Optimization of Location Assignment for Unit-Load AS/RS with a Dual-Shuttle

Çunkaş, Mehmet

doi:10.18201/ijisae.2019252783

“…One may clearly see that, for k e [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16], the dimensions of t p and t v increase while those of t h decrease as the number of operations increases.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, [3] performed a multi-objective optimization for the design of a shuttle-based automated storage/retrieval system using the non-dominated sorting genetic algorithm II. With regard to [6] they carried out a particle swarm optimization (PSO) to solve the problem of assigning a double-shuttle AS/RS to a unit load.…”

Section: State Of the Artmentioning

confidence: 99%

Multi-shuttle AS/RS dimensions optimization using a genetic algorithm—case of the multi-aisle configuration

Fandi

¹

,

Kouloughli

²

,

Ghomri

³

2022

Int J Adv Manuf Technol

5

0

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Over the past few years, automated storage and retrieval systems (AS/RSs) have been increasingly improving. It is worth mentioning that multi-shuttle storage/retrieval (S/R) machines were gradually introduced to the market some years ago. These machines, which possess a high speed of execution, are able to transport several pallets at a time during the same trip, as opposed to single-shuttle S/R machines which can carry only one pallet at a time. It should be noted that the installation of this type of system requires a significant financial investment, and therefore it is highly recommended that this system be well studied and designed prior to its installation. It is widely acknowledged that one of the most important objectives while designing an AS/RS is to achieve the shortest time for one single cycle. The present work aims at designing an AS/RS with optimal dimensions for the purpose of minimizing the time in a multi-cycle implementation. To do this, it was decided to consider a multi-aisle automated storage/ retrieval system (AS/RS) with a multi-shuttle S/R machine. In addition, a genetic algorithm (GA) was used for the optimization of the system.

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“…One may clearly see that, for k e [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16], the dimensions of t p and t v increase while those of t h decrease as the number of operations increases. In addition, the approximate functions of t p and t h are determined with the least squares method, using the LeastSquares command in Maple.…”

Section: Resultsmentioning

confidence: 99%

Multi-shuttle AS/RS Dimensions Optimization Using a Genetic Algorithm – Case of the Multi-aisle Configuration

Fandi

¹

,

Kouloughli

²

,

Ghomri

³

2021

Preprint

0

View full text Add to dashboard Cite

Over the past few years, automated storage and retrieval systems (AS/RSs) have been increasingly improving. It is worth mentioning that multi-shuttle storage/retrieval (S/R) machines were gradually introduced to the market some years ago. These machines, which possess a high speed of execution, are able to transport several pallets at a time during the same trip, as opposed to single-shuttle S/R machines which can carry only one pallet at a time. It should be noted that the installation of this type of system requires a significant financial investment, and therefore it is highly recommended that this system be well studied and designed prior to its installation. It is widely acknowledged that one of the most important objectives while designing an AS/RS is to achieve the shortest time for one single cycle. The present work aims at designing an AS/RS with optimal dimensions for the purpose of minimizing the time in a multi-cycle implementation. To do this, it was decided to consider a multi-aisle automated storage/ retrieval system (AS/RS) with a multi-shuttle S/R machine. In addition, a genetic algorithm (GA) was used for the optimization of the system.

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“…Twelve different layouts of the warehouse were used to investigate the effectiveness of the hybrid model. It was found that the hybrid model outperformed the standard ACO and GA. Cunkas and Ozer (2019) [28] optimized the assignment of storage positions for SKUs in a unit-load AS/RS. The S/R machines of the AS/RS are equipped with dual shuttles and use the quadruple command cycle.…”

Section: Metaheuristicsmentioning

confidence: 99%

Hybridizing Whale Optimization Algorithm With Particle Swarm Optimization for Scheduling a Dual-Command Storage/Retrieval Machine

Hsu¹,

Wang

²

2023

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Whale optimization algorithm (WOA) and particle swarm optimization (PSO) have been used individually usually. However, a separate use of them has a limitation. Hybridizing WOA with PSO is expected to evolve solutions better due to the cooperation between whales and seabirds. Developing such kind of model is the focus of this research. A framework has been further proposed to best utilize such hybridizations for developing simulation-based optimization approaches. The framework has the advantage of integrating metaheuristic, simulation, and optimization seamlessly. It can waive the rigorous and laborintensive optimization procedure required for traditional simulation. In this research, simulation-based optimization approaches are used to deal with the dual-command block scheduling problem of a manufacturing firm's storage/retrieval (S/R) machine in an automated storage/retrieval system. The S/R machine is mainly used to store/retrieve stock-keeping units in an automated storage/retrieval system. Three simulation-based optimization approaches, Hybrid1 (WOA+PSO), Hybrid2 (WOA+PSO), and Hybrid3 (WOA+PSO), have been developed. To investigate their effectiveness, experiments have been conducted to compare them with their base models, WOA and PSO, as well as the genetic algorithm (GA) and PWOA. The PWOA is an abbreviation of a hybridization of PSO and WOA proposed in a previous study. The experimental results show that Hybrid3 (WOA+PSO) outperforms Hybrid2 (WOA+PSO), Hybrid1 (WOA+PSO), WOA, PSO, PWOA, and GA. The uses of techniques such as hybridization, Neighborhood heuristic, and adaptive movements of whales empower Hybrid3 (WOA+PSO) the most.

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Optimization of Location Assignment for Unit-Load AS/RS with a Dual-Shuttle

Cited by 8 publications

References 17 publications

Multi-shuttle AS/RS dimensions optimization using a genetic algorithm—case of the multi-aisle configuration

Multi-shuttle AS/RS dimensions optimization using a genetic algorithm—case of the multi-aisle configuration

Multi-shuttle AS/RS Dimensions Optimization Using a Genetic Algorithm – Case of the Multi-aisle Configuration

Hybridizing Whale Optimization Algorithm With Particle Swarm Optimization for Scheduling a Dual-Command Storage/Retrieval Machine

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