Ant colony optimization for automated storage and retrieval system

Xing, Bo; Gao, Wenjing; Nelwamondo, Fulufhelo V.; Battle, Kimberly; Marwala, Tshilidzi

doi:10.1109/cec.2010.5586237

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…[17] present an integer programming model for optimizing the order picking problem. [18] propose an Max-Min Ant System (MMAS) algorithm for optimizing machine travel paths in automated storage/retrieval warehouses. [19] propose an ACO algorithm that detects congestion situations that arise when multiple order pickers traverse the same pick aisle simultaneously.…”

Section: Related Workmentioning

confidence: 99%

A Case Study on Optimization of Warehouses

Müller¹,

Krämer²,

Kounev³

et al. 2021

Preprint

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In warehouses, order picking is known to be the most labor-intensive and costly task in which the employees account for a large part of the warehouse performance. Hence, many approaches exist, that optimize the order picking process based on diverse economic criteria. However, most of these approaches focus on a single economic objective at once and disregard ergonomic criteria in their optimization. Further, the influence of the placement of the items to be picked is underestimated and accordingly, too little attention is paid to the interdependence of these two problems. In this work, we aim at optimizing the storage assignment and the order picking problem within mezzanine warehouse with regards to their reciprocal influence. We propose a customized version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing the storage assignment problem as well as an Ant Colony Optimization (ACO) algorithm for optimizing the order picking problem. Both algorithms incorporate multiple economic and ergonomic constraints simultaneously. Furthermore, the algorithms incorporate knowledge about the interdependence between both problems, aiming to improve the overall warehouse performance. Our evaluation results show that our proposed algorithms return better storage assignments and order pick routes compared to commonly used techniques for the following quality indicators for comparing Pareto fronts: Coverage, Generational Distance, Euclidian Distance, Pareto Front Size, and Inverted Generational Distance. Additionally, the evaluation regarding the interaction of both algorithms shows a better performance when combining both proposed algorithms.

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Section: Related Workmentioning

confidence: 99%

A Case Study on Optimization of Warehouses

Müller¹,

Krämer²,

Kounev³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The concept of equal utility is illustrated in Figure 1. When two decisions have the same utility, in optimization language is called multiple optimum solutions (Xing et. al., 2010).…”

Section: Is a Single Goal Optimization Subjective?mentioning

confidence: 99%

Is machine rationality subjective?

Marwala¹

2021

Rational Machines and Artificial Intelligence

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“…A multiobjective evolutionary algorithm is proposed by Nastasi to solve a management problem of automated warehouse [9]. Xing exploited ant colony optimization algorithm (ACO) to solve the picking path optimization of storage and retrieval machines [10]. Xin proposed the problem of multi-robot coordinated path planning in intelligent warehouse [11].…”

mentioning

confidence: 99%

A Novel Maximin-Based Multi-Objective Evolutionary Algorithm Using One-by-One Update Scheme for Multi-Robot Scheduling Optimization

Yang

Zhang

et al. 2021

IEEE Access

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With the continuous development of E-commerce, warehouse logistics is also facing emerging challenges, including more batches of orders and shorter order processing cycles. When more orders need to be processed simultaneously, some existing task scheduling methods may not be able to give a suitable plan, which delays order processing and reduces the efficiency of the warehouse. Therefore, the intelligent warehouse system that uses autonomous robots for automated storage and intelligent order scheduling is becoming mainstream. Based on this concept, we propose a multi-robot cooperative scheduling system in the intelligent warehouse. The aim of the multi-robot cooperative scheduling system of the intelligent storage is to drive many robots in an intelligent warehouse to perform the distributed tasks in an optimal (e.g., time-saving and energy-conserved) way. In this paper, we propose a multi-robot cooperative task scheduling model in the intelligent warehouse. For this model, we design a maximin-based multi-objective algorithm, which uses a one-by-one update scheme to select individuals. In this algorithm, two indicators are devised to discriminate the equivalent individuals with the same maximin fitness value in the environmental selection process. The results on benchmark test suite show that our algorithm is indeed a useful optimizer. Then it is applied to settle the multi-robot scheduling problem in the intelligence warehouse. Simulation experiment results demonstrate the efficiency of the proposed algorithm on the realworld scheduling problem. INDEX TERMS Many-objective optimization; Multi-objective optimization; Maximin fitness function;One-by-one update scheme; Multi-robot scheduling optimization I. INTRODUCTIONRecently, with the widespread application of autonomous robots, these cheap, small and smart robots have been widely employed in the intelligent storage management of the logistic industry [1]. In principle, the basic task of the warehouse system in the intelligent storage management is to transport goods, store goods and distribute goods efficiently [2]. Accordingly, the multi-robot coordination mechanism, if being really efficient and robust, it will make the entire intelligent warehouse management system more

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Ant colony optimization for automated storage and retrieval system

Cited by 11 publications

References 8 publications

A Case Study on Optimization of Warehouses

A Case Study on Optimization of Warehouses

Is machine rationality subjective?

A Novel Maximin-Based Multi-Objective Evolutionary Algorithm Using One-by-One Update Scheme for Multi-Robot Scheduling Optimization

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