Multi-Objective Production and Scheduling Optimization of Offshore Wind Turbine Steel Pipe Piles Based on Improved Hesitant Fuzzy Method

Abstract

This paper investigates the multi-objective optimization problem in the production of offshore wind turbine steel pipe piles (OWTSPP). Considering the particularity of the steel pipe pile production process, it is divided into a flexible flow shop scheduling (FFSS) stage and an open parallel shop scheduling (OPSS) stage, respectively. Mathematical models are established for each stage, and the critical path and production time information are obtained using a disjunctive graph model. Due to the inability of existing empirical scheduling methods to balance production goals, an improved Pythagorean hesitant fuzzy method (IPHFM) is proposed to solve the multi-objective optimization problem in steel pipe pile production. Specifically, the maximum completion time, machine total load, and total completion time are taken as optimization objectives. The improved Lagrange multiplier method with penalty terms is used to handle the constraints and objective functions, and a Lagrange objective function is generated. Then, the Lagrange objective function matrix is obtained by normalization and same-scale processing, and an algorithm is designed to obtain the Pareto front solution set. Finally, this paper compares the optimal scheduling plans under the empirical scheduling method and the improved method. The results show that the improved method can significantly improve production efficiency in both small-scale and large-scale production, with improvements of 15.7% and 22.16%, respectively.

Fusion Q-Learning Algorithm for Open Shop Scheduling Problem with AGVs

“…It can learn from data generated by scheduling systems according to different strategies and apply this knowledge to train its model during the solving process, thereby improving the training results of the scheduling system. The Q-value update computation is depicted by Equation (10). Table 2 shows the interpretation of the symbols in the Equation (10).…”

“…The Q-value update computation is depicted by Equation (10). Table 2 shows the interpretation of the symbols in the Equation (10).…”

“…In the new state, after taking an action, the agent examines all actions in the new state, selects the action with the maximum Q value, and then chooses that action. Using Equation (10), the Q value of the current action is updated after completing all calculations. After the update, the intelligent agent continues to explore the new state, repeating the above process.…”

“…The method was validated with the objective of minimizing makespan, demonstrating its reliability. Wang et al [10] employed a disjunctive graph model to extract production times for the open-shop scheduling in the wind turbine-production process. An enhanced Pythagorean hesitant fuzzy approach was utilized to address multi-objective concerns related to the maximum completion time and machine workload.…”