Pareto and Niche Genetic Algorithm for Storage Location Assignment Optimization Problem

Li, Meijuan; Chen, Xuebo; Liu, Chenqi

doi:10.1109/icicic.2008.655

Cited by 21 publications

(10 citation statements)

References 11 publications

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“…[10] apply local search algorithms for reorganizing the products in the warehouse to keep it operating efficiently. [11] propose a multi-objective genetic algorithm for optimizing the storage assignment problem in automated storage/retrieval warehouses.…”

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

“…For example, tabu search has shown its capability in making dynamic operation policies for product relocation [6]. Genetic algorithms have also been applied to address different storage location problems [20] [21] [22]. To achieve promising performance, a number of problem-specific search operators for the heuristic methods have been developed based on the insights of the characteristics of the problems.…”

Section: Related Studiesmentioning

confidence: 99%

A Bi-Level Optimization Model for Grouping Constrained Storage Location Assignment Problems

Xie

Mei

Ernst

et al. 2018

IEEE Trans. Cybern.

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In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method.

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“…However, a disadvantage of the proposed algorithm is the appearance of unnecessary small displacements of products. Li et al (2008) proposed a multi-objective mathematical model for storage location assignment. Two objectives are considered.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Pareto-Based Multi-Objective Optimization for Two-Block Class-Based Storage Warehouse Design

Sooksaksun¹

2012

Industrial Engineering and Management Systems

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This research proposes a Pareto-based multi-objective optimization approach to class-based storage warehouse design, considering a two-block warehouse that operates under the class-based storage policy in a low-level, picker-to-part and narrow aisle warehousing system. A mathematical model is formulated to determine the number of aisles, the length of aisle and the partial length of each pick aisle to allocate to each product class that minimizes the travel distance and maximizes the usable storage space. A solution approach based on multiple objective particle swarm optimization is proposed to find the Pareto front of the problems. Numerical examples are given to show how to apply the proposed algorithm. The results from the examples show that the proposed algorithm can provide design alternatives to conflicting warehouse design decisions.

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Pareto and Niche Genetic Algorithm for Storage Location Assignment Optimization Problem

Cited by 21 publications

References 11 publications

A Case Study on Optimization of Warehouses

A Case Study on Optimization of Warehouses

A Bi-Level Optimization Model for Grouping Constrained Storage Location Assignment Problems

Pareto-Based Multi-Objective Optimization for Two-Block Class-Based Storage Warehouse Design

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