic algorithm is significantly less than the solution time in the exact method. In addition, the relative difference in the quality of the results is limited to approximately one percent. These cases clearly indicate the effectiveness and efficiency of the proposed algorithm.The results indicate that the proposed electromagnetic algorithm is a highly effective approach for this problem and converges toward an optimal solution.

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References

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