So we aim to gain a more comprehensive understanding of the environmental performance of port equipment. In both studies, the use phase causes the greatest environmental impact within the entire life cycle of the equipment.
Life Cycle Impact Assessment and Interpretation of diesel-electric and battery-electric Straddle Carriers
The contribution from the use phase and the production phase to the 'particle formation' (see Figure 6) of a battery electric SC is very similar. Especially for the battery-electric (ice) SC, the use phase causes only 13% more emissions than the production phase.
Conclusion based on findings
Our preliminary results demonstrate the importance and high impact of the use phase on the overall LCA results. The research and development project “SuStEnergyPort” (simulation-based evaluation of measures for improving energy sustainability in port operations) is funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVi) in the context of the funding program “Innovative Hafentechnologien” ( IHATEC).
State of the art
This applies especially to container terminals (Stahlbock and Voß, 2008) and intermodal terminals due to the high transhipment numbers and the increasing requirements. Other simulation models consider container terminals as a whole, but focus on medium to large seaport container terminals and do not provide the flexibility needed for domestic terminals or intermodal terminals.
Objectives
This is also the reason why many simulation models focus on defined area of seaport container terminals, e.g. The integration of layout planning and simulation studies was - in a first step - developed for inland waterway container terminals and terminals for intermodal transport.
Methodology
The tool will be tested in the project by the Institute of Maritime Logistics at the Hamburg University of Technology, Fraunhofer Center for Maritime Logistics. The performance of the planned terminal layout can be tested using the generated simulation model.
Objects
Loading units are transport containers through which different goods can be transported and handled in a standardized way. Additional storage areas can be realized near the crane runway and must be served by an RS.
Processes
If an RS is used to unload the train, the driver unloads an accessible LU, the controller checks the LU for damage and whether it is correct, and then the RS transports it to the appropriate storage position and places it in the storage area. (or on a truck that takes the specific LU). If a crane is used in the train area, the controller first checks all LUs in the train before the crane starts unloading.
Strategies
These examples illustrate on the one hand that multiple similarities exist between seaport container terminals, barge container terminals and intermodal terminals. On the other hand, the relevant strategies for inland waterway container terminals and intermodal terminals are quite simple; some seaport-related strategies are not relevant (e.g. berth allocation, as most of the terminals considered have no or one berth), and there are other challenges such as e.g.
Relevant questions
For larger terminals, the following strategies are possible: reduce the distance to the planned storage position, reduce the distance to the current crane position, reduce the distance to the planned or current position on the train (during train loading or unloading), or reduce the distance to the empty space. in the storage area (especially with high occupancy of the storage area). The questions regarding the barge or the train are: What influence does logic have on the occupancy of tracks/berths.
Output parameters
Detecting gaps in the material flow of inland port container terminals along the Danube by simulation”. These transport patterns differ in the percentages of transport between container terminals and container terminals and other logistics nodes.
Constraints and Assumptions
The analysis is based on data from the Port of Hamburg obtained from various stakeholders in the port area. For this simulation model, four large container terminals in the Port of Hamburg, six empty container warehouses, six freight stations and five other logistics hubs were considered.
Structure of the Simulation Model
For the reservation process, the opening and/or closing hours of the source and drain must be taken into account, as well as the time windows already reserved for the source. In part (2), the list of all shipping orders and required time windows is imported into the simulation model.
Experimental Design
It is noted that the time windows reserved for experiment 1 and 4 are, as expected, distributed over all phases of the day. Since all shipments in these experiments are non-ITT, the opening hours of other logistics nodes limit the time windows applicable to container terminals.
Case Description
The trucks operate in 12-hour shifts, during which they try to maximize the number of deliveries to the terminal. Wood chips are delivered to the terminal and stored until sufficient quantities are available to fill a wood chip vessel destined for export markets.
Bulk Cargo Maritime Terminals
Wadhwa (2000) explores the use of additional ship loaders at a bulk export facility to improve the terminal's ship handling capacity. Therefore, one of the most important factors determining the throughput of a terminal is the lowest common denominator between the terminal port, the berth and the storage capacity.
Terminal Gate Congestion Management
Simulation can be an effective tool to understand the impact of a limited number of variables on the modeled system (Manuj, Mentzer, & Bowers, 2009) and allow researchers to develop and analyze "what if" scenarios (Crainic, Perboli, & Rosano, 2017). The impact of the measures on the terminal and the logistics chain is evaluated, as well as their performance when the throughput of the terminal is increased.
Model Specification
The distributions that best fit the empirical data were used as the model's parameters: (1) One truck type can load a maximum of 32-tonne product and the payload distribution is described by a +19 shifted beta distribution with parameters α= 9.77,β The second truck type can load a maximum of 45 tons and the payload distribution is described by a normal distribution with parametersµ= 38.7,σ The inter-arrival time is described by a gamma distribution with parametersk= 1.49,θ The unloading of the same product at two different ramps can occur if one ramp has completed more than 60% of its unloading cycle before the other begins. A different product can be unloaded if one ramp has completed more than 80% of its unloading cycle before the other starts; (5) The unloading times are described by a lognormal distribution with µ= 5.16,σ The driving time between the weighbridge and the unloading ramps is kept constant at 1 minute on arrival and 2 minutes on departure; (7) The weighbridge operation time is described by a normal distribution with parameters µ= 3.46,σ= 1.68;.
Simulation Results
In the 7-minute average arrival frequency scenario, both methods are on average 65% more efficient than the no-intervention case. In a 7-minute arrival frequency scenario, TAS and an extended unloading system could improve productivity by up to 40% compared to no intervention.
LCOE
Recent European LCOE values for conventional energy sources were not found during the literature review in this document. Nevertheless, the offshore wind industry is still under great pressure to reduce costs in order to improve competitiveness with other energy sources.
LCOE reduction potentials
Tables 1 and 2 show the still high costs of offshore wind energy compared to other energy sources. Bloomberg (2017) predicted a reduction in offshore wind spending by 71% by 2040 due to competition, experience and economies of scale.
Motion sensors
TLift Prep Duration in minutes to prepare liftin equipment on the installation vessel's crane hook. These savings are factored into the number of incidents (nT) and a factor of 2 due to return journeys.
Unmanned Systems (US) for access and inspection
LiDAR
Big Data and Digitally-based Decisions
To better plan logistics activities at sea, several researches have been carried out that present a blueprint for big data analysis and real-time decision-making: several decision support tools and simulation tools have been developed during the installation phase (see Scholz-Reiter et al., 2010 ; Lange et al., 2012; Ritter, 2016; Vis and Ursavas, 2016). Further studies have been carried out to optimize the vessel fleet during the O&M phase (see Endrerud, et al. 2014; Dewan, A. 2014; Stalhane, et al. 2016) and furthermore on big data to improve the maintenance of offshore wind farms (see Viharos et al., 2013; Nabati and Thoben, 2017).
Break even analysis comparison
Digitization and big data analytics can also help monitor critical key performance indicators. Some limitations to consider may be the cost of the technology and the ability to accurately analyze the data.
Research barriers
Made Smarter (2017) saw a need to adapt some digital technologies from more advanced sectors (nuclear or aerospace) to the offshore wind industry. Made Smarter (2017) further recommended more comprehensive and shared storage of geological or environmental data to reduce risks in the offshore wind industry.
Discussion and conclusion
In the literature search, we reviewed all types of electronic material related to the recycling of eolian textiles (scientific articles, research and project reports and other articles). It turns out that there is very little literature on the logistics of the recycling organization of aeolian textiles.
Background Information
Recommendations for Eol Textile Collection and Sorting Operations in Finland
Therefore, the textile collection processes used by charities should be encouraged to separate recyclable textiles from other textile material. Based on the experience of textile collection charities and of regional household waste organizations with eol textile collection pilots, textile collection faces a number of challenges which affect the sorting process.
Treatment of Sorted Textile Material
If the quantity of reusable clothes is suitable, their value will cover the cost of pre-sorting. And if the sorters manage to weed out the harmful material, the sorting organizations can reap some rewards, as this pre-sorting will smooth out the later stages of the process.
Cost Estimations for Organizing Eol Textile Recycling in Finland
Based on the results of the study, it seems possible to produce recycled textile raw material at a reasonable cost. Based on the results of the study, the authors present scenarios for the future development of the percentage of e-vehicles in the respective fleets, including the expected progress towards the given targets.
Background
Electric mobility
Road transport as one of many examples accounts for a large proportion of greenhouse gas (GHG) emissions worldwide. From 1990 to 2015, GHG emissions from road transport within the European Union and Iceland increased by 142 million tonnes of CO2, with road transport responsible for around 20% of all GHG emissions in Europe (around 10% globally) .
Governmental responsibility
At the same time, the overall European level of greenhouse gas emissions decreased by 1,336 million tonnes of CO2 (equivalent to 23.6%) (EEA, 2017). This resolution stipulated that by mid-2016, 900 electric vehicles for the public sector and 592 charging stations would be available (Hamburg, 2013).
Hamburg´s climate plan
To help increase their percentage of e-vehicles of the overall fleet, according to specific forecasts through systematic and comprehensive data evaluation. Finally, a prognosis was created for the development of the percentage of e-vehicles within the public organizations' fleets, including different future scenarios.
Analysis of the e-mobility share based on registration figures
Similar to the development in the fleets of public administrations, the number and percentage of e-vehicles in public companies increased: an increase from 6% e-car share in 2014 to 10% in 2016 was achieved with a continuous development of approx. Of all public organizations within FHH and MRH, those with a preferably large car fleet were invited to participate in the next step of the survey.
The questionnaire
This questionnaire contained the number and models of vehicles registered in the name of the individual study participant. A first evaluation of the questionnaire was carried out one day before the respective date of the interview.
Content and methodology of the questionnaire & planning tool
Evaluation of the questionnaires
11% of public enterprises declared very high user acceptance of e-vehicles and another 11% medium acceptance. Not all FHH organizations know the climate plan and vehicle procurement regulations in detail.
Findings of the survey
A positive example within the public administrations almost already reached the climate plan's target (50% share of e-cars) in 2016. Two positive examples within the public companies have almost already reached or exceeded the climate plan's target (35% share) of e-vehicles ) before the end of 2017.
Prognosis
For other companies, higher overall costs militated against purchasing e-vehicles due to their performance targets and no strategic budget was set aside for the transition to e-mobility.
Constructing scenarios
In this scenario, the share of e-vehicles would exceed the climate plan's target of 50%. In this scenario, the target of the Hamburg climate plan (35% share of e-cars) would be exceeded.
Definitions of Logistics Sprawl
In recent years, some studies have been carried out with the aim of understanding the extent of the phenomenon in metropolitan areas. This calculation was performed for all the companies using the average of the Euclidean distance (Sakai, Kawamura, & Hyodo, 2015).
Motivational Factors of Logistics Sprawl
The choice of the center of gravity is due to it being more sensitive to address changes over time (Dablanc and Rakotonarivo, 2010). Distribution centers tend to move to more inland regions of the country and carriers to the suburban regions of major cities.
Impacts of Logistics Sprawl
The logistical structure of the region consists of ten highways that connect the city of São Paulo with all regions of the state, as well as a ring road that connects most of them. Guarulhos airport is the region's international airport and there are two more airports in the city of São Paulo itself, namely an exclusive airport for executive aviation (Campo de Marte) and a civil aviation with domestic flights to the main Brazilian capitals (Congonhas Airport).
Data Gathering and Sampling
The research is divided into two parts: data collection from companies; sampling and georeferencing of the addresses where the companies are located. One of the biggest challenges was identifying the right places, some of which are difficult to identify, and Google Street View® photos were taken at different times (for example, the company was founded in 2016 and the photo of the address was taken in 2011 ). .
Geocoding
Eleven companies were terminated because they had the wrong city on the register and 66 companies because of a mismatch in the description of the activity, leaving 4561 in total. This phenomenon occurred with 59 companies and one company was rejected due to the lack of reliable information in the register, leaving 459 companies in the final sample.
Data Limitations
The shape of SPMA municipalities is taken from the Metropolis Studies Center (CEM/Cepid, 2018).
Analysis Tools and Method
GDP data for 2000 have been updated with the official inflation index (IPCA) also provided by IBGE (2017).
Sample and Analysis Representation
In addition, the average distance of the companies from the center of gravity has also increased over the years. The aim of the document is to present the essential steps for developing an innovative approach that supports logistics managers in the selection of sustainable logistics locations.
Analytic Hierarchy Process (AHP)
9 Absolute Significance Evidence favoring one activity over another is of the highest possible order. The weights of each criterion (also called local priorities) are calculated by normalizing the matrix and the sum of the columns and rows.
Strategic Environmental Assessment (SEA)
The criteria are based on the relevant environmental objectives of the respective investigation area. Based on the information as a result of steps 2 and 3, environmental criteria are determined.
Deductive Category Application
The methods vary from free interpretation of texts to qualitative content analysis, which follows strict rules (Gläser and Laudel, 2006, pp. 41-42). However, the structuring of qualitative data is the main technique in qualitative analysis: the characteristics of a text are identified using a system of categories (Diekmann, 2004, pp.512-513).
Development of Trend Catagories
