Abstract
Double-deep multi-tier shuttle warehousing systems (DMSWS) have been increasingly applied for store-and-retrieval stock-keeping unit tasks, with the advantage of a reduced number of aisles and improved space utilization. Scheduling different devices for retrieval tasks to increase system efficiency is an important concern. In this paper, a Pareto optimization model of task operations based on the cycle time and carbon emissions is presented. The impact of the rearrangement operation is considered in this model. The cycle time model is converted into a flow-shop scheduling model with parallel machines by analyzing the retrieval operation process. Moreover, the carbon emissions of the shuttle in the waiting process, the carbon emissions of the lift during the free process, and the carbon emissions of the retrieval operation are considered in the carbon emissions model, which can help us to evaluate the carbon emissions of the equipment more comprehensively during the entire retrieval task process. The elitist non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve the non-linear multi-objective optimization function. Finally, a real case is adopted to illustrate the findings of this study. The results show that this method can reduce carbon emissions and improve system efficiency. In addition, it also help managers to reduce operational costs and improve the utilization of shuttles.
Highlights
With the rapid development of automation technology and the related economy, the warehousing industry has gradually become a key link in economic activities [1].Warehousing systems are mainly used to store and retrieve materials and commodities
The aforementioned analysis of deep multi-tier shuttle warehousing systems (DMSWS) indicates that the total retrieval operation time, the total carbon emissions and the total waiting time of shuttles reflect the efficiency of the DMSWS from different perspectives
The task scheduling problem was studied for DMSWS, which have high space utilization
Summary
With the rapid development of automation technology and the related economy, the warehousing industry has gradually become a key link in economic activities [1]. There is a clear necessity to study DMSWS, especially in terms of task scheduling, which significantly affects the system efficiency This observation motivated us to examine the task scheduling problem of double-deep multi-tier shuttle warehousing systems. The task scheduling problem of DMSWS is different from TSP, as storage and retrieval tasks can be executed in parallel by multi-shuttles. Most previous studies on task scheduling in AS/RS and its variant have focused on TSP and its solution algorithms This case differs from DMSWS because multiple shuttles perform storage and retrieval tasks in parallel. We provide a multi-objective optimization model for task scheduling by optimizing the total working time, waiting time and carbon emissions. The cycle time model and carbon emissions model are proposed, while Section 5 presents the NSGA-II to solve the task scheduling problem.
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