Ancora, Gabriele
(2022)

*The three-dimensional single-bin-size bin packing problem: combining metaheuristic and machine learning approaches*, [Dissertation thesis], Alma Mater Studiorum Università di Bologna.
Dottorato di ricerca in

Ingegneria biomedica, elettrica e dei sistemi, 34 Ciclo. DOI 10.48676/unibo/amsdottorato/10476.

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## Abstract

The Three-Dimensional Single-Bin-Size Bin Packing Problem is one of the most studied problem in the Cutting & Packing category. From a strictly mathematical point of view, it consists of packing a finite set of strongly heterogeneous “small” boxes, called items, into a finite set of identical “large” rectangles, called bins, minimizing the unused volume and requiring that the items are packed without overlapping. The great interest is mainly due to the number of real-world applications in which it arises, such as pallet and container loading, cutting objects out of a piece of material and packaging design. Depending on these real-world applications, more objective functions and more practical constraints could be needed. After a brief discussion about the real-world applications of the problem and a exhaustive literature review, the design of a two-stage algorithm to solve the aforementioned problem is presented. The algorithm must be able to provide the
spatial coordinates of the placed boxes vertices and also the optimal boxes input sequence, while guaranteeing geometric, stability, fragility constraints and a reduced computational time. Due to NP-hard complexity of this type
of combinatorial problems, a fusion of metaheuristic and machine learning techniques is adopted. In particular, a hybrid genetic algorithm coupled with a feedforward neural network is used. In the first stage, a rich dataset is created starting from a set of real input instances provided by an industrial company and the feedforward neural network is trained on it. After its training, given a new input instance, the hybrid genetic algorithm is able to run using the neural network output as input parameter vector, providing as output the optimal solution. The effectiveness of the proposed works is confirmed via several experimental tests.

Abstract

The Three-Dimensional Single-Bin-Size Bin Packing Problem is one of the most studied problem in the Cutting & Packing category. From a strictly mathematical point of view, it consists of packing a finite set of strongly heterogeneous “small” boxes, called items, into a finite set of identical “large” rectangles, called bins, minimizing the unused volume and requiring that the items are packed without overlapping. The great interest is mainly due to the number of real-world applications in which it arises, such as pallet and container loading, cutting objects out of a piece of material and packaging design. Depending on these real-world applications, more objective functions and more practical constraints could be needed. After a brief discussion about the real-world applications of the problem and a exhaustive literature review, the design of a two-stage algorithm to solve the aforementioned problem is presented. The algorithm must be able to provide the
spatial coordinates of the placed boxes vertices and also the optimal boxes input sequence, while guaranteeing geometric, stability, fragility constraints and a reduced computational time. Due to NP-hard complexity of this type
of combinatorial problems, a fusion of metaheuristic and machine learning techniques is adopted. In particular, a hybrid genetic algorithm coupled with a feedforward neural network is used. In the first stage, a rich dataset is created starting from a set of real input instances provided by an industrial company and the feedforward neural network is trained on it. After its training, given a new input instance, the hybrid genetic algorithm is able to run using the neural network output as input parameter vector, providing as output the optimal solution. The effectiveness of the proposed works is confirmed via several experimental tests.

Tipologia del documento

Tesi di dottorato

Autore

Ancora, Gabriele

Supervisore

Co-supervisore

Dottorato di ricerca

Ciclo

34

Coordinatore

Settore disciplinare

Settore concorsuale

Parole chiave

packing, genetic algorithms, metaheuristics, machine learning, neural networks

URN:NBN

DOI

10.48676/unibo/amsdottorato/10476

Data di discussione

7 Novembre 2022

URI

## Altri metadati

Tipologia del documento

Tesi di dottorato

Autore

Ancora, Gabriele

Supervisore

Co-supervisore

Dottorato di ricerca

Ciclo

34

Coordinatore

Settore disciplinare

Settore concorsuale

Parole chiave

packing, genetic algorithms, metaheuristics, machine learning, neural networks

URN:NBN

DOI

10.48676/unibo/amsdottorato/10476

Data di discussione

7 Novembre 2022

URI

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