Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

Nastasi, Gianluca; Colla, Valentina; Cateni, Silvia; Campigli, Simone

doi:10.1007/s10845-016-1198-x

Cited by 33 publications

(26 citation statements)

References 30 publications

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“…Meanwhile, those cannot be flexibly applied in dynamic storage environment, and cannot guarantee the optimality with the large calculation and the long optimization time, so cannot meet the requirement of real-time application of stacking robot. Only a few research results can be used in stacking storage structure, such as literature [6] improved genetic algorithm and SHAA neural network, aiming to multi-point access complex tasks, which can be modified to apply to the stack robot application, though Even in the drawer-goods-shelf and stacking machine warehouse scenes, scheduling optimization is still the hardest combination optimization because of facing the NP-Hard problems with exponent computational complexity, which is even impossible to solve the optimization in limited time [7].…”

Section: Relational Workmentioning

confidence: 99%

“…Utility can comprehensively consider several indicators (that is, lots of measurements) together and only output one value, it can be expressed as formula (1). According to [6], genetic algorithm and SHAA neural network were used to determine the weight of the indicators. In different test Scenarios, the contribution of a series of indicators was tested for the overall measurement, so that the weight of the indicators can be decided and the maximum throughput of the warehouse can finally be achieved.…”

Section: Utility On the Stack Mixing-degree Indicatorsmentioning

confidence: 99%

“…At first we used the scheduling optimization model and algorithm from [6] as the solve solution. The utility function of [6] can be expressed as formula (2).…”

Section: Utility On the Stack Mixing-degree Indicatorsmentioning

confidence: 99%

“…The utility function of [6] can be expressed as formula (2). The meaning is the same as formula (1).…”

Section: Utility On the Stack Mixing-degree Indicatorsmentioning

confidence: 99%

“…However, [6] did not show good performance at stacking storage scheduling, so we changed the utility function, and converted [6] as the comparing. We only used the genetic algorithm of [6].…”

Section: Utility On the Stack Mixing-degree Indicatorsmentioning

confidence: 99%

See 4 more Smart Citations

Stacking Storage Scheduling Optimization for Profile Automated Warehouse Robot Based on Mixing-degree

Zhang¹,

Lin²,

Tsai³

et al. 2017

dtetr

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Abstract.Stacking storage structure has to be used in profile automated warehouse, which results that the above ones of a rack must to be removed before the rack moving. So the delay of the procedure increases. Aiming at the problem, we firstly defined the mixing-degree of the whole stacking-storage as an important indicator of warehouse wholeness, which can characterize warehouse long-term efficiency. Secondly, a utility function was built to describe the measurement of warehouse status based on the mixing-degree. Finally a scheduling optimization model on constraints of the initialization condition was proposed, with always calculating the warehouse mixing-degree and top statuses. Simulation and field experiment on real Nanping AL. Robots system shows that the proposed algorithm achieves good performance and efficiency in long-term, though no optimization in short-term, which roots from less average expectation of the top-racks moves.

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Section: Relational Workmentioning

confidence: 99%

Section: Utility On the Stack Mixing-degree Indicatorsmentioning

confidence: 99%

“…At first we used the scheduling optimization model and algorithm from [6] as the solve solution. The utility function of [6] can be expressed as formula (2).…”

Section: Utility On the Stack Mixing-degree Indicatorsmentioning

confidence: 99%

“…The utility function of [6] can be expressed as formula (2). The meaning is the same as formula (1).…”

Section: Utility On the Stack Mixing-degree Indicatorsmentioning

confidence: 99%

Section: Utility On the Stack Mixing-degree Indicatorsmentioning

confidence: 99%

See 3 more Smart Citations

Stacking Storage Scheduling Optimization for Profile Automated Warehouse Robot Based on Mixing-degree

Zhang¹,

Lin²,

Tsai³

et al. 2017

dtetr

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Quantum-Inspired Evolutionary Multiobjective Optimization for a Dynamic Production Scheduling Approach

Fiasché

Liberati

Gualandi³

et al. 2017

Smart Innovation, Systems and Technologies

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Knowledge discovery and predictive accuracy comparison of different classification algorithms for mould level fluctuation phenomenon in thin slab caster

Roy

Tiwari

2016

J Intell Manuf

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Mould level fluctuation (MLF) is one of the main reasons for surface defects in continuously cast slabs. In these study first, large scale mould level fluctuations has been categorized in three different cases based on actual plant data. Moreover, theoretical formulation has been investigated to better understand the underlying physics of flow. Next, exploratory data analysis is used for preliminary investigation into the phenomenon based on actual plant data. Finally, different classification algorithms were used to classify non-mould level fluctuation cases from MLF cases for two different scenarios-one where all mould level fluctuation cases are considered and in another where only a particular case of mould level fluctuation is considered. Classification algorithm such as recursive partitioning, random forest etc. has been used to identify different casting parameters affecting the phenomenon of mould level fluctuation. 70 % of the dataset used as training dataset and rest 30 % as the testing dataset. Prediction accuracy of these different classification algorithms along with an ensemble model has been compared on a completely unseen test set. Ladle change operation and superheat temperature has been identified as process parameters influencing the phenomenon of large scale mould level fluctuations.

show abstract

Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

Cited by 33 publications

References 30 publications

Stacking Storage Scheduling Optimization for Profile Automated Warehouse Robot Based on Mixing-degree

Stacking Storage Scheduling Optimization for Profile Automated Warehouse Robot Based on Mixing-degree

Quantum-Inspired Evolutionary Multiobjective Optimization for a Dynamic Production Scheduling Approach

Knowledge discovery and predictive accuracy comparison of different classification algorithms for mould level fluctuation phenomenon in thin slab caster

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