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Modeling of carbon dioxide comparable pollution
The transport industry represents considerable stress on the environment, causing various types of damage such as air pollution, global warming and the depletion of resources. As CO2 is the largest human-made greenhouse gas (GHG), certain gasses may also calculate their effects on carbon equivalent emissions (CO2) (Demir et al. 2015). CO2 emissions leads to climate changes and ecologically sustainable degradation of habitats and well-being threats (Dekker et al. 2012; Demir and others 2014; Bektas et al. 2016.). Because shipping in the sector is a major contributor to CO2 emissions, the (Demir et al . study (2013) found that pollution calculation is still being increasingly used in transport plans. Although contaminants are included in the preparation of programmes, they are mentioned only as a by-product and not included as an optimisation target. Normally, only cost optimization is considered and costs are time, distance, combined with service, etc. for multiple purposes. A variety of reasons could be linked to making it impossible to measure emissions. The potential causes are listed below. The amount of emissions depends on how much fuel a car requires to turn from diesel to petrol. Although energy usage is simple to calculate after travel, it is difficult to quantify energy use before travel begins because a number of variables are not fully understood. The following criteria include truck characteristics (resistance to weight, moving), transport and moving features (distance, stop) and quantity of transport (Eichlseder et al . 2009). The following factors include: several models were built to estimate emissions involving detailed inputs, as shown by (Demir et al. (2011) between Demir and the others. (2014). Aside from complete versions of these, emissions calculators are also available on the basis of measurements in the life and prescribed principles for standard cars (see, for example, IFEU 2011; Boulter and McCrae 2009;). Nevertheless, each model and simulator focus on overly simplistic assumptions that lead to variations between real and expected emissions. Pollution can be separated into three normal functional areas of GHG. This includes capital pollution from industries, emissions conditional from electricity, and all other contaminants, many supply chains contained processes [e.g. Suppliers, logistics, warehouses (Toffel & Sice 2011; Hoen et al. 2014). Pollution can be calculated as direct diesel contamination in the truck (tank-to-wheel, TTW) and fuel system (WTW) pollutants. The lack of waste for fuel production is especially important when it comes to electric trucks, since carbon emissions are zero equal (Kranke et al . 2011). Economic value of CO2 e emissions are uncertain. For the long run impact of pollution on climate change and quality of pollutants emitted cannot be accurately calculated, a number of factors are needed to calculate carbon costs, including varying discount levels for potential incidents and risk attitudes of decision-makers. In the basis of the model, the social costs of pollutants are calculated to be between EUR 0 and EUR 700 per ton of contaminants (Anthoff et al . 2011; Nordhaus 2011). Nevertheless, the numerical advantages of pollution cannot compare quickly to transport costs. In the road Case state transport, the Emission Model for Passenger and medium goods trucks (PHEM) TU Graz built is a fundamental basis for the Road Traffic Emission Factors Manual (HBEFA) for trucks operating in similar routes. Both assumptions are based on real-world equations that approximate a fixed travel period and includes both empty and fully driven cars (Eichlseder et al. 2009). The load ratio and engine fuel usage have a linear relationship and thus the gas use will safely be accomplished determined for various payloads. Besides the road type and the the load factor is also an essential aspect of the gradient. In accordance with Kno r̈ et al . (2011), the impact of the gradient on the fuel consumption of road transport is between 5% and 10%, which means that for hill and mountain countries, emissions estimated for flat countries should be increased between 1.05 and 1.1.
Literature review of multimodal transport
In this regard, domestic and foreign scholars have also done a lot of research on multimodal transport network planning in recent decades, and most of the model construction is based on the shortest path or the lowest total cost. (Tong Lu et al 2014). used ant colony algorithm to solve the multimodal transportation route selection problem with the shortest transportation time and transportation path as the optimization objective.
( Li Shuanglin et al.2012) have solved the multi-objective location-multimodal transportation problem with the shortest total delivery time and the smallest total loss of unsatisfied materials, a multi-objective genetic algorithm is used, in which two-dimensional coding non-dominant sorting is used.( Yang Qiuqiu et al.2013 , Yanmei et al.2015) constructed a dual-objective multimodal transportation shortest path selection model with the lowest cost and shortest time as the optimization goals. (Huang Lixia et al.2016). With the goal of minimizing the total cost and the total risk in the multimodal transportation process, a binocular 0-1 linear programming model was constructed. The above are all dual-objective optimization models based on Pareto analysis. Some articles consider many factors. (Bhatta-Charya et al. 2017) used mixed integer programming to optimize the multimodal transport network schedule considering multiple costs and additional capacity constraints. (Modesti et al.2009). Use an ad hoc utility function to weigh cost and time, and choose the best path to minimize the total cost, time, and customer inconvenience. (Sun et al. 2010) used Pareto optimality to select the optimal route of multimodal transportation to achieve the optimal total transportation cost and time, and then considered the commodity flow path, the specific timetable of railway services, carbon dioxide emissions, etc. The total cost of is the best. Although there are many considerations, it is transformed into a single objective function to solve. (Kangpol et al.2016 ) to reduce costs, lead time, risk and dioxide for carbon emissions, the objective function is determined through analytic hierarchy process and data envelopment analysis, and finally the optimal path is calculated through 0-1 planning. (He Zhuqing et al 2013) considered time and capacity constraints, and prioritized the time-sensitive logistics to minimize the overall cost, and solved it with a 0-1 integer programming. (Chen Yifei et al 2011) Convert carbon emissions and time into corresponding costs, and build a single-objective model of the total cost of the container multimodal transportation process. ( Li Gaobo,2014 )] changed the capacity Single-objective model of standard function.( Fu Xiaofeng et al.,2015 ) established cost-based Time-integrated single-object multimodal transport route selection model. (Wang Zhengbin et al.2014 ) calculated operating income, transshipment costs, storage costs due to delays and Loss costs, time value, these factors are transformed into costs and expenses to construct a single-objective planning problem. Although many factors are considered in these multi-objective planning studies, the final study is to transform the considered factors into a single objective function. Because of the existence of dynamic uncertainty in the transportation process, some scholars believe that customer demand and transportation time can be regarded as dynamic uncertainty in the transportation process. (Wang et al.2014) set the target as Minimal cost, a new mixed integer model with time constraints is proposed. Using image transformation, the problem can be turned into a shortest path model with nodal operation constraints and random characteristics. (Wang Hui et al.2010) Excellent Considering that the transportation demand is fuzzy, the optimization model of multimodal transportation and box transportation with the main purpose of total cost optimization is established, and solved by improved particle ant colony algorithm. (Zhang Dezhi et al.2018) limitedly considered the transit time and transportation differences in multimodal transport. Certainty, time window limitations, and the possibility of transhipment, etc., established a time-sensitive multimodal transport collaborative optimization model. However, the uncertain factors added in these studies are relatively single, and they are all single-objective optimization models. In general, the current multimodal transport route selection model is basically a single-objective optimization model. Even if multiple factors are considered, multiple factors are converted into single-objective programming problems for processing and solving. A few articles are establishing dual-objective optimization models. However, the research on multimodal transportation with uncertain information is mostly based on a single uncertain factor and a single objective function. Regarding the development trend of multimodal transport, (SteadieSeifi et al.2017) pointed out Multi-objective traffic planning in multimodal transport deserves more research. It is necessary to consider the integration of replacement resources and the simultaneous planning of multiple resources. Dynamics and data randomness are also important challenges for research.( Matthisen et al.2016 ) Pointed out Some issues related to the environment, such as low-carbon and sustainable development, are getting more and more attention. Therefore, from the perspective of multimodal transport operators, this chapter fully considers multiple factors that affect the choice of container multimodal transport process paths and transportation methods. At the same time, due to various reasons such as mechanical failures of vehicles, road maintenance, weather factors, or sudden traffic accidents, it is easy to cause uncertainty in transportation time and carbon emissions Transfer and consolidation refers to a form of logistics operation in which goods are transferred, consolidated, and distributed many times during the transportation process, and finally reach the destination. Transshipment collection has the characteristics of intensive operation, specialization of labor division, and flexible operation. It is widely used in logistics operations such as express delivery, aviation, and container multimodal transportation. At present, there are not many related literatures on the optimization of the operation of the transfer and consolidation, which is not compatible with the development of the current new transportation mode. Based on previous studies, this chapter puts forward a study on the optimization of transfer and consolidation operations in multimodal transportation. (Quan Jiaxiang et al. 2018). conducted a study on how to consolidate goods in 1998, and pointed out that in the process of reconsolidation, in order to minimize the empty load rate of the container, while unpacking and unloading, it is possible to consolidate an appropriate amount of goods. Using containers, this mode of transportation is later also called transit consolidation. Transit consolidation business. Specifically, it refers to the goods are transported to the consolidation point, where the secondary sorting and packaging are carried out at this node, and according to different destinations or different customers, they are repacked together with the local source of goods. A new type of logistics business for transportation. This business involves the simultaneous loading and unloading of goods with multiple ODs, the selection of goods to containers, and the coupling of container transportation routes and cargo transportation routes. There are the following studies for the simultaneous loading and unloading of multiple ODs. (Bu Lei et al.2013) studied the consolidation and assembly problems of a variety of goods, constructed a reasonable individual coding fitness function, and used genetic algorithms to optimize the consolidation and assembly problems of general spare parts. (Tasan et al. 2015) proposed a genetic algorithm on the basis of solving the VRP problem of simultaneous loading and unloading, and evaluated the method by solving multiple test problems, which proved the effectiveness of the algorithm. (Belgin et al.2016) considered the dual-echelon vehicle routing problem to pick up and deliver goods at the same time, using a hybrid heuristic algorithm based on variable neighborhood descent (VND) and local search (LS), and using single-level and two-level distribution systems Research on the simultaneous pickup and delivery system. There are certain similarities between the LCL problem and the LTL problem. ( Salvador et al.2016) used the branch and cut method to solve the problem of cooperation between LTL cargo carriers under dynamic capacity, and weighed the cost of holding and the cost of congestion by waiting for packing and transshipment. (Cheung et al.2015) provides a strategy to study the randomness and dynamics of the LTL service network route, find the dynamic shortest path and calculate the travel time through the network with random arc cost. The difference is that the problem of transfer and consolidation can be combined with multimodal transport, not just as a means of delivery. However, the research on multimodal transportation is mostly focused on the study of FCL. (Literature et al.2017 a) studied the network design of hubs with capacity constraints, and analyzed the multimodal transportation network from two levels: the establishment of hubs and the operation of different vehicles efficiency. (Literature et al.2019) considers the optimization research of the entire network from each network node, uses greedy algorithm and intelligent search to solve the multimodal transportation problem, and compares the running time of the two algorithms to obtain the optimal operating time. (Xie Xuemei et al.2016) considered the multimodal transportation of the whole vehicle, which considered the transportation cost, replacement cost, risk cost and time penalty cost, and used the genetic algorithm of binary coding to solve the problem. These studies model and solve the multimodal transportation problem of the entire container under specific conditions, and do not consider the integration of operations in the transportation of multiple OD flows in the container, that is, the optimization of the transfer and consolidation of goods. For the transportation of small batches and multiple batches of goods, from the perspective of space utilization, starting from the concept of free rides, the optimization strategies of multimodal transport operations are also very different in the process of cargo shipment and container transshipment. Therefore, this chapter considers the overall perspective of LCL, fully and evenly utilizes the load and volume of containers, reduces the empty load rate, and establishes a mixed-integer planning multimodal transport model. The objective function considers the minimization of operating costs, including transportation costs, Transshipment costs and node operating costs. Through the design of genetic algorithm and the verification of a certain scale of calculation examples, the selection of different container consolidation points and transfer points, transportation methods and transportation routes in the process of container transfer and consolidation can be effectively completed.
Truck platoon projects in the worldwide
Many platoon projects around in the world in different country is summary gives information about platooning in the world. Several leading team members have been investigating various facets of platooning in the past several decades. These structures specified platooning artifacts, platoon resources, and protocols according to their constructs.
A. SARTRE – In the European pedaling system SARTRE, an automobile is classified as a motorbike, a lead bike, another vehicle, a possible follow-up vehicle, a probable lead vehicle, a probable division driver, or a team leader vehicle [78][5]. Relevant platoon activities, such as the forming of a platoon, the entry of platoon, the maintenance of platoon, the release of the troop, and the Dismantling platoons are characterized as the use of platoon cases (Bergenhem et al., 2010; Robinson et al., 2010).
B. PATH – U.S.-based intercropping trailer project Guide extended to two types of platoon objects like lead car and prior driver and division assets such as inter-vehicle alignment, based on cross-platoon stance, clear team type action, number of platoon vehicles, length of the platoon, speed or rapid acceleration of the automobile, contact lag time as well as pause, and freeway. Operations such as holding the road, moving the path, entering, and dividing are being checked at this stage.
C. SCANIA – The Swedish carmaker SCANIA has launched two sedimentary ventures: SSDC and Intelligent real-time fleet control and management (iQFleet) (Bergenheim et al. 2012b). SCANIA has also developed two sedimentation schemes. In this context, Deng (2016) described the platoon class as a cluster of heavy-duty vehicles capable of platooning and three required characteristics: platoon ID, speed, and several pilots. Deng defined the action of the peloton as an advanced, basic operation. Simple operations involve acceleration, deceleration, and the necessary inter-vehicle space modification. Additionally, emerging structures are recognized for concentrating and party group sorting and dispersion.
Table of contents :
List of figures
List of Tables
List of abbreviations
General Introduction
Chapter 1. State of art and studied problems
1.1 Introduction
1.2 Definitions
1.2.1 Logistics
1.2.2 Green freight
1.2.3 Transshipment
1.2.4 Platooning
1.2.5 Multimodal logistic platform
1.2.6 Green Logistic management
1.2.7 Green transportation
1.2.8 Green transport corridor
1.2.9 Intelligent transportation system
1.3 Transport and the concept of transportation
1.4 Transport modals
1.4.1 Railway transportation
1.4.2 Road Transport
1.4.3 Waterway transportation
1.4.4 Air Transport
1.4.5 Pipeline transportation
1.5 Multimodal transport
1.6 Logistics and Characteristics of logistics
1.7 The relationship between multimodal transport and logistics
1.8 Freight transport processes
1.9 Freight trip multimodal Transport
1.10 Components of transport system
1.11 Factor transport system
1.12 Rationalize of transport
Chapter 2. Different transport systems using in goods transportation
2.1. Introduction
2.2. Modeling of carbon dioxide comparable pollution
2.3. Literature review of multimodal transport
2.4. Technology for truck platooning
2.5. Truck platoon projects in the worldwide
2.6. Solution Techniques
2.7. Aerial cable way Technology (ART)
2.8. Comparison of different Type of ropeway technology
Chapter 3. The Optimal Path of Goods Multimodal Transport
3.1. Introduction
3.2. The problem Description
3.3. Network model assumptions and mathematical formulation
3.4. Mixed-integer model for path optimization
3.5. Model network transformation and data processing
3.6. Short path Algorithmic (Dijkstra’s algorithm)
3.7. Description of the calculation example
3.8. Model conversion time
3.9. Switching cost
3.10. Container transportation time and transportation cost
Chapter 4. Optimizing freight Transfers at Multimodal logistic platform transit and consolidation.
4.1. Part one: Optimal of Multimodal Transport Base on platooning technology
4.1.1. Conceptual definition of truck platooning
4.1.2. Truck Platoon System overview
4.1.3. Environment concerns
4.1.4. Platoon truck Emission
4.1.5. Network model and Mathematical Model
4.1.6. Platooning considering operation costs
4.1.7. Result Platooning
4.2. Part 2 Cable car– As an Alternate Transport Solution
4.2.1. Overhead Conveyor
4.2.2. The Novel in the Proposed Model
4.2.3. Transport Conditions
4.2.4. Aerial cableways
4.2.5. Benefit Aerial Cable System
4.2.6. Cable car Time window
4.2.7. Time window constraints concept:
4.2.8. Calculate time window constraints of cable Cabin
4.2.9. Time windows model
4.2.10. Operation of overhead Conveyor
4.2.11. Traditional multimodal transport network description
4.2.12. Cable car Operation for small batches of goods Container Logistics
4.2.13. Problem assumptions
4.2.14. Modelling setting Parameter description and Decision variables:
4.2.15. Model establishment
4.2.16. Logistics Network adapt
4.2.17. Solving each model
4.2.18. Computational Example
General Conclusion and perspectives
Benefits of implementing platoon system
Benefits of implementing cable car
Perspectives
