[1]XIANG Qian,ZHOU Yayun,LU Zhiyi,et al.Multi-objective location optimization algorithm in response to dynamic constraints[J].CAAI Transactions on Intelligent Systems,2020,15(5):925-933.[doi:10.11992/tis.201906041]
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Multi-objective location optimization algorithm in response to dynamic constraints

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 925-933 Column: 学术论文—机器学习 Public date: 2020-09-05
Title:
Multi-objective location optimization algorithm in response to dynamic constraints
Author(s):
XIANG Qian;  ZHOU Yayun;  LU Zhiyi;  YU Yufeng
College of Mechanical Engineering, Donghua University, Shanghai 201620, China
Keywords:
automated storage and retrieval system; location optimization; dynamic constraints; continuous optimization; differential evolution; adaptive variation coefficient; analytic hierarchy process; multi-objective; Pareto solution
CLC:
TP18；F274
DOI:
10.11992/tis.201906041
Abstract:
Considering the storage location decision and optimization problems in automated storage and retrieval system, we propose a multi-objective logistics optimization algorithm, which considers various optimization objectives, such as the usage status of the pallet and dynamic changes in the allocable storage location. A multi-objective optimization model is established based on the equilibrium of roadway operations, the stability of the gravity center of shelves, and the shortest operation path. Based on the adaptive variation coefficients’ differential evolution algorithm, a random number encoding of the storage location is used to perform individual decoding according to the real-time feasible domain in response to the dynamic constraint condition. A Pareto optimal solution evaluation method based on the analytic hierarchy process is proposed to obtain the target weight related to the continuous optimization of a multi-batch operation, and a reasonable plan for the storage location decision is provided. The experimental results of the multi-batch operation show that the proposed algorithm is significantly better than the simple weighting algorithm, which can be effectively applied to dynamic location decision and optimization.
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