Hydrogen is believed as a promising energy carrier that contributes to deep decarbonization, especially for the sectors hard to be directly electrified. A grid-connected wind/hydrogen system is a typical configuration for hydrogen production. For such a system, a critical barrier lies in the poor cost-competitiveness of the produced hydrogen. Researchers have found that flexible operation of a wind/hydrogen system is possible thanks to the excellent dynamic properties of electrolysis. This finding implies the system owner can strategically participate in day-ahead power markets to reduce the hydrogen production cost. However, the uncertainties from imperfect prediction of the fluctuating market price and wind power reduce the effectiveness of the offering strategy in the market. In this paper, we proposed a decision-making framework, which is based on data-driven robust chance constrained programming (DRCCP). This framework also includes multi-layer perception neural network (MLPNN) for wind power and spot electricity price prediction. Such a DRCCP-based decision framework (DDF) is then applied to make the day-ahead decision for a wind/hydrogen system. It can effectively handle the uncertainties, manage the risks and reduce the operation cost. The results show that, for the daily operation in the selected 30 days, offering strategy based on the framework reduces the overall operation cost by 24.36%, compared to the strategy based on imperfect prediction. Besides, we elaborate the parameter selections of the DRCCP to reveal the best parameter combination to obtain better optimization performance. The efficacy of the DRCCP method is also highlighted by the comparison with the chance-constrained programming method.
How value is created through service has recently undergone massive changes. Centralized service provision with clear distinctions between service offerers and beneficiaries is increasingly being substituted by value creation within decentralized networks of distributed actors integrating digital resources. One of the drivers of this transformation is blockchain technology. Applying the lens of service-dominant logic and discussing examples of blockchain-based decentralized finance, we shed light on how properties of decentralized technology stimulate value creation in service ecosystems. With this conceptual research, we postulate five propositions of decentralized value creation along the axiomatic foundations of the service-dominant logic. We provide first definitions for decentralized service as well as decentralized service ecosystems. Thereby, we contribute with an extension of the service-dominant logic to the context of decentralized ecosystems. To our knowledge, this research is among the first to add to the growing literature on blockchain value creation from a service science perspective.
Purpose: The purpose of the paper is to identify the multiple types of data that can be collected and analyzed by practitioners across the cold chain, the ICT infrastructure required to enable data capture and how to utilize the data for decision making in cold chain logistics. Design/methodology/approach: Content analysis based literature review of 38 selected research articles, published between 2000 and 2016, was used to create an overview of data capture, technologies used for collection and sharing of data, and decision making that can be supported by the data, across the cold chain and for different types of perishable food products. Findings: There is a need to understand how continuous monitoring of conditions such as temperature, humidity, and vibration can be translated to support real-time assessment of quality, determination of actual remaining shelf life of products and use of those for decision making in cold chains. Firms across the cold chain need to adopt appropriate technologies suited to the specific contexts to capture data across the cold chain. Analysis of such data over longer periods can also unearth patterns of product deterioration under different transportation conditions, which can lead to redesigning the transportation network to minimize quality loss or to take precautions to avoid the adverse transportation conditions. Research limitations/implications: The findings need to be validated through further empirical research and modeling. There are opportunities to identify all relevant parameters to capture product condition as well as transaction data across the cold chain processes for fish, meat and dairy products. Such data can then be used for supply chain (SC) planning and pricing products in the retail stores based on product conditions and traceability information. Addressing some of the above research gaps will call for multi-disciplinary research involving food science and engineering, information technologies, computer science and logistics and SC management scholars. Practical implications: The findings of this research can be beneficial for multiple players involved in the cold chain like food processing companies, logistics service providers, ports and wholesalers and retailers to understand how data can be effectively used for better decision making in cold chain and to invest in the specific technologies, which will suit the purpose. To ensure adoption of data analytics across the cold chain, it is also important to identify the player in the cold chain, which will drive and coordinate the effort. Originality/value: This paper is one of the earliest to recognize the need for a comprehensive assessment for adoption and application of data analytics in cold chain management and provides directions for future research.
The technician routing and scheduling problem (TRSP) consists of technicians serving tasks subject to qualifications, time constraints and routing costs. In the literature, the TRSP is solved either to provide actual technician plans or for performing what-if analyses on different TRSP scenarios. We present a method for building optimal TRSP scenarios, e.g., how many technicians to employ, which technician qualifications to upgrade, etc. The scenarios are built such that the combined TRSP costs (OPEX) and investment costs (CAPEX) are minimized. Using a holistic approach we can generate scenarios that would not have been found by studying the investments individually. The proposed method consists of a matheuristic based on column generation. To reduce computational time, the routing costs of a technician are approximated. The proposed method is evaluated on data from the literature and on real-life data from a telecommunication company. The evaluation shows that the proposed method successfully suggests attractive scenarios. The method especially excels in ensuring that more tasks are serviced but also reduces travel time with around 16% in the real-life instance. We believe that the proposed method could constitute an important strategic tool in field service companies and we propose future research directions to further its applicability.
The real-time provision of high-quality estimates of the ocean wave parameters at appropriate spatial resolutions are essential for the sustainable operations of marine structures. Machine learning affords considerable opportunity for providing additional value from sensor networks, fusing metocean data collected by various platforms. Exploiting the ship-as-a-wave-buoy concept, this article proposes the integration of vessel-based observations into a wave-nowcasting framework. Surrogate models are trained using a high-fidelity physics-based nearshore wave model to learn the spatial correlations between grid points within a computational domain. The performance of these different models are evaluated in a case study to assess how well wave parameters estimated through the spectral analysis of ship motions can perform as inputs to the surrogate system, to replace or complement traditional wave buoy measurements. The benchmark study identifies the advantages and limitations inherent in the methodology incorporating ship-based wave estimates to improve the reliability and availability of regional sea state information.
This report is in the context of the DMA-DTU project on Market Based Measures (MBMs) The aim of this project is to provide an overview and discussion of potential Market Based Measures under the Initial IMO Strategy for the reduction of green house gas (GHG) emissions from ships. In this context, some related developments are also seen as directly relevant to the scope of the project, mainly in the context of the possible inclusion of shipping into the EU Emissions Trading System (ETS). In 2010 an Expert Group was appointed by the IMO’s Secretary General after solicitation of member states and was tasked to evaluate as many as eleven (11) separate MBM proposals, submitted by various member states and other organizations. All MBM proposals described programs and procedures that would target GHG reductions through either ‘in-sector’ emissions reductions from shipping, or ‘out-of-sector’ reductions via the collection of funds to be used for mitigation activities in other sectors that would contribute towards global reduction of GHG emissions.
Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.
This paper presents a detailed BC, NOx and SO2 emission inventory for ships in the Arctic in 2012 based on satellite AIS data, ship engine power functions and technology stratified emission factors. Emission projections are presented for the years 2020, 2030 and 2050. Furthermore, the BC, SO2 and O3 concentrations and the deposition of BC are calculated for 2012 and for two arctic shipping scenarios – with or without arctic diversion routes due to a possible polar sea ice extent in the future.
In 2012, the largest shares of Arctic ships emissions are calculated for fishing ships (45% for BC, 38% for NOx, 23% for SO2) followed by passenger ships (20%, 17%, 25%), tankers (9%, 13%, 15%), general cargo (8%, 11%, 12%) and container ships (5%, 7%, 8%). In 2050, without arctic diversion routes, the total emissions of BC, NOx and SO2 are expected to change by +16%, −32% and −63%, respectively, compared to 2012. The results for fishing ships are the least certain, caused by a less precise engine power – sailing speed relation.
The calculated BC, SO2, and O3 surface concentrations and BC deposition contributions from ships are low as a mean for the whole Arctic in 2012, but locally BC additional contributions reach up to 20% around Iceland, and high additional contributions (100–300%) are calculated in some sea areas for SO2. In 2050, the arctic diversion routes highly influence the calculated surface concentrations and the deposition of BC in the Arctic. During summertime navigation contributions become very visible for BC (>80%) and SO2 (>1000%) along the arctic diversion routes, while the O3 (>10%) and BC deposition (>5%) additional contributions, respectively, get highest over the ocean east of Greenland and in the High Arctic.
The geospatial ship type specific emission results presented in this paper have increased the accuracy of the emission inventories for ships in the Arctic. The methodology can be used to estimate shipping emissions in other regions of the world, and hence may serve as an input for other researchers and policy makers working in this field.
When a ship navigates at sea, the slamming impact can generate significant load pulses which move up along the hull plating. The effect of the moving pressure has so far not been explicitly considered in the Rules and Regulations for the Classification of Ships. Based on a modal superposition method and the Lagrange equation, this paper derives analytical solutions to study the elastic dynamic responses of fully clamped rectangular plates under moving pressure impact loads. The spatial variation of the moving slamming impact pressure is simplified to three types of impact loads, i.e. a rectangular pulse, a linearly decaying pulse and an exponentially decaying pulse. The dynamic responses of fully clamped rectangular plates under the moving slamming impact pressure are calculated in order to investigate the influence of the load pulse shapes and moving speed on the plate structural behaviour. It is found that the structural response of the plate increases with the increase of the moving speed. The response of the plate subjected to a moving pressure impact load is smaller than the case when the plate is subjected to a spatially uniform distributed impact load with the same load amplitude and load duration. In order to quantify the effect of the moving speed on the dynamic load, a Dynamic Moving Load Coefficient (DMLC) is introduced as the ratio between the dynamic load factor for the moving impact load and that under the spatially uniform distributed impact load. An expression for DMLC is proposed based on analyses of various scenarios using the developed analytical model. Finally an empirical formula which transforms the moving impact loads to an equivalent static load is proposed.
This paper presents experimental measurements of beaching times for buoyant microplastic particles released, both in the pre-breaking region and within the surf zone. The beaching times are used to quantify cross-shore Lagrangian transport velocities of the microplastics. Prior to breaking the particles travel onshore with a velocity close to the Lagrangian fluid particle velocity, regardless of particle characteristics. In the surf zone the Lagrangian velocities of the microplastics increase and become closer to the wave celerity. Furthermore, it is demonstrated that particles having low Dean numbers (dimensionless fall velocity) are transported at higher mean velocities, as they have a larger tendency to be at the free-surface relative to particles with higher Dean numbers. An empirical relation is formulated for predicting the cross-shore Lagrangian transport velocities of buoyant microplastic particles, valid for both non-breaking and breaking irregular waves. The expression matches the present experiments well, in addition to two prior studies.