Intelligent material distribution and optimization in the assembly process of large offshore crane lifting equipment. (September 2021)

• Record Type: Journal Article
• Authors: Shen, Xingwang; Liu, Shimin; Zhang, Can; Bao, Jinsong
• Is Part Of: Computers & industrial engineering. Volume 159(2021)
• Publication Date: 2021-09
• Subjects: Offshore crane lifting equipment -- Material distribution -- Improved Particle Swarm Optimization algorithm -- Vehicle-material matching relationship; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107496
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Propose an improved particle swarm optimization algorithm to solve material transport problem. Present a mathematical model to increase vehicle scheduling efficiency. Propose a two-stage hierarchical solution. Propose a new coding, resolution and location update method. Use actual cases to verify the advantages of the proposed method. Abstract: The constraint of the material distribution for offshore crane lifting equipment is complicated. Taking into account the characteristics of the material distribution problem of the general assembly of offshore lifting equipment, this paper proposes a scheduling method based on the improved Particle Swarm Optimization algorithm by considering vehicle material matching relationships. Firstly, this we establish a vehicle scheduling model for the final assembly logistics distribution process and then propose a two-stage hierarchical solution framework for minimizing the number of vehicles and the transportation distance. Secondly, the optimization aims to minimize the number of vehicles, we obtain the optimal solution by CPLEX Optimizer. The vehicle transportation distance is then optimized by the improved particle swarm optimization algorithm. The initial solution performance of the algorithm is improved by our heuristic rules. The discretization of the problem is achieved by coding, decoding and location updating methods according to the characteristics of the problem. Additionally, the updating mechanism of simulated annealing algorithm is introduced to update the location to avoid the algorithm falling into local optimum. Through case analysis and comparisons, the improved Particle Swarm Optimization based scheduling method proposed in this paper combined with the vehicle-material matching relationship can effectively improve the efficiency of the material distribution with better performance.