CN113780609A

CN113780609A - Method, device, computing equipment and medium for adjusting article distribution path

Info

Publication number: CN113780609A
Application number: CN202011351010.1A
Authority: CN
Inventors: 牛志强; 耿晓亮; 王煜; 董红宇; 张峰; 佟路
Original assignee: Beijing Jingdong Zhenshi Information Technology Co Ltd
Current assignee: Beijing Jingdong Zhenshi Information Technology Co Ltd
Priority date: 2020-11-26
Filing date: 2020-11-26
Publication date: 2021-12-10

Abstract

The present disclosure provides a method of adjusting an item delivery path, comprising: acquiring distribution network information and distribution task information; determining a distribution path according to the distribution network information and the distribution task information; performing a delivery task of the item via the delivery path; in the process of executing the distribution tasks, responding to the received dynamic demand information, and establishing a path optimization model according to the dynamic demand information, wherein the path optimization model is used for optimizing distribution paths under the condition that the distribution tasks are allowed to be interrupted; and adjusting the distribution path by using the path optimization model. The disclosure also provides a device, a computing device and a medium for adjusting the article distribution path.

Description

Method, device, computing equipment and medium for adjusting article distribution path

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a computing device, and a medium for adjusting an article distribution path.

Background

In the related art, in the process of planning the vehicle path, the service time of the delivery personnel at the customer site is not taken into consideration in the path planning, or the service time of the delivery personnel at the customer site is regarded as an indivisible period of time. This means that if a delivery person receives a delivery or pickup request with a tight time window during the delivery service at a certain customer point, the delivery person is constrained by the existing modeling and optimization solution method, and may not perform on time, which results in a decrease in customer satisfaction.

Disclosure of Invention

In view of the above, the present disclosure provides a method, an apparatus, a computing device and a medium for adjusting an article distribution path.

One aspect of the present disclosure provides a method of adjusting an article dispensing path, comprising: acquiring distribution network information and distribution task information; determining a distribution path according to the distribution network information and the distribution task information; performing a delivery task of the item via the delivery path; in the process of executing the distribution tasks, responding to the received dynamic demand information, and establishing a path optimization model according to the dynamic demand information, wherein the path optimization model is used for optimizing distribution paths under the condition that the distribution tasks are allowed to be interrupted; and adjusting the delivery path by using the path optimization model.

According to an embodiment of the present disclosure, the distributing network information includes: the positions of a plurality of areas to be delivered and the travel time between the plurality of areas to be delivered; the distribution task information includes: the starting time of the station, the latest returning time of the station and the remaining service time of each to-be-distributed area in the plurality of to-be-distributed areas.

According to an embodiment of the present disclosure, the determining a delivery path according to the delivery network information and the delivery task information includes: determining a shortest path passing through the plurality of areas to be distributed according to the positions of the plurality of areas to be distributed, the travel time among the plurality of areas to be distributed, the station departure time, the station latest return time and the remaining service time of each area to be distributed in the plurality of areas to be distributed, wherein the shortest path satisfies the condition that tau + ST + TT is less than or equal to LAT, wherein tau is the current time, LAT is the latest station return time, TT is the time spent by the distribution path, and ST is the sum of the remaining service times of the plurality of areas to be distributed.

According to an embodiment of the present disclosure, the dynamic demand information includes: the method comprises the steps that the position of a newly added area to be distributed, the residual service duration of the newly added area to be distributed and a time window are set, wherein the time window comprises a scheduled service starting time and a scheduled service ending time; the establishing of the path optimization model according to the dynamic demand information comprises: updating the distribution network information and the distribution task information according to the position of the newly-added area to be distributed, the residual service duration and the time window of the newly-added area to be distributed; and establishing a path optimization model with the net income maximum as a target according to the updated distribution network information and the distribution task information.

According to an embodiment of the present disclosure, establishing a path optimization model with a net profit being a maximum target according to the updated distribution network information and distribution task information includes: determining an objective function of the path optimization model according to the following formula:

wherein, the x_{i，j，t，t′}(τ) is a decision variable that indicates whether the spatio-temporal arc (i, j, t, t') was selected at time τ, and the ui_{，j，t，t′}Is the utility of selecting the spatio-temporal arc (i, j, t, t'), tt_{i，j，t，t′}Is the cost of selecting a spatio-temporal arc (i, j, t, t '), said spatio-temporal arc (i, j, t, t ') being a directed arc of a spatio-temporal point (i, t) pointing to the spatio-temporal point (i, t '), said spatio-temporal point (i, t) comprising the location information of the area i to be delivered and the time information at time t ', said spatio-temporal point (i, t ') comprising the location information of the area i to be delivered and the time information at time tAnd i is a positive integer, is less than or equal to the total number of the current areas to be distributed, and A is a space-time arc set.

According to an embodiment of the present disclosure, the establishing a path optimization model with a net profit maximum as a target according to the updated distribution network information and distribution task information further includes: determining constraints of the path optimization model according to the following formula:

wherein, c is_τIs a delivery area in which a delivery task is being executed at time t τ, the LAT is the latest return station time, and the ST_iIs the remaining service duration of the area i to be allocated, said T_i(τ) is the length of time that the area to be provisioned, i, has been served by the time t τ, P_τAnd the areas to be distributed which do not meet the service duration are collected.

According to an embodiment of the present disclosure, the adjusting the delivery path using the path optimization model includes: the recurrence relation is established according to the following formula:

where i, j ∈ N, where N is the set of regions to be allocated, N' is a subset of N- { i }, and where F_i，N″Is the shortest time from the scheduled start of service of area i to be delivered to the return to the station after all points in N ", tt_ijIs the travel time between areas i and j to be delivered, the ST_jIs the duration of the service required in the area j to be allocated; and solving the path optimization model to obtain a new distribution path.

Another aspect of the present disclosure provides an apparatus for adjusting an article delivery path, including an obtaining module, configured to obtain delivery network information and delivery task information; the determining module is used for determining a distribution path according to the distribution network information and the distribution task information; the execution module is used for executing the distribution task of the goods through the distribution path; and the adjusting module is used for responding to the received dynamic demand information in the process of executing the distribution tasks and adjusting the distribution route according to the dynamic demand information.

Another aspect of the disclosure provides a computing device comprising: one or more processors; storage means for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described above.

Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.

Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.

According to the embodiment of the disclosure, by establishing the path optimization model according to the dynamic demand information, the path optimization model is used for optimizing the delivery path under the condition that the delivery task is allowed to be interrupted, the problems that the existing modeling and optimization method cannot timely respond to a new demand generated in the service process, so that the invalid waiting time is long, and the customer satisfaction degree is reduced can be solved, and the customer satisfaction degree and the overall delivery benefit can be better improved compared with the related art.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

fig. 1 schematically illustrates an application scenario of the method and apparatus for adjusting an article delivery path according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a method of adjusting an item delivery path according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow chart of a method of adjusting an article dispensing path according to another embodiment of the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary configuration of an apparatus for adjusting an article dispensing path according to an embodiment of the present disclosure; and

FIG. 5 schematically illustrates a block diagram of a computer system suitable for implementing the methods of embodiments of the present disclosure, in accordance with embodiments of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

Embodiments of the present disclosure provide a method of adjusting an article dispensing path and an apparatus to which the method can be applied. The method comprises the steps of obtaining distribution network information and distribution task information; determining a distribution path according to the distribution network information and the distribution task information; performing a delivery task of the item via the delivery path; and in the process of executing the distribution task, responding to the received dynamic demand information, and adjusting the distribution route according to the dynamic demand information.

Fig. 1 schematically illustrates an exemplary application scenario 100 in which a method of adjusting an item delivery path may be applied according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of an application scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, the application scenario 100 according to the embodiment may include a station 10, a delivery vehicle 20, and

delivery areas

31, 32, 33, 34.

The delivery vehicle 20 starts from the station 10, sequentially passes through the

delivery areas

31, 32, and 33 according to the planned route, executes delivery tasks, and returns to the station 10 after the tasks have been executed. Initially the system will perform an initial vehicle path planning based on the distribution of the order at each customer site. If a new demand is dynamically generated during the delivery process, the delivery route is adjusted according to the new demand. The delivery vehicle 20 starts from the station 10 at the start time and performs delivery tasks to the

delivery areas

31, 32, 33 according to the initial vehicle path plan. Subsequently, during execution of the delivery job, status deduction is performed at preset time steps (unit time intervals), and status information such as the position information of the delivery vehicle 20, the order service status of each delivery area, and the like is updated at each time step. If no new delivery demand is generated on the way of performing the delivery task, the delivery vehicle 20 travels according to the originally planned route. If a new delivery demand is generated at a certain time, and the delivery area corresponding to the new demand is, for example, the delivery area 34, the optimization process is switched to. Taking the state information of the delivery vehicle 20 at the current moment as an input part of a subsequent optimization model, re-planning the route according to the remaining service duration of the

delivery areas

31, 32, 33 and 34 to obtain a new vehicle route, and entering the next time step. The above process is repeated, iteratively iterating the state deduction process and the optimization process until the delivery vehicle 20 returns to the station 10 by a prescribed time.

It should be understood that the number of stations, delivery vehicles, and delivery areas in fig. 1 are merely illustrative. There may be any number of stops, delivery vehicles, and delivery areas, as desired for the implementation.

It should be noted that the method for adjusting the article distribution route provided by the embodiment of the present disclosure may be generally executed by a terminal device, for example, a handheld terminal device used by a distributor or an on-board computer on a distribution vehicle. Accordingly, the device for adjusting the article distribution path provided by the embodiment of the disclosure can be generally arranged in the terminal equipment. The method for adjusting the article distribution path provided by the embodiment of the disclosure may also be executed by a server or a server cluster which is different from the terminal device and can communicate with the terminal device. Correspondingly, the device for adjusting the article distribution path provided by the embodiment of the disclosure may also be disposed in a server or a server cluster different from the terminal device and capable of communicating with the terminal device.

Fig. 2 schematically illustrates a flow chart of a method of adjusting an item delivery path according to an embodiment of the present disclosure.

As shown in fig. 2, the method includes acquiring distribution network information and distribution task information in operation S210.

According to an embodiment of the present disclosure, the distributing network information may include, for example: the location of multiple areas to be dispensed, the travel time between multiple areas to be dispensed, etc. The delivery task information may include, for example: the departure time of the site, the latest return time of the site, the remaining service duration of each to-be-distributed area in the plurality of to-be-distributed areas and the like.

Then, in operation S220, a delivery path is determined according to the delivery network information and the delivery task information.

According to the embodiment of the disclosure, the shortest path passing through the plurality of areas to be distributed can be determined according to the positions of the plurality of areas to be distributed, the travel time between the plurality of areas to be distributed, the departure time of the station, the latest return time of the station and the remaining service time of each area to be distributed in the plurality of areas to be distributed, wherein the shortest path satisfies τ + ST + TT ≦ LAT, where τ is the current time, LAT is the latest return time of the station, TT is the time spent on the distribution path, and ST is the sum of the remaining service times of the plurality of areas to be distributed. When initial route planning is performed (delivery vehicles do not depart from the station), τ is EDT, and EDT is the station departure time.

In operation S230, a distribution task of the item is performed via the distribution path.

According to the embodiment of the disclosure, if no dynamic demand information is received in the process of executing the delivery task, namely, the new delivery demand is not generated, the delivery task of the article is executed according to the preset delivery path.

In operation S240, in the process of executing the delivery tasks, in response to receiving the dynamic demand information, a path optimization model is established according to the dynamic demand information, and the path optimization model is used for optimizing the delivery paths in the case of allowing the delivery tasks to be interrupted.

In operation S250, the delivery path is adjusted using the path optimization model.

According to the embodiment of the disclosure, during the distribution process, the order system may receive the distribution order or the pickup order from the client at times, and the distribution order needs to be processed. The dynamic demand information corresponding to the orders includes: the position of the newly added area to be distributed, the remaining service duration and the time window of the newly added area to be distributed. The time window refers to the performance time required by the dynamic demand, for example, when a new order is received at 9:15 am and a pickup is required to the gate of a certain area within 1 hour, the time window of the dynamic demand is 9:15-10:15 am.

According to the embodiment of the disclosure, the distribution network information and the distribution task information can be updated according to the position of the newly-added area to be distributed, the remaining service duration and the time window of the newly-added area to be distributed. And establishing a path optimization model with the net income maximum as a target according to the updated distribution network information and the distribution task information. And then determining a target path as a new distribution path by using a path optimization model.

According to embodiments of the present disclosure, the net benefit is equal to the total utility available for delivery at each area to be delivered minus the sum of travel costs incurred when moving between areas to be delivered. In this embodiment, an objective function may be established with the net gain as the maximum target, and a constraint condition may be established according to the traffic balance constraint and the delivery area service time constraint, so as to determine the path optimization model.

After the path optimization model is established, the path optimization model can be solved through a recursion method to obtain a target path.

In addition, the related art cannot reasonably explain and describe the situation that the delivery vehicle makes multiple round trips between different delivery areas from the perspective of a model and an algorithm. According to the embodiment of the disclosure, description of the dynamic state of the delivery vehicle can be realized by representing the change of the position of the delivery vehicle, the order and the serviced condition of the responsible area with time.

The method for adjusting the distribution route according to the dynamic demand information will be further described with reference to the following embodiments. Those skilled in the art will appreciate that the following example embodiments are only for the understanding of the present disclosure, and the present disclosure is not limited thereto.

Step 1: the change of the relevant information of the delivery vehicle along with the time is expressed by a state deduction method.

The state deduction process is a conversion process which changes along with time and describes the relevant information of the distribution state at each time step. According to the embodiment of the disclosure, the status information of the delivery vehicles is changed with time, and the information includes the positions of the delivery vehicles at each moment, the delivery area sets which do not satisfy the service duration, the delivery area sets which satisfy the service duration, the delivery area set which does not satisfy the service duration, and the like. The purpose of the state deduction process is to comb and integrate the delivery vehicles and the order information at each moment to form input information of an optimization model.

Step 2: an abstract spatiotemporal network is established. The construction of the spatio-temporal network may include the following steps:

step 2.1: the physical nodes are abstracted from the actual scene. According to an embodiment of the present disclosure, a real scene includes physical areas such as sites and delivery areas (customer sites), which may be abstracted as non-sized nodes in a spatio-temporal network.

Step 2.2: and configuring a time coordinate for each node to form a space-time point.

Illustratively, in this embodiment, the earliest departure time of a station is EDT, and the latest return time is lat (lt), i.e., the latest return time of a delivery vehicle to the station. N discrete time points (n is a positive integer) are determined in a time range between the EDT and the LAT, and for each node, the discrete time points are respectively allocated to the node to obtain n space-time points. The space-time point contains both the spatial and temporal coordinates of the node. More specifically, set of allocation regions as M and set of time points as R, for

And (i, t) is added to the space-time point set V.

Step 2.3: and calculating the travel time between the two nodes according to the topological relation in the physical network. Here, the physical network refers to a distribution network composed of nodes (stations or distribution areas) and road segments (roads connecting the areas).

Step 2.4: a directed arc is used to connect two spatiotemporal points, forming a spatiotemporal arc (i.e., the connecting line between the two spatiotemporal points).

According to an embodiment of the present disclosure, the spatio-temporal arcs may be divided into travel arcs and service arcs, the construction processes of which are described below separately.

(1) And constructing a space-time travel arc set.

According to an embodiment of the disclosure, let the arc set be E and the travel time cost (travel arc cost for short) of the travel arc be tt_{i，j，t，t′}. In this embodiment, the travel arc cost is travel time per unit time cost. The unit time cost includes an operation cost, a maintenance cost, and the like. For the

Adding a spatio-temporal arc (i, j, t, t + tt) between the spatio-temporal point pair (i, t) and (j, t')_{i，j，t，t′}B) and add it to the spatio-temporal travel arc set A^TIn (1).

(2) And constructing a space-time service arc set.

According to the embodiment of the disclosure, the spatio-temporal service arcs can be divided into a reservation service arc and a dynamic service arc according to types, and the construction processes of the reservation service arc and the dynamic service arc are respectively described below.

1) And constructing a reservation service arc set.

According to an embodiment of the present disclosure, a service arc is a spatio-temporal arc connecting different times of the same site, i.e. a directed arc (i, i, t, t ') pointing from (i, t) to (i, t'). The specific construction mode is as follows: for the

At the time ofAdding the space-time arcs (i, i, t, t ') to the space-time pairs (i, t) and (i, t') and adding the space-time arcs to the reserved service arc set A^SdIn (1).

2) And constructing a dynamic service arc set.

According to the embodiment of the disclosure, as time goes by, a dynamic demand may occur in a certain delivery area at a certain time in the delivery process, and in order to distinguish the reservation demand and the dynamic demand of the same delivery area, according to the number of times of the dynamic demand occurring in the area, a corresponding number of 0 is added after the area number to be used as a virtual point of the area to show the distinction. Suppose that a new demand is generated 1 st time in distribution area i, with a time window of [ t ]_s，t_e]Wherein, t_sFor the earliest starting service time, t, in distribution area i_eThe latest start service time. Corresponding virtual delivery area iO, will (iO, t)_s) And (iO, t)_e) Adding (iO, iO, t) into the space-time point set V_s，t_e) Joining dynamic service arc set A^SrIn (1).

Step 2.5: setting the cost (actual cost or generalized cost) corresponding to each space-time arc

According to an embodiment of the disclosure, the cost of the travel arc is a travel time cost tt_{i，j，t，t′}Service arc cost is service duration profit u_{i，j，t，t′}. In this embodiment, the service duration profit u_{i，j，t，t′}Refers to the promotion or reward brought by an order processed within the duration of the service (t' -t). In addition, when a new demand is generated, the arc connecting the virtual areas needs to define a cost, which is u, taking the 1 st generation of the new demand in the distribution area i as an example_{i0，i0，t，t′}＝u_{i，i，t，t′}，tt_{i，j0，t，t′}＝tt_{i，j，t，t′}，tt_{j0，i，t，t′}＝tt_{j，i，t，t′}。

And step 3: a dynamic vehicle path optimization model is established that takes into account interruptible service.

Step 3.1: a decision variable is determined.

According to an embodiment of the present disclosure, x is as shown in formula (1)_{i，j，t，t′}(τ) is a decision variable that indicates whether the spatio-temporal arc (i, j, t, t') was selected by the dispenser in the path plan at time τ. Equal to 1 if selected, and 0 otherwise.

Step 3.2: and constructing an objective function.

According to an embodiment of the present disclosure, the solution objective of the present model is to maximize the net benefit (net benefit-cost) obtained by the distributor when the distributor performs distribution service while allowing interruptible service and requiring return to the site by a specified time, as shown in equation (2).

Wherein the first term is the total utility available for servicing at the distribution area, the second term is the sum of travel costs incurred while moving between distribution areas, and the difference yields the total revenue. u. of_{i，j，t，t′}Is the utility of selecting the spatio-temporal service arc (i, j, t, t '), if (i, j, t, t') is ∈ A^SThen u is_{i，j，t，t′}Not 0, otherwise u_{i，j，t，t′}Is 0. tt is a Chinese character_{i，j，t，t′}Is the cost of selecting the spatio-temporal travel arc (i, j, t, t '), if (i, j, t, t') is ∈ A^TThen tt_{i，j，t，t′}Not 0, otherwise tt_{i，j，t，t′}Is 0.

Step 3.3: and establishing space-time network flow balance constraint.

According to an embodiment of the present disclosure, a space-time network flow balance constraint is used to ensure that the ingress and egress traffic of each space-time point (i, t) is balanced, specifically, the space-time network flow balance constraint includes: for the starting point, there is only outgoing flow and no incoming flow; for the endpoint, only inflow flow, no outflow flow; for points between the starting point and the end point, the inflow flow rate is equal to the outflow flow rate. Illustratively, the constrained spatiotemporal network flow balance constraint is shown as equation (3).

Step 3.4: and establishing a service time constraint of the distribution area.

According to an embodiment of the disclosure, the delivery area service time constraint requires a delivery area set P that does not meet the service duration_τThe service time length of any area is less than or equal to the remaining unserviceable time length of the area. Consider the case where the service duration is less than the remaining unserviceable duration because the dispatcher may reduce the service duration in some areas by returning to the site by the specified time, as shown in equation (4).

Wherein, ST_iIs the total duration of the service required in zone i, T_i(τ) is the length of time that the zone i has been served by time t τ.

And 4, step 4: and (5) solving by using dynamic programming.

According to an embodiment of the present disclosure, a dynamic vehicle path optimization process, which takes into account interruptible service, is a core process that is activated only at the moment when a dynamic demand is generated. The process uses the state information generated in the state deduction process as input information, vehicle path optimization is carried out on distribution areas (including areas which newly generate dynamic demands) which do not meet the distribution duration, the vehicle path optimization is carried out in consideration of time windows which can interrupt service and meet the dynamic demands, the optimal distribution path is obtained and used as a driving plan of distribution vehicles before the next decision moment is reached, and the state deduction is carried out according to the plan.

Step 4.1: and constructing basic data.

According to the embodiment of the present disclosure, first, the distribution area, the service duration in each area, the time duration of returning to the site, and the like need to be defined, and the specific manner is as follows: i, j denotes delivery area, Depot is site, N is delivery set, i, j ∈ N, N "is a subset of N- { i }, m (N')Representing the number of elements in the N' set; f_i，N″Is the shortest time, tt, to return to the site after all points in N' have passed from the time of starting service in zone i_ijIs the travel time between distribution areas i and j, ST_jIs the duration of the service required in region j.

Step 4.2: and establishing a recurrence relation.

According to an embodiment of the present disclosure, the recurrence relation is as shown in equation (5):

step 4.3: and solving according to the recurrence relation.

More specifically, at step 4.3.1, the number of elements m (N ") in the N" set is 0, according to the recurrence relation

Find F_i，N″Value of (2), record

As a back node.

At step 4.3.2, m (N ″) m (N ″ +1, N "is any set containing a delivery area, and all possible N" s are traversed, based on the values of m (N ″), "and

find F_i，N″Value of (2), record

As a back node.

At step 4.3.3, step 4.3.2 is repeated until N "is a set containing all delivery areas except the site, i.e., N ″, N.

At step 4.3.4, output F_Depot，NThe value of (c) is taken as the time consumed for the shortest path.

Step 4.4: backtracking to obtain the shortest path.

More specifically, at step 4.4.1, the path sequence is set to Seq ═ { Depot } and the backtracking node is j.

At step 4.4.2, a backtrack is sent from the site j ═ pred _ node (depot).

At step 4.4.3, j ═ pred _ node (j), and is added to Seq.

At step 4.4.4, step (3) is repeated until j ═ Depot (i.e. back to the site) ends.

At step 4.4.5, the output Seq is the shortest path.

According to the embodiment of the disclosure, modeling is performed based on a space-time network, discretization processing is performed on time, space points are formed according to discrete time points and nodes in a physical space, space-time arcs are formed among different space points, a service arc is used for representing the distribution process of a distributor in a distribution area, travel space-time arcs are used for representing the movement process of the distributor between a station and the distribution area and between different distribution areas, then a dynamic vehicle path optimization model considering that distribution service can be interrupted is established, step-by-step solution is performed by using a dynamic planning algorithm, and the technical problem that the related technology cannot model and solve the condition that the distribution service can be interrupted is solved.

The method of fig. 2 is further described with reference to fig. 3 in conjunction with specific embodiments. Those skilled in the art will appreciate that the following example embodiments are only for the understanding of the present disclosure, and the present disclosure is not limited thereto.

Fig. 3 schematically illustrates a flow chart for adjusting an article dispensing path according to another embodiment of the present disclosure.

Fig. 3 shows that, at the initial time (time step t is 0), an initial route is planned based on the distribution network information and the distribution task information, and a distribution route, that is, an original route is obtained. And then performing the distribution task of the articles according to the original path.

In the course of executing the delivery task, the time steps are gradually accumulated as time advances (t ═ t + 1). The delivery status information is updated at each time step, and may include, for example, the location of the delivery vehicle at each time, a set of delivery areas (i.e., areas to be delivered) for which the service duration has not been met, a set of delivery areas for which the service duration has not been met, and the like. And after updating the delivery state information, judging whether the delivery vehicle returns to the station, and if the delivery vehicle returns to the station, finishing delivery. If the delivery vehicle does not return to the station, whether a dynamic demand is generated is judged.

If no dynamic demand is generated, the service is continued according to the original path. If a dynamic demand is generated, the dynamic demand is received, and the dynamic demand may include, for example, a delivery area where the dynamic demand is generated, a dynamic demand predicted service duration, a dynamic demand time window, and the like. And then updating the distribution state according to the dynamic requirement. And after the distribution state is updated, path optimization is carried out again for the distribution area which does not meet the service duration, and the optimized distribution path is obtained. And then, the distribution service is continued according to the updated distribution path.

Fig. 4 schematically illustrates a block diagram of an apparatus for adjusting an article dispensing path according to an embodiment of the present disclosure.

As shown in fig. 4, the apparatus 400 for adjusting an article dispensing path includes an obtaining module 410, a determining module 420, an executing module 430, a modeling module 440, and an adjusting module 450.

The obtaining module 410 may be configured to obtain distribution network information and distribution task information.

The determining module 420 may be configured to determine a distribution route according to the distribution network information and the distribution task information.

The execution module 430 may be configured to perform a delivery task for the item via the delivery path.

And the modeling module 440 is used for responding to the received dynamic demand information in the process of executing the delivery tasks, and establishing a path optimization model according to the dynamic demand information, wherein the path optimization model is used for optimizing the delivery path under the condition that the delivery tasks are allowed to be interrupted.

The adjustment module 450 adjusts the distribution route using the route optimization model.

Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.

For example, any of the obtaining module 410, the determining module 420, the executing module 430, the modeling module 440, and the adjusting module 450 may be combined in one module to be implemented, or any one of the modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 410, the determining module 420, the executing module 430, the modeling module 440, and the adjusting module 450 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the obtaining module 410, the determining module 420, the executing module 430, the modeling module 440, and the adjusting module 450 may be implemented at least in part as a computer program module that, when executed, may perform a corresponding function.

FIG. 5 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 5 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.

As shown in fig. 5, a computer system 500 according to an embodiment of the present disclosure includes a processor 501 that may perform various appropriate actions and processes according to a program stored in a non-transitory memory (ROM)502 or a program loaded from a storage portion 508 into a Random Access Memory (RAM) 503. The processor 501 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 501 may also include onboard memory for caching purposes. Processor 501 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the disclosure.

In the RAM 503, various programs and data necessary for the operation of the system 500 are stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. The processor 501 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 502 and/or the RAM 503. Note that the programs may also be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, system 500 may also include an input/output (I/O) interface 505, input/output (I/O) interface 505 also being connected to bus 504. The system 500 may also include one or more of the following components connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.

According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program, when executed by the processor 501, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include ROM 502 and/or RAM 503 and/or one or more memories other than ROM 502 and RAM 503 described above.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. A method of adjusting an article dispensing path, comprising:

acquiring distribution network information and distribution task information;

determining a distribution path according to the distribution network information and the distribution task information;

performing a delivery task of the item via the delivery path;

in the process of executing the distribution tasks, responding to the received dynamic demand information, and establishing a path optimization model according to the dynamic demand information, wherein the path optimization model is used for optimizing distribution paths under the condition that the distribution tasks are allowed to be interrupted; and

and adjusting the distribution path by utilizing the path optimization model.

2. The method of claim 1, wherein the distributing network information comprises: the positions of a plurality of areas to be delivered and the travel time between the plurality of areas to be delivered;

the distribution task information includes: the starting time of the station, the latest returning time of the station and the remaining service time of each to-be-distributed area in the plurality of to-be-distributed areas.

3. The method of claim 2, wherein the determining a delivery path based on the delivery network information and the delivery task information comprises:

determining the shortest path passing through the plurality of areas to be distributed according to the positions of the plurality of areas to be distributed, the travel time among the plurality of areas to be distributed, the station departure time, the station latest return time and the remaining service time of each area to be distributed in the plurality of areas to be distributed,

the shortest path satisfies the condition that tau + ST + TT is not more than LAT, wherein tau is the current time, LAT is the latest station returning time, TT is the time length spent by the distribution path, and ST is the sum of the remaining service time lengths of the plurality of areas to be distributed.

4. The method of any of claims 1 to 3, wherein the dynamic demand information comprises: the method comprises the steps that the position of a newly added area to be distributed, the residual service duration of the newly added area to be distributed and a time window are set, wherein the time window comprises a scheduled service starting time and a scheduled service ending time;

the establishing of the path optimization model according to the dynamic demand information comprises:

updating the distribution network information and the distribution task information according to the position of the newly-added area to be distributed, the residual service duration and the time window of the newly-added area to be distributed; and

and establishing a path optimization model with the net income maximum as a target according to the updated distribution network information and the distribution task information.

5. The method of claim 4, wherein the building a path optimization model with net revenue max objective based on the updated delivery network information and delivery mission information comprises:

determining an objective function of the path optimization model according to the following formula:

wherein, the x_{i，j，t，t′}(τ) is a decision variable indicating whether a spatio-temporal arc (i, j, t, t') was selected at time τ, and u is_{i，j，t，t′}Is the utility of selecting the spatio-temporal arc (i, j, t, t'), tt_{i，j，t，t′}The method comprises the steps of selecting the cost of a spatio-temporal arc (i, j, t, t '), wherein the spatio-temporal arc (i, j, t, t ') is a directed arc of a spatio-temporal point (i, t) pointing to the spatio-temporal point (i, t '), the spatio-temporal point (i, t) comprises position information of an area i to be distributed and time information at the time t, the spatio-temporal point (i, t ') comprises the position information of the area i to be distributed and the time information at the time t ', i is a positive integer, i is smaller than or equal to the total number of the current areas to be distributed, and A is a spatio-temporal arc set.

6. The method of claim 5, wherein the building a path optimization model with net revenue max objective based on the updated delivery network information and delivery mission information further comprises:

determining constraints of the path optimization model according to the following formula:

7. The method of claim 4, wherein said adjusting said delivery path using said path optimization model comprises:

the recurrence relation is established according to the following formula:

where i, j ∈ N, where N is the set of regions to be allocated, N' is a subset of N- { i }, and where F_i，N″Is the shortest time from the scheduled start of service of area i to be delivered to the return to the station after all points in N ", tt_ijIs the travel time between areas i and j to be delivered, the ST_jIs the duration of the service required in the area j to be allocated; and

and solving the path optimization model according to the recursion relational expression to obtain a new distribution path.

8. An apparatus for adjusting a dispensing path of an article, comprising:

the acquisition module is used for acquiring the distribution network information and the distribution task information;

the determining module is used for determining a distribution path according to the distribution network information and the distribution task information;

the execution module is used for executing the distribution task of the goods through the distribution path; and

the modeling module is used for responding to the received dynamic demand information in the process of executing the delivery tasks and establishing a path optimization model according to the dynamic demand information, and the path optimization model is used for optimizing a delivery path under the condition that the delivery tasks are allowed to be interrupted; and

and the adjusting module is used for adjusting the distribution path by utilizing the path optimization model.

9. A computing device, comprising:

one or more processors;

a storage device for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1 to 7.

10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 7.