CN-114580732-B - Urban logistics vehicle path optimization method considering dynamic efficiency of driver

CN114580732BCN 114580732 BCN114580732 BCN 114580732BCN-114580732-B

Abstract

The invention discloses an urban logistics vehicle path optimization method considering dynamic efficiency of a driver, which comprises the steps of determining a relation between fatigue and performance of the driver, namely, introducing a fatigue curve model, analyzing the relation between fatigue and performance of the driver, establishing a vehicle path optimization model, namely, determining problem targets and constraint conditions, determining symbolic representations of parameters and variables, establishing a mathematical model, and solving the established models, namely, encoding and evaluating solutions, initializing stage design, hiring stage design, following stage design and detecting stage design. The invention not only can enable logistics enterprises to reasonably plan the distribution path of the driver under the condition of limited resources, help the enterprises to improve the distribution efficiency and save the distribution cost, but also can enable the path planning process to give consideration to the benefits of the driver, alleviate the fatigue accumulation of the driver and improve the working experience of the driver. Both of these aspects are important to improve the competitiveness of the enterprise.

Inventors

ZHANG SHUZHU
QIU BINGBING

Assignees

浙江财经大学

Dates

Publication Date: 20260512
Application Date: 20220301

Claims (3)

1. The city logistics vehicle path optimization method considering the dynamic efficiency of the driver is characterized by comprising the following steps: S1, determining a relation between fatigue and performance of a driver; S2, establishing a vehicle path optimization model; s3, solving the established model; S1, determining the relation between fatigue and performance of a driver, and specifically comprising the following steps of: s11, introducing a fatigue curve model; s12, analyzing the relation between the fatigue and the performance of the driver; s2, establishing a vehicle path optimization model, which specifically comprises the following steps: S21, determining a problem target and constraint conditions; s22, determining symbolic representations of parameters and variables; S23, establishing a mathematical model; In the introduced fatigue curve model, a classical fatigue curve model in the field of human engineering is introduced, and the specific function expression is as follows: ; Wherein, the To be the degree of fatigue of the driver at time t, Is a fatigue index, which indicates the rate of fatigue accumulation; in the analysis of the relation between the fatigue and the performance of the driver, the fatigue curve model is converted, and the distribution speed function of the driver under the influence of the fatigue is determined, wherein the formula is as follows: ; Wherein, the For the delivery speed of the driver at time t, Is the initial speed; From the driver delivery speed function, the delivery speed of the driver is nonlinear, so the delivery speed versus distance function is expressed as: ; Wherein, the For the distance between node i and node j, And (3) with The time of arrival at node i and node j, respectively; Deriving time spent by driver from node i to node j The formula is: ; the symbolic representation of the determined parameters and variables includes the following parameters and parameter descriptions: a city logistics distribution network comprising a network of distribution points, ; A set of nodes is provided which, Wherein 0 is the distribution center, Is a node; an arc set, which is composed of routes among nodes, ; A collection of vehicles is provided with a set of vehicles, Wherein the vehicles are in one-to-one correspondence with the drivers; The distance between node i and node j; Maximum capacity of the vehicle; the demand of node i; the travel time of the vehicle from node i to node j; The time the vehicle arrives at node i; The longest continuous delivery time allowed by the driver; a 0-1 variable, which is 1 when the vehicle k travels from node i to node j, or 0; a 0-1 variable, which is 1 when vehicle k accesses node i, or 0 otherwise; the establishing the mathematical model comprises the steps of establishing a mathematical model for optimizing the vehicle path according to a problem target, constraint conditions and symbolic representations: (1); (2); (3); (4); (5); (6); (7); (8); (9); (10); (11); Equation (1) is an objective function equation representing minimizing total delivery time, equation (2) represents that each node can only be served by one vehicle, equation (3) represents that if vehicle k serves node j, node j must be accessed, equation (4) represents that if vehicle k serves node i, the service must be removed from node i after the end, equation (5) represents that vehicles all start from the delivery center, all nodes on the path must return to the delivery center after the service is completed, and each vehicle only travels along one delivery route, equation (6) represents that the sum of all node demands on each delivery path cannot exceed the maximum capacity of the vehicle, equation (7) represents that the number of vehicles used cannot exceed the number of vehicles owned by the delivery center, equation (8) represents that the time relationship between vehicles sequentially reaching two nodes, equation (9) represents that the time of delivery of each vehicle does not exceed the allowed continuous delivery time, and equations (10) and (11) are decision variable value constraints.
2. The urban logistics vehicle path optimization method considering dynamic efficiency of drivers according to claim 1, wherein said S3, solving the model established above, comprises the steps of: s31, encoding and evaluating a solution; s32, initializing stage design; s33, hiring a bee stage design; S34, following the bee stage design; s35, the bee stage design is detected.
3. The method for optimizing urban logistics vehicular path considering dynamic efficiency of driver as claimed in claim 1, wherein the problem is described in that a distribution center has a plurality of vehicles, providing logistics distribution service to a plurality of nodes, wherein the positions and demands of the nodes are known, each node can be serviced only by one vehicle and only once, the number of vehicles dispatched cannot exceed the number of vehicles owned by the distribution center, each vehicle has a capacity limit, so that the sum of the demands of the nodes on each distribution path cannot exceed the maximum capacity of the vehicles, each vehicle starts from the distribution center, and must return to the distribution center after all the nodes on the path are serviced, assuming that fatigue of the driver does not exist at the initial moment, but is accumulated continuously over time, the performance of the driver varies during the distribution due to the influence of fatigue during the distribution, and further, the continuous distribution time of the driver is limited within a preset time.

Description

Urban logistics vehicle path optimization method considering dynamic efficiency of driver Technical Field The invention belongs to the technical field of logistics optimization, and relates to an urban logistics vehicle path optimization method considering dynamic efficiency of a driver. Background In recent years, under the push of continuous growth of electronic commerce, urban logistics has become an indispensable part of the national life field. In urban logistics, the driver acts as a tie for the link between the business and the customer, and the distribution efficiency directly affects the distribution cost, distribution efficiency and customer satisfaction level of the business. In order to improve the delivery efficiency of the driver, the logistics dispatcher often makes an optimal delivery path scheme before the driver starts, but the actual delivery situation is often inconsistent with the optimal delivery path scheme. Because the distribution process may be affected by external factors such as weather and traffic, and internal characteristics such as fatigue of the driver, the actual driving path and the time taken for the driver to complete the distribution task often have a great difference from the scheme expected by the dispatcher when preparing the scheme. So far, researchers have paid more attention to the influence of external factors on the optimal delivery path, ignoring the influence of intrinsic characteristics of the driver, especially the important factor of driver fatigue. In today's high-intensity working environment, fatigue generated during distribution tends to affect the performance of the driver, resulting in a decrease in the distribution efficiency of the driver. Therefore, how to effectively optimize the distribution scheme in consideration of the dynamic performance of the driver caused by fatigue by enterprises and provide better distribution service for customers has become a urgent problem to be solved. Delivery optimization problems such as express delivery, takeaway, fresh and the like in urban logistics can be collectively referred to as vehicle path problems. The vehicle path problem is a typical NP-hard problem, and the research of the current solving method is mainly focused on meta-heuristic method. The artificial bee colony algorithm is a classical meta-heuristic algorithm, and is formally proposed in 2005 by Karaboga at the earliest. The algorithm simulates the intelligent behavior of the honey collection group of the bee colony in the nature, and searches for a better honey source by adopting the division of three roles of the bee, following the bee and the investigation bee, searching the honey source, summoning the bee for the honey source and discarding the honey source. In the actual optimization problem, the process of searching the optimal honey source by bees is the process of searching the optimal solution of the problem. The original artificial bee colony algorithm is suitable for solving the continuity problem, and the vehicle path problem belongs to the discreteness problem. Meanwhile, the artificial bee colony algorithm has the defects of strong exploration capacity and weak exploitation capacity. Therefore, it is necessary to design and improve the artificial bee colony algorithm so that it is suitable for solving the vehicle path problem. Disclosure of Invention The invention specifically considers that the performance of the driver in the distribution process is directly influenced by the fatigue of the driver. The method solves the problem by adopting an improved artificial bee colony algorithm, and provides an urban logistics vehicle path optimization method considering the dynamic efficiency of a driver, which comprises the following steps: S1, determining a relation between fatigue and performance of a driver; S2, establishing a vehicle path optimization model; and S3, solving the established model. Preferably, the step S1 of determining the relationship between the fatigue and the performance of the driver specifically includes the steps of: s11, introducing a fatigue curve model; And S12, analyzing the relation between the fatigue and the performance of the driver. Preferably, the step S2 of establishing a vehicle path optimization model specifically includes the following steps: S21, determining a problem target and constraint conditions; s22, determining symbolic representations of parameters and variables; s23, establishing a mathematical model. Preferably, the step S3 of solving the established model includes the following steps: s31, encoding and evaluating a solution; s32, initializing stage design; s33, hiring a bee stage design; S34, following the bee stage design; s35, the bee stage design is detected. Preferably, in the introduced fatigue curve model, a classical fatigue curve model in the field of ergonomics is introduced, and the specific function expression is: ft＝1-e-λt wherein f t is the fatigue level of the driver at time