Optimization of Storage Allocation for a Robotic Mobile Fulfillment System Considering Multiple Workstations

This paper explores a storage allocation problem for the Robotic Mobile Fulfillment System (RMFS) considering the situation of multiple workstations. A mixed integer programming model is formulated as the joint optimization of storage allocation and mobile rack scheduling (SAMRS), to minimize the to...

Full description

Saved in:
Bibliographic Details
Main Authors: Xu, Zhenqi, Ding, Zhenhao, Weng, Wei, Zheng, Meimei
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Adaptive Large Neighborhood Search
Greedy algorithms
Integer programming
Multiple workstations
Publishing
Resource management
RMFS
Robots
Search problems
Simulated annealing
Storage allocation optimization
Surges
Workstations
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper explores a storage allocation problem for the Robotic Mobile Fulfillment System (RMFS) considering the situation of multiple workstations. A mixed integer programming model is formulated as the joint optimization of storage allocation and mobile rack scheduling (SAMRS), to minimize the total distance AGV traveled. Then, an efficient meta-heuristic based on Adaptive Large Neighborhood Search and Greedy Algorithm (ALNS-GA) is proposed to solve the model. Based on the data from the distribution center of a publishing house in Shanghai, we conduct a case study to demonstrate the effectiveness of the algorithm. In small-scale cases, ALNS-GA is faster and more stable than GUROBI while ensuring optimality. In large-scale cases, ALNS-GA is far better than the two random methods.
ISSN:2161-8089
DOI:10.1109/CASE59546.2024.10711726