This thesis investigates how to sustainably meet the thermal demand of an Indigenous-led Aquaponic Greenhouse in Alberta’s Boreal Forest using renewable heating technologies. Drawing on systems engineering, Traditional Ecological Knowledge (TEK), and an integrative design framework grounded in Value Sensitive Design (VSD) and multi-criteria decision-making (MCDM), the study balances technical, economic, environmental, and social dimensions to inform decision-making.

A conceptual greenhouse design was developed, incorporating nutrient-film technique and soil-based growing systems alongside passive solar measures such as south-facing glazing, night curtains, and thermal energy storage. These passive features alone were found to reduce the greenhouse’s thermal load by up to 47%. Three renewable heating technologies including biomass boilers, biodigester systems with biogas, and ground source heat pumps were evaluated, individually and in hybrid configurations, against a natural gas baseline. A multi-criteria assessment using the Best-Worst Method revealed that hybrid systems generally outperform single-technology options by balancing cost, reliability, scalability, and environmental impact. Specifically, a biodigester–natural gas combination proved more cost-effective and flexible, while a ground source heat pump–biomass boiler system offered greater potential for off-grid operation and lower emissions.

The findings highlight that scaling up the greenhouse size enhances financial viability, though a pilot phase is recommended to address uncertainties and ensure reliability. Overall, this research demonstrates the feasibility of integrating TEK and community engagement with robust engineering methodologies to support food sovereignty, reduce greenhouse gas emissions, and foster Indigenous self-determination in remote communities. The proposed framework can guide similar socio-technical projects, promoting holistic, value-driven innovation in community-scale infrastructure systems.

Excess inventory negatively impacts financial performance due to increased holding and working capital costs, stock value depreciation and lost sales from other products. For durable finished goods inventory within e-commerce, this issue of excess stock could be amplified given the risk of long-term accumulation and specific nature of goods, as well as the industry's competitive emphasis on broad product assortments and short delivery times. Despite improvements in forecasting, procurement and inventory management, the problem of excess stock is likely to persist, highlighting the importance of effective reactive control. This study proposes a binary programming model for optimizing reactive control strategies for excess inventory through cost minimization, tailored to the use case of a large e-commerce company, which remains specified due to confidentiality. The total costs consist of holding costs, working capital costs, transport costs and opportunity costs from lost revenue opportunities. The model considers three control strategies: 'keep in stock', 'return', 'liquidate', and incorporates warehouse capacity constraints. The model has been deployed for optimizing the excess stock from 27 November 2024, consisting of over 44 thousand excess items. Under limited warehouse capacity, the model yielded a 19% retention rate, 67% return rate, 14% liquidation rate, resulting in total excess stock costs of - €166k. Under unlimited warehouse capacity, eliminating opportunity costs of keep-in-stock, the model yielded a 31% retention rate, 59% return rate, 11% liquidation rate, resulting in total excess stock costs of - €215k. Thus, in both capacity scenarios, profit could be generated through optimal excess stock management. Optimal control decisions showed significant sensitivity to capacity constraints, return eligibility, expected stock days and the financial benefit of clearing compared to regular sale. Excess stock costs are mainly impacted by the opportunity costs of keep-in-stock and clearing, which are mostly sensitive to the rate at which excess stock depreciates in value, and the expected days in stock. Working capital, inventory holding and transport costs only have little effect. Given the clear relationships, optimal control decisions can be closely approximated with simple decision rules based on stock metrics, presenting a time-efficient alternative to exact optimization. Concluding, the optimization of reactive control strategies can significantly improve the performance of excess stock of the company. In the broader context of durable finished goods e-commerce platforms, this study suggest that incorporating opportunity costs alongside direct costs, and differentiating excess stock management strategies based on seasonality, could enhance excess stock management.

Cargo airlines are increasingly pressured to enhance sustainability across all aspects of their operations, including the ground handling services managed by global partners. These operations are integral to reducing the overall environmental impact of the aviation industry, necessitating effective Partner Relationship Management (PRM) that incorporates sustainability objectives.

Managing sustainability across a diverse and global network of partners presents significant challenges, as these partners vary widely in their willingness and capability to implement sustainable practices. The complexity of this task is compounded by the lack of a structured approach to evaluate and guide these partners toward achieving sustainability goals. The central question addressed in this research is: ”How can a Decision Support System (DSS) be developed to guide Partner Relationship Management (PRM) for the operations of existing partners of cargo airlines to achieve sustainability goals?”

To tackle this issue, a DSS was developed, utilising Multi-Criteria Decision Making (MCDM) with the Best-Worst Method (BWM). The DSS was applied to KLM Cargo’s operations, focusing on segmenting partners based on their capabilities and willingness to meet sustainability goals, specifically zero emissions and zero waste. This segmentation enabled the creation of tailored strategies for each partner segment.

The DSS effectively categorised partners and identified key trends, such as the performance of GHAs operating multiple outstations and the area in which the outstations are operating. This categorisation facilitated the development of a step-by-step strategy, including specified components of strategies for each segment and the introduction of a checklist to assess the current sustainability practices of each partner. These tools proved essential for guiding PRM toward the achievement of sustainability goals.

This research offers a robust framework for sustainability management that can be generalised across the cargo airline industry and potentially other sectors. Future research should focus on validating the DSS in different contexts, refining the segmentation techniques, and further developing the checklist to ensure comprehensive sustainability assessments and strategy development across diverse operational environments.

Dry port has emerged as a critical element of transport infrastructure, eliciting substantial research and investment for its development. The strategic selection of dry port locations not only enhances the effectiveness of connections between seaports and hinterlands but also supports the sustainable advancement of the logistics industry, given that dry port operations can integrate with more environmentally friendly transportation modes, particularly inland waterway transport. Extensive research has been conducted to identify optimal dry port locations within the framework of inland waterway container terminals. Nevertheless, these studies primarily focus on developed economies, leaving a notable research void in developing countries. This paper aims to propose a methodological framework for selecting the most suitable dry port location, with a particular emphasis on integration with inland waterway transport in developing nations. This study implements a combination of the Best-Worst Method (BWM) and Elimination Et Choix Traduisant la Realité III (ELECTRE III) in this domain. An analytical case study of Northern Vietnam, considering five alternative dry ports, is conducted to demonstrate the efficacy of the proposed framework. Twenty-seven Vietnamese experts, categorized into three groups—policymakers and consultants, dry port investors and operators, and dry port users—participate in the decision-making process, contributing insights to this case study. An aggregated group decision-making approach is employed. Four principal criteria—economic, accessibility, location, and environmental—are utilized to assess and rank the five alternatives. The findings reveal that a reduction in transport cost is the most critical sub-criterion, while environmental considerations and railway accessibility receive the lowest priority.

To aid the efforts of electrifying public bus systems and to reduce the impact public transit has on emissions in cities, it is necessary for planners to be well informed when making the decision to electrify the transport system. The methodological approach shows that there is value in considering the development of a new transport system over simply electrifying an existing transport network, as there is a trade-off between investment characteristics and passenger satisfaction. Significant cost savings could be realized by jointly considering line planning, infrastructure location, vehicle, battery and fleet size. This has been investigated by developing a decision making model that selects a lineplan seeking to maximize passenger satisfaction, whilst taking into account, vehicle scheduling, battery capacity and charging station location. The model demonstrated that there is a clear trade-off between these aspects and that a multitude of pareto optimal solutions that can be obtained, depending on weighting that decision-makers assign to the objectives. The simplicity of turning the objective of this methodology into a revenue-maximization function, should make this model a suitable starting point for more serious modelling of investment decision in the future.

This thesis addresses the increasing vulnerabilities in modern supply chains, particularly within the maritime industry. Emphasizing the crucial role of procurement in risk mitigation, the study challenges conventional lean management, advocating for agile supply chain strategies. It introduces the Integrated Supplier Matrix (ISM) to enhance supplier segmentation, combining aspects of the Purchasing Portfolio Matrix (PPM) and Supplier Potential Matrix (SPM).

Conducted as a case study at Allseas, the research employs the Best Worst Method (BWM) to rank risks, identifying the uniqueness of the product, rules and regulations, and external factors as significant concerns. Through graphical representations and ISM-based supplier segmentation, relationships between supplier willingness, capabilities, risks and profit impact are established. The findings stress the importance of communication and trust in supplier relationship management (SRM) trade-offs, including inventory reliance, contract durations, sourcing strategies, and addressing non-performing suppliers.

The research provides insights for maritime managers, recommending the integration of theoretical SRM approaches with multi-criteria decision-making methods for resilient supply chains. The suggested shift from lean to agile management and the ISM implementation offers practical strategies to optimize relationships, navigate complexities, and establish adaptive procurement approaches, effectively mitigating supply chain risks.

The current trend of technology being increasingly integrated into the healthcare industry is heavily influencing the supply chains of companies involved. Thus, companies need to find ways to remain competitive, one of which is introducing process optimization in (parts of) their supply chains. In large organizations whose portfolios involve a variety of products and processes, the problem of choosing a focus area for implementing process optimization arises. Due to its complexity and various influencing factors and decision-makers, this issue can be regarded as a multi-criteria decision-making (MCDM) problem. This thesis presents a novel framework for finding a solution by means of combining two MCDM methods, Linear Best-Worst Method (BWM) for determining the weights of the relevant criteria, and ELECTRE III for ranking the chosen alternatives. The framework was applied to a real case within a manufacturing unit of Philips in The Netherlands, where a focus area is chosen among 8 alternative process steps in the assembly of an Image-Guided Therapy Medical System. Six decision-makers within Philips were chosen and interviewed in order to collect the necessary data, and they chose a set of 7 relevant criteria based on which the alternative process steps would be ranked. The results obtained from the analysis ranked the Cabling 2 step of the assembly process as the first one, so it was recommended that process optimization is implemented in this step. This framework contributes to existing research by addressing the research gap in choosing a focus area for process optimization, as well as by presenting a new MCDM method. Furthermore, it offers an easy-to-use, stepwise procedure for solving this issue for companies in the industry.

Fast-Moving Consumer Goods (FMCG) companies face the critical challenge of selecting appropriate locations for their distribution centers to maintain operational efficiency and customer satisfaction. This thesis introduces a structured 5 step of phased method to address the distribution center site selection problem, considering various company-specific strategic objectives. The method incorporates determining factors identification, constraints selection, requirements outlining, alternatives generation and alternatives evaluation. A morphological chart is used in the alternative generation phase to create a range of potential locations alternatives. The Multi-Criteria Decision-Making (MCDM) method is applied for the systematic evaluation of the alternatives. The thesis suggests that expanding the set of determining factors, incorporating a broader range of stakeholder inputs and further enriching the alternatives through diversified means could enhance the method’s applicability and accuracy. This method offers FMCG companies a robust decision-making tool for strategic distribution center location selection, catering to individual business objectives and market dynamics.

This research lays forward a framework where supplier segmentation, supplier collaboration models, and supplier development strategies are integrated, specifically for remanufacturing. As remanufacturing is gaining popularity this pioneering research may serve as a first step towards more research for
companies that are willing to drive remanufacturing across the whole supply chain. This research compares two proven supplier segmentation methods and links it with supplier new collaboration and existing supplier development strategies. As a result of this study, an integrated framework is proposed. For future research, the framework can be applied to other cases or the framework could be extended.

SITA is the world’s leading specialist in air transport communications and information technology which works with around 400 air transport members and has 2800 customers in 190 countries. TS6 kiosk is the newest generation of its kiosk family and is facing a complicated situation now. Usually, the production of a kiosk is using make-to-order methods. However, if this method is adopted, the customers of SITA can not receive their productions within the expected periods of time. Besides this, SITA also can not get the procurement discount from the suppliers if they only purchase a low volume and do not make changes. In order to solve this problem, demand forecasting is conducted using the historical sales data of the TS6 kiosk. Through the literature review, suitable qualitative forecasting methods and quantitative forecasting methods which were mainly used in the FMCG industry are got together and combined to increase the forecast accuracy in this research to come out with the final forecast result of the TS6 kiosk. This research also explores the possibility of demand forecasting for the slow-moving consuming industry. By successfully conducting the final forecast, the result can help SITA to shorten procurement lead time so as to meet customers’ expectations as well as save total costs.
KeyWords: Demand Forecasting, Quantitative Forecasting Methods, Qualitative Forecasting Methods, ARIMA, SARIMA, Residual Analysis.

The growth in the e-commerce platforms and online shopping has been increasing exponentially in the last decade. Businesses and logistics companies are finding new ways to ensure fast, sustainable, and safe deliveries to customers through existing means. However, to meet the ever increasing demand of deliveries, innovative shipping or delivery alternatives have been on the rise across the world. Crowd-Shipping is one such concept which aims at taking advantage of regular travellers with unused space and additional time who can pick up packages, in order to deliver packages to customers who lie along or near the same route for a small compensation. Since the Netherlands has witnesses massive e-commerce growth and demand for efficient and fast deliveries, this study aimed at how Crowd-Shipping can potentially be implemented in the Netherlands through considering a set of drivers, barriers and a business model innovation perspective. The study first investigates the existing research on the concept, focusing on the drivers, barriers and business model aspects. In order to explore these findings and potentially more or findings, we conducted the methodology which consisted of interviews and surveys. Post the interviews and surveys, a thorough analysis of the findings was conducted through content analysis, descriptive statistics and correlations. The results from the research indicate the importance of the findings from the literature with respect to existing drivers, barriers and business model perspectives while also providing insight into more drivers, barriers and business model perspectives that need to be considered for Crowd-Shipping deployment. These results are discussed in detail providing in-depth insight on the Crowd-Shipping aspects mentioned. We conclude the research by suggesting how Crowd-Shipping can be implemented in the Netherlands considering these drivers, barriers and business model aspects, following which, further research recommendations are provided.

Aircraft LRU (Line Replaceable Unit) MROs (Maintenance, Repair Organization) struggle to meet service levels. To stay competitive, literature describes two strategies; increasing contracted fleet size and not reaching projected repair turn around times. Besides these two strategies, literature does not describe the use of triple echelon order fulfillment for global contracts but it is recommended Sprong (2019) Munsters (2019). This research aims to find relations between the service level and the two strategies from the perspective of expansion rules and operations. A discrete time-step simulation is proposed because the systems component availability changes over time and because of the stochastic behavior of repair time and component failure. The found relations are firstly that send from base operational procedures and secondly that mbk stock influence the theoretic repair time performance. The second relation is that when adding more fleet units (apply a bigger pool), the current performance will deteriorate.

One of the ways that Transmission System Operators (TSOs) can become more sustainable is by integrating sustainable goals in their projects using the associated procurement and tender processes. However, there are several limitations related to this integration. This research aims to identify these challenges by looking at the Dutch TSO, TenneT and its specific context. Furthermore, the research aims to provide a decision-support framework through which these limitations can be countered and the integration of sustainable goals in projects can be achieved.
First, a literature study was conducted to define sustainability and the concepts associated with it. To properly understand the context of TenneT and identify the limitations associated with sustainability, company professionals were interviewed. This pointed to the need for prioritisation and gradual integration of sustainability measures. A desktop study was also conducted to identify the company’s sustainable goals, their associated measures, and their real-life applications. A decision-support framework for integrating sustainable goals in projects, based on Multi-Criteria Decision-Analysis (MCDA) was proposed. Using the Best-Worst Method (BWM), as the selected MCDA method, developed by Rezaei (2015), a sustainability workshop was organised where decision-makers from TenneT were asked to select relevant sustainability goals for TenneT, and the ones selected by three or more decision-makers were then used for performing the BWM. To avoid focusing on already well-performing sustainability goals, the performance of their associated measures was studied, using a Maturity Model based on the work of Silvius and Schipper (2015). Finally, an Importance-Performance Analysis (Martilla and James 1977) was performed, to assess future actions for each sustainable goal. The BWM showed that the Planet aspect was selected as the most important one for stimulating sustainability in projects. Additionally, Reducing Emissions was selected as the most important sub-aspect while in the People aspect, Health and Safety ranked first. The Maturity Model showed that the least integrated goals in projects are Human Rights, Labour Conditions and Decent Work, Energy Consumption, Materials and Natural Resources and Circularity and Waste. The BWM and Maturity Model also produced interesting results regarding the awareness of decision-makers and how they perceive sustainable goals and the performance of their associated measures in projects.
Based on the Importance-Performance analysis, the research suggests integrating the sustainable goal of Circularity and Waste in the company’s following projects. This can be achieved by using existing sustainable measures, such as the Raw Material Passport (RMP), in the tender process. Using measures such as the RMP in the pre-qualification phases or selection phase, as entry requirements with specific targets to be met, or as an award criterion, aiming to create added value for the project, can stimulate the contractors and suppliers to act more sustainably. This will in turn increase TenneT’s sustainable performance and will help the company meet its own sustainable targets.

Transport networks are operated under a pre-designed plan to carry out the trans- portation tasks. Perturbations can inevitably arise exogenously or endogenously, and cause disturbances to the transit of passengers or the delivery of freights. A transitional plan is needed to deal with the perturbations and accomplish the trans- port tasks. The transitional plan is referred to as perturbations management in this study. The first contribution of this work is that we construct the re-planning models for multi-class networks. The perturbation management for the single-class transport network, for instance, a passenger railway network is well studied, however, it is still missing for multi-class transport networks. In real applications, passenger flows and freight flow sometimes co-exist in one system. The volume of freight transport keeps increasing. On the other hand, there are usually multi-mode services associated to finish the transit of one object. The current research is centred around this point. We study perturbation management for multi-class networks, which involve the mixed passenger and freight railway networks and deploy various modes of service vehicles. There are several important research questions regarding the management of heterogeneous traffic flows in case of delays. The first challenge is to get an overview of the focus of the existing literature in rail bound networks and find out what are the promising directions for further research. Second, concerning the modeling part, the challenge is to come up with a replanning strategy and determine the control objective. Another task is to find out how to manage the different flows involved, either for the served clients or for the serving vehicles. Last concern is to explore how to achieve flexibility for the integrated network. The second contribution of this work is that the replanning models arrange the heterogeneous traffic according to their characteristics. When delays occur to a mixed passenger and freight railway network, the replanning gives more flexibility to the freight trains. The underlying reason is that the freight clients, i.e., the shippers care mainly about the finally delivery time instead of the taken route or the waiting time at the intermediate stations. While in the synchromodality replanning, the three modes of service are scheduled according to their applied sub-networks. In the multimodal transport network, the large capacity vehicles are aligned with the freight flows. Another contribution of this thesis is that the models achieve the synchronization of the involved flows. This is vital to perturbation management, even if the applied networks are different. We pursue the synchronization of flows from both time and space dimensions. We adjust the scheduling of the metro vehicles in agreement with the massive passenger flows (Chapter 3). We align the scheduling of three modes of service vehicles according to the delayed preceding vehicles or shipment release (Chapter 5). Meanwhile, the passenger transfer management (Chapter 3 and Chapter 4) and the freight flows re-assignment (Chapter 4 and Chapter 5) is integrated with the re-scheduling. The synergy of the rescheduling and re-assignment can optimally deploy the affected capacity to accomplish the transport tasks and minimize the operation costs.

This thesis studies the factors that influence physical distribution structure design. Distribution Structure Design (DSD) concerns the spatial layout of the distribution channel as well as the location(s) of logistics facilities. Despite the frequent treatment of DSD in supply chain handbooks, an empirically validated conceptual framework of factors is still lacking. This thesis studies DSD inmultiple industry sectors (Fashion, Consumer Electronics, Online Retail) and proposes a conceptual framework.

The aim of this thesis is to investigate how the collection of industrial waste can be managed with different types of sub-contraction. Literature review was conducted in order to obtain knowledge about how this type of problems can be modelled along with the types of sub-contraction and how the logistical performance could be assessed. Interviews were performed to get a better understanding of the decision making for this type of problems. Thereafter, two mathematical models were formulated in order to model the decisions at tactical and operational level. In order to get more insights the developed models were implemented with real-life data. Following, further testings were performed to determine the efficiency of the developed models and to obtain managerial insights. A key conclusion was that among the three types of sub-contraction that were examined, the results obtained from the route based and the tour based type give the most realistic solutions. Overall, the results indicate that the introduction of sub-contraction in waste collection brings considerable cost-savings.

This research aims to identify what the customers’ acceptance is regarding various technological alternatives designed to prevent unnecessary apparel returns within the context of apparel e-commerce. This is done by applying a more qualitative approach and operationalization of the Technology Acceptance Model (TAM), whereby less data is required to produce reliable results. As such, a Multi-Criteria Decision-Analysis (MCDA) approach is used, wherein the novel Bayesian Group Best-Worst Method (BWM) is applied to infer the optimal group weights of the indicators (i.e. criteria) that influence customers’(users’) technology acceptance (TA). This is done within the context of apparel e-commerce and with the application of qualitative tools such as an online BWM survey and expert interviews. This research contributes to the empirical application of the novel Bayesian BWM, in the specific field of apparel e-commerce and proves that users’ technology acceptance can be predicted by applying the aforementioned MCDA approach as well.