Container collaborative allocation method based on idle container combined transportation

Abstract

The invention provides a container collaborative allocation method based on idle container combined transportation, which comprises the following steps of: starting from cargo transportation requirements, constructing a model by considering various factors such as loading container transportation, idle container transportation, container type combined transportation and the like, and introducing a swarm intelligence algorithm to optimize and solve the model. The intelligent output of the container allocation scheme of the middle and old railways is realized, a decision basis is provided for the formulation of container transportation operation and allocation plans, and the method plays an important role in guaranteeing the freight efficiency and earnings of the middle and old railways.

Description

Technical Field

[0001] The invention relates to the field of railway container transportation, and in particular to a container coordinated deployment method based on idle container intermodal transportation. Background Art

[0002] The opening of the China-Laos Railway international freight train has made the logistics and transportation between China and Laos more frequent and smooth, providing new impetus for the development of surrounding areas; containers, as an important carrier of railway freight business, have the advantages of high operating efficiency, easy management and transportation; in recent years, with the continuous expansion of overseas railway freight business, the volume of container transportation has increased rapidly; however, due to the differences in natural conditions, cargo sources, cargo flows and development conditions in different regions of Laos, the flow of containers is uneven in time and space, and the problem of container operation and management has gradually become prominent, which has a great impact on freight efficiency and revenue; therefore, it is inevitable to allocate containers to meet the needs of freight business; in addition, due to the transportation management problems of idle containers, the allocation volume and cost of idle containers have greatly increased, which has reduced customer satisfaction to a certain extent; the optimal allocation of idle container resources is one of the important links in freight organization, and the allocation effect directly affects the efficiency and revenue of freight business; in view of this, it is a key issue in current work to build a reasonable container allocation method and further improve the efficiency and revenue of China-Laos Railway freight.

[0003] In actual transportation, different types of containers are selected for different cargo demands and customer needs. In order to avoid insufficient idle containers or inability to meet customer cargo transportation needs in time, intermodal transport of containers often occurs. At the same time, how to maintain the balance of container transportation and ensure railway efficiency is one of the key contents of the China-Laos Railway freight business. Therefore, on the basis of ensuring the efficiency and revenue of the China-Laos Railway freight, it is of great significance to study the idle container allocation method based on intermodal transport according to the actual cargo transportation needs of customers.

[0004] The present invention combines the actual needs of container transportation on the China-Laos Railway, studies the coordinated allocation plan of empty and loaded containers on the China-Laos Railway based on container intermodal transport, and comprehensively considers multiple factors such as container transportation revenue, container intermodal transport cost, idle container transportation cost, and loss due to unmet transportation demand, to achieve intelligent output of the coordinated allocation plan for idle containers on the China-Laos Railway, which can be further applied to overseas container transportation business, and provide a decision-making basis for the operation and allocation plan of containers in railway transportation. Summary of the invention

[0005] In order to solve the above problems, the present invention aims to maximize railway revenue and provides a method for coordinated container allocation based on idle container intermodal transport. The method comprehensively considers four factors, namely, container transport revenue, container intermodal transport expenditure, idle container transport cost and loss due to unmet transport demand, to construct an objective function. The model solution module adopts an improved teaching and learning optimization algorithm to realize the intelligent solution and output of the idle container allocation plan. The present invention takes the actual cargo transportation demand as the starting point and can provide auxiliary support for the overseas container operation management and planning of the China-Laos Railway. The present invention specifically adopts the following technical solutions:

[0006] A container coordinated deployment method based on idle container intermodal transport, the method comprising the following steps:

[0007] (1) Model construction

[0008] The model takes the maximum railway revenue as the optimization goal, comprehensively considers the loading box revenue, box intermodal transportation costs, freight demand loss costs, and idle box allocation costs. The objective function is shown in the following formula:

[0009]

[0010] Among them, I represents the total railway revenue, i and j represent the freight stations of the China-Laos Railway, m represents the type of container, and n represents the type of goods to be transported. represents the unit revenue of transporting n kinds of goods from station i to station j, W mn Indicates the unit weight of m boxes loaded with n types of goods. It represents the number of loaded boxes of m boxes carrying n types of goods in 1 day. It represents the unit cost of deploying m types of idle boxes from station i to station j to load n types of goods. is the unit loss cost when the n-type cargo transportation demand from station i to station j is not completed, is the demand for transport of n types of goods from station i to station j, is the number of idle boxes of type m that travel from station i to station j within one day, The unit cost of deploying m types of idle boxes from station i to station j.

[0011] The constraints are as follows:

[0012] 1.1 Invalidity constraint on idle container transfer: For a certain type of container, the idle container transported from station i to station j is not allowed to be transferred from station j to other stations again, which is used to control the flow direction of the idle containers of this type, namely:

[0013]

[0014] in, It represents the number of idle boxes of type m shipped from station j to station i within 1 day;

[0015] 1.2 Freight plan constraints: The goods loaded in the loading box should first meet the mandatory tasks assigned by the superior, and secondly meet other transportation tasks in the freight plan, namely:

[0016]

[0017] in, A mandatory transportation task for n types of goods from station i to station j;

[0018] 1.3 Container inventory constraints: The adequacy of the container inventory at the station directly determines whether the daily transportation plan can be completed continuously. Therefore, the station should ensure that the container inventory is sufficient, that is:

[0019]

[0020] Among them, t m represents the container turnover time of container type m, S m is the inventory of containers of type m;

[0021] 1.4 Container demand and supply constraints;

[0022] In actual freight production, due to various restrictions, the number of idle boxes required at each station follows a random distribution:

[0023]

[0024] The above formula indicates that at the confidence level α i Under the condition of Obey random distribution; among them, It follows a normal distribution;

[0025] The number of idle boxes generated at each site follows a random distribution:

[0026]

[0027] The above formula indicates that at the confidence level β i Under the condition of Obey random distribution; among them, It follows a normal distribution.

[0028] Preferably, the method further comprises the steps of:

[0029] (3) Model solution

[0030] The improved teaching and learning optimization algorithm is used to solve the model; the teaching and learning optimization algorithm simulates the teaching activities of a class in real life, and regards the members of the class as an evolutionary group; the individual with the best performance in the group is regarded as the teacher, and the other individuals are learners; the improved teaching and learning optimization algorithm is divided into two stages, "teaching stage" and "learning stage", to achieve the continuous improvement and evolution of individual performance; the teaching stage is for students to learn from teachers and improve their own performance, and the learning stage is for students to learn from each other, thereby giving individuals a variation mechanism;

[0031] 3.1: Initialization parameters: number of iterations N, number of groups n, initial position of students X i , where i = 1, 2, 3, ..., n;

[0032] 3.2: Calculate the initial individual performance: According to the model constraints, calculate the linear constraints and nonlinear constraints of teaching and learning optimization, calculate the group performance, and select the best individual as the teacher X teacher (t), t is the current iteration number;

[0033] 3.3: Teaching stage: refers to students learning from teachers; the one with the highest fitness function value in the group is regarded as a teacher, and imparts knowledge to other students through the following formula to improve the group's performance;

[0034] X i (t+1)=aX i (t)+b×r(X teacher (t)-TF i ×X mean (t))

[0035] Among them, X i (t+1) is the state of the i-th student after learning, X i (t) is the state of the i-th student before learning, r i is the learning step of the i-th student, which is a random number between 0 and 1. teacher is the optimal teaching position in the group, TF i Indicates the length of the position change, the value is a random number between 0 and 1, X mean is the average state of the students in the group; a represents the proportion of the state that is retained in the evolution process, and b represents the proportion of the state of the learner:

[0036]

[0037] b=1-a

[0038] 3.4: Learning stage: refers to learning through interaction between students; for individual X i (t), set the distance X i(t) The nearest individual is Y i (t), in the early stage, the two individuals make progress together, and then turn into a competitive relationship in the later stage; the cooperation and competition between individuals are simulated by introducing the Lotka-Volterra model:

[0039] When the two parties are in a cooperative relationship:

[0040]

[0041] Among them, g1 represents the individual growth rate, U represents the upper bound of the group search space, and I1 is the individual X i (t) is the fitness function value, I2 is the fitness function value of individual Y i (t) the fitness function value;

[0042]

[0043] Among them, g2 represents the individual growth rate;

[0044] When the two are in competition:

[0045]

[0046]

[0047] The present invention has the following specific beneficial effects:

[0048] The idle container collaborative allocation optimization plan based on container intermodal transport starts from the cargo transportation demand, considers multiple factors such as loaded container transportation, idle container transportation, and container intermodal transport to build a model, introduces swarm intelligence algorithm to optimize and solve the model, and realizes the intelligent output of the China-Laos Railway container allocation plan, providing decision-making basis for the formulation of container transportation operations and allocation plans, which plays an important role in ensuring the freight efficiency and revenue of the China-Laos Railway. DETAILED DESCRIPTION

[0049] The idle box collaborative allocation optimization method based on box intermodal transport of the present invention mainly includes three modules: precondition preset, model construction, and model solution. The symbol definitions are shown in the following table:

[0050] Table 1 Model symbol interpretation table

[0051]

[0052]

[0053] 1. Preconditions The present invention considers three types of boxes commonly used in China-Laos Railway freight: general standard boxes, refrigerated boxes, i.e., open top boxes;

[0054] Assume that initially, each station has a certain amount of idle box resources.

[0055] When the loaded box arrives at the station, it can be used as an idle box resource for the next link.

[0056] Container transshipment, repair and scrapping are not taken into account.

[0057] At the end of a certain period, if the container has not been transported, storage costs will be incurred.

[0058] For the transport of containers, only rail transport is considered.

[0059] 2. Optimization model of container coordinated deployment for idle container intermodal transport

[0060] (1) Model construction

[0061] The model takes the maximum railway revenue as the optimization goal, comprehensively considers the loading box revenue, box intermodal transportation costs, freight demand loss costs, and idle box allocation costs. The objective function is shown in the following formula:

[0062]

[0063] The constraints are as follows:

[0064] 1) Invalidity constraint on idle box transfer: For a certain type of box, the idle box transported from station i to station j is not allowed to be transferred to other stations by station j again, which is used to control the flow direction of the idle boxes of this type, that is:

[0065]

[0066] 2) Freight plan constraints: The cargo loaded in the loading box should first meet the mandatory tasks issued by the superior, and secondly meet other transportation tasks in the freight plan, that is:

[0067]

[0068] 3) Container inventory constraints: The adequacy of the station's container inventory directly determines whether the daily transportation plan can be completed continuously. Therefore, the station should ensure that the container inventory is sufficient, that is:

[0069]

[0070] 4) Container demand and supply constraints;

[0071] In actual freight production, due to various restrictions, the number of idle boxes required at each station follows a random distribution:

[0072]

[0073] The number of idle boxes generated at each site follows a random distribution:

[0074]

[0075] The above formula indicates that at the confidence level β i Under the condition of Obey random distribution; among them, It follows a normal distribution.

[0076] 3. Model optimization solution

[0077] The improved teaching and learning optimization algorithm is used to solve the model; the teaching and learning optimization algorithm simulates the teaching activities of a class in real life, and regards the class members as an evolutionary group; the individual with the best performance in the group is regarded as a teacher, and the other individuals are learners; the improved teaching and learning optimization algorithm is divided into two stages, "teaching stage" and "learning stage", to achieve continuous improvement and evolution of individual performance; the teaching stage is for students to learn from teachers and is used for students to improve their own performance, and the learning stage is for students to learn from each other, thereby giving individuals a variation mechanism.

[0078] 3.1: Initialization parameters: N, n, X i , where i = 1, 2, 3,…, n.

[0079] 3.2: Calculate the initial individual performance: According to the model constraints, calculate the linear constraints and nonlinear constraints of teaching and learning optimization, calculate the group performance, and select the best individual as the teacher X teacher (t).

[0080] 3.3: Teaching stage: refers to students learning from teachers; the one with the highest fitness function value in the group is regarded as the teacher, who imparts knowledge to other students through the following formula to improve the group's performance.

[0081] X i (t+1)=aX i (t)+b×r(X teacher (t)-TF i ×X mean (t))

[0082]

[0083] b=1-a

[0084] 3.4: Learning stage: refers to learning through interaction between students; for individual X i (t), Y i (t), in the early stage, the two individuals make progress together, and then turn into a competitive relationship in the later stage; the cooperation and competition between individuals are simulated by introducing the Lotka-Volterra model:

[0085] When the two parties are in a cooperative relationship:

[0086]

[0087] When the two are in competition:

[0088]

[0089] The steps for teaching and learning the optimization algorithm to solve the model are as follows;

[0090] Step 1: Import the loading box data, idle box data and cargo transportation demand of each station box type by cargo type.

[0091] Step 2: Initialize parameters; set parameters such as group size and maximum number of iterations.

[0092] Step 3: Calculate the individual scores in the group, and the best individual is recorded as X teacher (t).

[0093] Step 4: Execute the teaching phase; individuals in the group move to X teacher (t) Learning, using a greedy mechanism to update its own state.

[0094] Step 5: Execute the learning phase; if t < n / 3, individuals in the group learn from each other in a collaborative evolutionary manner, otherwise, individuals in the group learn from each other in a competitive manner.

[0095] Step 6: When the end condition is met, output the optimal X teacher (t) Corresponding idle box allocation plan; otherwise, repeat Step 2 to Step 6.

Claims

1. A container coordinated deployment method based on idle container intermodal transport, the method comprising the following steps: (1) Model construction The model takes the maximum railway revenue as the optimization goal, and the objective function is as follows: in, I represents the total railway revenue, i and j represent the freight stations of the China-Laos Railway, m represents the type of container, and n represents the type of goods to be transported. represents the unit revenue of transporting n kinds of goods from station i to station j, W mn Indicates the unit weight of m boxes loaded with n types of goods. It indicates the number of m boxes loaded with n types of goods in one day. It represents the unit cost of deploying m types of idle boxes from station i to station j to load n types of goods. is the unit loss cost caused by the unfulfilled transportation demand of n types of goods from station i to station j, is the demand for transport of n types of goods from station i to station j, is the number of idle boxes of type m that travel from station i to station j within one day, The unit cost of deploying m-type idle boxes from station i to station j; The constraints are: (1.1) For a certain type of box, the idle box shipped from station i to station j is not allowed to be transferred to other stations by station j, which is used to control the flow direction of the idle boxes of this type of box: in, It represents the number of idle boxes of type m shipped from station j to station i within 1 day; (1.2) The cargo in the loading box must first meet the mandatory tasks assigned by the superior, and secondly meet other transportation tasks in the freight plan: in, A mandatory transportation task for n types of goods from station i to station j; (1.3) Whether the container inventory at the station is sufficient directly determines whether the daily transportation plan can be completed continuously. The station should ensure that the container inventory is sufficient: Among them, t m represents the container turnover time of container type m, S m is the inventory of containers of type m; (1.4) Constraining the demand and supply of containers, the number of idle containers required at each station follows a random distribution: The above formula indicates that at the confidence level α i Under the condition of Obeying a random distribution, where Follows normal distribution; The number of idle boxes generated at each site follows a random distribution: The above formula indicates that at the confidence level β i Under the condition of Obeying a random distribution, where It follows a normal distribution.

2. The container coordinated deployment method based on idle container intermodal transport according to claim 1, further comprising the following steps: (3) Model solution The improved teaching and learning optimization algorithm is used to solve the model: 3.1: Initialization parameters: number of iterations N, number of groups n, initial position of students X i , where i = 1, 2, 3, ..., n; 3.2: Calculate the initial individual performance: According to the model constraints, calculate the linear constraints and nonlinear constraints of teaching and learning optimization, calculate the group performance, and select the best individual as the teacher X teacher (t), t is the current iteration number; 3.3: Teaching stage: refers to students learning from teachers; the group with the highest fitness function value is regarded as the teacher, and imparts knowledge to other students through the following formula to improve the group's performance X i (t+1)=aX i (t)+b×r(X teacher (t)-TF i ×X mean (t)) in, X i (t+1) is the state of the i-th student after learning, X i (t) is the state of the i-th student before learning, r i is the learning step of the i-th student, which is a random number between 0 and 1. teacher is the optimal teaching position in the group, TF i Indicates the length of the position change, the value is a random number between 0 and 1, X mean is the average state of the students in the group; a represents the proportion of the state that is retained in the evolution process, and b represents the proportion of the state of the learner: b=1-a 3.4: Learning stage: refers to learning through interaction between students; for individual X i (t), set the distance X i (t) The nearest individual is Y i (t), in the early stage, the two individuals make progress together, and then turn into a competitive relationship in the later stage; the cooperation and competition between individuals are simulated by introducing the Lotka-Volterra model: When the two parties are in a cooperative relationship: Among them, g1 represents the individual growth rate, U represents the upper bound of the group search space, and I1 is the individual X i (t) is the fitness function value, I2 is the fitness function value of individual Y i (t) the fitness function value; Among them, g2 represents the individual growth rate; When the two are in competition:

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