The essence of 3D object packing problem is to load a set of distinct boxes with given dimensions in containers to maximize volume utilization. The goal of this research is to support with algorithmic techniques the design of package layouts that meet the functional relationships between the parts and match market needs including cost, safety, comfort, as well as economic and environmental aspects. An analysis of 3D object packing algorithms, created by foreign authors, and commercial three-dimensional pallet packing software packages was performed in this work; also methods’ advantages and disadvantages were pointed out. A model of three-dimensional rectangular object packing into containers of the same shape was formed, and the software solving 3D object packing problems was created according to this model. The tests proved that the suggested method is effective and beneficial.

Šio darbo tikslas – sukurti trimačių dėžių su papildomu orientacijos erdvėje apribojimu pakavimo optimizavimo algoritmą, o realizavus jį programinėmis priemonėmis – ištirti jo efektyvumą su atsitiktinai sugeneruotų kraunamų dėžių rinkiniais ir prie skirtingų algoritmo veikimo parametrų. Taip pat sukurti ir pakavimo optimizavimo sprendinio vizualizavimo trimatėje erdvėje programinę įrangą. / Presented work covers one of the most complex areas of combinatorial optimization – three dimensional bin packing problem. Solution methods of this problem are applied in the real world from logistics, packing optimization to VLSI circuit and automobile engineering. Several heuristic packing algorithms suggested by other authors are analyzed. Approach based on tree-search and wall building strategy is chosen to create a 3D packing optimization algorithm. A bin orientation in space restriction is added to classical 3D bin packing problem to make it more complex and more suited for real world applications. A prototype of created algorithm is created and tested with randomly generated data collections. Each data sample is processed with and without bin orientation in space restriction. Influence of restriction and maximal tree width on packing efficiency and computational time is statistically analyzed. Visualization tool based on Microsoft Direct X technology is created to view results of packing optimization.

In modern day logistics, small and medium sized consumer goods are often manually handled and packaged in fulfillment centres and warehouses. When time efficiency is prioritised, there exists the risk to pick too big a package and thus waste space which can naturally lead to unwanted consequences such as unnecessary transports being booked and excess package material being used. This in turn leads to cost inefficiency and increased environmental impact. One novel way of tackling this issue is to use algorithms made to solve optimization problems such as theff3D bin packing problem to pre-calculate which package to use when assembling an order. However, as fill rates improve, it may become difficult for warehouse workers to fit all items into the selected package, especially when dealing with a larger number of items. To help with this, modern techniques such as spatial augmented reality can be used to visualize the packing process in real time, showing workers which item to place next, and where. Recent studies have shown improved packing speeds using this approach, however further improvements could potentially optimize the usability even further. In this study, a novel spatial AR application was used in conjunction with a depth camera to not only indicate where to place items, but to progress the instructions automatically only if they were placed correctly. The aid-tool was tested on 26 participants and showed significant improvements by a factor of 3.29 for a fill rate of 90%, slight but insignificant improvements by a factor of 1.18 for a fill rate of 85% and performed worse by a factor of 0.71 for a fill rate of 81% when compared against unassisted packing. The study concluded that the chosen spatial AR tool showed promise for fill rates of 90% and above, but the results should be treated with caution as the sample size and methodological flaws severely limited the scope of the findings. / Inom modern logistik skickas små och medelstora konsumtionsvaror ofta via manuella packflöden i terminaler och lager. När tidseffektivitet prioriteras så finns risken att ett för stort paket väljs vilket leder till tomt extautrymme och därmed oönskade konsekvenser såsom onödiga transporter och överflödigt packmaterial. Detta leder i sin tur till kostnadsineffektivitet och miljöpåverkan. Ett nytt sätt att hantera detta problem är att använda algoritmer som löser optimeringsproblem såsom 3D-lådpackningsproblemet för att på förhand räkna ut vilket paket som ska användas när en order packas. Dock kan det bli svårt för lagerarbetarna att passa in alla varor i det utvalda paketet när fyllnadsgraden blir högre, särskilt när antalet varor är högt. För att förenkla detta kan moderna tekniker så som spatial augmented reality användas för att visualisera packningsprocessen i realtid för packaren, och visa vilken vara som skall placeras härnäst, samt var. Nylig forskning har påvisat förbättrade packningstider med detta tillvägagångssätt, men vidare förbättringar skulle potentiellt kunna optimera användarvänligheten ytterligare. I denna studie utvecklades ett nytt spatialt AR-verktyg som användes i kombination med en djupkamera för att inte enbart visa var varor skulle placeras utan dessutom enbart stega framåt i instruktionerna då de placerats korrekt. Hjälpmedelsverktyget testades på 26 deltagare och visade en signifikant förbättring med en faktor av 3.29 för en 90%:ig fyllnadsgrad, en liten men insignifikant förbättring med faktor 1.18 för en fyllnadsgrad på 85% samt presterade sämre med en faktor 0.71 för en 81%:ig fyllnadsgrad i jämförelse med oassisterad packning. Studien kom fram till att det valda AR-hjälpmedlet visade lovande resultat för fyllnadsgrader inkl. och över 90%, men resultaten bör hanteras med försiktighet då en liten urvalsstorlek och metodologiska brister kraftigt begränsade resultatens styrka.

Whether you are in industrial manufacturing striving to optimize your supply chain, international or domestic carrier committed to lower your operational costs, retailer dedicated to run your distribution network more efficiently, or anywhere else where the words "cargo", "freight", "shipment" are in your business language, automated load planning and optimization will significantly improve your business process. While the concept of computerized simulation of 3D load building is not new, with some companies offering software solutions for "virtual" loading, no one has a single product that is capable to handle complex variety of business rules and constraints of the modern transportation industry. We are analyzing the problems related with 3D box loading and then to analyze existing 3D load building systems. After accumulating all analyzes data we are having enough experience to create our own algorithm. And we did it! We create 3D box loading algorithm witch is fast and accurate and 3D box loading system which is using this new algorithm. To ensure that we succeeded we compare this new system with existing 3D box loading software to make sure that it’s really useful for solving logistic problems. After testing we are sure that we made a big job and we have created system, which can compete with the logistic IT leaders made solutions.

This thesis explores the integration of IoT protocols to enhance supply chain efficiency and sustainability by developing a flexible automated system. The research covers the integration of a palletization optimizer with a flexible robotic platform, a project conducted in collaboration with OpiFlex and Linköping University. Flexibility and sustainability in production, particularly in the food and beverage industry, are critical yet challenging to achieve. This research addresses these challenges by proposing a system that aligns the output with customer needs by combining these technologies. The research employs a combination of case study and exploratory methodologies. The development approach synthesizes elements from Set-Based Design, Point-Based Design, and Agile development frameworks. The primary research questions focus on identifying the best system architecture for integrating the palletization optimizer with a lower-level automation platform and outlining the steps needed to transform this integration into a commercially viable product. The system includes the optimizer, capable of processing customer orders and configuring products on mixed output pallets, integrated with a flexible robotic system provided by OpiFlex. The work involved evaluating communication protocols, MQTT, OPC UA, and TCP/IP, and designing robust interactions and interfaces between the subsystems. The results demonstrate the system's architecture and interaction protocols.  The thesis concludes with a discussion of the results in comparison to the application scenario and the standards consulted. The conclusion is that the chosen interface practices should remain largely intact but be re-developed using an OPC UA-based architecture. The main reasons for this are its support for both pub/sub and client-server models, increased security, and greater support for enterprise application integration. However, depending on the specific application, the downsides of OPC UA may outweigh its benefits.