2025_001

The environmental effects of e-commerce are both difficult to calculate and, as evidence suggests, relevant. Over the last years, e-commerce has consistently grown. The e-commerce share of the turnover of all EU-27 enterprises (except financial) increased from 13% in 2010 to 18% in 2019 and jumped to 20% in 2020 and 2021. Cross-border transactions also increased ad many consumers purchased from sellers in a non-EU country. Covid-19 pandemic accelerated this evolution since many more consumers and retailers in the EU shifted from physical stores to online. In fact, there are several issues that need to be consider estimating the relative environmental impact e-commerce produces with respect to physical buying options.

The extant literature suggests that there are some theoretical as well as practical issues that one must consider. Among these one can recall: 1) the substitution or complementarity of on-line and off-line purchases; 2) the fulfillment arrangements that the seller provides (express vs regular) delivery; 3) the management of missed deliveries and returned items; 4) the mix between home and click-and-pick deliveries; 5) the type of vehicles used and their average load factors; 6) the induced changes e-commerce can generate with respect to shopping habits… and possibly many others.

The assumption of positive environmental consequences linked to e-commerce have not been supported either by convincing theoretical reasonings or hard practical evidence.

Two are the crucial questions one needs to address to reverse this situation which is extremely relevant given the current and expected increase in the volume of e-commerce throughout the world. In more detail, we need to ask ourselves: (1) How do we improve our understanding and management of the environmental effects deriving from e-commerce?; (2) Which are the policy interventions one should deploy to foster the adoption and diffusion of sustainable e-commerce solutions? (3) Which are the methods to communicate the environmental impacts of e-commerce to end consumers (e.g., storytelling)?

Since the literature is currently using alternative methods and conceptual paradigms when estimating the environmental impact of e-commerce, the thesis will systematically investigate, analyze, and critically compare them with the intent of providing a credible and transparent evaluation of the pros and cons of the different options while also making explicit the underlying assumptions each one relies on.

In fact, determining the most accurate estimates about the environmental impact e-commerce produces is essential to ensure communicating the correct and reliable information to end consumers allowing them autonomously and consciously take their purchase decisions.

2025_002

Urban freight transport (UFT) is now recognized as critical in pursuing Sustainable Development Goals given the rise in demand within cities. In fact, UFT is both essential to ensure high standard of livings as well as responsible for relevant environmental and social costs.

Experts suggest adopting a participatory approach to UFT planning so to define the best possible compromise solution that can account for the heterogeneity of preferences among stakeholders (e.g., transport providers, retailers, public administration, and citizens), the substantial interaction effects, and the low level of cooperation among them. While consensus is building around the need for participatory planning, at the same time the engagement of citizens is typically low since freight transport is not considered important from their perspective while being the source of many negative externalities produced within the city boundaries.

The level and width of the engagement of the various stakeholders is a critical element to produce good compromise solutions capable of dealing with the hard-to-solve problems local decision makers are confronted with when taking decisions.

The thesis will investigate the possible role gamification can play in promoting awareness and engagement among citizens whose level of knowledge of the environmental, social, and economic implications deriving from the actions they frequently take (e.g., buy goods online).

The thesis will delve on both the theoretical and practical gamification principles that one has to consider when developing a gamification strategy capable of increasing citizens’ awareness with respect to the impact their daily choices have on their lives, while also upgrading their understanding of the phenomenon. The thesis will also analyze the possible methods one could use to measure the impact the gamification might have in terms of the aims pursued.

The student could also consider developing a simple game and test its impact in a pilot study simulating the adoption of such an approach in practice.

2025_003

Seafood consumption has seen significant growth globally, and consumers increasingly demand information about what they are eating to ensure nutritional value, food safety, and sustainable practices. Meeting these demands relies on the ability to accurately identify seafood and trace its journey throughout the supply chain. Currently, technologies such as RFID tags, QR codes, and other IoT-based systems are used to identify seafood or its transport containers. While effective, these methods remain vulnerable to manipulation, highlighting the need for more robust identification methods. One promising approach is the use of DNA-based identification for seafood traceability. Research on DNA barcoding of seafood products has already begun to address this need. The aim of this master’s thesis is to conduct a systematic literature review (SLR) on the state of the art in DNA-based seafood traceability, following the PRISMA guidelines. Given that this research is part of an ongoing project on seafood traceability, the study may be extended with a case study at the DNA lab in Kristiansund, if time allows.

2025_004

Drones improve the efficiency and speed of response and reduce the risk to human lives by performing tasks in hazardous or inaccessible areas. Their flexibility in deployment makes them a powerful tool in disaster relief and recovery operations.

Governments often implement lockdowns and enforce social distancing measures during epidemics, particularly when vaccines and cures are not readily available. In such scenarios, drones can play a crucial role in minimizing person-to-person contact by taking over tasks traditionally carried out by humans, such as delivering essential supplies, collecting medical samples, and monitoring body temperatures.

Beyond these tasks, drones have a wide range of other applications, including surveillance of quarantined areas, disinfecting public spaces, and assisting with the transportation of medical equipment to remote or inaccessible regions. Drones can also be used for real-time data collection, helping authorities track and respond to the spread of the virus more effectively.

This topic explores a new approach to humanitarian logistic problems with the application of multiple drones for disaster relief. Additionally, it studies alternatives of truck-and-drone combinations to minimize delivery time and costs, along with a sensitivity analysis.

2025_005

In the aftermath of a disaster, drones can be invaluable for a wide range of tasks due to their speed, flexibility, and ability to access hard-to-reach areas.

Drones can be involved in Search and Rescue Operations, effectively locating survivors and assessing damage. Drones equipped with thermal imaging cameras can scan large areas quickly to detect heat signatures of survivors trapped under rubble, in forests, or in floodwaters, and provide real-time aerial footage to help rescue teams assess the extent of damage and prioritize areas for immediate intervention.

In areas cut off from traditional transportation routes due to collapsed bridges, landslides, or flooding, drones can deliver critical medical supplies such as vaccines, first-aid kits, or blood units. Drones can deliver food, water, and other essential supplies to remote or isolated locations where ground-based delivery is impossible or delayed.

Drones equipped with high-resolution cameras can fly over disaster-stricken areas to survey damage to infrastructure. This allows governments and aid agencies to prioritize repair work and mobilize resources efficiently. Drones can inspect power lines, gas pipelines, and communication towers to assess damage, enabling quicker restoration of essential services.

Drones can be utilized to create up-to-date 3D maps of affected areas, providing detailed information for disaster response teams. This helps responders understand the landscape changes and plan their operations more effectively. They can gather data on environmental hazards such as landslides, flash floods, or chemical spills, aiding in evacuation plans or identifying further risks.

As well as drones can provide recording for legal and insurance purposes. Footage from drones can serve as documentation for legal claims, insurance assessments, and post-disaster evaluations.

This topic explores the potential of using drones in emergency response operations. As a solution for a real-life problem based on a realistic case study, it proposes an efficient emergency response plan with the application of drones.

2025_006

Several logistical challenges can be modelled as pure binary integer programming (BIP) problems. Examples include facility location problems, cutting stock problems, facility location problems, and airline crew scheduling, in addition to many other planning problems. The resulting problems are hard to solve, and heuristic solution methods are often developed for the particular problem class at hand, rather than addressing the general BIP.

However, some heuristics for the BIP have been implemented and tested as a part of two master theses at Molde University college [1, 2] and in related research [3, 4].

One challenge when using heuristics for BIP is the lack of information about solution quality, as only primal bounds are sought. In this thesis topic we propose to investigate a new idea for generating dual bounds: using decision diagrams for optimization.

The core of the idea is to use a form of dynamic programming on a relaxation of the problem [5]. This allows us to calculate an upper bound (for a maximization problem). One may also consider restricted versions of the problem and use that to calculate lower bounds (for a maximization problem). The proposed research question is: how good are the upper bounds that can be found in reasonable time using relaxed decision diagrams for BIPs?

This topic is suitable for one or more students with high ambitions and good programming skills. It is possible to work within an existing framework for solving BIPs implemented in C++.

Contacts: Lars Magnus Hvattum and Bård Inge Pettersen

[1] A. Reznik. 2021. Heuristics for binary integer programming problems. Master thesis, Molde University College, https://himolde.brage.unit.no/himolde-xmlui/handle/11250/2779735

[2] M .Drozd. 2024. Adaptive large neighborhood search for binary integer programming problems. Master thesis, Molde University College, https://himolde.brage.unit.no/himolde-xmlui/handle/11250/3196905

[3] H. Bentsen, A. Hoff, and L.M. Hvattum. Exponential extrapolation memory for tabu search. EURO Journal on Computational Optimization, 10, 100028, 2022.

[4] K. Danielsen and L.M. Hvattum. 2025. Solution-based versus attribute-based tabu search for binary integer programming. International Transactions in Operations Research, forthcoming.

[5] W.J. van Hoeve. 2024. An Introduction to Decision Diagrams for Optimization. TUTORIALS in Operations Research.

2025_007

The transition from fossil fuel-based to renewable energy systems is needed for the green transformation to decarbonize our economic systems and mitigate climate change. Offshore Wind Energy is one of the necessary sources in this transition.

Given the urgency of effectively mitigating climate change, the market share of renewables is expected to increase rapidly.

It is crucial for offshore wind farms to minimize their operation and maintenance expenses. Vessels are the most significant component of logistics costs in offshore operations.

This thesis will analyze O&M vessels planning activities and propose methods and models to optimize this process.

2025_008

Nowadays, oil is the primary source of energy in the world, and natural gas is the third. The two sources that share the same supply chain are responsible for more than 50% of the world's energy supply. Since the 1970s, oil and gas have been the primary export commodities in Norway, playing a crucial role in the country's economy.

In Norway, oil & gas production is 100% offshore.

Now, numerous aspects are adding dichotomies to the oil and gas industry. On the one hand, we have higher demand because the Europeans need to shift gas suppliers to Western countries, and on the other hand, the planet needs to reduce emissions.

This situation challenges Offshore Logistics, which must improve its capacity, reduce emissions, and maintain or lower costs.

This thesis should analyze the Offshore Oil and gas Supply Chain, identify challenges, and propose ways to mitigate them.

2025_009

Nowadays, oil is the primary source of energy in the world, and natural gas is the third. The two sources that share the same supply chain are responsible for more than 50% of the world's energy supply. Since the 1970s, oil and gas have been the primary export commodities in Norway, playing a crucial role in the country's economy.

In Norway, oil & gas production is 100% offshore.

Now, numerous aspects are adding dichotomies to the oil and gas industry. On the one hand, we have higher demand because the Europeans need to shift gas suppliers to Western countries, and on the other hand, the planet needs to reduce emissions. This situation challenges Offshore Logistics, which must improve its capacity, reduce emissions, and maintain or lower costs.

This thesis should review the literature about quantitative methods applied to optimize vessel operation in the Offshore Oil and gas industry and/or implement a quantitative approach to optimize vessel planning (at strategic, tactical, or operational levels)

2025_010

In February 2025, Brazilian Ports and Airports Minister and Norway’s Ambassador to Brazil, representing the Norwegian Ministry of Climate and Environment, signed a Memorandum of Understanding (MoU) to advance sustainable maritime transport and support international climate targets. The agreement aims to establish a shipping corridor between Brazil and Norway for vessels powered by advanced technology and low- or zero-carbon fuels, significantly cutting greenhouse gas emissions. (https://www.datamarnews.com/noticias/brazil-and-norway-sign-agreement-to-establish-green-shipping-corridor/ )

To establish this corridor, it will be necessary to have a logistical infrastructure in place to support the operation. This thesis, based on the literature and available technologies, will analyze the logistical needs of each green technology considered, identifying gaps and measuring the challenges to implementing logistical infrastructure.

2025_011

The transition from fossil fuel-based to renewable energy systems is needed for the green transformation, which will decarbonize our economic systems and mitigate climate change.

Given the urgency of effectively mitigating climate change, Hydrogen and Ammonia will be crucial as energy carriers.

Ammonia and Hydrogen have special storage and transportation requirements. Their market is new and is currently being developed, as well as shortly.

This thesis aims to analyze the hydrogen and ammonia supply chain, identify challenges and bottlenecks, and propose strategies to mitigate these potential issues while considering socioeconomic factors.

2025_012

This thesis investigates the complex relationship between urban logistics, spatial planning, and climate neutrality, with a focus on how cities can sustainably manage increasing demand for last-mile deliveries. The research begins with the identification of key trends reshaping the urban freight landscape, including the rise of e-commerce, decentralized delivery models, and the integration of Sustainable Urban Logistics Plans (SULPs). A benchmarking analysis will compare best practices from leading cities in implementing microhubs, smart loading zones, and curbside management strategies.

The study will explore the role of advanced technologies—such as Digital Twins, eXtended Reality (XR) tools, and data-driven analytics—in enhancing the efficiency and sustainability of urban logistics. These tools enable simulation and real-time management of freight flows and space allocation. Particular attention will be given to the challenges faced by resource-constrained cities, including infrastructure limitations, regulatory gaps, and unique mobility patterns.

The objective is to propose adaptable, scalable solutions that promote climate-neutral urban freight systems while making optimal use of limited urban space. The findings aim to inform city planners, policymakers, and logistics stakeholders on integrating innovative, inclusive, and environmentally re

2025_013

This thesis focuses on defining and structuring the data ecosystem required to support effective and sustainable urban logistics planning. The research begins by identifying key data sources, including traffic sensors, municipal infrastructure records, GPS-enabled fleet management systems, and other relevant inputs. Existing data collection methods will be mapped and evaluated, with attention to both technological capabilities and legal constraints such as privacy regulations and data ownership.

A core objective is the development of a structured framework for managing the full data lifecycle—acquisition, storage, validation, and interoperability. The framework will emphasize secure handling of both real-time and historical datasets, ensuring reliability and scalability. In parallel, the study will assess the feasibility and potential impact of establishing a centralized, secure data-sharing platform that consolidates urban mobility data across public and private actors. A SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis will guide the evaluation of this platform’s effectiveness in supporting informed decision-making, policy design, and logistics optimization.

The research aims to provide actionable guidelines for city planners, technology providers, and logistics operators on how to leverage integrated data systems for more efficient, transparent, and sustainable urban freight and mobility strategies.

2025_014

This master’s project focuses on designing and implementing Guardrails—constraints that restrict undesired outputs—and iterative re-prompting techniques to improve the trustworthiness of Generative AI systems. The work would address the limitations and biases of current models and explores methods to ensure outputs align with predefined expectations. Validation may include case studies from sectors like logistics, healthcare, manufacturing, space, and energy.

2025_015

This thesis builds on findings from SINTEF’s tertiary study on the production, logistics, and end-of-life phases of AI hardware. It aims to develop a multi-agent AI model to simulate and analyze material flows across different stages of the AI hardware lifecycle. The goal is to understand how these flows influence societal outcomes and align with the United Nations Sustainable Development Goals (UN SDGs).

2025_016

This thesis focuses on optimizing single-drug chemotherapy treatment schedules using Mixed-Integer Nonlinear Programming (MINLP). The approach aims to model complex treatment protocols of single-drug chemotherapy for cancer treatment to enhance the effectiveness, safety, and personalization of chemotherapy planning, and solve it using Reinforcement Learning. For efficiency evaluation, the developed model and methodology will be implemented on real patient data.

2025_017

Background:

AI-enabled enterprise systems have the potential to significantly streamline management processes that have traditionally required extensive manual work. Especially processes that depend on detailed information stored in their enterprise systems. Examples of time-consuming and costly manual tasks that can benefit from AI-driven automation include generating reports and preparing applications for certifications required by authorities.

Research focus:

Investigate how AI-enabled enterprise systems can support the automation of management operations, using a case study at Molde University College.

Students:

This thesis is suitable for students interested in the digitalization and automation of business processes through AI-enabled enterprise systems.

2025_018

Background:
Increased urbanization has transformed freight and passenger transportation within urban areas into a critical challenge. Urban transportation is a lifeline for citizens, urban retailers, and industries. At the same time, it has a significant negative impact on the quality of life in cities through congestion, emissions, and space consumption. Urban logistics initiatives have long emphasized the need for collaborative and environmentally friendly solutions to mitigate these effects. However, they face organizational and technological barriers that weaken their long-term robustness.

Recent research suggests that sharing economy initiatives have been presented as potential solutions for greener and more affordable transport (Cassetta et al., 2017; Vazifeh et al., 2018). However, their role in strengthening resilience – the capacity of urban transport systems to adapt, recover, and maintain critical functions under stress – remains underexplored.

Research focus:
This thesis will explore how sharing economy principles can contribute to resilience in urban passenger transport, with a particular focus on the social dimension of sustainability.

Methodology:
The thesis is expected to be qualitative in nature, based on one or more case studies. Candidates are encouraged to collect empirical data through interviews, document analysis, or observations, ideally in collaboration with a company, municipality, or platform provider.

Contribution:
The study will provide insights into how sharing economy practices can strengthen resilience in urban transportation and inform city governance and sustainable urban mobility strategies.

2025_019

Background
The concept of a circular economy (CE) pushes the boundaries of environmental sustainability by promoting closed-loop systems, where waste becomes input and resources circulate continuously. Unlike traditional sustainable supply chain management (SCM), which often seeks to minimize negative impacts (e.g., emissions, waste, overconsumption), the CE framework emphasizes a paradigm shift: designing production and consumption systems that are self-sustaining and regenerative. Despite its growing popularity, the alignment between CE and sustainable SCM is still poorly understood—particularly with respect to energy use, carbon emissions, and broader systemic impacts on industries and societies.

Research Focus
This thesis invites students to investigate how sustainable supply chain strategies can be enhanced, reshaped, or challenged by the principles of the circular economy. The focus is on uncovering whether CE represents a complementary extension of sustainable SCM—or whether it constitutes a competing paradigm with different priorities.

Methodology
The thesis will be carried out as a qualitative study. Students are encouraged to conduct a single case study or multiple comparative case studies (e.g., in manufacturing, retail, or logistics) to explore how CE principles are applied in practice. The exact problem definition and empirical context will be refined in dialogue with the supervisor and—if possible—with a collaborating company.

Contribution
The expected contribution is twofold:

- Theoretical – providing insights into how CE and SCM can be conceptually aligned, and whether CE pushes the limits of environmental sustainability beyond traditional SCM frameworks.

- Practical – offering companies and policymakers guidance on how to integrate CE principles into supply chain strategies to strengthen both resilience and sustainability in the long term.

2025_020

Background
Offshore oil and gas operations rely on highly complex and globalized supply chains, where logistics plays a crucial role in ensuring uninterrupted exploration, drilling, and production activities. These supply chains are continuously exposed to uncertainty and disruptions, such as harsh weather conditions, geopolitical tensions, accidents, or technical failures. In much of the supply chain literature, disruptions are framed primarily as challenges and risks that undermine efficiency and performance. However, in offshore oil and gas, disruptions often function differently: they are inherent to the operating environment and, rather than being exceptional, they are expected and planned for. This makes resilience competence not only a risk-management tool, but a core operational capability that shapes how offshore logistics is organized and performed.

Research Focus
The thesis could investigate how resilience competence is built, maintained, and applied in offshore logistics operations within the oil and gas sector. The focus is on identifying the key practices, capabilities, and organizational learning processes that enable supply chains to withstand and recover from disruptions.

Methodology
The thesis will adopt a qualitative research design, such as case studies or semi-structured interviews with industry practitioners (e.g., supply base managers, vessel operators, logistics coordinators). Comparative case studies across companies or regions may also be considered. Document analysis (e.g., contingency plans, reports) can supplement interviews to provide a holistic perspective.

Contribution
This research will provide:

- Theoretical contribution – a deeper understanding of resilience competence in offshore logistics, complementing broader discussions of supply chain resilience in high-risk industries.

- Practical contribution – actionable insights for managers in oil and gas companies on how to design and strengthen resilience strategies in their logistics operations, thereby improving safety, preparedness, and operational continuity.

2025_021

Problemstilling:

Hvordan kan Helse Nord RHF utnytte prinsippene fra flertrinns lagerstyring for å utforme en robust, kostnadseffektiv og beredskapsorientert lagerstruktur på tvers av sine helseforetak?

Relevans:

• For Helse Nord med både      store sykehus (UNN, NLSH) og mindre lokasjoner (Kirkenes,      Hammerfest) vil en differensiert flertrinnsmodell muliggjøre bedre balanse      mellom tilgjengelighet og kostnad.

• Lagerstrategien må ta høyde for      både daglig drift og beredskap.

Metode:

• Litteraturstudie og      modelloversikt

• Kartlegging av dagens      lagerplassering og omløpshastighet

• Case-basert scenarioanalyse      (f.eks. «to-echelon»- vs «tre-echelon»-modell for utvalgte      produktkategorier)

• Simulering av responstid og      kostnad

2025_022

Problemstilling:

Hvilke lokasjoner i Helse Nord er mest hensiktsmessige som forsyningsknutepunkt i et fremtidig samarbeidende forsyningsnettverk – basert på volum, tilgjengelighet og transportavstand?

Relevans:

• Gir grunnlag for å vurdere om,      hvor og hvordan en regional forsyningssentral eller mellomlagerstrukturkan      etableres.

• Kan støtte vurderinger rundt REFIL      og evt. knutepunkt i f.eks. Kirkenes, Harstad eller Bodø.

Metode:

• Nettverksanalyse      (multi-warehouse, multi-product)

• Bruk av GIS og avstandsmatriser

• Analyse av eksisterende      forsendelsesstrømmer (via Clockwork-data hvis mulig)

• Modellering i f.eks. Python, Excel      Solver eller spesialisert nettverksdesignverktøy

2025_023

Problemstilling:

Hvordan bør sikkerhetslager og kapasiteter dimensjoneres i Helse Nord for å sikre kostnadseffektiv forsyning under usikker etterspørsel – særlig for kritiske varer og i spredt geografi?

Relevans:

• Aktuelt for styrket      forsyningsberedskap i tråd med beredskapsplanene og behov for      differensiering (kritisk vs ikke-kritisk forsyning)

• Underbygger ROS- og      pandemiberedskap

Metode:

• Kvantitativ modellering med      normalfordelt etterspørsel

• Datainnsamling fra varelager og      forbruksmønster

• Sensitivitetsanalyse på ledetid,      variasjon og forsyningsruter

• Sammenligning av «decentralized vs      pooled inventory» med simulerte risikoeksponeringer

2025_024

This thesis aims to explore the potential of Artificial Intelligence (AI) to streamline and automate order processing workflows within a large-scale transport and logistics operation. Veøy is one of the largest privately owned transport companies in Norway, with over 450 trucks, more than 700 employees, and operations across Norway, the Nordics, and Europe. The company handles a significant volume of daily bookings and transport orders originating from multiple sources, including EDI messages, emails, and phone calls.

Currently, the process of receiving, classifying, and assigning these orders to the correct vehicle, route, and departure schedule relies heavily on manual routines. This can be time-consuming, prone to error, and resource-intensive. The objective of this research is to investigate whether AI-based solutions—such as Natural Language Processing (NLP), machine learning classification models, and automated decision-support systems—can improve efficiency, accuracy, and scalability in this critical part of Veøy’s operations.

The study will begin with a detailed mapping of the existing order-handling process, identifying bottlenecks, manual decision points, and opportunities for automation. Data availability and quality will be assessed, including EDI transaction logs, historical order data, and operational performance metrics. A conceptual AI-driven framework will then be designed, outlining how data can be ingested, processed, and used for automated assignment and scheduling decisions.

The research will also address key challenges such as system integration with existing transport management systems, data governance, and organizational readiness for digitalization. A feasibility analysis, potentially including a prototype or proof-of-concept model, will evaluate the expected impact on key performance indicators such as lead time, on-time delivery, and administrative workload.

The thesis ultimately aims to deliver actionable recommendations for how Veøy can implement AI-supported order processing to reduce manual work, increase operational reliability, and improve customer service.

2025_025

This thesis investigates how Artificial Intelligence (AI) can be applied to optimize workforce planning for a large-scale transport company. Veøy employs between 500 and 600 drivers whose work schedules must be planned weeks in advance while satisfying multiple and often conflicting constraints. These include compliance with EU and Norwegian regulations on driving and rest time, adherence to the Working Environment Act, alignment with collective agreements and company-specific agreements, and fulfilment of strict customer delivery requirements.

Currently, this planning process is largely manual, relying on tools such as Excel spreadsheets and requiring significant administrative effort. The research aims to explore how AI-driven optimization models and decision-support tools can create more efficient, legally compliant, and predictable work schedules for drivers, while reducing manual workload and supporting fair distribution of shifts.

The study will begin by mapping the current planning process in detail, identifying key data sources, including historical schedules, route demands, vehicle availability, and regulatory requirements. A conceptual optimization framework will then be developed, using methods such as mathematical programming, heuristic algorithms, or machine learning to generate feasible and robust driver schedules.

Attention will be given to ensuring that the proposed solution integrates with existing transport management and HR systems, handles real-world complexities such as last-minute changes and absences, and remains transparent and auditable for both management and employees. The feasibility of implementing such a solution will be evaluated through a combination of simulation, scenario analysis, and stakeholder feedback.

The expected outcome of the thesis is a set of practical recommendations and a prototype scheduling model that can help Veøy transition from manual planning to a digital, data-driven workforce scheduling process. This will support better compliance, improved resource utilization, and greater predictability for drivers and customers alike.

2025_026

This thesis explores how Artificial Intelligence (AI) and advanced optimization techniques can be applied to improve daily fleet planning and route scheduling for Veøy, one of Norway’s largest privately-owned transport companies. Veøy operates around 450 trucks distributed across 11 branches, serving customers nationwide and internationally. Every day, transport planners must allocate capacity across hundreds of vehicles to ensure all customer orders are executed in line with agreed delivery windows and contractual obligations.

Currently, this “fleet puzzle” is largely solved through manual work, with significant time spent matching orders to available trucks and routes. The objective of the research is to develop a data-driven and automated decision-support framework capable of maximizing vehicle utilization, improving cost efficiency, and ensuring operational compliance with driver schedules and delivery commitments.

The study will begin by mapping the current planning process and collecting data from the company’s order management system (load board), including daily demand, historical traffic patterns, vehicle availability, and cost structures. A core focus will be the design of an optimization engine that can:

• Generate optimal daily delivery plans based on factors such as real-time traffic data (roadworks, congestion,      accidents), fill rates, and economic performance of each route.

• Account for driver schedules and working time regulations, ensuring compliance while balancing capacity utilization.

• Provide predictive insight for future resource needs by leveraging historical data on seasonal demand patterns and vehicle downtime (e.g., sick leave or maintenance).

The research will also consider scenarios where traffic planners manually design routes but receive AI-powered feedback about potential inefficiencies, such as routes being too long, underperforming financially, or exceeding driver work limits. A feasibility study, possibly including simulation or prototype development, will be conducted to estimate potential improvements in utilization rates, profitability, and planning time.

The thesis aims to deliver actionable recommendations and a conceptual framework for implementing AI-supported fleet and route optimization, helping Veøy achieve near-100% utilization of its capacity while reducing manual workload and improving decision quality for planners.

2025_027

Background:
The Norwegian seafood industry is one of Norway’s most important export sectors, characterized by high requirements for quality, traceability, and sustainability. The transport from fishing vessels to landing facilities, through processing plants and subsequent transport stages, forms a complex supply chain influenced by multiple variables — including choice of transport mode, temperature control, time management, port infrastructure, coordination among actors, and regulatory as well as sustainability considerations. In addition, increasing documentation requirements related to climate footprint, efficiency, and food safety call for both digitalization and improved collaboration between actors in the supply chain.

Research Problem:
How can supply chains for transporting fish/biomass from vessels via landing facilities and transporters to the market be optimized in terms of efficiency, cost, quality, and sustainability?

Research Questions:

1. What are the main challenges in current logistics processes for transporting fish/biomass from catch to market?

2. How do different transport variables (mode of transport, frequency, lead time, route selection, temperature control, etc.) affect the efficiency and quality of the logistics chain?

3. How can digitalization and data sharing between vessels, landing facilities, and transporters improve coordination and reduce waste and climate impact?

4. What measures can be implemented to balance cost efficiency with the requirements for food safety, traceability, and sustainability?

Method:

• Case study of a selected value chain (e.g., pelagic fish or whitefish). The pelagic segment is the most accessible for data collection, given existing projects and regulatory requirements, while the whitefish segment—though somewhat more advanced—is also highly relevant.

• Data collection: Interviews with shipowners, landing facilities, transporters, and exporters; collection of transport and production data; document review.

• Analyses: Process mapping (value stream mapping), quantitative analysis of transport data (lead time, temperature, cost, CO₂ footprint), and scenario analyses of alternative logistics models.

Expected Contributions:

• Identification of bottlenecks and improvement opportunities in the transport segment from vessel to market.

• Recommendations for improved coordination and digitalization of transport logistics.

• Insights into how transport variables affect end results in terms of quality, efficiency, and environmental performance.

• Recommendations that can strengthen competitiveness and promote more sustainable seafood logistics.

2025_028

Suggested Research Questions

1. What sustainability requirements and reporting expectations are manufacturing companies currently facing, and how do these shape their strategic priorities?

Focuses on identifying the main drivers, regulatory, market-based, and stakeholder-related, that influence sustainability efforts in manufacturing.

2. How do sustainability and reporting obligations affect operational processes, resource management, and innovation within manufacturing firms?

Examines the internal impact, for example, changes to production methods, efficiency goals, or environmental performance tracking.

3. In what ways do sustainability reporting requirements influence collaboration, transparency, and performance expectations across manufacturing supply chains?

Explores the external impact, how manufacturers communicate with, evaluate, and coordinate with their suppliers.