Multi-vehicle-type city-wide distribution vehicle scheduling optimization method based on traffic restriction and number restriction rules

Abstract

The invention relates to a multi-vehicle-type city-wide distribution vehicle scheduling optimization method based on a traffic restriction and number restriction rule, and belongs to the field of path planning. The method comprises the following steps: 1) integration of dynamic traffic restriction rules: ensuring that a scheduling scheme adapts to a dynamic traffic environment by acquiring urban traffic restriction information in real time; 2) multi-objective optimization: comprehensively considering distribution time, cost and environmental protection requirements, and introducing a weight factor to flexibly adjust importance of each objective; and 3) loading time optimization: introducing an M / M / 1 / infinity queuing theory model, accurately measuring the loading time of the vehicle in a distribution center, and bringing the loading time into time cost calculation to optimize the overall distribution efficiency. The method can effectively improve the efficiency and environmental protection of city-wide distribution, reduces the operation cost, and has a good application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of path planning and relates to a method for optimizing the vehicle scheduling of multi-vehicle same-city distribution based on traffic restriction rules. Background Art

[0002] With the acceleration of the urbanization process and the booming development of the e-commerce industry, the demand for same-city distribution services has shown a significant growth trend. This growth has not only brought business opportunities but also posed new challenges to urban traffic management and environmental protection. Especially in big cities, the problems of traffic congestion and environmental pollution have become increasingly prominent, prompting the emergence of a series of traffic restriction policies.

[0003] Traffic restriction rules usually include restrictions on the driving time and area of vehicles within a specific area, aiming to reduce the vehicle flow in the central urban area, thereby effectively alleviating traffic congestion, improving traffic efficiency, and promoting the development of a more environmentally friendly logistics model. However, the implementation of these policies has brought additional challenges to the same-city distribution industry. On the one hand, enterprises need to strictly comply with traffic restriction regulations when planning distribution routes and times, which increases the complexity and difficulty of scheduling; on the other hand, in order to meet the high expectations of customers, enterprises also need to ensure that goods can be delivered on time, which to a certain extent exacerbates the increase in logistics costs.

[0004] Existing same-city distribution vehicle scheduling methods mostly adopt traditional optimization algorithms, such as genetic algorithms, ant colony algorithms, etc. Although they can solve the scheduling problem to a certain extent, they are unable to cope when faced with dynamically changing traffic restriction rules. These methods have the following main problems and deficiencies:

[0005] 1) Static scheduling model: Unable to respond to changes in traffic restriction rules in real time. Most traditional scheduling methods are based on static models and cannot respond to changes in traffic restriction rules in real time. This may lead to the failure of the scheduling plan during actual execution, affecting distribution efficiency. Lack of dynamic adjustment ability. When traffic restriction rules (such as license plate number restrictions, area restrictions, etc.) change, it is necessary to re-run the algorithm to obtain a new scheduling plan, which is time-consuming and inefficient in actual operation.

[0006] 2) Single-objective optimization: Ignoring multi-objective factors. Existing scheduling algorithms usually only focus on a single objective, such as minimizing the distribution time cost, while ignoring other important factors, such as carbon emissions and fuel consumption costs. Unable to balance multi-objective requirements. In actual distribution, multiple objectives need to be considered comprehensively, such as time cost, carbon emissions, etc. Traditional models are difficult to take these objectives into account simultaneously.

[0007] 3) Neglect of traffic restriction rules: Traffic restriction rules are not incorporated into the model. Many traditional scheduling methods fail to integrate traffic restriction rules (such as license plate number restrictions, area restrictions, etc.) into the optimization model, resulting in the generated scheduling plans possibly violating traffic restriction regulations during actual implementation, leading to fines or delays. Insufficient handling of dynamic traffic restriction rules. Traffic restriction rules may change at any time, and traditional algorithms lack a dynamic adjustment mechanism and cannot update the scheduling plan in a timely manner to adapt to new traffic restriction rules.

[0008] 4) Improper management of loading time: Ignoring loading time. Traditional scheduling methods usually assume that the loading time is fixed and known, but in fact, the loading time may vary due to factors such as the type of goods and loading and unloading equipment. Ignoring this will result in a discrepancy between the actual delivery time and the planned time, affecting the overall efficiency. Uncertainty of loading time. The uncertainty of loading time may lead to delays in subsequent tasks. Traditional algorithms fail to effectively handle this problem, affecting the coordination of the entire delivery process. Summary of the Invention

[0009] In view of this, the purpose of the present invention is to provide a multi-vehicle same-city distribution vehicle scheduling optimization method based on traffic restriction rules, to solve the deficiencies in the existing research such as insufficient application in the field of traffic restrictions, single-objective optimization and mostly static scheduling. Through the synergistic effect of the application of traffic restriction rules and dynamic scheduling, the optimal path for same-city distribution is solved, so as to balance the delivery time and loading rate under the optimal path condition.

[0010] To achieve the above purpose, the present invention provides the following technical solutions:

[0011] A multi-vehicle same-city distribution vehicle scheduling optimization method based on traffic restriction rules, including:

[0012] 1) Integration of dynamic traffic restriction rules: By obtaining urban traffic restriction information in real time, ensure that the vehicle scheduling plan adapts to the dynamic traffic environment;

[0013] 2) Construction of a multi-objective optimization model: Considering delivery time, cost and environmental protection requirements comprehensively, introduce a weight factor to flexibly adjust the importance of each objective;

[0014] 3) Optimization of loading time: Introduce the M / M / 1 / ∞ queuing theory model to measure the loading time of vehicles at the distribution center, incorporate the loading time into the time cost calculation, and optimize the overall delivery efficiency.

[0015] [[ID=~26]]Further, the prerequisite for constructing a multi-objective optimization model is:

[0016] (1) The demand points, i.e., customers and distribution centers, are discrete points located on a plane;

[0017] (2) Each distribution center is independent of each other and there is no cooperative relationship;

[0018] (3) The goods and delivery services provided by fuel trucks are homogeneous, and the efficiency of all distribution centers as service desks is the same;

[0019] (4) When the distribution center is occupied by a truck as a service station, if other trucks arrive and there are no free service stations, they will be put in the waiting queue;

[0020] (5) The demand rate (demand per unit time) of each point follows a Poisson distribution, and the service time of the service desk follows a negative exponential distribution, and the first-come, first-served principle is adopted;

[0021] (6) The demand point does not know the busyness of each facility in advance, so it chooses the nearest facility to serve it;

[0022] (7) There are three types of delivery vehicles: micro, light and medium, and the number of vehicles of each type is M1, M2 and M3 respectively.

[0023] Furthermore, a multi-objective optimization model is constructed, including objective functions and constraints; the multi-objective objectives are minimizing fuel cost F1, minimizing carbon emissions F2, and minimizing time cost F3.

[0024] Furthermore, the objective function of the multi-objective optimization model is:

[0025]

[0026]

[0027]

[0028] Among them, U k is the distribution center, k∈{1,2,...,K}, K represents the number of distribution centers; U0 represents the departure distribution center; d ij is the distance from demand point i to j, i, j∈{1,2,3,...,N}, N is the number of demand points; From demand point i to distribution center U k distance; For the distribution center U k The distance to demand point j;

[0029] Is the delivery from demand point i to demand point j at time t from the last number k nm Vehicle delivery, if If It means that the vehicle is not used for delivery; k nmis the last license plate number of the fuel vehicle, n∈{1,2,3}, m∈{M1,M2,M3}, where n=1 means the fuel vehicle is a mini truck, in which case m∈{M1}; n=2 means the fuel vehicle is a light truck, in which case m∈{M2}; n=3 means the fuel vehicle is a medium truck, in which case m∈{M3}; k nm ∈{1,2,3,4,5,6,7,8,9,0}, n represents the model number, m represents the vehicle number; β n is the fuel consumption per unit distance of the n-th truck; γ n is the carbon emission per unit distance of the n-th type of truck; v n is the average speed of the n-th truck; μ is the average demand processed by a single service station in the distribution center per unit time (service rate); λ is the demand generated by the distribution center per unit time (demand rate);

[0030] x ij 1 Is the delivery from demand point i to demand point j delivered by the first type of truck, i.e., mini truck? ij 1 =1 means delivery by type 1 truck; when x ij 1 =0 means not delivered by type 1 truck;

[0031] x ij 2 Is the delivery from demand point i to demand point j delivered by the second type of truck, that is, light truck? ij 2 =1 means delivery by the second type of truck; when x ij 2 =0 means not delivered by type 2 truck;

[0032] x ij 3 Is the delivery from demand point i to demand point j delivered by the third type of truck, that is, the medium-sized truck? ij 3 =1 means delivery by the third type of truck; when x ij 3 =0 means not delivered by type 3 truck;

[0033] Whether the distribution center U is visited during the delivery from demand point i to demand point j k ,like It means that the distribution center U was visited on the way. k ;like It means that the distribution center U will not be visited during the journey. k .

[0034] Furthermore, formula (1) represents the fuel consumption F1, and the fuel consumption is proportional to the driving distance of the vehicle; when it means that the distribution center U is passed through on the way from demand point i to demand point j k , in this case this term is zero; when it means that the distribution center U is not passed through on the way from demand point i to demand point j k , in this case represents the fuel consumption directly from demand point i to demand point j; represents the fuel consumption when passing through the distribution center U on the way from demand point i to demand point j k .

[0035] Furthermore, formula (2) represents the carbon emission F2, and the carbon emission is proportional to the driving distance of the vehicle; when it means that the distribution center U is passed through on the way from demand point i to demand point j k , in this case this term is zero; when it means that the distribution center U is not passed through on the way from demand point i to demand point j k , in this case represents the carbon emission directly from demand i to demand point j; represents the carbon emission when passing through the distribution center U on the way from demand point i to demand point j k .

[0036] Furthermore, formula (3) represents the time cost F3, where represents the transportation time, represents the average total service time received by the vehicle at the distribution center, i.e., the total loading time.

[0037] Furthermore, the constraint conditions of the multi-objective optimization model are:

[0038]

[0039]

[0040]

[0041]

[0042] q nm ≤Q n (8)

[0043] x ij 1 +x ij 2 +x ij3 ≤1 (9)

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] where d i is the demand volume of demand point i; is the load capacity of the m - th fuel truck of the n - th truck when it arrives at demand point i; is the load capacity of the m - th fuel truck of the n - th truck when it leaves demand point i; is the load capacity of the m - th fuel truck of the n - th truck when it arrives at distribution center U k ; is the load capacity of the m - th fuel truck of the n - th truck when it leaves distribution center U k ; q nm is the dynamic load capacity of the m - th fuel truck of the n - th truck; is the pick - up volume of the m - th fuel truck of the n - th truck at distribution center U k ; Q n is the maximum load capacity of the n - th fuel truck; is the total goods collection volume of distribution center U k ; P represents the set of demand points;

[0056] is the departure time of the vehicle from demand point i during the transportation from demand point i to demand point j; is the arrival time of the vehicle at demand point j during the transportation from demand point i to demand point j;

[0057] r nm is the last digit of knm Whether the truck is restricted on that day, where R d is the restricted tail number on the d-th day in the cycle;

[0058] r t is whether the time t belongs to the restricted period, where, M s1 is the start time of the morning rush hour, M s2 is the start time of the evening rush hour, M e1 is the end time of the morning rush hour, M e2 is the end time of the evening rush hour;

[0059] g ij is whether the path from demand point i to demand point j is in the restricted path. If g ij = 1, it means it is in the restricted path; if g ij = 0, it means it is not in the restricted path;

[0060] is the tail number k nm whether it is the restricted tail number.

[0061] Furthermore, formula (4) indicates that the load of the m-th truck of the n-th type of vehicle leaving demand point i is equal to the load when arriving at demand point k minus the demand of demand point k;

[0062] Formula (5) indicates that the load of the m-th truck of the n-th type of vehicle leaving the distribution center U k is equal to the load when arriving at the distribution center U k plus the pick-up quantity at this distribution center;

[0063] Formula (6) The load of the fuel truck leaving each distribution center does not exceed its maximum load;

[0064] Formula (7) The load of the fuel truck when arriving at each demand point does not exceed its maximum load;

[0065] Formula (8) The dynamic load of the fuel truck at all times does not exceed its maximum load;

[0066] Formula (9) indicates that the delivery between two demand points can only be carried out by one type of truck;

[0067] The value of the fuel consumption per unit distance in formula (10) must be less than or equal to the fuel consumption per unit of the medium-sized truck β3;

[0068] Formula (11) Transportation time limit. During the direct transportation process of the vehicle from demand point i to j, the departure time from point i plus the transportation time is equal to the arrival time at j;

[0069] The transportation time limit in formula (12) means that during the transportation process of the vehicle from demand point i to j, it needs to pass through the distribution center U. k ;

[0070] The speed during transportation in formula (13) must be less than or equal to the speed of the minivan.

[0071] In formula (14), only during off-peak hours can the restricted-number vehicles be conditionally selected for distribution. At other times, the restricted-number vehicles are unconditionally involved in distribution, and the non-restricted-number vehicles can participate in distribution at any time.

[0072] Formula (15) shows the quantitative relationship between the total goods collection volume at the distribution center U k and the total pick-up volume of the trucks here. This invention allows for a surplus in the goods collection volume at the distribution center.

[0073] Formulas (16) and (17) indicate that a demand point can only be visited by one vehicle.

[0074] Formula (18) means that the vehicle departs from the distribution center, completes distribution and goods collection, and finally returns to the distribution center.

[0075] Formula (19) means that the pick-up volume of the vehicle at the distribution center U k should be less than or equal to the maximum load capacity of the vehicle at this time.

[0076] Formula (20) represents the constraint condition for whether the distribution from demand point i to j is carried out by a vehicle with the last digit k nm When g ij , r t are both 1, that is, when the restricted path, restricted tail number, and restricted time are all satisfied simultaneously, the value of must be 0, and in other cases

[0077] The value of

[0078] can be 0 or 1.

[0079] Furthermore, the simulated annealing algorithm is used to solve the multi-objective optimization model.

[0080] The beneficial effects of the present invention are as follows:

[0079] (1) Integration of dynamic traffic restriction rules: The present invention incorporates the real-time updated urban traffic restriction rules into the scheduling algorithm to ensure that the vehicle scheduling plan can adapt to the changing traffic environment. By docking with the data interface of the urban traffic management department, the model can obtain the latest traffic restriction information, such as traffic restriction time, traffic restriction number, etc., so as to make the optimal scheduling decision.

[0080] (2) Multi-objective optimization: Considering the complexity in practical applications, the present invention adopts a multi-objective optimization strategy, taking into account distribution efficiency, cost control, and environmental protection requirements. Specifically, the model aims to minimize the total distribution time, reduce transportation costs, and lower carbon emissions. By introducing weight factors, the importance of each objective can be flexibly adjusted according to the needs of different scenarios.

[0081] (3) Using a digital map to construct a distance matrix provides high-precision location and road information, ensuring the accuracy of distance calculation; supports real-time updates, can promptly reflect traffic changes, and optimize route selection; has versatility, and in addition to distance calculation, can also provide additional information such as road congestion conditions. The present invention constructs a model based on the advantages of the above digital map, which is conducive to more effectively solving the vehicle scheduling problem under traffic restriction rules.

[0082] (4) Introducing the M / M / 1 queuing model can better reflect the loading time. By analyzing historical data to estimate the average arrival rate and service rate, the average waiting time of the vehicle can be calculated, avoiding time waste, and significantly reducing the additional time cost caused by too long loading time. At the same time, optimize resource utilization and improve the overall distribution efficiency.

[0083] Other advantages, objectives, and features of the present invention will to some extent be described in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following specification. Brief Description of the Drawings

[0084] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0085] Figure 1 It is a flowchart for implementing the dynamic same-city logistics vehicle scheduling optimization method based on traffic restriction rules provided by the present invention. Detailed Embodiments

[0086] The following illustrates the embodiments of the present invention through specific specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present invention schematically, and the following embodiments and the features in the embodiments can be combined with each other without conflict.

[0087] Please refer to Figure 1, a dynamic same-city distribution vehicle scheduling optimization method provided by the present invention. First, dynamic traffic restriction rule integration. This model incorporates the real-time updated city traffic restriction rules into the scheduling algorithm to ensure that the vehicle scheduling plan can adapt to the constantly changing traffic environment. By docking with the data interface of the city traffic management department, the model can obtain the latest traffic restriction information, such as restricted travel periods, restricted areas, etc., and thus make the optimal scheduling decision. Second, multi-objective optimization. Considering the complexity in practical applications, the model adopts a multi-objective optimization strategy, taking into account distribution efficiency, cost control, and environmental protection requirements. Specifically, the model aims to minimize the total distribution time, reduce transportation costs, and lower carbon emissions. By introducing weight factors, the importance of each objective can be flexibly adjusted according to the requirements of different scenarios. Third, the M / M / 1 / ∞ queuing model is introduced to measure the loading time of vehicles at the distribution center. In the actual distribution process, the loading time of vehicles at the distribution center is an important factor, which directly affects the overall distribution efficiency and cost. To more accurately manage and optimize the loading time, this model introduces the M / M / 1 / ∞ queuing theory model. The M / M / 1 / ∞ model is a classic queuing theory model applicable to describing a queuing system with a single service desk, where both the arrival process and the service process follow the Poisson distribution.

[0088] To better understand this solution, some concepts are first explained.

[0089] Traffic restriction rules: refer to a series of regulations that restrict vehicle travel within a specific time and area, aiming to relieve traffic congestion, reduce air pollution, ensure traffic safety, etc. This model only considers the restricted travel time, restricted area, and the last digit of the restricted vehicle license plate, and this restriction applies to all vehicle types;

[0090] Traditional fuel-consuming vehicles: Vehicles with a fixed and known loading capacity that consume fuel as energy. Since fuel-consuming vehicles have a relatively long driving range when full of fuel, there is no travel distance constraint for fuel-consuming vehicles in the present invention. In addition, the refueling time is short and can be ignored compared to the mileage time. The present invention introduces three types of fuel trucks: micro, light, and medium.

[0091] Secondly, the following assumptions are made for this solution:

[0092] (1) The demand points and the distribution centers are all discrete points located on a plane; (2) Each distribution center is independent of each other and there is no cooperative relationship; (3) The goods and distribution services provided by fuel trucks are homogeneous, so the working efficiency of the distribution center as a service desk is the same; (4) When a distribution center as a service desk is occupied by a truck and other trucks arrive and there is no idle service desk, they are included in the waiting queue; (5) The demand rate (demand quantity per unit time) of each point follows a Poisson distribution, and the service time of the service desk follows a negative exponential distribution, and the first-come, first-served principle is adopted; (6) The demand points (i.e., customers) do not know the busy situation of each facility in advance, so they all choose the nearest facility to serve them; (7) There are three types of distribution vehicles, namely, micro, light, and medium, and the number of each type of vehicle is M1, M2, and M3 respectively.

[0093] Construct a multi-objective optimization model:

[0094] The present invention is a multi-objective optimization, including objectives: fuel cost F1, carbon emission F2, and time cost F3. The model can be divided into two parts: the objective function and the constraint conditions. The sets, parameters, and decision variables defined in the present invention are listed as follows.

[0095] U k : Distribution center, U0 represents the departure distribution center, k ∈ {1, 2,..., K};

[0096] N: Demand point, N = 1, 2, 3,.....;

[0097] d i : The demand quantity of customer point i, i ∈ N;

[0098] k nm : The tail number of the fuel vehicle, n ∈ {1, 2, 3}, m ∈ {M1, M2, M3}, where n = 1 means the fuel vehicle is a micro truck, and at this time m ∈ {M1}; n = 2 means the fuel vehicle is a light truck, and at this time m ∈ {M2}; n = 3 means the fuel vehicle is a medium truck, and at this time m ∈ {M3}. k nm ∈ {1, 2, 3, 4, 5, 6, 7, 8, 9, 0}, n represents the vehicle type number, and m represents the vehicle number. r nm : The tail number is k nm Whether the truck with tail number k is restricted from driving on the same day;

[0099]

[0100] r t : Whether the moment t belongs to the restricted driving period;

[0101]

[0102] Rd : The license plate tail number restricted on the d-th day in a cycle; it is specifically adjusted according to the traffic restriction rules of the city. In this invention, taking Chongqing as an example, d ∈ {1, 2, 3, 4, 5, 6, 7}, and d = 1, 2, 3, 4, 5, 6, 7 represent Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday respectively.

[0103] M s1 : The start time of the morning rush hour;

[0104] M s2 : The start time of the evening rush hour;

[0105] M e1 : The end time of the morning rush hour;

[0106] M e2 : The end time of the evening rush hour;

[0107] t: The current time

[0108] : The departure time from point i during the transportation of the vehicle from demand point i to j;

[0109] : The arrival time at point j during the transportation of the vehicle from demand point i to j;

[0110] d ij : The distance between customer i and j, i, j ∈ {1, 2, 3,..., N};

[0111] : The distance from demand point i to the distribution center U k ; i ∈ {1, 2, 3,..., N};

[0112] : The distribution center U k to the distance of demand point j, j ∈ {1, 2, 3,..., N};

[0113] v n : The average speed of the n-th fuel truck, n ∈ {1, 2, 3};

[0114] : The tail number k nm Whether it is a restricted license plate tail number;

[0115] g ij : Whether the path from demand point i to demand point j is in the restricted path. If g ij = 1, it means it is in the restricted path; if g ij = 0, it means it is not in the restricted path, i, j ∈ {1, 2, 3,..., N};

[0116] Qn : The maximum load capacity of the nth fuel truck, where n ∈ {1, 2, 3};

[0117] β n : The fuel consumption per unit distance of the nth truck. In the present invention, the fuel is set as diesel. n ∈ {1, 2, 3}, and n = 1, 2, 3 represent mini, light, and medium fuel trucks respectively;

[0118] γ n : The carbon emission per unit distance of the nth truck. In the present invention, the fuel is set as diesel. n ∈ {1, 2, 3}, and n = 1, 2, 3 represent mini, light, and medium fuel trucks respectively;

[0119] : The load capacity of the mth fuel truck of the nth type when it arrives at demand point i. n ∈ {1, 2, 3}, and n = 1, 2, 3 represent mini, light, and medium fuel trucks respectively. m ∈ {M1, M2, M3};

[0120] : The load capacity of the mth fuel truck of the nth type when it leaves demand point i. n ∈ {1, 2, 3}, and n = 1, 2, 3 represent mini, light, and medium fuel trucks respectively. m ∈ {M1, M2, M3};

[0121] : The load capacity of the mth fuel truck of the nth type of vehicle when it arrives at distribution center U k when. n ∈ {1, 2, 3}, and n = 1, 2, 3 represent mini, light, and medium fuel trucks respectively. m ∈ {M1, M2, M3};

[0122] : The load capacity of the mth fuel truck of the nth type of vehicle when it leaves distribution center U k when. n ∈ {1, 2, 3}, and n = 1, 2, 3 represent mini, light, and medium fuel trucks respectively. m ∈ {M1, M2, M3};

[0123] q nm : The dynamic load capacity of the mth fuel truck of the nth type of vehicle. n ∈ {1, 2, 3}, and n = 1, 2, 3 represent mini, light, and medium fuel trucks respectively. m ∈ {M1, M2, M3};

[0124] Distribution center U k Total goods collection volume;

[0125] μ: The average demand processed within a single service desk in the distribution center per unit time (service rate);

[0126] λ: The demand generated in the distribution center per unit time (demand rate);

[0127] The decision variables are defined as follows:

[0128] : Whether the delivery from demand point i to demand point j is made by vehicle with tail number k nm at time t. If it means it is delivered by this vehicle; if it means it is not delivered by this vehicle; i, j ∈ {1, 2,..., N};

[0129] x ij 1 : Whether the delivery from demand point i to demand point j is made by the first type of truck (i.e., micro truck); when x ij 1 = 1, it means it is delivered by the first type of truck; when x ij 1 = 0, it means it is not delivered by the first type of truck;

[0130] x ij 2 : Whether the delivery from demand point i to demand point j is made by the second type of truck (i.e., light truck); when x ij 2 = 1, it means it is delivered by the second type of truck; when x ij 2 = 0, it means it is not delivered by the second type of truck;

[0131] x ij 3 : Whether the delivery from demand point i to demand point j is made by the third type of truck (i.e., medium truck); when x ij 3 = 1, it means it is delivered by the third type of truck; when x ij 3 = 0, it means it is not delivered by the third type of truck;

[0132] : Whether the distribution center U is visited during the delivery from demand point i to demand point j k . If it means the distribution center U is visited during the journey k ; if it means the distribution center U will not be visited during the journey k . i, j ∈ {1, 2,..., N}, U k ∈ {1, 2,..., U};

[0133] : The pickup quantity of the m - numbered fuel truck of type n at the distribution center U k , where n ∈ {1, 2, 3}, and n = 1, 2, 3 represent micro, light, and medium fuel trucks respectively.

[0134] Its objective function is as follows:

[0135]

[0136]

[0137]

[0138] The constraint conditions are as follows:

[0139]

[0140]

[0141]

[0142]

[0143] q nm ≤Q n (8)

[0144] x ij 1 +x ij 2 +x ij 3 ≤1 (9)

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156] Equation (1) represents the fuel consumption F1, and the fuel consumption is proportional to the driving distance of the vehicle. When When it indicates that the distribution center U is passed through on the way from i to j k , at this time this item is zero; when it indicates that the distribution center U is not passed through on the way from i to j k , at this time It represents the fuel consumption brought directly from demand point i to demand point j It represents the fuel consumption when passing through the distribution center U on the way from demand point i to demand point j k of the fuel consumption

[0157] Equation (2) represents the carbon emission F2, and the carbon emission is proportional to the driving distance of the vehicle. When it indicates that the distribution center U is passed through on the way from i to j k , at this time this item is zero; when it indicates that the distribution center U is not passed through on the way from i to j k , at this time It represents the carbon emission brought directly from demand i to demand point j It represents the carbon emission when passing through the distribution center U on the way from demand point i to demand point j k of the carbon emission

[0158] Equation (3) represents the time cost F3, where represents the transportation time represents the average total service time received by the vehicle at the distribution center, that is, the total loading time

[0159] Equation (4) means that the load of the m-th truck of the n-th type of vehicle leaving demand point i is equal to the load when arriving at demand point k minus the demand of demand point k

[0160] Equation (5) means that the load of the m-th truck of the n-th type of vehicle leaving the distribution center U k is equal to the load when arriving at the distribution center U k plus the pick-up quantity at this distribution center

[0161] Equation (6) means that the load of the fuel truck leaving each distribution center does not exceed its maximum load

[0162] Equation (7) means that the load of the fuel truck when arriving at each demand point does not exceed its maximum load

[0163] Equation (8) means that the dynamic load of the fuel truck never exceeds its maximum load

[0164] Equation (9) means that the distribution between two demand points can only be carried out by one type of truck

[0165] The fuel consumption value per unit distance of Equation (10) must be less than or equal to the unit fuel consumption β3 of medium-sized trucks;

[0166] Equation (11) is the transportation time limit. During the direct transportation of the vehicle from demand point i to j, the departure time from point i plus the transportation time equals the arrival time at j;

[0167] Equation (12) is the transportation time limit. When the vehicle transports from demand point i to j, it needs to pass through the distribution center U k , and the specific meaning is referred to (11);

[0168] The speed during transportation in Equation (13) must be less than or equal to the speed of mini-trucks;

[0169] Equation (14) means that only during non-peak hours can the restricted-number vehicles be conditionally selected for distribution. During other times, the restricted-number vehicles are unconditionally involved in distribution, and non-restricted-number vehicles can be involved in distribution at any time.

[0170] Equation (15) is the quantitative relationship between the total cargo collection volume of the distribution center U k and the total pick-up volume of the trucks here. This invention allows for a surplus in the cargo collection volume of the distribution center;

[0171] Equations (16) and (17) indicate that a customer point can only be visited by one vehicle;

[0172] Equation (18) indicates that the vehicle departs from the distribution center, completes distribution and cargo collection, and finally returns to the distribution center;

[0173] Equation (19) indicates that the pick-up volume of the vehicle at the distribution center U k should be less than or equal to the maximum load capacity of the vehicle at this time;

[0174] Equation (20) represents the constraint condition for whether the distribution from demand point i to j is executed by a vehicle with the last digit k nm . When g ij , r t are both 1, that is, when the restricted path, restricted tail number, and restricted time are all satisfied simultaneously, the value of must be 0, and in other cases

[0175] Embodiment:

[0176] Use the simulated annealing algorithm to solve the model:

[0177] The present invention relates to an optimization method based on the simulated annealing algorithm, which is characterized by including the following steps:

[0178] 1) Initialize parameters: Set the initial temperature to 100, the final temperature to 1, the temperature decay coefficient to 0.90, and the maximum number of iterations to 100.

[0179] 2) Temperature control mechanism: The higher the initial temperature, the greater the probability that the algorithm accepts a worse solution at the initial stage of the search, which helps to jump out of the local optimum and achieve extensive exploration. As the temperature gradually decreases, the search process changes from extensive exploration to local refinement.

[0180] 3) Cooling process: After each iteration, the current temperature is multiplied by the temperature decay coefficient (0.90), that is, the temperature decreases by 10% of the initial temperature after each iteration until the temperature drops to the final temperature.

[0181] 4) Iteration termination condition: When the temperature drops to the final temperature or the maximum number of iterations is reached, the algorithm terminates and outputs the optimized result. Through the above method, the present invention can effectively avoid local optimal solutions in complex optimization problems and achieve global optimization.

[0182] 5) Parameter settings

[0183] This embodiment consists of one distribution center, twenty demand points, one consolidation center, and three vehicle types. To simplify the consideration of the model, the present invention assumes that the numbers of the three vehicle types are one micro vehicle, two light vehicles, and one medium vehicle respectively. The numerical values of the parameters in this model are shown in Table 1.

[0184] Table 1 Parameter value table of each parameter

[0185] Symbol Value Symbol Value <![CDATA[v1]]> 60 km / h <![CDATA[γ1]]> 3 <![CDATA[v2]]> 55 km / h <![CDATA[γ2]]> 4 <![CDATA[v3]]> 50 km / h <![CDATA[γ3]]> 5 <![CDATA[Q1]]> 1600 kg λ 0.5 vehicles / hour <![CDATA[Q2]]> 2000 kg μ 1 vehicle / hour <![CDATA[Q3]]> 2500 kg <![CDATA[β2]]> 3 <![CDATA[β1]]> 2 <![CDATA[β3]]> 4

[0186] The restricted paths and restricted time schedules are shown in Table 2.

[0187] Table 2 Restricted paths and restricted time schedules

[0188]

[0189] The distances between the demand points in this model are shown in Table 3, with the unit of kilometers.

[0190] Table 3 Distances between the demand points

[0191]

[0192]

[0193] The distances between the distribution center and the demand points are shown in Table 4, with the unit of kilometers.

[0194] Table 4 Distances between the distribution center and the demand points

[0195]

[0196] The demand quantities of the demand points are shown in Table 5, with the unit being kilograms.

[0197] Table 5 Demand Quantities of the Demand Points

[0198]

[0199] The distances between the collection center and each demand point are shown in Table 6, with the unit being kilometers.

[0200] Table 6 Distances between the Collection Center and Each Demand Point

[0201]

[0202] In this embodiment, to achieve a flexible response to dynamic traffic restriction rules, the system allows users to manually input key information for the dispatching day, including the departure time of the vehicle, whether it is a weekday, and in the case of a weekday, the specific vehicle information affected by the license plate number restriction. In this way, when the traffic restriction rules change, the system can quickly adjust the dispatching plan according to the input data without re-running the algorithm, thereby effectively improving the dispatching efficiency and ensuring the smooth execution of the distribution task. In this embodiment, the weighted coefficients of fuel, carbon emissions, and time cost are set to 0.2, 0.3, and 0.5 respectively. The user inputs the following information through the interface to adapt to the dynamic traffic restriction rules: Is today a weekday? (yes / no): yes; Enter departuretime(0 - 24): 8; Enter restricted vehicle numbers(e.g., 1 2): 1.

[0203] Finally, the optimal dispatching results are obtained. The distribution routes of the vehicles are 7→20→12→15→4; 3→19→8→11→16; 18→13→14→17→5; 2→6→10→9→1. The loading quantities of the vehicles at the collection center are: 313, 434, 368, 336. The fuel cost is 2116, the carbon emissions cost is 2826, the time cost is 25, and the total weighted cost is 1283. (Note: All are retained as integers) The complete driving routes of the vehicles are shown in Table 7.

[0204] Table 7 Complete Driving Routes of the Vehicles

[0205]

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A multi-vehicle same-city distribution vehicle scheduling optimization method based on traffic restriction rules, characterized in that, The method includes: 1) Dynamic traffic restriction rule integration: By obtaining real-time urban traffic restriction information, ensure that the vehicle scheduling plan adapts to the dynamic traffic environment; 2) Construct a multi-objective optimization model: Considering the delivery time, cost, and environmental protection requirements comprehensively, introduce a weight factor to adjust the importance of each objective; 3) Loading time optimization: Introduce the M / M / 1 / ∞ queuing theory model to measure the loading time of vehicles at the distribution center, incorporate the loading time into the time cost calculation, and optimize the overall distribution efficiency.

2. The multi-vehicle same-city distribution vehicle scheduling optimization method based on traffic restriction rules according to claim 1, wherein The preconditions for constructing the multi-objective optimization model are: (1) The demand points, i.e., customers and the distribution center, are discrete points located on a plane; (2) The distribution centers are independent of each other and there is no cooperative relationship; (3) The goods and distribution services provided by fuel trucks are homogeneous, and the working efficiency of all distribution centers as service desks is the same; (4) A distribution center as a service desk is occupied by a truck. If other trucks arrive and there is no idle service desk, they are included in the waiting queue; (5) The demand rate at each point follows a Poisson distribution, and the service time of the service desk follows a negative exponential distribution, adopting the first-come, first-served principle; (6) The demand points do not know the busy situation of each facility in advance, so they all choose the nearest facility to serve them; (7) There are three types of distribution vehicles, namely, micro, light, and medium. The number of each type of vehicle is M1, M2, and M3 respectively.

3. The multi-vehicle same-city distribution vehicle scheduling optimization method based on the traffic restriction and number plate restriction rules according to claim 1, wherein, Construct a multi-objective optimization model, including an objective function and constraints; The multi-objectives are to minimize the fuel cost F1, minimize the carbon emissions F2, and minimize the time cost F3.

4. The multi-vehicle same-city distribution vehicle scheduling optimization method based on the traffic restriction and license plate number restriction rules according to claim 3, wherein, The objective function of the multi-objective optimization model is: Among them, U k is the distribution center, k ∈ {1, 2,..., K}, where K represents the number of distribution centers; U0 represents the departure distribution center; d ij is the distance from demand point i to j, i, j ∈ {1, 2, 3,..., N}, and N is the number of demand points; is the distance from demand point i to the distribution center U k ; is the distance from the distribution center U k to demand point j; Whether the delivery from demand point i to demand point j is made by the vehicle with the last digit k at time t nm If It means it is delivered by this vehicle. If It means it is not delivered by this vehicle; k nm is the last digit of the fuel vehicle. n ∈ {1, 2, 3}, m ∈ {M1, M2, M3}, where n = 1 means the fuel vehicle is a mini truck, and at this time m ∈ {M1}; n = 2 means the fuel vehicle is a light truck, and at this time m ∈ {M2}; n = 3 means the fuel vehicle is a medium truck, and at this time m ∈ {M3}; k nm ∈ {1, 2, 3, 4, 5, 6, 7, 8, 9, 0}, n represents the vehicle type number, and m represents the vehicle number; β n is the fuel consumption per unit distance of the nth truck; γ n is the carbon emission per unit distance of the nth truck; v n is the average speed of the nth truck; μ is the average demand processed within a single service desk in the distribution center per unit time; λ is the demand generated by the distribution center per unit time; x ij 1 Whether the delivery from demand point i to demand point j is made by the first type of truck, i.e., a minivan. When x ij 1 = 1, it means the delivery is made by the first type of truck; when x ij 1 = 0, it means the delivery is not made by the first type of truck; x ij 2 indicates whether the delivery from demand point i to demand point j is made by the second type of truck, i.e., the light truck. When x ij 2 = 1, it means the delivery is made by the second type of truck; when x ij 2 = 0, it means the delivery is not made by the second type of truck; x ij 3 Whether the delivery from demand point i to demand point j is made by the third type of truck, i.e., the medium-sized truck. When x ij 3 = 1, it means the delivery is made by the third type of truck; when x ij 3 = 0, it means the delivery is not made by the third type of truck; Whether the distribution center U is visited during the delivery from demand point i to demand point j k , if it means that the distribution center U is visited on the way k ; if it means that the distribution center U will not be visited on the way k .

5. The multi-vehicle same-city distribution vehicle scheduling optimization method based on traffic restriction rules according to claim 4, wherein, Formula (1) represents the fuel consumption F1, and the fuel consumption is proportional to the driving distance of the vehicle; when it means that during the journey from demand point i to demand point j, the distribution center U is passed through k , and at this time this item is zero; when it means that during the journey from demand point i to demand point j, the distribution center U is not passed through k , and at this time represents the fuel consumption brought directly from demand point i to demand point j; represents the fuel consumption when passing through the distribution center U during the journey from demand point i to demand point j k .

6. The multi-vehicle same-city distribution vehicle scheduling optimization method based on the traffic restriction rules according to claim 4, characterized in that, Equation (2) represents the carbon emission F2, and the carbon emission is proportional to the driving distance of the vehicle; when it means that the distribution center U is passed on the way from demand point i to demand point j k , and at this time this item is zero; when it means that the distribution center U is not passed on the way from demand point i to demand point j k , and at this time represents the carbon emission directly brought from demand i to demand point j; represents the carbon emission when the distribution center U is passed on the way from demand point i to demand point j k .

7. The multi-vehicle same-city distribution vehicle scheduling optimization method based on traffic restriction rules according to claim 4, wherein Formula (3) represents the time cost F3, where represents the transportation time, represents the average total service time received by the vehicle at the distribution center, i.e., the total loading time.

8. The multi-vehicle same-city distribution vehicle scheduling optimization method based on the traffic restriction rules according to claim 4, characterized in that, The constraints of the multi-objective optimization model are: q nm ≤Q n (8) x ij 1 +x ij 2 +x ij 3 ≤1 (9) where d i is the demand volume of demand point i; is the load capacity of the m - th fuel truck of the n - th truck when it arrives at demand point i; is the load capacity of the m - th fuel truck of the n - th truck when it leaves demand point i; is the load capacity of the m - th fuel truck of the n - th truck when it arrives at distribution center U k ; is the load capacity of the m - th fuel truck of the n - th truck when it leaves distribution center U k ; q nm is the dynamic load capacity of the m - th fuel truck of the n - th truck; is the pick - up volume of the m - th fuel truck of the n - th truck at distribution center U k ; Q n is the maximum load capacity of the n - th fuel truck; is the total goods collection volume of distribution center U k ; P represents the set of demand points; The departure time of the vehicle from demand point i during the transportation process from demand point i to demand point j; The arrival time of the vehicle at demand point j during the transportation process from demand point i to demand point j; r nm is the last digit of k nm whether the truck is restricted by number on the same day, where R d is the restricted tail number on the d-th day in the cycle; r t Whether it belongs to the restricted driving period at time t, where M s1 is the start time of the morning rush hour, M s2 is the start time of the evening rush hour, M e1 is the end time of the morning rush hour, M e2 is the end time of the evening rush hour; g ij Whether the path from demand point i to demand point j is in the restricted path. If g ij = 1, it means it is in the restricted path; if g ij = 0, it means it is not in the restricted path; is the last digit k nm whether it is the restricted license plate number digit.

9. The multi-vehicle same-city distribution vehicle scheduling optimization method based on the traffic restriction rules according to claim 8, wherein Use the simulated annealing algorithm to solve the multi-objective optimization model.

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