Modelling and Optimisation of Space Allocation and layout Problems

This thesis investigates the development of optimisation-based, decision-making frameworks for allocation problems related to manufacturing, warehousing, logistics, and retailing. Since associated costs with these areas constitute significant parts to the overall supply chain cost, mathematical models of enhanced fidelity are required to obtain optimal decisions for i) pallet loading, ii) assortment, and iii) product shelf space, which will be the main research focus of this thesis. For the Manufactures Pallet loading problems (MPLP), novel single- and multi-objective Mixed Integer Linear Programming (MILP) models have been proposed, which generate optimal layouts of improved 2D structure based on a block representation. The approach uses a Complexity Index metric, which aids in comparing 2 pallet layouts that share the same pallet size and number of boxes loaded but with different box arrangements. The proposed algorithm has been tested against available data-sets in literature. In the area of Assortments (optimal 2D packing within given containers) , an iterative MILP algorithm has been developed to provide a diverse set of solutions within pre-specified range of key performance metrics. In addition, a basic software prototype, based on AIMMS platform, has been developed using a user-friendly interface so as to facilitate user interaction with a visual display of the solutions obtained. In Shelf- Space Allocation (SSAP) problem, the relationship between the demand and the retailer shelf space allocated to each item is defined as space elasticity. Most of existing literature considers the problem with stationary demand and fixed space elasticities. In this part of the thesis, a dynamic framework has been proposed to forecast space elasticities based on historical data using standard time-series methodologies. In addition, an optimisation mathematical model has been implemented using the forecasted space elasticities to provide the retailer with optimal shelf space thus resulting into closer match between supply and demand and increased profitability. The applicability and effectiveness of the proposed framework is demonstrated through a number of tests and comparisons against literature data-sets

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Operational Research and Machine Learning Applied to Transport Systems

The New Economy, environmental sustainability and global competitiveness drive inno- vations in supply chain management and transport systems. The New Economy increases the amount and types of products that can be delivered directly to homes, challenging the organisation of last-mile delivery companies. To keep up with the challenges, deliv- ery companies are continuously seeking new innovations to allow them to pack goods faster and more efficiently. Thus, the packing problem has become a crucial factor and solving this problem effectively is essential for the success of good deliveries and logistics. On land, rail transportation is known to be the most eco-friendly transport system in terms of emissions, energy consumption, land use, noise levels, and quantities of people and goods that can be moved. It is difficult to apply innovations to the rail industry due to a number of reasons: the risk aversion nature, the high level of regulations, the very high cost of infrastructure upgrades, and the natural monopoly of resources in many countries. In the UK, however, in 2018 the Department for Transport published the Joint Rail Data Action Plan, opening some rail industry datasets for researching purposes. In line with the above developments, this thesis focuses on the research of machine learning and operational research techniques in two main areas: improving packing operations for logistics and improving various operations for passenger rail. In total, the research in this thesis will make six contributions as detailed below. The first contribution is a new mathematical model and a new heuristic to solve the Multiple Heterogeneous Knapsack Problem, giving priority to smaller bins and consid- ering some important container loading constraints. This problem is interesting because many companies prefer to deal with smaller bins as they are less expensive. Moreover, giving priority to filling small bins (rather than large bins) is very important in some industries, e.g. fast-moving consumer goods. The second contribution is a novel strategy to hybridize operational research with ma- chine learning to estimate if a particular packing solution is feasible in a constant O(1) computational time. Given that traditional feasibility checking for packing solutions is an NP-Hard problem, it is expected that this strategy will significantly save time and computational effort. The third contribution is an extended mathematical model and an algorithm to apply the packing problem to improving the seat reservation system in passenger rail. The problem is formulated as the Group Seat Reservation Knapsack Problem with Price on Seat. It is an extension of the Offline Group Seat Reservation Knapsack Problem. This extension introduces a profit evaluation dependent on not only the space occupied, but also on the individual profit brought by each reserved seat. The fourth contribution is a data-driven method to infer the feasible train routing strate- gies from open data in the United Kingdom rail network. Briefly, most of the UK network is divided into sections called berths, and the transition point from one berth to another is called a berth step. There are sensors at berth steps that can detect the movement when a train passes by. The result of the method is a directed graph, the berth graph, where each node represents a berth and each arc represents a berth-step. The arcs rep- resent the feasible routing strategies, i.e. where a train can move from one berth. A connected path between two berths represents a connected section of the network. The fifth contribution is a novel method to estimate the amount of time that a train is going to spend on a berth. This chapter compares two different approaches, AutoRe- gressive Moving Average with Recurrent Neural Networks, and analyse the pros and cons of each choice with statistical analyses. The method is tested on a real-world case study, one berth that represent a busy junction in the Merseyside region. The sixth contribution is an adaptive method to forecast the running time of a train journey using the Gated Recurrent Units method. The method exploits the TD’s berth information and the berth graph. The case-study adopted in the experimental tests is the train network in the Merseyside region

Improved Cardinality Bounds for Rectangle Packing Representations

Axis-aligned rectangle packings can be characterized by the set of spatial relations that hold for pairs of rectangles (west, south, east, north). A representation of a packing consists of one satisfied spatial relation for each pair. We call a set of representations complete for n ∈ ℕ if it contains a representation of every packing of any n rectangles. Both in theory and practice, fastest known algorithms for a large class of rectangle packing problems enumerate a complete set R of representations. The running time of these algorithms is dominated by the (exponential) size of R. In this thesis, we improve the best known lower and upper bounds on the minimum cardinality of complete sets of representations. The new upper bound implies theoretically faster algorithms for many rectangle packing problems, for example in chip design, while the new lower bound imposes a limit on the running time that can be achieved by any algorithm following this approach. The proofs of both results are based on pattern-avoiding permutations. Finally, we empirically compute the minimum cardinality of complete sets of representations for small n. Our computations directly suggest two conjectures, connecting well-known Baxter permutations with the set of permutations avoiding an apparently new pattern, which in turn seem to generate complete sets of representations of minimum cardinality

RoCKIn@Work: Industrial Robot Challenge

RoCKIn@Work was focused on benchmarks in the domain of industrial robots. Both task and functionality benchmarks were derived from real world applications. All of them were part of a bigger user story painting the picture of a scaled down real world factory scenario. Elements used to build the testbed were chosen from common materials in modern manufacturing environments. Networked devices, machines controllable through a central software component, were also part of the testbed and introduced a dynamic component to the task benchmarks. Strict guidelines on data logging were imposed on participating teams to ensure gathered data could be automatically evaluated. This also had the positive effect that teams were made aware of the importance of data logging, not only during a competition but also during research as useful utility in their own laboratory. Tasks and functionality benchmarks are explained in detail, starting with their use case in industry, further detailing their execution and providing information on scoring and ranking mechanisms for the specific benchmark

Reconnaissance and Documentation (RAD)

The Reconnaissance and Documentation (RAD) mission aims to utilize a Low Earth Orbit satellite using machine learning enabled image recognition and optical remote sensing to observe countries currently experiencing Stage Nine of the United Nations’ Ten Stages of Genocide. The primary objective of the RAD satellite, Leza, is to observe high-risk countries at adequate spatial and temporal resolutions to capture evidence of genocide. The secondary objective of Leza is to process images on-board, so flagged images serving as evidence may be distributed to proper authorities, the United Nations, and mainstream media outlets as soon as possible. Using remote sensing to survey the surface of the planet is far from a new concept but using it to uphold current international human rights laws is revolutionary. Evidence gathered during the operational lifetime of the satellite could be used not only to persecute those inflicting chaos, but also to push for new policies on the international level. A prototype system that will test the machine learning software on the ground before utilization aboard Leza includes a drone, Olorun, and testing payload, OWL. The Olorun drone will act as a testing platform for image recognition software developed as part of the OWL payload. OWL will use a pre-trained neural net to evaluate if 3D modeled test beds of simulated evidence of genocide can be identified. This prototype will also analyze the capability to downlink images of interest and discard irrelevant photos. Testing of the Olorun and OWL will be completed in April 2022

Monitoring Environmental Trends In Levels of Influenza Virus and SARS-COV-2 in Prescott, AZ

Every year, the Centers for Disease Control and Prevention and state health agencies collect surveillance data for cases of influenza. During the flu season of 2019, SARSCoV- 2, which causes the symptoms known as COVID-19, caused a global pandemic. In turn, the surveillance and testing data showed a dramatic drop in influenza case numbers compared to previous years. Influenza is one of the deadliest viruses in human history, so it seems unlikely that this drastic change would occur due to the emergence of a similar virus. This research is designed to show that the prevalence of influenza in the community of Prescott, Arizona is much the same as during most flu seasons and is comparable to the prevalence of SARS-CoV-2. To do so, environmental sampling of a gas station, courthouse, urgent care center, a Walmart and a university library was conducted to obtain a base-level of viral RNA present on various highly touched surfaces throughout the fall and winter viral respiratory season, which runs from October through April each year. RNA extraction to isolate the viral RNA present in the environment was performed. Levels of viral RNA present were quantified through real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR). The results of the RT-qPCR will be interpreted to quantify the levels of influenza and SARS-CoV-2 RNA present on the sampled environmental surfaces. This data will be compared to an analysis of the public health data throughout the 2021-2022 viral respiratory season

Studying Aspects of Teamwork and Communication in a Virtual Reality Environment

This study aims to look at levels of teamwork and communication in virtual reality gaming systems. Researchers hope to analyze participants’ communication during the study with the assistance of Virtual Reality. This will allow an experimental view of how subjects interact together when presented with a difficult situation that requires communication to be their top priority if they wish to succeed as a team. Researchers believe that this experiment will allow a better look into the human element of Virtual Reality. This data will prove useful for a variety of applications beyond this study including, but not limited to, consumer, military and computerbased training simulations

Eaglenautics: Sae Aero West Design Competition Team

Eaglenautics is an engineering club affiliated with Embry-Riddle Aeronautical University. Every year, Eaglenautics participates in the SAE Aero West Design Competition. This competition challenges teams to design a competitive R/C scale aircraft from the ground up. Eaglenautics tackles this challenge by using a modified design-build-fly (DBF) process by adding a simulation step after the design step. Simulation software allows for a faster convergence to a design before the build process starts. Eaglenautics utilizes simulation programs like XFLR5 and OpenVSP to aid with the design of the aircraft to save time building multiple aircraft iterations. This process is especially helpful for Eaglenautics because of the size and complexity of the aircrafts. The competition requires teams to design a heavy lift aircraft that must carry steel plates and a minimum of one soccer ball. Due to these competition requirements, this year’s aircraft has a 6.8 ft wingspan and a length of about 6.4 ft. The gross take-off weight of the aircraft will be about 30-32 lbf. The process that Eaglenautics follows to design aircraft more closely mimics a typical design process of companies in the Aerospace industry. This in turn provides students with experience that is applicable to Capstone projects and jobs in the Aerospace industry. In the picture is an example of the aircraft’s finale iteration of the wing and vertical and horizontal stabilizers in XFLR5. An XFLR5 simulation was performed that showed the streamlines of the air leaving the wing and control surfaces