CN117371628A - Subway construction engineering material transportation path planning method under carbon emission reduction target - Google Patents
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Abstract
The invention discloses a subway construction engineering material transportation path planning method under a carbon emission reduction target, which relates to the technical field of traffic planning, and comprises the steps of firstly collecting information of load and oil consumption and analyzing the relation between the load and the oil consumption, performing data fitting by using python, and establishing a carbon emission calculation model; then, based on a real vector road map network, analyzing a network topology structure of the road map network, and constructing a subway engineering material transportation road map network model: secondly, analyzing constraint conditions of engineering material transportation, introducing a concept of carbon transaction, associating carbon emission with transportation cost, and establishing a transportation path selection optimization model taking minimum carbon emission and minimum transportation cost as objective functions; and finally, carrying out improvement optimization on the genetic algorithm according to the inadequacy of the genetic algorithm and the characteristics of the combined model of the minimum carbon emission and the minimum cost in consideration of the carbon transaction policy, and designing an improved genetic algorithm suitable for solving the engineering material path planning model.
Description
Technical Field
The invention relates to the technical field of traffic planning, in particular to a subway construction engineering material transportation path planning method under a carbon emission reduction target.
Background
And the reasonable carbon emission policy is implemented, so that the carbon emission and transportation cost can be reduced better, road congestion can be relieved, popularization of multi-type intermodal transportation can be promoted, and virtuous circle of comprehensive transportation development can be formed.
At present, in the multi-mode intermodal process, based on the consideration of transportation environmental protection, more and more students develop path optimization research under the carbon emission policy, and the existing research proves that the carbon emission policy has a significant influence on path decision. However, net characteristics and regional economy of a real multi-modal network can affect the implementation of carbon emission policies. At this time, the significance of the carbon emission policy on the path decision may also vary. At present, most of researches adopt a virtual network set by people, whether experimental results and analysis conclusions can be applied to the actual transportation process or not is still waiting for further confirmation. Therefore, how to construct a rational multi-modal intermodal multi-objective decision method that can quantify the impact of carbon emission policies based on uncertain carbon emission policies is a challenge.
Disclosure of Invention
The invention aims to provide a subway construction engineering material transportation path planning method under a carbon emission reduction target, so as to solve the problem that the carbon emission of transportation tools is minimized in the transportation process of subway engineering materials.
In order to solve the technical problems, the invention adopts the following technical scheme: a subway construction engineering material transportation path planning method under a carbon emission reduction target comprises the following steps:
step1: collecting information of load and oil consumption, analyzing the relation between the load and the oil consumption, performing data fitting by using python, and establishing a carbon emission calculation model according to IPCC standard and combining the fitting function;
step2: based on a real vector road map network, analyzing a network topology structure of the road map network, carrying out abstract expression on the real road map network, and constructing a subway engineering material transportation road map network model:
step3: analyzing constraint conditions of engineering material transportation, introducing a concept of carbon transaction, associating carbon emission with transportation cost, combining the carbon emission with the transportation cost as a combined optimization target, and establishing a transportation path selection optimization model taking minimum carbon emission and minimum transportation cost as objective functions;
step4: and (3) according to the inadequacy of the genetic algorithm and the characteristics of the combined model of the minimum carbon emission and the minimum cost of the carbon transaction policy, carrying out improvement optimization on the genetic algorithm, and designing an improved genetic algorithm suitable for solving the engineering material path planning model.
The technical scheme of the invention is further improved as follows: the specific implementation steps of Step1 are as follows:
step1.1: and (3) data collection: in a plurality of cities or a plurality of areas, an intelligent sensing system and a dynamic weighing system automatically acquire transportation tool load information of engineering materials and corresponding oil consumption information thereof through an internet of things system, and the acquired data are stored into a transportation vehicle load-oil consumption data set;
step1.2: carrying out data fitting on the data collected in step1.1 by using python to a transport vehicle load-oil consumption data set to obtain a functional relation between the transport vehicle load and the vehicle oil consumption;
step1.3: and according to the IPCC standard, combining a functional relation between the load and the oil consumption of the transport vehicle to construct a carbon emission calculation model.
The technical scheme of the invention is further improved as follows: the formula of the carbon emission calculation model in Step1.3 is as follows:
E＝y ij ×V ij ×D ij ×F ij
wherein E represents transport carbon emissions; i represents the type of the transport means, j represents the type of the energy source; y is ij Representing hundred kilometers of fuel consumption of a transport means i using an energy source j, wherein the unit is L/hundred kilometers; x is x ij The unit of the vehicle load is ton, which is the vehicle load of the transport means i using the energy source j; v (V) ij Representing the number of vehicles i using energy j; d (D) ij Representing the distance travelled by a vehicle i using an energy source j over a period of time in hundred kilometres; f (F) ij Representing the carbon emission factor of the vehicle i using the energy source j.
The technical scheme of the invention is further improved as follows: the specific implementation steps of Step2 are as follows:
step2.1: extracting a vector road map network: downloading map vector data of a required area on an OpenStreetMap, importing a map into an ArcGIS, extracting linear data, namely vector road map network data, and storing the linear data as a road map network map;
step2.2: constructing a subway engineering material transportation road map network model: and (3) removing redundant data, checking and correcting a topological structure, and projecting coordinates from the road map network map stored in the step (Step2.1) to obtain an available road network, and continuously extracting nodes and setting parameters of the available road network, and determining road weights to obtain the subway engineering material transportation road map network model.
The technical scheme of the invention is further improved as follows: the specific implementation steps of Step3 are as follows:
step3.1: and (3) constructing an integer programming model: respectively constructing a carbon emission minimum-order integer programming model and a cost minimum-order integer programming model;
step3.2: constructing a combined path planning model: and combining two optimization targets of the transportation cost and the carbon emission into one target by utilizing a carbon transaction mechanism, taking minimization as an objective function, and establishing a transportation path selection optimization model taking the minimum carbon emission and the minimum transportation cost as the objective function.
The technical scheme of the invention is further improved as follows: the carbon emission minimum-order integer programming model in the step3.1 is constructed as follows:
in multiple intermodal routing, carbon emissions fall into several aspects:
1) In transporting engineering materials from source A to worksite operation face B, carbon emissions from fuel consumption of the transport vehicle:
2) In road network nodes, carbon emission converted by engineering material transportation mode
And synthesizing the carbon emission amounts, and establishing a mathematical model formula of a carbon emission objective function as follows:
the construction process of the cost minimum target integer programming model is as follows:
1) Transportation cost for transporting engineering material at source point A to working surface B of construction site
2) In the road network node e or e', the engineering material transportation mode is converted into cost
3) Traffic quota for transporting engineering material of road network node e to road network node e
And (3) integrating the transportation costs, and establishing a cost minimum target integer programming model formula as follows:
in the above formula, wherein: a represents a source node; b represents a working surface node of a construction site; e represents the required road network node; i represents a transport means; j represents the type of energy source; y is ij Representing hundred kilometers of fuel consumption of a transport means i using an energy source j, wherein the unit is L/hundred kilometers; x is x ij The vehicle load of a transport means i using energy j is expressed in tons; v (V) ij Representing the number of vehicles i using energy j; d (D) ij Representing the distance travelled by a vehicle i using an energy source j over a period of time in hundred kilometres; f (F) ij Representing the carbon emission factor of the vehicle i using the energy source j;representing a transport carbon dioxide emission factor at road network node e; c i Representing the unit transportation cost of i transportation means; f (f) i Representing the transport quota for i transport vehicles; />Representing a conversion cost when converting from i vehicles to i' vehicles; p is p A Representing the product yield of source A; q B Representing the demand of the work site operation surface B for products; />Indicating operation from source point A to the worksiteFace B uses engineering material traffic for i vehicles; />Representing the engineering material traffic of i transport means from one road network node e to another road network node e';representing the engineering material traffic using i kinds of transportation means from the source point A to a certain road network node e; />Representing the engineering material traffic using i kinds of transportation means from a certain road network node e to a construction site operation surface B; />Representing the transport distance from source point a to worksite operating face B; />Representing the transportation distance from source point a to road network node e; />Representing a transportation distance from the road network node e to the road network node e'; />A transportation distance from the road network node e to the construction site operation surface B; />Representing the transportation time from the source point A to the working surface B by using the i transportation mode; />Representing the transportation time from the source point A to the road network node e by using the i transportation mode; />Representing a transportation time from the road network node e to the road network node e' by using the i transportation mode; />Representing the transportation time from the road network node e to the construction site operation surface B by using the i transportation mode; />Representing transit time for converting the transportation mode from i transportation mode to i' at the road network node e; x is x AB Representing a decision variable, which is a Boolean value, and 1 when the nodes A to B have a transportation task, or 0 when the nodes A to B have a transportation task; />Representing nodes A to B to be transported by a seed transportation mode; t represents the total engineering material transportation time; e represents the total carbon emission.
The technical scheme of the invention is further improved as follows: constraint conditions of the carbon emission minimum target integer programming model and the cost minimum target integer programming model constructed in the Step3.1 are as follows:
(1) In multi-modal transportation, the total time consumption must be within a specified time, wherein the transportation time consists of the line transportation time and the transportation means transition time at the road network node, as follows:
(2) The total yield is greater than the total demand, and the formula is as follows:
q B ≤p A
(3) The transportation capacity is limited as follows:
(4) Only one transportation mode can be selected between the city and the road network nodes, and the formula is as follows:
(5) Decision variables can only be 0 or 1; and all parameters are non-negative numbers, the formula is as follows: k, k 1 ,k 2 ∈{0,1}。
The technical scheme of the invention is further improved as follows: the transportation path selection optimization model formula taking the minimum carbon emission and the minimum transportation cost as the objective function in the step3.2 is as follows:
MinZ＝C 4 +MinC
C 4 ＝Y(MinE-P)+1000*LU*MinE
wherein P represents annual carbon emission allowance of enterprises; y represents the equivalent price of carbon dioxide in yuan per ton; l represents the value of a LIME comprehensive coefficient table; u represents the exchange rate of Japanese and Renminbi.
The technical scheme of the invention is further improved as follows: the NSGA-II is adopted in Step4 to solve the double-target mixed multi-mode intermodal route optimization problem with the cost and the carbon emission as targets, and the specific process is as follows:
step4.1: encoding
In order to facilitate computer operation, a natural number coding mode is selected for coding a genetic algorithm;
the individual chromosome is divided into two sections for coding, and the former section represents nodes needing to pass through and is expressed as 1,2,3,4 and … … n; the latter section adopts transportation modes, namely 1 and 2 are used for representing highways and subways respectively, so that the total length of the chromosome is 2n-1, n nodes are represented by adopting n-1 transportation modes, the transportation mode codes are correspondingly inserted into the node codes, 0 represents that the node codes do not pass through, and the corresponding transportation mode codes are also 0;
step4.2: fitness function
Sequencing the objective function values from small to large by using a ranking function, and taking the objective function in the step Step3.2 as an fitness function;
step4.3: fast non-dominant ordering
Layering the population by using a rapid non-dominant sorting method, selecting individuals on the same layer by using a crowding degree operator, selecting individuals with high crowding degree into the next generation population, and keeping diversity of the individuals new;
step4.4: crossover and mutation
Performing cross operation by adopting a recombin advanced recombination operator, and selecting 0.4-0.99 of cross probability by adopting multipoint cross;
adopting a mutation advanced mutation function to carry out mutation, wherein the probability of mutation is 0.0001-0.1;
step4.5: elite strategy
The parent population and the offspring population are mixed to generate a new population, and then layering is carried out by a non-dominant rapid ordering method, because the scale of the mixed population is twice that of the original population, the population is selected by using the layering and crowding degree of the population, and the N most excellent individuals are selected to form the new population, so that the excellent individuals of the parent are ensured to be kept continuously.
By adopting the technical scheme, the invention has the following technical progress:
the subway construction engineering material transportation path planning method under the carbon emission reduction target starts from two aspects of carbon emission and transportation cost of subway engineering material transportation, researches the multi-target path optimization problem of subway engineering material transportation, obtains a reasonable solution under the condition of meeting constraint requirements, realizes the relative balance between carbon emission and cost in the subway engineering material transportation process, and has important significance for researching the subway engineering material transportation problem;
the invention has the following specific beneficial effects:
(1) Is beneficial to reducing the transportation cost. The transportation cost of subway engineering materials is the most important component of subway construction stage. It is counted that the transportation cost is about 50% -60% of the total subway construction cost. Therefore, optimizing the transportation road and reducing the transportation cost are important links for reducing the subway construction cost and realizing the management and optimization of the subway construction cost;
(2) Is beneficial to reducing carbon emission generated by subway engineering transportation. With the increasing distance, the fuel and other consumption of the vehicles is greatly increased, and the environmental impact of the discharged pollutants is also great. The transportation path of the subway engineering materials is reasonably planned, so that carbon emission generated in the transportation process can be reduced, the energy utilization efficiency is improved, and the effect of carbon emission reduction is achieved;
(3) Is favorable for the balance of environmental factors and economic factors. The trade-off between environmental factors and economic factors is comprehensively considered, and the best combination between the environmental factors and the economic factors is sought. The reduction of environmental pollution at the cost of great economic benefit is not achievable in today's engineering decisions. Therefore, the economic single-target system for material transportation in the subway construction process is expanded into an economic and environmental double-target system, and the method has more practicability.
Drawings
FIG. 1 is a schematic flow chart of a subway construction engineering material transportation path planning method under a carbon emission reduction target;
FIG. 2 is a schematic diagram of a multi-modal intermodal testing network and transportation mileage;
FIG. 3 is a schematic diagram showing the relation between fuel consumption and load of the diesel vehicle in embodiment 1 of the present invention;
FIG. 4 is a diagram showing the relation between the power consumption and the load of the electric car in the embodiment 1 of the present invention;
FIG. 5 is a flow chart of the procedure of NSGA-II of the present invention.
Detailed Description
The invention is further illustrated by the following examples:
as shown in fig. 1, a method for planning a transportation path of a subway construction engineering material under a carbon emission reduction target is characterized in that: comprises the following steps:
step1: collecting information of load and oil consumption, analyzing the relation between the load and the oil consumption, performing data fitting by using python, and establishing a carbon emission calculation model according to IPCC standard and combining the fitting function;
step2: based on a real vector road map network, analyzing a network topology structure of the road map network, and carrying out abstract expression on the real road map network to construct a general subway engineering material transportation road map network model;
step3: analyzing constraint conditions of engineering material transportation, introducing a concept of carbon transaction, associating carbon emission with transportation cost, combining the carbon emission with the transportation cost as a combined optimization target, and establishing a transportation path selection optimization model taking minimum carbon emission and minimum transportation cost as objective functions;
step4: and (3) according to the inadequacy of the genetic algorithm and the characteristics of the combined model of the minimum carbon emission and the minimum cost of the carbon transaction policy, carrying out improvement optimization on the genetic algorithm, and designing an improved genetic algorithm suitable for solving the engineering material path planning model.
Example 1
The specific implementation steps of Step1 are as follows:
step1.1: and (3) data collection: in a plurality of cities or a plurality of areas, the intelligent sensing system and the dynamic weighing system automatically acquire the transportation tool load information of engineering materials and the corresponding oil consumption information thereof through the Internet of things system, and the collected data are stored into a transportation vehicle load-oil consumption data set.
Step1.2: data fitting was performed on the transport vehicle load-fuel consumption dataset using python from the data collected in step1.1 to obtain a functional relationship between transport vehicle load and vehicle fuel consumption, as shown in fig. 3 and 4.
Step1.3: and according to the IPCC standard, combining a functional relation between the vehicle load and the oil consumption to construct a carbon emission calculation model.
E＝y ij ×V ij ×D ij ×F ij
Wherein E represents transport carbon emissions; i represents the type of the transport means, j represents the type of the energy source; y is ij Representing hundred kilometers of fuel consumption of a transport means i using an energy source j, wherein the unit is L/hundred kilometers; x is x ij The unit of the vehicle load is ton, which is the vehicle load of the transport means i using the energy source j; v (V) ij Representing the number of vehicles i using energy j; d (D) ij Representing the distance travelled by a vehicle i using an energy source j over a period of time, in units ofHundred kilometers; f (F) ij Representing the carbon emission factor of the vehicle i using the energy source j.
Specifically, step2 is implemented as follows:
step2.1: extracting a vector road map network: map vector data of a required area is downloaded on an OpenStreetMap, a map is imported into an ArcGIS, linear data, namely vector road map network data, is extracted, and the map is stored as a road map network map.
Step2.2: constructing a subway engineering material transportation road map network model: and (3) removing redundant data, checking and correcting a topological structure, and projecting coordinates from the road map network map stored in the step (Step2.1) to obtain an available road network, and continuously extracting nodes and setting parameters of the available road network, and determining road weights to obtain the subway engineering material transportation road map network model.
Specifically, step3 is implemented as follows:
step3.1: and (3) constructing an integer programming model: respectively constructing a carbon emission minimum-order integer programming model and a cost minimum-order integer programming model;
the carbon emission minimum-order integer programming model construction process is as follows:
in multiple intermodal routing, carbon emissions fall into several aspects:
as shown in fig. 2, a is a source site, B is an operation surface, and (1) in fig. 2 is a site temporary stacking point, (2) is a track laying base (large temporary facility), (3) is an engineering material warehouse (small temporary facility), (4) is a processing site (large temporary facility), and (5) is a site processing site (small temporary facility).
1) In transporting engineering materials from source A to worksite operation face B, carbon emissions from fuel consumption of the transport vehicle:
2) In road network nodes, carbon emission converted by engineering material transportation mode
And synthesizing the carbon emission amounts, and establishing a mathematical model formula of a carbon emission objective function as follows:
the construction process of the cost minimum target integer programming model is as follows:
1) Transportation cost for transporting engineering material at source point A to working surface B of construction site
2) In the road network node e or e', the engineering material transportation mode is converted into cost
3) Traffic quota for transporting engineering material of road network node e to road network node e
And (3) integrating the transportation costs, and establishing a cost minimum target integer programming model formula as follows:
in the above formula, wherein: a represents a source node; b represents a working surface node of a construction site; e represents the required road network node; i represents a transport means; j represents the type of energy source; y is ij Representing hundred kilometers of fuel consumption of a transport means i using an energy source j, wherein the unit is L/hundred kilometers; x is x ij Representation ofThe vehicle load of the transport means i using the energy source j is in tons; v (V) ij Representing the number of vehicles i using energy j; d (D) ij Representing the distance travelled by a vehicle i using an energy source j over a period of time in hundred kilometres; f (F) ij Representing the carbon emission factor of the vehicle i using the energy source j;representing a transport carbon dioxide emission factor at road network node e; c i Representing the unit transportation cost of i transportation means; f (f) i Representing the transport quota for i transport vehicles; />Representing a conversion cost when converting from i vehicles to i' vehicles; p is p A Representing the product yield of source A; q B Representing the demand of the work site operation surface B for products; />Representing the engineering material traffic using i vehicles from source point a to worksite operating face B; />Representing the engineering material traffic of i transport means from one road network node e to another road network node e';representing the engineering material traffic using i kinds of transportation means from the source point A to a certain road network node e; />Representing the engineering material traffic using i kinds of transportation means from a certain road network node e to a construction site operation surface B; />Representing the transport distance from source point a to worksite operating face B; />Representing the transportation distance from source point a to road network node e; />Representing a transportation distance from the road network node e to the road network node e'; />A transportation distance from the road network node e to the construction site operation surface B; />Representing the transportation time from the source point A to the working surface B by using the i transportation mode; />Representing the transportation time from the source point A to the road network node e by using the i transportation mode; />Representing a transportation time from the road network node e to the road network node e' by using the i transportation mode; />Representing the transportation time from the road network node e to the construction site operation surface B by using the i transportation mode; />Representing transit time for converting the transportation mode from i transportation mode to i' at the road network node e; x is x AB Representing a decision variable, which is a Boolean value, and 1 when the nodes A to B have a transportation task, or 0 when the nodes A to B have a transportation task; />Representing nodes A to B to be transported by a seed transportation mode; t represents the total engineering material transportation time; e represents the total carbon emission.
Step3.2: constructing a combined path planning model: combining two optimization targets of transportation cost and carbon emission into one target by utilizing a carbon transaction mechanism, taking minimization as an objective function, and establishing a transportation path selection optimization model taking minimum carbon emission and minimum transportation cost as objective functions;
the carbon transaction is a transaction in which a buyer pays a certain amount to a seller to obtain a certain amount of carbon dioxide emission rights, and thus the carbon dioxide emission rights are formed, using the carbon dioxide emission rights as a commodity. The basic principle of carbon transaction is that one party of the contract obtains greenhouse gas emission reduction amount by paying the other party, and the buyer can use the purchased emission reduction amount to slow down the greenhouse effect so as to achieve the aim of emission reduction. Among the 6 greenhouse gases that are required to be abated, carbon dioxide (CO 2) is the largest, so this trade is in terms of carbon dioxide equivalent per ton (tCO 2 e), and is therefore commonly referred to as a "carbon trade". Its Market for transactions is known as the Carbon Market (Carbon Market). In 2005, carbon emissions rights became an international commodity with the validation of the kyoto protocol. The standard for carbon emissions trading is known as nuclear evidence emission reduction (CER).
In combination with the carbon trade policy, the environmental cost generated by the carbon emission cost is internally converted into the economic cost of enterprises. The environmental cost was internalized using the damage computing environmental impact assessment method LIME (life-cycle Impact Assessment Method based on Endpoint Modeling) developed in Japan.
Carbon emission costs can be divided into four categories, carbon emission costs, carbon trade costs, carbon abatement costs, and carbon or common costs. In converting carbon emissions into costs, the cost source is divided into two parts, the first part is economic cost generated when purchasing carbon emission quota, and the second part is carbon emission cost, and the carbon treatment cost is not considered in the enterprise, and the carbon tax policy is not implemented in China, so that the carbon treatment cost and carbon or carbon cost are not considered. The conversion formula of the carbon emission cost and the economic cost is as follows:
C 4 ＝Y(MinE-P)+1000*LU*MinE
wherein P represents annual carbon emission allowance for the enterprise; y represents the equivalent price of carbon dioxide in yuan per ton; l represents the value of a LIME comprehensive coefficient table; u represents the exchange rate of Japanese and Renminbi.
Consider a carbon emission minimum and cost minimum combination model for a carbon trade policy:
MinZ＝C 4 +MinC
constraint conditions of the constructed carbon emission minimum-order integer programming model and the cost minimum-order integer programming model are as follows:
(1) In multi-modal transportation, the total time consumption must be within a specified time, wherein the transportation time consists of the line transportation time and the transportation means transition time at the road network node, as follows:
(2) The total yield is greater than the total demand, and the formula is as follows:
q B ≤p A
(3) The transportation capacity is limited as follows:
(4) Only one transportation mode can be selected between the city and the road network nodes, and the formula is as follows:
(5) Decision variables can only be 0 or 1; and all parameters are non-negative numbers, the formula is as follows: k, k 1 ,k 2 ∈{0,1}。
Specifically, step4 is implemented as follows:
because the established multi-mode intermodal route optimization model relates to a model based on multi-target optimization problems with carbon emission as a target and cost as a target, and because the multi-target optimization solving algorithm has the characteristics of rapidness, high robustness and continuous optimization, the NSGA-II is adopted to better solve the double-target mixed multi-mode intermodal route optimization problems with the cost and the carbon emission as the target.
In general, many local extreme points are generated in the combinatorial optimization problem, most of these points are discrete, multidimensional and nonlinear, and the solution result needs to meet many constraints, which is an NP-hard problem. Therefore, it is impossible to precisely find an optimal solution. Heuristic algorithms are the best means to solve this type of problem. Genetic algorithms are widely used in global optimization as heuristic algorithms that have been developed most successfully. The invention researches the basic principle of genetic algorithm, combines the specific characteristics of path selection optimization, adopts the improved algorithm of genetic algorithm, and solves the model by adopting the non-dominant ordered genetic algorithm (NSGA-II) with elite strategy.
The NSGA-II algorithm reduces the complexity of the non-inferior sorting genetic algorithm, has the advantages of high running speed and good convergence of solution sets, and becomes a benchmark for the performance of other multi-objective optimization algorithms. Solving the combined optimization problem by using NSGA-II algorithm can efficiently perform multi-objective optimization by using methods such as rapid non-dominant sorting, crowding degree calculation and the like; the solution precision is high, and a good multi-objective optimization effect can be achieved by selecting a proper solution from a plurality of non-dominant layers; although NSGA-II algorithm has higher efficiency and accuracy, the basic algorithm idea is very simple and easy to realize.
The basic idea of the NSGA-II algorithm of the invention is as follows:
the first step: randomly generating an initial population with N as a scale, and performing rapid non-dominant sorting on the initial population to generate offspring according to a genetic algorithm to obtain a first generation population;
and a second step of: combining all populations including father and offspring, continuing to perform rapid non-dominant sorting operation, then performing crowding degree calculation on individuals in each non-dominant layer, and selecting proper individuals as new father populations according to a non-dominant relationship and crowding degree indexes;
and a third step of: a new population of offspring is generated by three basic steps of the genetic algorithm and then a second step is performed until the end of the program condition is met. FIG. 5 is a flow chart of the NSGA-II procedure.
The specific steps of Step4 are as follows:
step4.1: encoding
Genetic algorithms are coded for ease of computer operation. The quality of the code directly affects the result of the operation of the genetic algorithm. The method is characterized in that the problem of optimizing the multi-mode intermodal route selection is researched, and a natural number coding mode is selected.
The individual chromosome is divided into two sections for coding, and the former section represents nodes needing to pass through and is expressed as 1,2,3,4 and … … n; the latter section is the transport mode used, denoted by 1 and 2, respectively, for roads and railways. The total length of the chromosome is 2n-1, which means that n nodes adopt n-1 transportation modes. The transport mode code is inserted into the node code accordingly. And 0 means that the node is not passed, and the corresponding transportation mode code is also 0.
Step4.2: fitness function
The objective function is taken as the fitness function, but when the individuals of the next generation are selected, the function with large fitness value is more likely to enter the population of the new generation, but the minimization is taken as the objective function, so that the objective function is required to be correspondingly processed, and the ranking function is used for ranking the objective function from small to large.
Step4.3: fast non-dominant ordering
And layering the population by using a rapid non-dominant sorting method, selecting individuals on the same layer by using a crowding degree operator, and selecting individuals with high crowding degree into the next generation population to keep diversity of the individuals new.
Assuming M (M > 1) objective functions and a population size of N, layering the population by a rapid non-dominant ranking algorithm, wherein the detailed method is as follows:
(1) calculating fitness value by comparing fitness functions to obtain dominant and non-dominant relationship between individuals i and j,
(2) if no fitness function value of any individual j is greater than the fitness function value of individual i, then j is marked as the dominant individual;
(3) setting two parameters Y for each individual i i And W is i ，Y i Is the number of dominant individuals i in the population, W i Is a collection of individuals governed by individual i;
(4) find all Y i Individual of =0, deposit it into S 1 ；
(5) Then for set S 1 Each individual k in (1) finds its dominant set S k Will S k Y of each individual p p Subtracting 1, corresponding to the number of dominant individuals k minus 1, then Y k -individual k of 1=0 is deposited in set H;
(6) will S 1 As a first-order non-dominant set of individuals, and assigning the individuals within the set an identical non-dominant sequence, e.g., i 1 The method comprises the steps of carrying out a first treatment on the surface of the The ranking operation described above is then continued for H and the corresponding non-dominant order is assigned until the entire population is ranked.
Step4.4: crossover and mutation
The crossing is to perform crossing operation on the paired individual chromosomes in the initial population according to a certain ratio, so as to obtain a new generation subgroup, and perform crossing operation by adopting a recombin advanced recombination operator and perform multipoint crossing. The greater the crossover probability, the more adequate the crossover, but the better the population will be destroyed; otherwise, the continuity of the good individuals is protected, the globally optimal solution is found, but the calculation speed is slow. The crossover rate is typically 0.4-0.99. Mutation is the exchange of a gene on a chromosome with an equivalent gene value, resulting in a new chromosome. Mutation is performed herein using a mutation advanced mutation function. Similarly, too large a probability of variation increases the diversity of the solution, and too wide a range of values and too small a probability keeps the solution stable, but an immature convergence solution is easily obtained, so the value is generally 0.0001 to 0.1.
Step4.5: elite strategy
The parent population and the offspring population are mixed to generate a new population, and then layering is carried out by using a non-dominant rapid ordering method, because the scale of the mixed population is twice that of the original population, the population is selected by using the layering and crowding degree of the population, and the N individuals with the most excellent characteristics are selected to form the new population, so that the excellent individuals of the parent are ensured to be kept continuously.
The multi-modal intermodal route selection model based on the non-dominant ranking genetic algorithm with elite strategy combines the special transportation mode selection problem in multi-modal intermodal, establishes a multi-modal intermodal route optimization model aiming at minimizing the total carbon emission and minimizing the cost, introduces a carbon transaction policy, converts an environmental impact index into an economic index, and combines the two models into a combined model comprising the economic cost and the environmental cost. The method has the advantages that the path optimization under the influence of the carbon emission policy is carried out, the defect of the influence of the current large-space-scale regional carbon emission policy on traffic decision is overcome, the important influence is exerted on the mode selected by enterprises in engineering material transportation, the influence of transportation on greenhouse effect is reduced, and the irreplaceable effect is brought to society and even the whole earth economy. Considering the accounting of the carbon emission, the multi-mode intermodal route selection is optimized from two aspects of environment and economy so as to calculate the economic cost brought by the carbon emission based on the carbon emission, thereby enabling enterprises to voluntarily walk down the carbonization route, having practical application value for enterprise members on a supply chain, and being one of important means for sustainable development of enterprises.
Claims (9)
1. A subway construction engineering material transportation path planning method under a carbon emission reduction target is characterized by comprising the following steps of: comprises the following steps:
step1: collecting information of load and oil consumption, analyzing the relation between the load and the oil consumption, performing data fitting by using python, and establishing a carbon emission calculation model according to IPCC standard and combining the fitting function;
step2: based on a real vector road map network, analyzing a network topology structure of the road map network, carrying out abstract expression on the real road map network, and constructing a subway engineering material transportation road map network model:
step3: analyzing constraint conditions of engineering material transportation, introducing a concept of carbon transaction, associating carbon emission with transportation cost, combining the carbon emission with the transportation cost as a combined optimization target, and establishing a transportation path selection optimization model taking minimum carbon emission and minimum transportation cost as objective functions;
step4: and (3) according to the inadequacy of the genetic algorithm and the characteristics of the combined model of the minimum carbon emission and the minimum cost of the carbon transaction policy, carrying out improvement optimization on the genetic algorithm, and designing an improved genetic algorithm suitable for solving the engineering material path planning model.
2. The method for planning the transportation path of the subway construction engineering materials under the carbon emission reduction target according to claim 1, which is characterized in that: the specific implementation steps of Step1 are as follows:
step1.1: and (3) data collection: in a plurality of cities or a plurality of areas, an intelligent sensing system and a dynamic weighing system automatically acquire transportation tool load information of engineering materials and corresponding oil consumption information thereof through an internet of things system, and the acquired data are stored into a transportation vehicle load-oil consumption data set;
step1.2: carrying out data fitting on the data collected in step1.1 by using python to a transport vehicle load-oil consumption data set to obtain a functional relation between the transport vehicle load and the vehicle oil consumption;
step1.3: and according to the IPCC standard, combining a functional relation between the load and the oil consumption of the transport vehicle to construct a carbon emission calculation model.
3. The method for planning the transportation path of the subway construction engineering materials under the carbon emission reduction target according to claim 2, which is characterized in that: the formula of the carbon emission calculation model in Step1.3 is as follows:
E＝y ij ×V ij ×D ij ×F ij
wherein E represents transport carbon emissions; i represents the type of the transport means, j represents the type of the energy source; y is ij Representing hundred kilometers of fuel consumption of a transport means i using an energy source j, wherein the unit is L/hundred kilometers; x is x ij The unit of the vehicle load is ton, which is the vehicle load of the transport means i using the energy source j;V ij representing the number of vehicles i using energy j; d (D) ij Representing the distance travelled by a vehicle i using an energy source j over a period of time in hundred kilometres; f (F) ij Representing the carbon emission factor of the vehicle i using the energy source j.
4. The method for planning the transportation path of the subway construction engineering materials under the carbon emission reduction target according to claim 1, which is characterized in that: the specific implementation steps of Step2 are as follows:
step2.1: extracting a vector road map network: downloading map vector data of a required area on an OpenStreetMap, importing a map into an ArcGIS, extracting linear data, namely vector road map network data, and storing the linear data as a road map network map;
step2.2: constructing a subway engineering material transportation road map network model: and (3) removing redundant data, checking and correcting a topological structure, and projecting coordinates from the road map network map stored in the step (Step2.1) to obtain an available road network, and continuously extracting nodes and setting parameters of the available road network, and determining road weights to obtain the subway engineering material transportation road map network model.
5. The method for planning the transportation path of the subway construction engineering materials under the carbon emission reduction target according to claim 1, which is characterized in that: the specific implementation steps of Step3 are as follows:
step3.1: and (3) constructing an integer programming model: respectively constructing a carbon emission minimum-order integer programming model and a cost minimum-order integer programming model;
step3.2: constructing a combined path planning model: and combining two optimization targets of the transportation cost and the carbon emission into one target by utilizing a carbon transaction mechanism, taking minimization as an objective function, and establishing a transportation path selection optimization model taking the minimum carbon emission and the minimum transportation cost as the objective function.
6. The method for planning the transportation path of the subway construction engineering materials under the carbon emission reduction target according to claim 5, which is characterized in that: the carbon emission minimum-order integer programming model in the step3.1 is constructed as follows:
in multiple intermodal routing, carbon emissions fall into several aspects:
1) In transporting engineering materials from source A to worksite operation face B, carbon emissions from fuel consumption of the transport vehicle:
2) In road network nodes, carbon emission converted by engineering material transportation mode
And synthesizing the carbon emission amounts, and establishing a mathematical model formula of a carbon emission objective function as follows:
the construction process of the cost minimum target integer programming model is as follows:
1) Transportation cost for transporting engineering material at source point A to working surface B of construction site
2) In the road network node e or e', the engineering material transportation mode is converted into cost
3) Traffic quota for transporting engineering material of road network node e to road network node e
And (3) integrating the transportation costs, and establishing a cost minimum target integer programming model formula as follows:
in the above formula, wherein: a represents a source node; b represents a working surface node of a construction site; e represents the required road network node; i represents a transport means; j represents the type of energy source; y is ij Representing hundred kilometers of fuel consumption of a transport means i using an energy source j, wherein the unit is L/hundred kilometers; x is x ij The vehicle load of a transport means i using energy j is expressed in tons; v (V) ij Representing the number of vehicles i using energy j; d (D) ij Representing the distance travelled by a vehicle i using an energy source j over a period of time in hundred kilometres; f (F) ij Representing the carbon emission factor of the vehicle i using the energy source j;representing a transport carbon dioxide emission factor at road network node e; c i Representing the unit transportation cost of i transportation means; f (f) i Representing the transport quota for i transport vehicles; />Representing a conversion cost when converting from i vehicles to i' vehicles; p is p A Representing the product yield of source A; q B Representing the demand of the work site operation surface B for products; />Representing the engineering material traffic using i vehicles from source point a to worksite operating face B; />Representing the engineering material traffic of i transport means from one road network node e to another road network node e'; />Representing the engineering material traffic using i kinds of transportation means from the source point A to a certain road network node e; />Representing the engineering material traffic using i kinds of transportation means from a certain road network node e to a construction site operation surface B; />Representing the transport distance from source point a to worksite operating face B; />Representing the transportation distance from source point a to road network node e; />Representing a transportation distance from the road network node e to the road network node e'; />A transportation distance from the road network node e to the construction site operation surface B; />Representing the transportation time from the source point A to the working surface B by using the i transportation mode; />Representing the transportation time from the source point A to the road network node e by using the i transportation mode; />Representing a transportation time from the road network node e to the road network node e' by using the i transportation mode; />Representing the transportation time from the road network node e to the construction site operation surface B by using the i transportation mode; />Representing transit time for converting the transportation mode from i transportation mode to i' at the road network node e; x is x AB Representing a decision variable, which is a Boolean value, and 1 when the nodes A to B have a transportation task, or 0 when the nodes A to B have a transportation task; />Representing nodes A to B to be transported by a seed transportation mode; t represents the total engineering material transportation time; e represents the total carbon emission.
7. The method for planning the transportation path of the subway construction engineering materials under the carbon emission reduction target according to claim 6, which is characterized in that: constraint conditions of the carbon emission minimum target integer programming model and the cost minimum target integer programming model constructed in the Step3.1 are as follows:
(1) In multi-modal transportation, the total time consumption must be within a specified time, wherein the transportation time consists of the line transportation time and the transportation means transition time at the road network node, as follows:
(2) The total yield is greater than the total demand, and the formula is as follows:
q B ≤p A
(3) The transportation capacity is limited as follows:
(4) Only one transportation mode can be selected between the city and the road network nodes, and the formula is as follows:
(5) Decision variables can only be 0 or 1; and all of the parameters are non-negative numbers,
the formula is as follows: k, k 1 ,k 2 ∈{0,1}。
8. The method for planning the transportation path of the subway construction engineering materials under the carbon emission reduction target according to claim 7, which is characterized in that: the transportation path selection optimization model formula taking the minimum carbon emission and the minimum transportation cost as the objective function in the step3.2 is as follows:
MinZ＝C 4 +MinC
C 4 ＝Y(MinE-P)+1000*LU*MinE
wherein P represents annual carbon emission allowance of enterprises; y represents the equivalent price of carbon dioxide in yuan per ton; l represents the value of a LIME comprehensive coefficient table; u represents the exchange rate of Japanese and Renminbi.
9. The method for planning the transportation path of the subway construction engineering materials under the carbon emission reduction target according to claim 8, which is characterized in that: the NSGA-II is adopted in Step4 to solve the double-target mixed multi-mode intermodal route optimization problem with the cost and the carbon emission as targets, and the specific process is as follows:
step4.1: encoding
In order to facilitate computer operation, a natural number coding mode is selected for coding a genetic algorithm;
the individual chromosome is divided into two sections for coding, and the former section represents nodes needing to pass through and is expressed as 1,2,3,4 and … … n; the latter section adopts transportation modes, namely 1 and 2 are used for representing highways and subways respectively, so that the total length of the chromosome is 2n-1, n nodes are represented by adopting n-1 transportation modes, the transportation mode codes are correspondingly inserted into the node codes, 0 represents that the node codes do not pass through, and the corresponding transportation mode codes are also 0;
step4.2: fitness function
Sequencing the objective function values from small to large by using a ranking function, and taking the objective function in the step Step3.2 as an fitness function;
step4.3: fast non-dominant ordering
Layering the population by using a rapid non-dominant sorting method, selecting individuals on the same layer by using a crowding degree operator, selecting individuals with high crowding degree into the next generation population, and keeping diversity of the individuals new;
step4.4: crossover and mutation
Performing cross operation by adopting a recombin advanced recombination operator, and selecting 0.4-0.99 of cross probability by adopting multipoint cross;
adopting a mutation advanced mutation function to carry out mutation, wherein the probability of mutation is 0.0001-0.1;
step4.5: elite strategy
The parent population and the offspring population are mixed to generate a new population, and then layering is carried out by a non-dominant rapid ordering method, because the scale of the mixed population is twice that of the original population, the population is selected by using the layering and crowding degree of the population, and the N most excellent individuals are selected to form the new population, so that the excellent individuals of the parent are ensured to be kept continuously.
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