The length of stay (LOS) is an important indicator of the efficiency of hospital management. Reduction in the number of inpatient days results in decreased risk of infection and medication side effects, improvement in the quality of treatment, and increased hospital profit with more efficient bed management. The purpose of this thesis is to determine which factors are associated with length of hospital stay, in the covid period using the collected data. In this thesis the student will apply a data mining approach to understand the management process in the covid period. The data is from a Portuguese hospital.

Process mining is a data analytics approach which has shown promising results in healthcare including the potential to improve the management of chronic diseases such as cardiovascular disease (CVD). Cardiology is a branch of medicine that diagnoses and treats heart and blood vessel illnesses include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other heart conditions. CVDs are the world's leading cause of disease-related death. In 2015, the top two cardiovascular diseases, coronary heart disease and stroke, caused 15 million deaths worldwide. From an economic standpoint, CVD has a significant impact on healthcare expenses, productivity loss, and the care of persons with chronic illnesses. A big problem is lowering the cost of CVD care while also enhancing the quality of care.

Helping healthcare professionals develop a better understanding of how to improve CVD care pathways may result in better outcomes for patients.   A Data analytics process will be applied to the collected big data from a hospital. Process mining can be applied in healthcare settings to give new insights that help enhance patient treatment processes (also known as care pathways).

FRAM is the brand name of Møre and Romsdal (M&R) counties’ integrated public transport service system. The system combines multiple ferry-, express boat-, and bus services, with the latter accounting for some 9 million passengers and ticket revenues of 170 million NOK per year.

FRAM offers an extensive set of ticket options, so travellers can pick the best fare that fits their individual needs. Among other things, passengers can choose between paying onboard (e.g., cash/credit card) or using off-bord payment solutions (e.g., FRAM-app) to purchase tickets before the trip. From an operational perspective, FRAM prefers off-board payments as they reduce the bus drivers’ workload and increase the average system speed. FRAM currently charges a 20-kroner service fee for all onboard tickets to incentivise travellers to use off-board payment options. Despite this premium, some 40 per cent of our customers still purchase their tickets on board, suggesting that some traveller segments might value the option to pay onboard higher than 20 kroner.

A successful MSc-project analyses passengers’ valuation of onboard/off-board payment options in M&R and proposes adequate surcharge levels for different traveller segments.

Covid-19 has impacted society, with the outbreak affecting all segments of the human population and altering human activities and behavior. E-grocery shopping is becoming a growing trend in the worlds and the trend in Norway shows growth in the e-grocery with more Norwegians using the channel. Previous studies showed a growth in the demand for E-groceries in Norway, when pre and during COVID-19 periods were compared. Therefore, this thesis aims to understand how much of the consumer’s habits were maintained in a post pandemic period. Additionally, gain information on the consumer behavior and choice of the channel in grocery shopping in Norway. The data for the study should be collected in a form of revealed and stated preference questionnaires. Econometric analysis and logistic regressions are the base for analyzing the collected data.

This thesis is supported by manufacturer of prefabricated concrete structures ElementPartner AS, located at Åndalsnes (focal company), Norway and provider of rail freight services OnRail Scandinavia.

The focal company currently ships concrete structures manufactured at Åndalsnes to customers around the country by road, with articulated trucks with a special kind of semi-trailers, called In-loaders (see picture below). This type of transport is rather costly, and also has a significant environmental footprint. The company wants to explore the potential for transferring part of these transport to more environmental-friendly rail solutions, and has partnered with rail-freight operator OnRail to assess both technical solutions and the market such a concept might have. Since these structures are too big to be transported on regular trailers, and would not fit into standard containers, tailormade solutions for handling, transporting, and securing the cargo is a key technological challenge. However, this thesis will focus on a market analysis and an environmental impact analysis under the assumption that feasible technical solutions could be found.

A master’s thesis in logistics will be defined related to the project, mainly focusing on the potential market adoption of the concept and a mapping of the environmental benefits that could be harvested if the solution is successfully implemented. The detailing of the content of the thesis will be done in co-operation with the focal company during the autumn of 2023, but a preliminary list of research questions to be answered could be:

1. How much of the current transports of the focal company could be transferred from road to rail if the concept is fully adopted?

2. What is the likely market potential within the prefabricated concrete structure industry in Norway/Scandinavia for this new solution?

3. Are there other industries (e.g. manufacturers of large glass structures) that could also be interested in adopting such a solution?

4. What is the environmental footprint of the current solution?

a. What is the current environmental footprint of the annual transports of the focal company?

b. What is the likely current environmental footprint of annual Norwegian transports of concrete structures on road?

5. What is the environmental footprint of these transports under a rail-based solution?

a. What is the likely environmental footprint of the annual transports of the focal company if the new concept is fully adopted?

b. What is the likely environmental footprint of annual Norwegian transports of concrete structures if all actors adopt the new solution?

Contact person HiMolde: Professor Harald M. Hjelle (harald.hjelle@himolde.no)

Contact person at ElementPartner AS: Consultant Odd Tore Finnøy (odd.finnoy@element-partner.no)

Homepage of ElementPartner AS (Norwegian text only): https://elementpartner.no

Homepage of OnRail: https://www.onrail.no/home-english

The topic is suggested by the SiMolde Canteen (https://simolde.no/en/kantine)

Background

The canteen at Molde University College experiences a classic peak-hour problem with most of the customers coming lunch time creating a bottleneck for the operation of the canteen. The canteen is a part of the Student Welfare Organization in Molde (SiMolde) being organized as a legal entity responsible for the welfare of students at universities, university colleges, scientific universities and other colleges in Norway. https://en.wikipedia.org/wiki/Student_welfare_organisation. Since the canteen is closely related to the operation of HiMolde, cooperation ideas on lecture scheduling, library services, bookstore services might also be explored.

Proposed Main Problem:

How to handle the high variation in arrival of customers while keeping or increasing the total throughput over the opening hours? Other problem statements including the combination with the operation of HiMolde services are welcome.

Tasks could be:

First map the current operation of the canteen. Detailed data for each day is available. Second, do explorative research of different models for handling the peek hour problem, e.g., fast food, sushi conveyor systems, pre-paid models (buffets, or single customer), airport lounge systems, and canteen services combined with other services (literature review, visit places with similar operations). Third, analyze models, compare-and-contrast results. Forth, suggest improvements. Possible research methods include explorative research, AI/ML based on available data for predictions, lean and case study.

Advisors:

Supervisors: Professor Bjørn Jæger bjorn.jager@himolde.no, Steinar Westlie Kristoffersen steinar.w.kristoffersen@himolde.no, and Head of Canteen Tone Anita Åbelvold tone.a.abelvold@simolde.no.

Crowdshipping business model characteristics, comparison, and typologies.

Currently cities are suffering from increase in congestion and pollution, also due to freight deliveries mostly linked to e-commerce increase. Private companies are searching for new solutions to deliver goods efficiently while minimizing cost, emissions, and congestion.

Crowdshipping, or a service that uses non-professional bringers to deliver packages to final costumers, is considered a promising option to jointly deal with these three daunting issues. However, research is suggesting that crowdshipping can be performed according to very different business models bearing completely different implications with respect to the mentioned critical issues mentioned above.

This thesis analyzes, compares, and typifies the main and most relevant crowdshipping companies worldwide. The investigation will rely on an internet search and in-depth interviews. On the base of the business model canvas model, the various companies will be grouped within corwdshipping’ business model types. Taking advantage of previous literature, each business model typology will be evaluated in terms of the most likely impacts it will have on cost, congestion, and emission. Thus, allowing to discover when exactly e crowdshipping service is actually contributing to the improvement of the economic and environmental conditions city dwellers are experiencing.

The world’s economy operates largely upon linear economic principles. A traditional linear economy follows the “take-make-dispose” approach where the natural raw materials are extracted and then manufactured into products, which when not needed anymore, eventually discarded as waste. This model encourages improper use of limited natural resources and causes abundant waste production resulting in severe harm to the environment. A circular economy (CE) is a sustainable, restorative, and regenerative alternative to the current linear economy and is gaining popularity worldwide.

Amongst various digital technologies, Artificial intelligence (AI) is a crucial enabler for CE and can aid significantly with the adoption and implementation of CE in real-world applications. Combining the power of AI with a vision for a CE represents a significant, and as yet, largely untapped opportunity to harness one of the great technological developments of our time. It will support efforts to fundamentally reshape the economy into one that is regenerative, resilient, and fit for the long term.

However, despite a great deal of discussion around the use of AI in CE, there is actually little clarity on how AI is being used in a practical sense within the CE. To address this research gap, students will be required to investigate how AI can accelerate the transition towards a CE.

It is recommended to apply AI in two value chains of your choice and look at how AI is being used, or could be used in future, to enable and accelerate the CE transition in these sectors.

Industrial symbiosis (IS) is a commercial collaboration where companies exchange residual and surplus products such as water, energy, or materials. Normally, the companies are located in close proximity to each other, as long distances can impede the exchange of resources, or remove the business and environmental case for doing so. For companies, it can be advantageous to participate in the symbiosis, as it helps them achieve their environmental objectives (such as reduce CO2 emissions), reduce costs and find a market for their residual and/or by-products.

Circular economy (CE) and IS are often flagged as political objectives but despite good intentions, there are few significant examples of the CE and IS operating in practice. Most material production value chains are still linear, creating side-streams, wastes and energy flows that are not optimally utilized.

This strategy fits perfectly in a country like Norway, with large distances and a small population, where national infrastructure and markets for circular materials can be demanding in terms of profitability and access to secondary raw materials. Bringing together companies that are located in close proximity to collaborate can therefore be crucial to achieving circularity.

Based hereon, the thesis aims to study how such an IS can be established in achieving CE. The focus should be on the collaboration among the companies in Møre og Romsdal county.

The use of plastics has increased twentyfold in the past 50 years[1]. While the material has many benefits, there are negative consequences if it becomes waste or pollution. In 2016, the Ellen MacArthur Foundation published a report which showed that most plastic packaging is used only once, and only 14% is collected for recycling. 95% of the value of plastic packaging material, worth USD 80-120 billion annually, is lost to the economy. In yet another report published in 2017 on plastics, it is stated that without fundamental redesign and innovation, about 30% of plastic packaging will never be reused or recycled.

How can such a situation be avoided? In this master thesis, students will investigate how companies can apply circular principles to the plastic packaging in a way that it never becomes waste or pollution.

Potential cases: companies in the food, maritime and healthcare industries.

[1] https://archive.ellenmacarthurfoundation.org/explore/plastics-and-the-circular-economy

A digital twin (DT) is a virtual replica of an object, being, or system that can be continuously updated with data from its physical counterpart. The physical element or system being twinned can also be called a physical twin (PT). A DT is updated from real-time data, and uses simulation, machine learning, and reasoning to help decision-making. The main advantages of using a DT are: the possibility of create and predict future scenarios, predict changes in a system already functional and, to test performance capabilities prior to implementation.

By allowing simulating and predicting the behavior of systems, these models can help system’s developers and managers make better decisions. To mirror the real world, the model receives input from sensors that register variables of interest. The accuracy of the input received from the system is key for the reliability of the model. However, malfunction and the action of malicious agents can introduce errors into the data, which may result in errors in the model. In this proposal, we are specifically interested in the risk of deliberate attacks to digital twins systems and how to address these threats.

Many cities are moving away from traditional data collection instruments to automated and diverse sources. Cities that incorporate the use of traffic sensors and build intelligent transport systems to manage traffic also have a huge stream of real-time data. The advent of smart cities and smart management of many traditionally manual utilities are making a variety of data streams available (and in some cases open) to be exploited for analysis. Nevertheless, this high volume of data in smart city initiatives, brings a need to secure such data.  Cason and Wierschem (2020) discuss securing information and communication systems of smart city through the transportation sector and identify several trust issues and vulnerabilities in such systems. Promoting transparency and trust are two of the most relevant features that blockchain can bring to applications. This proposal investigates the feasibility, advantages and drawbacks of bringing blockchain into smart city applications.

Keywords: smart cities, blockchain technology, applications

In the rapidly evolving landscape of logistics and supply chain management, the integration of Artificial Intelligence (AI) has emerged as a transformative force, offering the potential to optimize operations, enhance decision-making, and improve overall efficiency. However, the deployment of AI systems in logistics often faces a significant challenge – the lack of transparency and interpretability in AI models, which can hinder trust, accountability, and effective collaboration among stakeholders.

Machine Learning (ML) models, which are integral components of AI systems, are often perceived as "black boxes." It can be difficult to explain how these models arrive at particular predictions or recommendations, especially in complex logistics and supply chain contexts. This opacity in AI decision-making raises concerns about the reliability and comprehensibility of AI-driven processes in critical logistical operations.

Explainable Artificial Intelligence (XAI) offers a solution to this pressing issue. XAI is the development of AI systems and models that can provide clear, understandable, and interpretable explanations for their decisions and actions. By enabling humans to comprehend why a particular decision was made by an AI model, XAI aims to bridge the gap between the powerful predictive capabilities of AI and the need for transparency and accountability in logistics and supply chain management.

This master's thesis would aim to investigate the need and implications of integrating XAI in supply chain optimization and logistics. It will delve into the challenges posed by the current lack of transparency in AI models, exploring the potential consequences of such opacity in critical logistics operations. This study would seek to demonstrate how XAI can enhance transparency, accountability, and collaboration among stakeholders in logistics, ultimately leading to more effective and reliable AI-driven decision-making processes.

The integration of digital technologies such as the Internet of Things (IoT), blockchain, artificial intelligence, and big data analytics has transformed traditional supply chains into digitized and digitalized models, promising enhanced efficiency and competitiveness. These technologies offer numerous opportunities, including streamlined logistics, cost reduction, and improved sustainability. However, they also introduce a host of challenges and ethical dilemmas, spanning data privacy, cybersecurity, labor displacement, environmental impact, algorithmic bias, and fair trade practices.

This master's thesis would seek to provide a comprehensive understanding of the social and ethical consequences arising from the digitalization of supply chains. By delving into the intricate relationship between digitalization, society, and ethics in the context of supply chains, this research would aim to shed light on an essential aspect of our evolving world. It is intended that this thesis would meaningfully contribute towards more ethical, socially responsible, and sustainable business practices in the digital age.