A method for optimizing the size of self-owned fleets in trunk transport

Abstract

The present invention discloses a method for optimizing the size of a self-owned fleet for trunk transportation, including: considering the influence of multiple vehicle types, outsourced transportation and return transportation at the same time, taking the maximization of the total profit of the logistics enterprise's trunk transportation throughout the year as the objective function, and constructing a self-owned fleet size optimization model; based on monthly profit and annual profit, constructing inner and outer nested optimization algorithms, using the nested optimization algorithm to optimize the objective function, and outputting the size of the self-owned fleet and the optimal transportation plan for each month under the size of the self-owned fleet. The present invention simultaneously considers outsourced transportation, multiple vehicle types and return transportation factors, constructs a mathematical model with the goal of maximizing annual profit, and is closer to the actual situation in the trunk transportation scenario; improves the traditional optimization algorithm, and proposes a monthly and annual target nested optimization algorithm, which can effectively solve the above-mentioned mathematical model, and solves the problems of low optimization efficiency and easy to fall into local optimality.

Description

Technical Field

[0001] The present invention relates to the research field of transport resource allocation optimization, and in particular to a method for optimizing the scale of a self-owned fleet for trunk transport. Background Art

[0002] Logistics companies refer to economic organizations that provide logistics activities. In recent years, with the support of Internet + freight technology, more and more logistics companies have chosen to adopt the means of re-outsourcing, integrating self-owned transportation capacity with social transportation capacity to achieve a transportation model that combines self-reliance with outsourcing. Through this strategy, logistics companies can meet seasonal fluctuations in transportation demand on the one hand, and reduce costs and increase efficiency on the other hand, improve corporate market competitiveness, and thus cope with the risks brought about by fluctuations in trunk transportation demand.

[0003] For logistics companies, trunk transport business accounts for a considerable proportion. However, due to the large volatility of urban demand for trunk transport, if logistics companies have too much transport capacity, it will cause problems such as idle transport vehicles, reduced corporate profits, and lack of flexible assets; too little transport capacity will require a lot of outsourcing, resulting in reduced service levels and weakened market competitiveness. Therefore, how logistics companies can reasonably allocate the size of their own fleets participating in trunk transport has become an urgent problem to be solved.

[0004] At present, there have been studies on how to reasonably configure the size of truck or bus fleets. However, most of the existing research on vehicle size is carried out on a certain industry and is an overall resource allocation optimization. For example, research is conducted on buses or taxis in a certain city, but research on the fleet size of specific logistics companies is scarce. The only relevant research is limited to calculating the reasonable number of self-owned vehicles, and does not consider outsourcing transportation and configuring multiple vehicle models for simultaneous transportation. Summary of the invention

[0005] Purpose of the invention: In view of the above problems, the purpose of the present invention is to provide a method for optimizing the size of self-owned fleets for trunk transport, which takes outsourced transport into consideration and can reasonably configure the size of self-owned fleets participating in trunk transport, helping logistics companies to reduce costs and increase efficiency.

[0006] Technical solution: A method for optimizing the size of a self-owned fleet for trunk transport of the present invention comprises: considering the influence of multiple vehicle types, outsourced transport and return transport at the same time, taking the maximization of the total profit of the trunk transport of the logistics enterprise throughout the year as the objective function, and constructing a self-owned fleet size optimization model;

[0007] Based on monthly and annual profits, inner and outer nested optimization algorithms are constructed, and the objective function is optimized using the nested optimization algorithm to output the size of the self-owned fleet and the optimal transportation plan for each month under the size of the self-owned fleet.

[0008] Further, the objective function expression is:

[0009]

[0010] In the formula, F is the total profit for the whole year, Y is the set of months, and R y is the income of month y, C y is the expenditure for month y;

[0011] The income R of month y y Including the order income of the logistics company's own vehicles for outbound and return transportation, as well as the price difference income of outsourced transportation, the expression is:

[0012]

[0013] In the formula, H is the vehicle model set, r h is the transport revenue coefficient related to the transport volume, k is the return order discount, is the optimal number of departures of self-owned vehicle type h in month y, a h is the freight rate of self-owned vehicle type h, d is the trunk transportation distance, q y is the outsourced transportation volume in month y, and u is the price difference income generated per unit of outsourced transportation volume;

[0014] Expenditure C in month y y Including vehicle use cost, long-term idle cost, short-term idle cost and delay penalty cost, the expression is:

[0015]

[0016] Where b h is the cost of using the self-owned vehicle model h, x l_h is the long-term idle cost of the self-owned vehicle model h, s h ' is the initial number of vehicles of self-owned model h, s h is the optimal number of vehicles of self-owned vehicle type h, is the rounding symbol, x s_h is the short-term idle cost of self-owned vehicle model h, T is the average time required for a single transport, f is the delay penalty cost, p1 is the delay penalty probability of self-transportation, p2 is the delay penalty probability of outsourced transportation, l h is the rated load of the self-provided vehicle model h, is the full load factor of all vehicle models; h is a Boolean variable. When the vehicle model h is idle for a long time, that is, when s h '≥s h When h =1, otherwise, o h =0.

[0017] Furthermore, the following constraints are set for the objective function:

[0018]

[0019] In the formula, the constraints are expressed in order: the autonomous transportation tasks formulated must be completed by the logistics company's own vehicles; the daily working hours of each driver shall not exceed 8 hours; the number of departures and the number of vehicles are both non-negative integers.

[0020] Furthermore, the transport revenue coefficient r h If the transport input is related to the transport volume and the transport supply exceeding the transport demand does not generate income, then the transport income coefficient r h The calculation expression is:

[0021]

[0022] In the formula, K y is the transportation demand in month y;

[0023] Outsourced transportation volume y It refers to the transportation volume generated by outsourced vehicles when the transportation volume of the logistics company's own vehicles cannot meet the transportation demand of the month. The outsourced transportation volume q y The calculation expression is:

[0024]

[0025] In the formula, λ y It is a Boolean variable. When the self-provided transport capacity in month y Unable to meet transportation demand K y , when outsourcing transportation is required, that is, when When y =1, otherwise, λ y =0.

[0026] Furthermore, the inner layer adopts genetic algorithm and the outer layer adopts adaptive genetic algorithm.

[0027] Further, the nested optimization algorithm comprises the following steps:

[0028] Step 1, determine the two decision variables of the objective function, including the monthly transportation plan of the logistics enterprise and the size of the self-owned fleet; the monthly transportation plan is used as the decision variable of the inner genetic algorithm, and the goal of the inner genetic algorithm is to maximize the profit of each month; the size of the self-owned fleet is used as the decision variable of the outer genetic algorithm, and the goal of the outer genetic algorithm is to maximize the total profit of the whole year; the monthly transportation plan refers to the number of departures of each model of each month and the outsourced transportation volume, and the size of the self-owned fleet refers to the number of vehicles of each model of each model configured by the logistics enterprise to participate in the trunk transportation throughout the year;

[0029] Step 2, setting the parameters of the inner and outer genetic algorithms according to the known data and constraints, including population size, number of decision variables and value range, crossover probability, mutation probability and maximum number of iterations;

[0030] Step 3, initialize the outer population, bring the outer population individuals into the inner layer, execute the inner genetic algorithm, and output the optimal transportation plan and the maximum profit of the month;

[0031] Step 4, execute the outer adaptive genetic algorithm, and output the optimized size of the self-owned fleet participating in trunk transportation, as well as the optimal transportation plan for each month under the size of the self-owned fleet.

[0032] Further, the step 3 comprises the following steps:

[0033] Step 31, initialize the inner population according to the individuals of the outer population brought in, so that the inner population satisfies the constraint conditions;

[0034] Step 32, calculating the inner layer fitness; the inner layer fitness is the trunk line transportation profit of the logistics enterprise in that month;

[0035] Step 33, determine whether the number of inner layer iterations meets the termination condition. If not, perform selection, crossover and mutation operations in sequence to generate a new generation of population and recalculate the inner layer fitness; if satisfied, terminate the iteration and output the optimal transportation plan and maximum profit for the month.

[0036] Further, the step 4 comprises the following steps:

[0037] Step 41, calculating the outer layer fitness according to the output of the inner layer genetic algorithm: each individual in the outer layer population corresponds to 12 inner layer populations, and the sum of the maximum profits of each month output after executing the inner layer genetic algorithm 12 times is the fitness of the outer layer algorithm, and the optimization is performed with the goal of maximizing the total profit for the whole year;

[0038] Step 42, adaptively adjusting the outer genetic operator: according to the fitness of the outer algorithm and the number of iterations of the current outer algorithm, adjusting the outer genetic operator so that the outer algorithm increases the probability of retaining the dominant individuals at the beginning of the iteration and avoids the algorithm from falling into the local optimum at the end of the iteration;

[0039] Step 43, determine whether the number of outer layer iterations meets the termination condition. If not, perform selection, crossover and mutation operations in sequence to generate a new generation of population, bring the new population individuals into the inner layer again, and return to step 31; if satisfied, terminate the iteration, output the optimized size of the self-owned fleet participating in trunk transportation, and the optimal transportation plan for each month under the size of the self-owned fleet.

[0040] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0041] 1. The present invention takes into account outsourcing transportation, multiple vehicle types and return transportation factors, and constructs a mathematical model with the goal of maximizing annual profits, which is closer to the actual situation in the trunk transportation scenario;

[0042] 2. Improve the traditional optimization algorithm and propose a monthly and annual target nested optimization algorithm, which can effectively solve the mathematical model proposed above and solve the problems of low optimization efficiency and easy to fall into local optimality. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is the overall flow chart of the present invention;

[0044] Figure 2 It is a schematic diagram of the mathematical model;

[0045] Figure 3 This is the flowchart of the nested optimization algorithm. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0047] like Figure 1 As shown in the flowchart, the method for optimizing the size of a self-owned fleet for trunk transportation described in this embodiment includes: considering the impact of multiple vehicle types, outsourced transportation and return transportation at the same time, taking the maximization of the total profit of the logistics enterprise's trunk transportation throughout the year as the objective function, and constructing a self-owned fleet size optimization model, such as Figure 2 As shown;

[0048] Based on monthly and annual profits, inner and outer nested optimization algorithms are constructed, and the objective function is optimized using the nested optimization algorithm to output the size of the self-owned fleet and the optimal transportation plan for each month under the size of the self-owned fleet.

[0049] The objective function expression is:

[0050]

[0051] In the formula, F is the total profit for the whole year, Y is the set of months, and R y is the income of month y, C y is the expenditure for month y;

[0052] The income R of month y y Including the order income of the logistics company's own vehicles for outbound and return transportation, as well as the price difference income of outsourced transportation, the expression is:

[0053]

[0054] In the formula, H is the vehicle model set, r his the transport revenue coefficient related to the transport volume, k is the return order discount, is the optimal number of departures of self-owned vehicle type h in month y, a h is the freight rate of self-owned vehicle type h, d is the trunk transportation distance, q y is the outsourced transportation volume in month y, and u is the price difference income generated per unit of outsourced transportation volume;

[0055] Expenditure C in month y y Including vehicle use cost, long-term idle cost, short-term idle cost and delay penalty cost, the expression is:

[0056]

[0057] Where b h is the cost of using the self-owned vehicle model h, x l_h is the long-term idle cost of the self-owned vehicle model h, s h ' is the initial number of vehicles of self-owned model h, s h is the optimal number of vehicles of self-owned vehicle type h, is the rounding symbol, x s_h is the short-term idle cost of self-owned vehicle model h, T is the average time required for a single transport, f is the delay penalty cost, p1 is the delay penalty probability of self-transportation, p2 is the delay penalty probability of outsourced transportation, l h is the rated load of the self-provided vehicle model h, is the full load factor of all vehicle models; h is a Boolean variable. When the vehicle model h is idle for a long time, that is, when s h '≥s h When h =1, otherwise, o h =0.

[0058] The following constraints are set for the objective function:

[0059]

[0060] In the formula, the constraints are expressed in order: the autonomous transportation tasks formulated must be completed by the logistics company's own vehicles; the daily working hours of each driver shall not exceed 8 hours; the number of departures and the number of vehicles are both non-negative integers.

[0061] Transport revenue coefficient r h If the transport input is related to the transport volume and the transport supply exceeding the transport demand does not generate income, then the transport income coefficient r h The calculation expression is:

[0062]

[0063] In the formula, K yis the transportation demand in month y;

[0064] Outsourced transportation volume y It refers to the transportation volume generated by outsourced vehicles when the transportation volume of the logistics company's own vehicles cannot meet the transportation demand of the month. The outsourced transportation volume q y The calculation expression is:

[0065]

[0066] In the formula, λ y It is a Boolean variable. When the self-provided transport capacity in month y Unable to meet transportation demand K y , when outsourcing transportation is required, that is, when When y =1, otherwise, λ y =0.

[0067] The inner layer adopts genetic algorithm, and the outer layer adopts adaptive genetic algorithm.

[0068] like Figure 3 As shown, the nested optimization algorithm includes the following steps:

[0069] Step 1: Determine the two decision variables of the objective function, including the monthly transportation plan of the logistics company and the size of its own fleet.

[0070] The monthly transportation plan is used as the decision variable of the inner genetic algorithm, and the goal of the inner genetic algorithm is to maximize the profit of each month. The size of the self-owned fleet is used as the decision variable of the outer genetic algorithm, and the goal of the outer genetic algorithm is to maximize the total profit of the whole year. The monthly transportation plan refers to the number of departures of each model in each month and the outsourced transportation volume. The size of the self-owned fleet refers to the number of vehicles of each model configured by the logistics enterprise to participate in trunk transportation throughout the year.

[0071] The optimal transportation plan for each month will change with the optimal fleet size throughout the year. The two decision variables are correlated. If they are iterated at the same time, the normal correspondence will be lost and an invalid solution will be generated. Therefore, the traditional genetic algorithm cannot solve it.

[0072] Step 2: According to the collected historical order data of trunk transportation and the constraints, the parameters of the inner and outer genetic algorithms are set, including the population size, the number and value range of decision variables, the crossover probability, the mutation probability and the maximum number of iterations.

[0073] Step 3, initialize the outer population, bring the outer population individuals into the inner layer, execute the inner genetic algorithm, and output the optimal transportation plan and the maximum profit of the month, which specifically includes the following steps:

[0074] Step 31, initialize the inner population according to the individuals of the outer population brought in, so that the inner population satisfies the constraint conditions;

[0075] Step 32, calculate the inner layer fitness; the inner layer fitness is the trunk line transportation profit of the logistics enterprise in that month, and the calculation expression of the inner layer fitness is:

[0076]

[0077] Step 33, determine whether the number of inner layer iterations meets the termination condition. If not, perform selection, crossover and mutation operations in sequence to generate a new generation of population and recalculate the inner layer fitness; if satisfied, terminate the iteration and output the optimal transportation plan and maximum profit for the month.

[0078] Step 4, execute the outer adaptive genetic algorithm, output the optimized size of the self-owned fleet participating in trunk transportation, and the optimal transportation plan for each month under the size of the self-owned fleet, which specifically includes the following steps:

[0079] Step 41, calculating the outer layer fitness according to the output of the inner layer genetic algorithm: each individual in the outer layer population corresponds to 12 inner layer populations, and the sum of the maximum profits of each month output after executing the inner layer genetic algorithm 12 times is the fitness of the outer layer algorithm, and the optimization is performed with the goal of maximizing the total profit for the whole year;

[0080] Step 42, adaptively adjust the outer genetic operator: according to the fitness of the outer algorithm and the number of iterations of the current outer algorithm, adjust the outer genetic operator to increase the probability of retaining the dominant individuals in the early stage of the iteration and avoid the algorithm from falling into the local optimum at the end of the iteration.

[0081] The improvement of adaptive genetic algorithm over traditional genetic algorithm lies in the adaptive adjustment of crossover operator and mutation operator. c and mutation operator P m The adaptive adjustment formula is as follows:

[0082]

[0083]

[0084] Where P c is the adaptive crossover probability, P c-max With P c-min are the maximum and minimum crossover probabilities, respectively, max is the maximum number of iterations, it er is the current iteration number, f er is the current individual fitness, f av is the average fitness value of the population, P m is the adaptive mutation probability, P m-max With P m-min are the maximum and minimum mutation probabilities respectively.

[0085] Through the above-mentioned adaptive adjustment, on the one hand, the algorithm can avoid falling into the local optimum, and on the other hand, the dominant individuals of the population can be retained, so that the population can evolve to the global optimum faster, thereby improving the optimization efficiency.

[0086] Step 43, determine whether the number of outer layer iterations meets the termination condition. If not, perform selection, crossover and mutation operations in sequence to generate a new generation of population, bring the new population individuals into the inner layer again, and return to step 31; if satisfied, terminate the iteration, output the optimized size of the self-owned fleet participating in trunk transportation, and the optimal transportation plan for each month under the size of the self-owned fleet.

[0087] The effect of the method for optimizing the size of a self-owned fleet for trunk transportation of the present invention is further verified by the following examples. This embodiment takes a logistics enterprise in Shanghai as an example to explain the method in detail.

[0088] A logistics company in Shanghai is a modern third-party logistics company that adopts the cooperative transportation model of "independent + outsourcing" to provide customers with efficient, reliable, professional and diversified logistics services mainly based on truck transportation. According to the survey, the company mainly operates two transportation trunk lines, Shanghai-Shenzhen and Shanghai-Chongqing, and the order volume accounts for 90% of the company's total trunk line transportation orders.

[0089] By investigating the details of the transport orders of the two trunk lines and making predictions, the market transport demand forecast data for 2022 is obtained, as shown in Table 1. It is known that the transport distance d of the Shanghai-Shenzhen trunk line is 1470km, and the initial self-owned fleet size is: 3 5t vehicles, 3 10t vehicles, and 12 28t vehicles; the transport distance d of the Shanghai-Chongqing trunk line is 1700km, and the initial self-owned fleet size is: 1 5t vehicle, 4 10t vehicles, and 16 28t vehicles. The remaining relevant parameters of trunk line transportation are shown in Table 2, where the self-owned vehicle model h is 1, 2, and 3, respectively, indicating the models of 5t vehicles, 10t vehicles, and 28t vehicles.

[0090] Table 1 Forecast data on transportation demand in 2022

[0091]

[0092] Table 2 Parameters related to trunk line transportation

[0093]

[0094] After repeated tests, the parameters of the nested optimization algorithm are set as follows: the inner algorithm population size is 30, the maximum number of iterations is 50, the crossover probability is 0.9, and the mutation probability is 0.05; the outer adaptive genetic algorithm population size is 50, the maximum number of iterations is 200, and the maximum crossover probability P c-maxis 0.9, the minimum crossover probability P c-min is 0.5, the maximum mutation probability P m-max is 0.1, the minimum mutation probability P m-min is 0.02.

[0095] After the nested optimization algorithm, the optimal fleet size and the maximum total profit for the whole year are output, as shown in Table 3, and the optimal transportation plan under the self-owned fleet size is recorded, as shown in Table 4.

[0096] Table 3 Optimal size of self-owned fleet in 2022

[0097]

[0098] Table 4 Optimal transportation plan for 2022

[0099]

[0100]

[0101] In order to verify the effectiveness of the optimization scheme, the trunk line transportation operation conditions before and after the optimization were compared. As shown in Table 5, by optimizing the size of the self-owned fleet of the two transportation trunk lines, the maximum annual profit of the Shanghai-Shenzhen trunk line increased by 8.9%, and the maximum annual profit of the Shanghai-Chongqing trunk line increased by 8.6%.

[0102] Table 5 Comparison of trunk line transportation operations before and after optimization

[0103]

[0104] In summary, by using the method for optimizing the size of self-owned fleets for trunk transport that nests monthly and annual profit targets, logistics companies can complete the work of reasonably configuring the size of self-owned fleets for trunk transport in advance, so as to achieve the purpose of reducing costs and increasing efficiency. In general, the method proposed in the present invention can effectively optimize the size of self-owned fleets involved in trunk transport.

Claims

1. A method for optimizing the size of a self-owned fleet for trunk transportation, characterized in that: The method includes: considering the impact of multiple vehicle types, outsourcing transportation and return transportation at the same time, taking the maximization of the annual total profit of the logistics enterprise's trunk transportation as the objective function, and constructing a self-owned fleet size optimization model; Based on monthly and annual profits, inner and outer nested optimization algorithms are constructed, and the objective function is optimized using the nested optimization algorithm to output the size of the self-owned fleet and the optimal transportation plan for each month under the size of the self-owned fleet; The objective function expression is: In the formula, F is the total profit for the whole year, Y is the set of months, and R y is the income of month y, C y is the expenditure for month y; The income R of month y y Including the order income of the logistics company's own vehicles for outbound and return transportation, as well as the price difference income of outsourced transportation, the expression is: In the formula, H is the vehicle model set, r h is the transport revenue coefficient related to the transport volume, k is the return order discount, is the optimal number of departures of self-owned vehicle type h in month y, a h is the freight rate of self-owned vehicle type h, d is the trunk transportation distance, q y is the outsourced transportation volume in month y, and u is the price difference income generated per unit of outsourced transportation volume; Expenditure C in month y y Including vehicle use cost, long-term idle cost, short-term idle cost and delay penalty cost, the expression is: Where b h is the cost of using the self-owned vehicle model h, x l_h is the long-term idle cost of the self-owned vehicle model h, s h ' is the initial number of vehicles of self-owned model h, s h is the optimal number of vehicles of self-owned vehicle type h, is the rounding symbol, x s_h is the short-term idle cost of self-owned vehicle model h, T is the average time required for a single transport, f is the delay penalty cost, p1 is the delay penalty probability of self-transportation, p2 is the delay penalty probability of outsourced transportation, l h is the rated load of the self-provided vehicle model h, is the full load factor of all vehicle models; h is a Boolean variable. When the vehicle of model h is idle for a long time, that is, when s h '≥s h When h =1, otherwise, o h =0.

2. The method for optimizing the size of a self-owned fleet for trunk transportation according to claim 1 is characterized in that: The following constraints are set for the objective function: In the formula, the constraints are expressed in order: the autonomous transportation tasks formulated must be completed by the logistics company's own vehicles; the daily working hours of each driver shall not exceed 8 hours; the number of departures and the number of vehicles are both non-negative integers.

3. The method for optimizing the size of a self-owned fleet for trunk transportation according to claim 2 is characterized in that: Transport revenue coefficient r h If transport input is related to transport volume and transport supply exceeding transport demand does not generate revenue, then the transport revenue coefficient r h The calculation expression is: In the formula, K y is the transportation demand in month y; Outsourced transportation volume y It refers to the transportation volume generated by outsourced vehicles when the transportation volume of the logistics company's own vehicles cannot meet the transportation demand of the month. The outsourced transportation volume q y The calculation expression is: In the formula, λ y It is a Boolean variable. When the self-provided transport capacity in month y Unable to meet transportation demand K y , when outsourcing transportation is required, that is, when When y =1, otherwise, λ y =0.

4. The method for optimizing the size of a self-owned fleet for trunk transportation according to claim 1, characterized in that: The inner layer adopts genetic algorithm, and the outer layer adopts adaptive genetic algorithm.

5. The method for optimizing the size of a self-owned fleet for trunk transportation according to claim 4 is characterized in that: The nested optimization algorithm comprises the following steps: Step 1, determine the two decision variables of the objective function, including the monthly transportation plan of the logistics enterprise and the size of the self-owned fleet; the monthly transportation plan is used as the decision variable of the inner genetic algorithm, and the goal of the inner genetic algorithm is to maximize the profit of each month; the size of the self-owned fleet is used as the decision variable of the outer genetic algorithm, and the goal of the outer genetic algorithm is to maximize the total profit of the whole year; the monthly transportation plan refers to the number of departures of each model of each month and the outsourced transportation volume, and the size of the self-owned fleet refers to the number of vehicles of each model of each model configured by the logistics enterprise to participate in trunk transportation throughout the year; Step 2, setting the parameters of the inner and outer genetic algorithms according to the known data and constraints, including population size, number of decision variables and value range, crossover probability, mutation probability and maximum number of iterations; Step 3, initialize the outer population, bring the outer population individuals into the inner layer, execute the inner genetic algorithm, and output the optimal transportation plan and the maximum profit of the month; Step 4, execute the outer adaptive genetic algorithm, and output the optimized size of the self-owned fleet participating in trunk transportation, as well as the optimal transportation plan for each month under the size of the self-owned fleet.

6. The method for optimizing the size of a self-owned fleet for trunk transportation according to claim 5 is characterized in that: The step 3 comprises the following steps: Step 31, initialize the inner population according to the individuals of the outer population brought in, so that the inner population satisfies the constraint conditions; Step 32, calculating the inner layer fitness; the inner layer fitness is the trunk line transportation profit of the logistics enterprise in that month; Step 33, determine whether the number of inner layer iterations meets the termination condition. If not, perform selection, crossover and mutation operations in sequence to generate a new generation of population and recalculate the inner layer fitness; if satisfied, terminate the iteration and output the optimal transportation plan and maximum profit for the month.

7. The method for optimizing the size of a self-owned fleet for trunk transportation according to claim 6 is characterized in that: The step 4 comprises the following steps: Step 41, calculating the outer layer fitness according to the output of the inner layer genetic algorithm: each individual in the outer layer population corresponds to 12 inner layer populations, and the sum of the maximum profits of each month output after executing the inner layer genetic algorithm 12 times is the fitness of the outer layer algorithm, and the optimization is performed with the goal of maximizing the total profit for the whole year; Step 42, adaptively adjusting the outer genetic operator: according to the fitness of the outer algorithm and the number of iterations of the current outer algorithm, adjusting the outer genetic operator so that the outer algorithm increases the probability of retaining the dominant individuals at the beginning of the iteration and avoids the algorithm from falling into the local optimum at the end of the iteration; Step 43, determine whether the number of outer layer iterations meets the termination condition. If not, perform selection, crossover and mutation operations in sequence to generate a new generation of population, bring the new population individuals into the inner layer again, and return to step 31; if satisfied, terminate the iteration, output the optimized size of the self-owned fleet participating in trunk transportation, and the optimal transportation plan for each month under the size of the self-owned fleet.

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