The three-dimensional single-bin-size bin packing problem: combining metaheuristic and machine learning approaches

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.

System innovation and life cycle thinking in packaging value chain: the circularity of plastics.

Compared to other, plastic materials have registered a strong acceleration in production and consumption during the last years. Despite the existence of waste management systems, plastic_based materials are still a pervasive presence in the environment, with negative consequences on marine ecosystem and human health. The recycling is still challenging due to the growing complexity of product design, the so-called overpackaging, the insufficient and inadequate recycling infrastructure, the weak market of recycled plastics and the high cost of waste treatment and disposal. The Circular economy package, the European Strategy for plastics in a circular economy and the recent European Green Deal include very ambitious programmes to rethink the entire plastic value chain. As regards packaging, all plastic packaging will have to be 100% recyclable (or reusable) and 55% recycled by 2030. Regions are consequently called upon to set up a robust plan able to fit the European objectives. It takes on greater importance in Emilia Romagna where the Packaging valley is located. This thesis supports the definition of a strategy aimed to establish an after-use plastics economy in the region. The PhD work has set the basis and the instruments to establish the so-called Circularity Strategy with the aim to turn about 92.000t of plastic waste into profitable secondary resources. System innovation, life cycle thinking and participative backcasting method have allowed to deeply analyse the current system, orientate the problem and explore sustainable solutions through a broad stakeholder participation. A material flow analysis, accompanied by a barrier analysis, has supported the identification of the gaps between the present situation and the 2030 scenario. Eco-design for and from recycling (and a mass _based recycling rate (based on the effective amount of plastic wastes turned into secondary plastics), valorized by a value_based indicator, are the key-points of the action plan.