Scheduling the Production of Aerospace Structural Parts with Dual Resource Constraints

doi:10.3901/JME.2018.09.055

Abstract

Abstract: The production scheduling method of aerospace structural parts with dual resource constraints of critical equipment and operator is investigated, where structural parts' processing time varies with the operator's ability, and the key components and processes have specific equipment and operator requirements constraints. A mathematical model of the flexible job shop scheduling with dual resource constraints (FJSSDR) is constructed to describe the equipment、operator and process information of the parts. A nested ant colony-genetic hybrid algorithm (NACGHA) is designed to solve the FJSSDR problem. Aiming at the dual resource selection problem, the mapping relationship between resource selection problem and ant colony parallel search is structured. Genetic algorithm is used to solve the optimal scheduling problem by taking the selected double resource constraints as the processing constraint information of the job. The equipment processing sequence is optimized with avoiding conflict of key operator resources, where the objectives of fullest utilization of critical equipment and makespan minimization are considered. The ant colony algorithm pheromones are updated by integrating the objective value obtained by ant colony and genetic algorithm to improve the performance. At last, a scheduling case of aerospace structural parts production shop is analyzed. The case study demonstrates that the proposed NACGHA method has good performance on FJSSDR in terms of reducing resource total load, avoiding resource conflict and improving the utilization of key equipment.

Key words: dual resource constraints, flexible scheduling, nested ant colony-genetic hybrid algorithm, production of aerospace structural parts

CLC Number:

TP301

ZHOU Yaqin, YANG Changqi, LÜ Youlong, JIN Yongqiao, ZHANG Jie. Scheduling the Production of Aerospace Structural Parts with Dual Resource Constraints[J]. Journal of Mechanical Engineering, 2018, 54(9): 55-63.

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