1,354,719 research outputs found
Remote sensing green-up date from SPOT-VEGETATION data at Daring Lake (1998-2012), link to GeoTIFF
No full textOne of the reported changes in arctic and boreal ecosystems in response to warming climate is the advance of the leaf and flower appearance in spring. We developed a remote sensing (RS) method, using 1km spatial resolution SPOT-VGT sensor, to estimate the date of boreal ecosystem green-up without detrimental effect of snow on the signal.
The tif file is made of 15 images, one for each year from 1998 to 2012. The pixel value gives the date at which the ecosystem starts green-up (expressed as the day of year) as estimated in Delbart et al. 2005
Capacity planning under uncertainty in synchromodal transport
No full textThe shift towards more sustainable modalities such as rail transport is effective in reducing the negative externalities caused by unimodal road transport. However, despite the more sustainable alternatives being potentially cheaper, the share of road transport in continental freight transport in the EU remains high and has even increased in the last decade. The low adoption rate of intermodal transport, the transport of goods with multiple modalities in which freight remains in the same loading unit, is due to it being perceived as slower, less reliable and less flexible compared to road transport. The need for flexibility arises from the various uncertainties encountered in transport networks. Synchromodal transport is an extension of intermodal transport which leverages the advantages of multiple transport modalities with an integrated approach. It addresses the lower flexibility of intermodal transport, which forms a major barrier to the modal shift. The main features of synchromodal transport are synchronised operations between actors in the supply chain, mode-free booking, and real-time planning.
This dissertation focuses on rail capacity planning under uncertainty in a synchromodal context from the perspective of logistics service providers (LSPs). LSPs are tasked with transporting customer orders through transport services. LSPs typically do not own the vehicles with which their orders are shipped, instead resorting to buying transport capacity from carriers such as rail operators. In contrast to road transport, rail services generally follow fixed schedules and transport capacity is bought for several months at a time. This causes greater demand uncertainty for LSPs compared to road transport, since their own customer orders only arrive on short notice. Due to this demand uncertainty, capacity plans are updated in the short term when demand is known. However, since rail capacity is bought externally, this introduces uncertainty on the remaining amount of capacity at the time of replanning. Two LSPs that make use of both rail and road transport were consulted to discover their main challenges related to intermodal transport. Both LSPs found the demand uncertainty when making long-term rail capacity commitments and the capacity uncertainty for short-term adjustments the most challenging aspects of intermodal planning.
A literature review on uncertainty in intermodal and synchromodal transport is performed first to identify researched uncertainties and existing measures to mitigate their impact. The considered capacity planning decisions can be modelled as a service network design problem (SND). Findings from the literature review indicate that short-term replanning of intermodal services is not considered in existing SND models. Consequently, the impact of capacity uncertainty when replanning is not researched either. Moreover, in reality, the consulted LSPs do not wait for complete information before replanning their capacity. They update their capacity decisions twice: once based on partial information and once when complete demand information is available.
In the second part of this thesis, we investigated the impact of intermodal capacity replanning with stochastic demand and capacity. To this end, we developed a three-stage SND model. Initial capacity decisions are made in the first stage, which are updated in the second stage based on partial demand information, and updated with complete information in the third stage. Capacity updates take on the form of additional bookings and cancellations. The model with two replanning stages is compared against a model with a single replanning stage, which only updates capacity when complete information is available, and a model without replanning. Additional comparisons against the case with perfect information indicates the cost of uncertainty. A full factorial experiment is performed to compare the performance across various market conditions. Our results reveal that replanning leads to improved planning under uncertainty, which is reflected by lower costs and higher rail shares. As such, it is effective in stimulating the modal shift. Factors which positively influence the impact of replanning are cheaper and more rail capacity on the market, more direct rail connections, more transhipment options, and increased demand uncertainty. In addition, replanning is more advantageous at lower costs of cancelling capacity and if the individual demand is negatively correlated with the market demand. Introducing replanning with complete information yields the largest improvements. Further cost savings and rail share increases of a second replanning stage, with partial information, are smaller. As such, there are diminishing returns with multiple replanning stages. The advantages of replanning with partial information increase with the amount of uncertainty that is resolved at the time of replanning.
Coordinated planning is a core feature of synchromodality. However, the majority of literature on synchromodal transport focuses on the real-time aspect, whereas research on cooperation as a means to mitigate uncertainty is scarce. Therefore, in the third part of this thesis, cooperative capacity planning is researched, with and without replanning. A centralised cooperative approach between two LSPs is compared against the case with no cooperation. In the considered cooperative approach, the demands are pooled, reducing the demand volume fluctuations over the planning horizon and enabling a more efficient allocation of capacity to customer orders. Results of a sensitivity analysis indicate that cooperation is more advantageous in cases with sufficient capacity on the market for both participants. Cooperation yields the largest improvements for LSPs that experience large fluctuations in demand volume over their planning horizon, for instance if their demand structure consists of few large orders with uncertain time windows or due to low demand volumes. As such, smaller LSPs benefit more from cooperation. In addition, collaborative savings and rail share increases are larger with negatively correlated demand volumes between cooperants compared to uncorrelated and positively correlated demand. Our findings reveal that cooperation is effective at mitigating the impact of uncertainty, even in combination with replanning. However, as part of the uncertainty is mitigated through cooperation, the impact of replanning is diminished. The impact of a single replanning stage with complete information remains significant. In contrast, under cooperation, replanning with partial information only leads notable improvements if a large amount of uncertainty is resolved at the time of replanning
Capacity planning under uncertainty in synchromodal transport
No full textThe shift towards more sustainable modalities such as rail transport is effective in reducing the negative externalities caused by unimodal road transport. However, despite the more sustainable alternatives being potentially cheaper, the share of road transport in continental freight transport in the EU remains high and has even increased in the last decade. The low adoption rate of intermodal transport, the transport of goods with multiple modalities in which freight remains in the same loading unit, is due to it being perceived as slower, less reliable and less flexible compared to road transport. The need for flexibility arises from the various uncertainties encountered in transport networks. Synchromodal transport is an extension of intermodal transport which leverages the advantages of multiple transport modalities with an integrated approach. It addresses the lower flexibility of intermodal transport, which forms a major barrier to the modal shift. The main features of synchromodal transport are synchronised operations between actors in the supply chain, mode-free booking, and real-time planning.
This dissertation focuses on rail capacity planning under uncertainty in a synchromodal context from the perspective of logistics service providers (LSPs). LSPs are tasked with transporting customer orders through transport services. LSPs typically do not own the vehicles with which their orders are shipped, instead resorting to buying transport capacity from carriers such as rail operators. In contrast to road transport, rail services generally follow fixed schedules and transport capacity is bought for several months at a time. This causes greater demand uncertainty for LSPs compared to road transport, since their own customer orders only arrive on short notice. Due to this demand uncertainty, capacity plans are updated in the short term when demand is known. However, since rail capacity is bought externally, this introduces uncertainty on the remaining amount of capacity at the time of replanning. Two LSPs that make use of both rail and road transport were consulted to discover their main challenges related to intermodal transport. Both LSPs found the demand uncertainty when making long-term rail capacity commitments and the capacity uncertainty for short-term adjustments the most challenging aspects of intermodal planning.
A literature review on uncertainty in intermodal and synchromodal transport is performed first to identify researched uncertainties and existing measures to mitigate their impact. The considered capacity planning decisions can be modelled as a service network design problem (SND). Findings from the literature review indicate that short-term replanning of intermodal services is not considered in existing SND models. Consequently, the impact of capacity uncertainty when replanning is not researched either. Moreover, in reality, the consulted LSPs do not wait for complete information before replanning their capacity. They update their capacity decisions twice: once based on partial information and once when complete demand information is available.
In the second part of this thesis, we investigated the impact of intermodal capacity replanning with stochastic demand and capacity. To this end, we developed a three-stage SND model. Initial capacity decisions are made in the first stage, which are updated in the second stage based on partial demand information, and updated with complete information in the third stage. Capacity updates take on the form of additional bookings and cancellations. The model with two replanning stages is compared against a model with a single replanning stage, which only updates capacity when complete information is available, and a model without replanning. Additional comparisons against the case with perfect information indicates the cost of uncertainty. A full factorial experiment is performed to compare the performance across various market conditions. Our results reveal that replanning leads to improved planning under uncertainty, which is reflected by lower costs and higher rail shares. As such, it is effective in stimulating the modal shift. Factors which positively influence the impact of replanning are cheaper and more rail capacity on the market, more direct rail connections, more transhipment options, and increased demand uncertainty. In addition, replanning is more advantageous at lower costs of cancelling capacity and if the individual demand is negatively correlated with the market demand. Introducing replanning with complete information yields the largest improvements. Further cost savings and rail share increases of a second replanning stage, with partial information, are smaller. As such, there are diminishing returns with multiple replanning stages. The advantages of replanning with partial information increase with the amount of uncertainty that is resolved at the time of replanning.
Coordinated planning is a core feature of synchromodality. However, the majority of literature on synchromodal transport focuses on the real-time aspect, whereas research on cooperation as a means to mitigate uncertainty is scarce. Therefore, in the third part of this thesis, cooperative capacity planning is researched, with and without replanning. A centralised cooperative approach between two LSPs is compared against the case with no cooperation. In the considered cooperative approach, the demands are pooled, reducing the demand volume fluctuations over the planning horizon and enabling a more efficient allocation of capacity to customer orders. Results of a sensitivity analysis indicate that cooperation is more advantageous in cases with sufficient capacity on the market for both participants. Cooperation yields the largest improvements for LSPs that experience large fluctuations in demand volume over their planning horizon, for instance if their demand structure consists of few large orders with uncertain time windows or due to low demand volumes. As such, smaller LSPs benefit more from cooperation. In addition, collaborative savings and rail share increases are larger with negatively correlated demand volumes between cooperants compared to uncorrelated and positively correlated demand. Our findings reveal that cooperation is effective at mitigating the impact of uncertainty, even in combination with replanning. However, as part of the uncertainty is mitigated through cooperation, the impact of replanning is diminished. The impact of a single replanning stage with complete information remains significant. In contrast, under cooperation, replanning with partial information only leads notable improvements if a large amount of uncertainty is resolved at the time of replanning
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Three-stage service network design in rail-road networks with demand and capacity uncertainty
No full textThis paper addresses the service network design (SND) problem in a railroad network with demand and capacity uncertainty from the perspective of a logistics service provider (LSP) in a synchromodal transport setting. Based on consultations with LSPs, we propose a three-stage model that includes an initial stage to book capacity, an intermediate stage to update capacity decisions with partial information in the form of improved demand forecasts, and a third stage with complete information for capacity updates, recourse actions and routing decisions. Stochasticity is included by means of a scenario generation approach which results in a scenario tree. The impact of replanning rail capacity once when complete information is available, and twice with both partial and complete information, is compared against the case without replanning on a set of theoretical instances with a full factorial design. Results indicate that replanning once when complete information is available yields large cost savings and increases the share of rail transport. Replanning is more advantageous with cheaper train services, networks with more transhipment options, more rail capacity, and higher levels of demand uncertainty. Introducing an additional replanning stage, with partial information, yields diminishing returns, though savings increase with the amount of uncertainty that is resolved at the time of replanning
A three-stage service network design model in synchromodal transport
No full textSynchromodal transport is potentially cheaper and more sustainable than unimodal road transport. However, capacity on high-capacity transport modes such as trains is usually determined a longer period in advance than truck capacity. When logistics service providers buy capacity on those services, they might lack complete information on their transport demand. Capacity decisions are then updated when additional information becomes available. This study proposes a service network design model to assist capacity decisions from the perspective of logistics service providers in a synchromodal setting.
Existing literature addresses this stochastic problem with two-stage models in which the initial capacity decisions are taken based on a known demand distribution and recourse actions are taken once complete demand information is available. Our model differs by including a third intermediate stage during which updates are performed with partial information. Two types of uncertainty are included: stochastic demand and a stochastic decrease of the capacity available on the market, taking into account the overall demand on the market
A capacity decision support model for synchromodal transport under uncertainty
No full textIntermodal transport is the transport of goods in the same loading unit and with multiple modes. The combination of road transport with high-capacity transport modes can lead to more sustainable long-haul freight shipping. A drawback of high-capacity transport modes such as trains and barges is their lower flexibility to uncertain events compared to trucks. This lower flexibility is still a major barrier to the modal switch. Synchromodal transport is an extension of intermodal transport which attempts to resolve this lower flexibility. Under synchromodality, mode-free booking allows LSPs to freely switch freight between available modes, which can lead to more efficient capacity utilisation.
This work presents a three-stage decision support model to assist capacity decisions in a synchromodal network under uncertain demand from the perspective of LSPs. Logistics service providers typically book slots on high-capacity transport modes such as trains and barges several months in advance, before complete demand information is available. It is assumed that the number of available capacity that can be booked decreases and becomes more expensive as the delivery date grows nearer.
Capacity decisions are a part of service network design (SND) problems. Existing research on SND employs two-stage models to account for stochasticity. In such models, capacity decisions are taken in the first stage before demand is known. Updates are performed in the second stage, at which point it is assumed that complete demand information is available. In reality, LSPs update their transport plan continuously as new information becomes available, instead of waiting until all demand is known. Our proposed model differs from two-stage models by considering a third intermediate stage to more accurately represent real-life decision making. Capacity adjustments can be made during this intermediate stage, where it is assumed that part of the demand is known
A three-stage service network design model for intermodal transport under uncertainty
No full textIntermodal transport is the transport of freight with multiple modes whereby freight remains in the same loading unit. The combination of road transport with other high-capacity transport modes such as trains and barges is potentially more sustainable than unimodal road transport for long-haul freight shipping. A drawback of those high-capacity transport modes is their lower flexibility compared to trucks, hindering the modal shift. Logistics service providers typically book capacity in advance on those services, at which point complete demand information is missing. This work aims to assist logistics service providers in their implementation of intermodal transport, which is done with a capacity decision support model.
The proposed model tackles a service network design problem with stochastic demand. In this type of problem, the aim is to select transport services based on the estimated demand and route freight through those services. In academic literature, these types of problems are studied with two-stage models. The first stage takes place before demand has materialised and only the demand distribution is known. Capacity on each service is determined at this stage. The second stage occurs in the short term when complete demand information is available. Routing decisions are taken at this stage, as well as recourse actions in case the capacity selected in the first stage is insufficient. A shortcoming of these two-stage models is that recourse actions are only performed once complete demand information is available. In reality, transport orders arrive up to a few days in advance when it might be too late to perform large updates to the transport plan. Logistics service providers revise their capacity gradually over time as new orders arrive. To more accurately represent reality, we propose a three-stage model that includes an additional intermediate stage.
A logistics service provider is the decision maker in our proposed stochastic optimisation model. It considers a rail-road intermodal network in which train services are offered by third parties. Since different logistics service providers can book those same services, the remaining amount of capacity on the market declines over time. Included uncertainties are stochastic demand and a stochastic decrease in available capacity. Regarding demand, it is assumed that only the distribution of total demand is known in the first stage. In the second stage, additional information on the total demand in the transport market leads to more accurate forecasts of demand volume. Demand materialises in the third stage. The model includes capacity decisions taken in each of the three stages and routing decisions in the last stage. Stochasticity is captured with a scenario tree and the objective function minimises the average cost over all scenarios. The model is solved with an exact algorithm with a time limit.
This work is supported by VLAIO (cSBO project DISpATch, HBC.2016.0412
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