10,056 research outputs found

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    On the Mechanism of Building Core Competencies: a Study of Chinese Multinational Port Enterprises

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    This study aims to explore how Chinese multinational port enterprises (MNPEs) build their core competencies. Core competencies are firms’special capabilities and sources to gain sustainable competitive advantage (SCA) in marketplace, and the concept led to extensive research and debates. However, few studies include inquiries about the mechanisms of building core competencies in the context of Chinese MNPEs. Accordingly, answers were sought to three research questions: 1. What are the core competencies of the Chinese MNPEs? 2. What are the mechanisms that the Chinese MNPEs use to build their core competencies? 3. What are the paths that the Chinese MNPEs pursue to build their resources bases? The study adopted a multiple-case study design, focusing on building mechanism of core competencies with RBV. It selected purposively five Chinese leading MNPEs and three industry associations as Case Companies. The study revealed three main findings. First, it identified three generic core competencies possessed by Case Companies, i.e., innovation in business models and operations, utilisation of technologies, and acquisition of strategic resources. Second, it developed the conceptual framework of the Mechanism of Building Core Competencies (MBCC), which is a process of change of collective learning in effective and efficient utilization of resources of a firm in response to critical events. Third, it proposed three paths to build core competencies, i.e., enhancing collective learning, selecting sustainable processes, and building resource base. The study contributes to the knowledge of core competencies and RBV in three ways: (1) presenting three generic core competencies of the Chinese MNPEs, (2) proposing a new conceptual framework to explain how Chinese MNPEs build their core competencies, (3) suggesting a solid anchor point (MBCC) to explain the links among resources, core competencies, and SCA. The findings set benchmarks for Chinese logistics industry and provide guidelines to build core competencies

    Wildlife trade in Latin America: people, economy and conservation

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    Wildlife trade is among the main threats to biodiversity conservation and may pose a risk to human health because of the spread of zoonotic diseases. To avoid social, economic and environmental consequences of illegal trade, it is crucial to understand the factors influencing the wildlife market and the effectiveness of policies already in place. I aim to unveil the biological and socioeconomic factors driving wildlife trade, the health risks imposed by the activity, and the effectiveness of certified captive-breeding as a strategy to curb the illegal market in Latin America through a multidisciplinary approach. I assess socioeconomic correlates of the emerging international trade in wild cat species from Latin America using a dataset of >1,000 seized cats, showing that high levels of corruption and Chinese private investment and low income per capita were related to higher numbers of jaguar seizures. I assess the effectiveness of primate captive-breeding programmes as an intervention to curb wildlife trafficking. Illegal sources held >70% of the primate market share. Legal primates are more expensive, and the production is not sufficiently high to fulfil the demand. I assess the scale of the illegal trade and ownership of venomous snakes in Brazil. Venomous snake taxa responsible for higher numbers of snakebites were those most often kept as pets. I uncover how online wildlife pet traders and consumers responded to campaigns associating the origin of the COVID-19 pandemic. Of 20,000 posts on Facebook groups, only 0.44% mentioned COVID-19 and several stimulated the trade in wild species during lockdown. Despite the existence of international and national wildlife trade regulations, I conclude that illegal wildlife trade is still an issue that needs further addressing in Latin America. I identify knowledge gaps and candidate interventions to amend the current loopholes to reduce wildlife trafficking. My aspiration with this thesis is to provide useful information that can inform better strategies to tackle illegal wildlife trade in Latin America

    Cost-effective non-destructive testing of biomedical components fabricated using additive manufacturing

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    Biocompatible titanium-alloys can be used to fabricate patient-specific medical components using additive manufacturing (AM). These novel components have the potential to improve clinical outcomes in various medical scenarios. However, AM introduces stability and repeatability concerns, which are potential roadblocks for its widespread use in the medical sector. Micro-CT imaging for non-destructive testing (NDT) is an effective solution for post-manufacturing quality control of these components. Unfortunately, current micro-CT NDT scanners require expensive infrastructure and hardware, which translates into prohibitively expensive routine NDT. Furthermore, the limited dynamic-range of these scanners can cause severe image artifacts that may compromise the diagnostic value of the non-destructive test. Finally, the cone-beam geometry of these scanners makes them susceptible to the adverse effects of scattered radiation, which is another source of artifacts in micro-CT imaging. In this work, we describe the design, fabrication, and implementation of a dedicated, cost-effective micro-CT scanner for NDT of AM-fabricated biomedical components. Our scanner reduces the limitations of costly image-based NDT by optimizing the scanner\u27s geometry and the image acquisition hardware (i.e., X-ray source and detector). Additionally, we describe two novel techniques to reduce image artifacts caused by photon-starvation and scatter radiation in cone-beam micro-CT imaging. Our cost-effective scanner was designed to match the image requirements of medium-size titanium-alloy medical components. We optimized the image acquisition hardware by using an 80 kVp low-cost portable X-ray unit and developing a low-cost lens-coupled X-ray detector. Image artifacts caused by photon-starvation were reduced by implementing dual-exposure high-dynamic-range radiography. For scatter mitigation, we describe the design, manufacturing, and testing of a large-area, highly-focused, two-dimensional, anti-scatter grid. Our results demonstrate that cost-effective NDT using low-cost equipment is feasible for medium-sized, titanium-alloy, AM-fabricated medical components. Our proposed high-dynamic-range strategy improved by 37% the penetration capabilities of an 80 kVp micro-CT imaging system for a total x-ray path length of 19.8 mm. Finally, our novel anti-scatter grid provided a 65% improvement in CT number accuracy and a 48% improvement in low-contrast visualization. Our proposed cost-effective scanner and artifact reduction strategies have the potential to improve patient care by accelerating the widespread use of patient-specific, bio-compatible, AM-manufactured, medical components

    Modelling and Solving the Single-Airport Slot Allocation Problem

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    Currently, there are about 200 overly congested airports where airport capacity does not suffice to accommodate airline demand. These airports play a critical role in the global air transport system since they concern 40% of global passenger demand and act as a bottleneck for the entire air transport system. This imbalance between airport capacity and airline demand leads to excessive delays, as well as multi-billion economic, and huge environmental and societal costs. Concurrently, the implementation of airport capacity expansion projects requires time, space and is subject to significant resistance from local communities. As a short to medium-term response, Airport Slot Allocation (ASA) has been used as the main demand management mechanism. The main goal of this thesis is to improve ASA decision-making through the proposition of models and algorithms that provide enhanced ASA decision support. In doing so, this thesis is organised into three distinct chapters that shed light on the following questions (I–V), which remain untapped by the existing literature. In parentheses, we identify the chapters of this thesis that relate to each research question. I. How to improve the modelling of airline demand flexibility and the utility that each airline assigns to each available airport slot? (Chapters 2 and 4) II. How can one model the dynamic and endogenous adaptation of the airport’s landside and airside infrastructure to the characteristics of airline demand? (Chapter 2) III. How to consider operational delays in strategic ASA decision-making? (Chapter 3) IV. How to involve the pertinent stakeholders into the ASA decision-making process to select a commonly agreed schedule; and how can one reduce the inherent decision-complexity without compromising the quality and diversity of the schedules presented to the decision-makers? (Chapter 3) V. Given that the ASA process involves airlines (submitting requests for slots) and coordinators (assigning slots to requests based on a set of rules and priorities), how can one jointly consider the interactions between these two sides to improve ASA decision-making? (Chapter 4) With regards to research questions (I) and (II), the thesis proposes a Mixed Integer Programming (MIP) model that considers airlines’ timing flexibility (research question I) and constraints that enable the dynamic and endogenous allocation of the airport’s resources (research question II). The proposed modelling variant addresses several additional problem characteristics and policy rules, and considers multiple efficiency objectives, while integrating all constraints that may affect airport slot scheduling decisions, including the asynchronous use of the different airport resources (runway, aprons, passenger terminal) and the endogenous consideration of the capabilities of the airport’s infrastructure to adapt to the airline demand’s characteristics and the aircraft/flight type associated with each request. The proposed model is integrated into a two-stage solution approach that considers all primary and several secondary policy rules of ASA. New combinatorial results and valid tightening inequalities that facilitate the solution of the problem are proposed and implemented. An extension of the above MIP model that considers the trade-offs among schedule displacement, maximum displacement, and the number of displaced requests, is integrated into a multi-objective solution framework. The proposed framework holistically considers the preferences of all ASA stakeholder groups (research question IV) concerning multiple performance metrics and models the operational delays associated with each airport schedule (research question III). The delays of each schedule/solution are macroscopically estimated, and a subtractive clustering algorithm and a parameter tuning routine reduce the inherent decision complexity by pruning non-dominated solutions without compromising the representativeness of the alternatives offered to the decision-makers (research question IV). Following the determination of the representative set, the expected delay estimates of each schedule are further refined by considering the whole airfield’s operations, the landside, and the airside infrastructure. The representative schedules are ranked based on the preferences of all ASA stakeholder groups concerning each schedule’s displacement-related and operational-delay performance. Finally, in considering the interactions between airlines’ timing flexibility and utility, and the policy-based priorities assigned by the coordinator to each request (research question V), the thesis models the ASA problem as a two-sided matching game and provides guarantees on the stability of the proposed schedules. A Stable Airport Slot Allocation Model (SASAM) capitalises on the flexibility considerations introduced for addressing research question (I) through the exploitation of data submitted by the airlines during the ASA process and provides functions that proxy each request’s value considering both the airlines’ timing flexibility for each submitted request and the requests’ prioritisation by the coordinators when considering the policy rules defining the ASA process. The thesis argues on the compliance of the proposed functions with the primary regulatory requirements of the ASA process and demonstrates their applicability for different types of slot requests. SASAM guarantees stability through sets of inequalities that prune allocations blocking the formation of stable schedules. A multi-objective Deferred-Acceptance (DA) algorithm guaranteeing the stability of each generated schedule is developed. The algorithm can generate all stable non-dominated points by considering the trade-off between the spilled airline and passenger demand and maximum displacement. The work conducted in this thesis addresses several problem characteristics and sheds light on their implications for ASA decision-making, hence having the potential to improve ASA decision-making. Our findings suggest that the consideration of airlines’ timing flexibility (research question I) results in improved capacity utilisation and scheduling efficiency. The endogenous consideration of the ability of the airport’s infrastructure to adapt to the characteristics of airline demand (research question II) enables a more efficient representation of airport declared capacity that results in the scheduling of additional requests. The concurrent consideration of airlines’ timing flexibility and the endogenous adaptation of airport resources to airline demand achieves an improved alignment between the airport infrastructure and the characteristics of airline demand, ergo proposing schedules of improved efficiency. The modelling and evaluation of the peak operational delays associated with the different airport schedules (research question III) provides allows the study of the implications of strategic ASA decision-making for operations and quantifies the impact of the airport’s declared capacity on each schedule’s operational performance. In considering the preferences of the relevant ASA stakeholders (airlines, coordinators, airport, and air traffic authorities) concerning multiple operational and strategic ASA efficiency metrics (research question IV) the thesis assesses the impact of alternative preference considerations and indicates a commonly preferred schedule that balances the stakeholders’ preferences. The proposition of representative subsets of alternative schedules reduces decision-complexity without significantly compromising the quality of the alternatives offered to the decision-making process (research question IV). The modelling of the ASA as a two-sided matching game (research question V), results in stable schedules consisting of request-to-slot assignments that provide no incentive to airlines and coordinators to reject or alter the proposed timings. Furthermore, the proposition of stable schedules results in more intensive use of airport capacity, while simultaneously improving scheduling efficiency. The models and algorithms developed as part of this thesis are tested using airline requests and airport capacity data from coordinated airports. Computational results that are relevant to the context of the considered airport instances provide evidence on the potential improvements for the current ASA process and facilitate data-driven policy and decision-making. In particular, with regards to the alignment of airline demand with the capabilities of the airport’s infrastructure (questions I and II), computational results report improved slot allocation efficiency and airport capacity utilisation, which for the considered airport instance translate to improvements ranging between 5-24% for various schedule performance metrics. In reducing the difficulty associated with the assessment of multiple ASA solutions by the stakeholders (question IV), instance-specific results suggest reductions to the number of alternative schedules by 87%, while maintaining the quality of the solutions presented to the stakeholders above 70% (expressed in relation to the initially considered set of schedules). Meanwhile, computational results suggest that the concurrent consideration of ASA stakeholders’ preferences (research question IV) with regards to both operational (research question III) and strategic performance metrics leads to alternative airport slot scheduling solutions that inform on the trade-offs between the schedules’ operational and strategic performance and the stakeholders’ preferences. Concerning research question (V), the application of SASAM and the DA algorithm suggest improvements to the number of unaccommodated flights and passengers (13 and 40% improvements) at the expense of requests concerning fewer passengers and days of operations (increasing the number of rejected requests by 1.2% in relation to the total number of submitted requests). The research conducted in this thesis aids in the identification of limitations that should be addressed by future studies to further improve ASA decision-making. First, the thesis focuses on exact solution approaches that consider the landside and airside infrastructure of the airport and generate multiple schedules. The proposition of pre-processing techniques that identify the bottleneck of the airport’s capacity, i.e., landside and/or airside, can be used to reduce the size of the proposed formulations and improve the required computational times. Meanwhile, the development of multi-objective heuristic algorithms that consider several problem characteristics and generate multiple efficient schedules in reasonable computational times, could extend the capabilities of the models propositioned in this thesis and provide decision support for some of the world’s most congested airports. Furthermore, the thesis models and evaluates the operational implications of strategic airport slot scheduling decisions. The explicit consideration of operational delays as an objective in ASA optimisation models and algorithms is an issue that merits investigation since it may further improve the operational performance of the generated schedules. In accordance with current practice, the models proposed in this work have considered deterministic capacity parameters. Perhaps, future research could propose formulations that consider stochastic representations of airport declared capacity and improve strategic ASA decision-making through the anticipation of operational uncertainty and weather-induced capacity reductions. Finally, in modelling airlines’ utility for each submitted request and available time slot the thesis proposes time-dependent functions that utilise available data to approximate airlines’ scheduling preferences. Future studies wishing to improve the accuracy of the proposed functions could utilise commercial data sources that provide route-specific information; or in cases that such data is unavailable, employ data mining and machine learning methodologies to extract airlines’ time-dependent utility and preferences

    Consolidation of Urban Freight Transport – Models and Algorithms

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    Urban freight transport is an indispensable component of economic and social life in cities. Compared to other types of transport, however, it contributes disproportionately to the negative impacts of traffic. As a result, urban freight transport is closely linked to social, environmental, and economic challenges. Managing urban freight transport and addressing these issues poses challenges not only for local city administrations but also for companies, such as logistics service providers (LSPs). Numerous policy measures and company-driven initiatives exist in the area of urban freight transport to overcome these challenges. One central approach is the consolidation of urban freight transport. This dissertation focuses on urban consolidation centers (UCCs) which are a widely studied and applied measure in urban freight transport. The fundamental idea of UCCs is to consolidate freight transport across companies in logistics facilities close to an urban area in order to increase the efficiency of vehicles delivering goods within the urban area. Although the concept has been researched and tested for several decades and it was shown that it can reduce the negative externalities of freight transport in cities, in practice many UCCs struggle with a lack of business participation and financial difficulties. This dissertation is primarily focused on the costs and savings associated with the use of UCCs from the perspective of LSPs. The cost-effectiveness of UCC use, which is also referred to as cost attractiveness, can be seen as a crucial condition for LSPs to be interested in using UCC systems. The overall objective of this dissertation is two-fold. First, it aims to develop models to provide decision support for evaluating the cost-effectiveness of using UCCs. Second, it aims to analyze the impacts of urban freight transport regulations and operational characteristics on the cost attractiveness of using UCCs from the perspective of LSPs. In this context, a distinction is made between UCCs that are jointly operated by a group of LSPs and UCCs that are operated by third parties who offer their urban transport service for a fee. The main body of this dissertation is based on three research papers. The first paper focuses on jointly-operated UCCs that are operated by a group of cooperating LSPs. It presents a simulation model to analyze the financial impacts on LSPs participating in such a scheme. In doing so, a particular focus is placed on urban freight transport regulations. A case study is used to analyze the operation of a jointly-operated UCC for scenarios involving three freight transport regulations. The second and third papers take on a different perspective on UCCs by focusing on third-party operated UCCs. In contrast to the first paper, the second and third papers present an evaluation approach in which the decision to use UCCs is integrated with the vehicle route planning of LSPs. In addition to addressing the basic version of this integrated routing problem, known as the vehicle routing problem with transshipment facilities (VRPTF), the second paper presents problem extensions that incorporate time windows, fleet size and mix decisions, and refined objective functions. To heuristically solve the basic problem and the new problem variants, an adaptive large neighborhood search (ALNS) heuristic with embedded local search heuristic and set partitioning problem (SPP) is presented. Furthermore, various factors influencing the cost attractiveness of UCCs, including time windows and usage fees, are analyzed using a real-world case study. The third paper extends the work of the second paper and incorporates daily and entrance-based city toll schemes and enables multi-trip routing. A mixed-integer linear programming (MILP) formulation of the resulting problem is proposed, as well as an ALNS solution heuristic. Moreover, a real-world case study with three European cities is used to analyze the impact of the two city toll systems in different operational contexts

    Hunting Wildlife in the Tropics and Subtropics

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    The hunting of wild animals for their meat has been a crucial activity in the evolution of humans. It continues to be an essential source of food and a generator of income for millions of Indigenous and rural communities worldwide. Conservationists rightly fear that excessive hunting of many animal species will cause their demise, as has already happened throughout the Anthropocene. Many species of large mammals and birds have been decimated or annihilated due to overhunting by humans. If such pressures continue, many other species will meet the same fate. Equally, if the use of wildlife resources is to continue by those who depend on it, sustainable practices must be implemented. These communities need to remain or become custodians of the wildlife resources within their lands, for their own well-being as well as for biodiversity in general. This title is also available via Open Access on Cambridge Core

    Industry 4.0: product digital twins for remanufacturing decision-making

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    Currently there is a desire to reduce natural resource consumption and expand circular business principles whilst Industry 4.0 (I4.0) is regarded as the evolutionary and potentially disruptive movement of technology, automation, digitalisation, and data manipulation into the industrial sector. The remanufacturing industry is recognised as being vital to the circular economy (CE) as it extends the in-use life of products, but its synergy with I4.0 has had little attention thus far. This thesis documents the first investigating into I4.0 in remanufacturing for a CE contributing a design and demonstration of a model that optimises remanufacturing planning using data from different instances in a product’s life cycle. The initial aim of this work was to identify the I4.0 technology that would enhance the stability in remanufacturing with a view to reducing resource consumption. As the project progressed it narrowed to focus on the development of a product digital twin (DT) model to support data-driven decision making for operations planning. The model’s architecture was derived using a bottom-up approach where requirements were extracted from the identified complications in production planning and control that differentiate remanufacturing from manufacturing. Simultaneously, the benefits of enabling visibility of an asset’s through-life health were obtained using a DT as the modus operandi. A product simulator and DT prototype was designed to use Internet of Things (IoT) components, a neural network for remaining life estimations and a search algorithm for operational planning optimisation. The DT was iteratively developed using case studies to validate and examine the real opportunities that exist in deploying a business model that harnesses, and commodifies, early life product data for end-of-life processing optimisation. Findings suggest that using intelligent programming networks and algorithms, a DT can enhance decision-making if it has visibility of the product and access to reliable remanufacturing process information, whilst existing IoT components provide rudimentary “smart” capabilities, but their integration is complex, and the durability of the systems over extended product life cycles needs to be further explored
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