A permutation-combination heuristics for crane-based automated storage and retrieval systems considering order fulfillment time and energy consumption

Haolan Zhou; Gang Chen; Yujun Lu; Xiaoya Cheng; Hao Xin; Haolan Zhou; Gang Chen; Yujun Lu; Xiaoya Cheng; Hao Xin

doi:10.3934/mbe.2024006

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 1: 116-143. doi: 10.3934/mbe.2024006

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Research article

A permutation-combination heuristics for crane-based automated storage and retrieval systems considering order fulfillment time and energy consumption

1.
School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
2.
Faculty of Automation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310059, China

Academic Editor: Vladimir Mityushev

Received: 27 August 2023 Revised: 16 November 2023 Accepted: 27 November 2023 Published: 08 December 2023

An automated storage and retrieval system (AS/RS) is a key component of enterprise logistics. Its performance metrics include, e.g., order fulfillment time and energy consumption. A crane-based automated storage and retrieval system (CB-AS/RS) is used as the study subject in this paper to build a location allocation model with the goal of minimizing order fulfillment time and minimizing energy consumption. The two-objective problem is transformed into a single-objective problem by the weight method. A genetic algorithm (GA) is used to optimize and simulate the model using spatial mapping coding. A permutation-combination heuristics (PCH) is proposed that follows the coding method and cross-operation of the GA and conducts both arrange-operation and change-operation. During the simulation, the influence of different storage utilization rates and different output and input instruction quantities in a batch of orders on the results is considered. Experimental results show that the results of the PCH algorithm are better than the GA and the optimization results are more stable. In this paper, we provide an optimization idea for the CB-AS/RS researchers and managers.
- crane-based automated storage and retrieval system,
- genetic algorithm,
- permutation-combination heuristics,
- order fulfillment time,
- energy consumption,
- location allocation
Citation: Haolan Zhou, Gang Chen, Yujun Lu, Xiaoya Cheng, Hao Xin. A permutation-combination heuristics for crane-based automated storage and retrieval systems considering order fulfillment time and energy consumption[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 116-143. doi: 10.3934/mbe.2024006

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Abstract

An automated storage and retrieval system (AS/RS) is a key component of enterprise logistics. Its performance metrics include, e.g., order fulfillment time and energy consumption. A crane-based automated storage and retrieval system (CB-AS/RS) is used as the study subject in this paper to build a location allocation model with the goal of minimizing order fulfillment time and minimizing energy consumption. The two-objective problem is transformed into a single-objective problem by the weight method. A genetic algorithm (GA) is used to optimize and simulate the model using spatial mapping coding. A permutation-combination heuristics (PCH) is proposed that follows the coding method and cross-operation of the GA and conducts both arrange-operation and change-operation. During the simulation, the influence of different storage utilization rates and different output and input instruction quantities in a batch of orders on the results is considered. Experimental results show that the results of the PCH algorithm are better than the GA and the optimization results are more stable. In this paper, we provide an optimization idea for the CB-AS/RS researchers and managers.

References

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