Optimizing Heavy Lift Plans for Industrial Construction Sites Using Dijkstra’s Algorithm

Ali Mousaei, Hosein Taghaddos, S. Marzieh Bagheri, Ulrich Hermann

Journal of Construction Engineering and Management · 2021 · 23 citations · 35 references

Concepts

TL;DR

Planning lift activities of mobile cranes for modular projects can raise productivity and improve safety, yet selecting among many lift options remains challenging; despite prior work, optimality can still be improved to reduce costs. The study presents an integrated framework that automatically generates an optimal lift plan for a predetermined sequence, proposing an enhanced graph‑based optimization algorithm for concurrent heavy lift operations. The framework employs Dijkstra’s search algorithm to select the optimal lift option for each module. The framework was validated on a modular construction project in Alberta, Canada, and outperformed prior lift‑planning algorithms, achieving a significant reduction in total project cost.

Abstract

Planning lift activities of mobile cranes for a modular project can raise productivity and improve safety. An optimized lift plan is essential for multiple lifts due to high rental cost of heavy lift cranes. However, choosing among a large number of available lift options (e.g., types, configurations, and locations of cranes) to develop multilift plans can be challenging. Despite numerous efforts in this area, there is still room for improvement to enhance the optimality of solutions, which leads to considerable cost reduction. This paper introduces an integrated framework to automatically generate an optimum lift plan based on a predetermined lifting sequence. Dijkstra's search algorithm is utilized in the current study to select each module's optimum lift option. This study contributes to the body of knowledge by proposing an enhanced graph-based optimization algorithm for multiple concurrent heavy lift operations. The suggested framework is validated successfully in an actual modular construction project in Alberta, Canada. The results are also compared with the previously developed lift planning algorithms, showing a remarkable reduction in the project's total cost.

References

35