CN113743739B - AGV scheduling method based on mixed integer programming and combined optimization algorithm - Google Patents
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Abstract
The invention discloses an AGV scheduling method based on a mixed integer programming algorithm, which comprises the following steps: (1) acquiring a job instruction in a job queue; (2) searching all idle AGVs; (3) Calculating penalty points of the AGV matched with each operation instruction respectively; (4) Inputting the penalty points matched with the AGV and each operation instruction into a mixed integer programming model, and returning an optimal matching result of the AGV and the operation instruction; (5) And scheduling the AGVs to execute the operation instructions matched with the AGVs according to the optimal matching result, wherein the AGVs are automatic guided vehicles. According to the AGV scheduling method based on the mixed integer programming and combination optimization algorithm, the condition that the AGV reaches the target position is comprehensively considered, the distance and the electric quantity are subjected to penalty, the penalty matched with each operation instruction by the AGV is input into the mixed integer programming model, the optimal matching result of the AGV and the operation instruction is returned, and the scheduling efficiency of the AGV can be improved.
Description
Technical Field
The invention belongs to the technical field of automatic wharf transportation, and particularly relates to an AGV scheduling method based on a mixed integer programming and combination optimization algorithm.
Background
The front operation band of the container terminal refers to the area between the front edge line of the container yard and the front line of the container yard, and has the function of serving the operation of loading and unloading ships on the quay bridge and the operation of entering and exiting the container yard, and an inner collection card driven manually is usually used in a manual wharf and a semi-automatic wharf. In recent years, along with the continuous rising of labor cost, more and more wharfs convert horizontal transport equipment from an inner collecting card into an AGV (Automated Guided Vehicle, automatic guided vehicles), and the use of the AGV greatly improves the production efficiency and saves the cost.
An AGV (Automated Guided Vehicles) is a transport cart equipped with an automatic guidance device such as electromagnetic or optical, capable of traveling along a predetermined guidance path, having safety protection and various transfer functions, and has an important connection function for transferring and circulating container cargoes due to the characteristics of automation, intellectualization and parallel operation. The main target of the container terminal is high efficiency and low consumption, the AGVs are horizontal conveying equipment for connecting QC (Quay bridge) and ASC (Automated Stacking Crane, automatic track Crane), the AGVs are reasonably scheduled, the idle load distance of the AGVs and the energy consumption of the AGVs can be reduced, the waiting time of the AGVs/QCs and the AGVs/ASCs is reduced, the utilization rate of the AGVs is improved, and therefore the AGVs are key modules of the automatic terminal. In the prior art, an AGV scheduling problem model with the aim of minimum weighted sum of factors influencing AGV scheduling is usually established, and weights are determined according to the priority of the influencing factors, and the method is from the viewpoint of cost, and the improvement of AGV efficiency is difficult to consider under the condition of reducing cost. Based on the above, how to invent a method capable of improving the scheduling efficiency of the AGV is a technical problem which is mainly solved by the invention.
Disclosure of Invention
The invention provides an AGV scheduling method based on a mixed integer programming algorithm, which aims at solving the technical problem that the AGV scheduling efficiency of container terminal loading and unloading operation in the prior art is low.
In order to achieve the aim of the invention, the invention is realized by adopting the following technical scheme:
an AGV scheduling method based on a mixed integer programming algorithm comprises the following steps:
(1) Acquiring a job instruction in a job queue;
(2) Searching all idle AGVs;
(3) Calculating the penalty points matched with each operation instruction by the AGV, wherein the penalty point factors comprise: any combination of early, late, distance, and power;
(4) Inputting the penalty points matched with the AGV and each operation instruction into a mixed integer programming model, and returning an optimal matching result of the AGV and the operation instruction;
(5) And scheduling the AGVs to execute the operation instructions matched with the AGVs according to the optimal matching result, wherein the AGVs are automatic guided vehicles.
Further, before the step (3), the method further comprises:
and screening the job instructions, screening out job instructions needing to be scheduled, and updating a job queue.
Further, the operation instructions comprise a ship unloading operation instruction and a ship loading operation instruction, and the dispatch of each AGV has a time window;
screening the job instruction includes:
selecting a job instruction with interaction time within the time window and a job instruction with interaction time earlier than the current time as job instructions to be scheduled;
for a ship unloading operation instruction, the interaction time is QC-AGV interaction time;
for a shipping operation instruction, the interaction time is AGV-ASC interaction time;
the QC-AGV interaction time is the interaction time of the quay crane and the automatic guided vehicle, and the AGV-ASC interaction time is the interaction time of the automatic guided vehicle and the automatic track crane.
Further, the method for estimating the interaction time comprises the following steps:
the estimated time required by the quay crane to execute each operation instruction respectively comprises the following steps:
QC-AGV interaction time = shoreside bridge main trolley operation time + shoreside bridge portal trolley to platform position time;
AGV-ASC interaction time = coastal bridge main trolley operation time-coastal bridge portal trolley operation time-travel time from the box receiving point to the coastal bridge box feeding point-turn-around time-AGV/ASC or bracket box receiving time.
Further, in the step (2), the method for searching all idle AGVs includes:
and searching all AGVs which reach the target position and have no pre-dispatching task, and the AGVs which reach the target position within the set time and have no pre-dispatching task.
Further, before the step (3), the method further comprises:
matching all AGVs with each operation instruction respectively, and if the total number of the operation instructions is less than or equal to the total number of the AGVs, carrying out full matching on all the operation instructions and all the AGVs; if the total number of the operation instructions is larger than the total number of the AGVs, the operation instructions with the same number as the total number of the AGVs are selected, and full matching is performed.
Further, if the total number of job instructions is greater than the total number of AGVs, the selection mode in the job instruction step with the same number as the total number of AGVs is:
and sequencing the priority of the operation instructions according to QC-AGV interaction time or AGV-ASC interaction time, wherein the higher the interaction time is, the higher the priority is, and the operation instructions with the same number as the total number of AGVs are selected from high to low according to the priority.
Further, the mixed integer programming model includes:
wherein m is ij A represents penalty situation, a, for the ith vehicle to execute task j ij = {0,1} indicates whether or not execution is performed, and a value of 1 is performed, and 0 indicates that no execution is performed.
Furthermore, the mixed integer programming model is calculated by adopting a branch-and-bound method.
Further, in each penalty factor, the early arrival and the late arrival refer to the arrival target position state of the previous operation instruction executed by the AGV, the distance refers to the distance between the AGV and the bank bridge or the automatic track crane, the distance is positively correlated with the penalty, the electric quantity is the residual electric quantity of the AGV, and the electric quantity is negatively correlated with the penalty.
Compared with the prior art, the invention has the advantages and positive effects that: according to the AGV scheduling method based on the mixed integer programming and combination optimization algorithm, the condition that the AGV reaches the target position is comprehensively considered, the distance and the electric quantity are subjected to penalty, the penalty matched with each operation instruction by the AGV is input into the mixed integer programming model, the optimal matching result of the AGV and the operation instruction is returned, and the scheduling efficiency of the AGV can be improved.
Other features and advantages of the present invention will become apparent upon review of the detailed description of the invention in conjunction with the drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of one embodiment of a mixed integer programming algorithm based AGV scheduling method in accordance with the present invention;
FIG. 2 is a schematic diagram of an adopted branch-and-bound method of the AGV scheduling method based on the mixed integer programming algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples.
It should be noted that, in the description of the present invention, terms such as "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus are not to be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Example 1
The embodiment provides an AGV scheduling method based on a mixed integer programming algorithm, which comprises the following steps:
s1, acquiring a job instruction in a job queue; all the job instructions to be executed are arranged to form a job queue.
S2, searching all idle AGVs;
s3, calculating the penalty points of the AGV matched with each operation instruction respectively, wherein the penalty point factors comprise: any combination of early, late, distance, and power;
s4, inputting the penalty points matched with the AGV and each operation instruction into a mixed integer programming model, and returning an optimal matching result of the AGV and the operation instruction;
and S5, scheduling the AGVs to execute the operation instructions matched with the AGVs according to the optimal matching result, wherein the AGVs are automatic guided vehicles.
According to the AGV scheduling method based on the mixed integer programming algorithm, available AGVs are matched with the operation instructions in the operation queue, only the box receiving or the running tasks without the instructions are considered on the premise, and finally the matching result with the lowest total penalty is selected, and interaction is conducted with the equipment control system through the distribution module and the workflow. The AGV reaches the target position state, the distance and the electric quantity condition are comprehensively considered to carry out penalty points, the penalty points matched with the AGV and each operation instruction are input into the mixed integer programming model, and the optimal matching result of the AGV and the operation instruction is returned, so that the dispatching efficiency of the AGV can be improved.
Preferably, before step S3 in this embodiment, the method further includes:
and screening the job instructions, screening out the job instructions to be scheduled, and updating the job queue. The updated job queue should be operated on in the subsequent step.
The scheduling method mainly considers three factors: the first is the priority or urgency of the job instruction. At the EMT (projected work time) corresponding to each work order, the AGV is dispatched with a definable time window. If the predicted job time falls well within the time window, it is of medium priority. If the predicted job time is already later than the time window, the task is considered to have a high priority. Conversely, if the predicted job time is earlier than the time window, the priority of the job instruction is considered low. The second is the empty distance or energy consumption of the AGV. If only the priority of the operation instruction is considered for task dispatch, the local optimal solution with the minimum idle distance of the overall AGV is ignored; if only the idle distance and the minimum are considered, the influence of the priority on the dispatching cannot be accurately reflected, so that the operation of the quay crane is finally influenced. The third is the time the AGV is cycled. AGV cycle time determines the ratio of the quay crane to the AGV. If this factor is not taken into account, the number of vehicles required for dock production increases, resulting in wasted equipment. The invention gives consideration to the most important factors, so that the distribution of the vehicles can meet the optimal effect in a short period.
The operation instructions comprise a ship unloading operation instruction and a ship loading operation instruction, and the dispatch of each AGV is provided with a time window;
screening the job instructions includes:
and selecting the job instruction with the interaction time within the time window and the job instruction with the interaction time earlier than the current time as the job instruction to be scheduled. That is, the job instructions are arranged in accordance with the degree of urgency, and the job instructions with high priority and medium priority are reserved, and when the number of AGVs is insufficient, the job instructions with low priority are temporarily not processed.
For the ship unloading operation instruction, the interaction time is QC-AGV interaction time.
For a shipping job instruction, the interaction time is AGV-ASC interaction time.
The QC-AGV interaction time is the interaction time of the quay crane and the automatic guided vehicle, and the AGV-ASC interaction time is the interaction time of the automatic guided vehicle and the automatic track crane.
The method for estimating the interaction time comprises the following steps:
the estimated time required by the quay crane to execute each operation instruction respectively comprises the following steps:
QC-AGV interaction time = shoreside master car operation time + shoreside portal car to platform position time.
AGV-ASC interaction time = coastal bridge main trolley operation time-coastal bridge portal trolley operation time-travel time from the box receiving point to the coastal bridge box feeding point-turn-around time-AGV/ASC or bracket box receiving time.
Shipping box ASC operation time = shipping AGV/ASC interaction time-ASC box time-ASC heavy load cart movement time-ASC box grabbing time-ASC empty cart movement time if there is a tipping over, tipping box ASC operation time.
In step S2, the method for finding all the idle AGVs includes:
and searching all AGVs which reach the target position and have no pre-dispatching task, and the AGVs which reach the target position within the set time and have no pre-dispatching task. To ensure that the AGV to be scheduled must be an empty AGV that can be scheduled.
Because this scheme is to need to dispatch for the instruction of waiting to operate and distribute the AGV to carry out the operation, need to ensure that the operation instruction after the matching is one-to-one with the AGV, if the quantity of idle AGVs is not less than the quantity of operation instruction then no problem, but if the quantity of idle AGVs is less than the quantity of operation instruction, can't guarantee that every operation instruction can distribute the AGV, then need match the operation instruction to make to satisfy above-mentioned condition.
Before step S3, the method further includes:
matching all AGVs with each operation instruction respectively, and if the total number of the operation instructions is less than or equal to the total number of the AGVs, carrying out full matching on all the operation instructions and all the AGVs; if the total number of the operation instructions is larger than the total number of the AGVs, the operation instructions with the same number as the total number of the AGVs are selected, and full matching is performed.
After the job orders are screened, all AGVs are fully matched with each job order.
Preferably, if the total number of operation instructions is greater than the total number of AGVs, the selection manner in the operation instruction step with the same number as the total number of AGVs is:
and sequencing the priority of the operation instructions according to the QC-AGV interaction time or the AGV-ASC interaction time, wherein the higher the interaction time is, the higher the priority is, and the operation instructions with the same number as the total number of AGVs are selected from high to low according to the priority.
The more urgent the interaction time is, the more urgent the priority is.
The mixed integer programming model in this embodiment includes:
wherein m is ij A represents penalty situation, a, for the ith vehicle to execute task j ij = {0,1} indicates whether or not execution is performedA row is executed when the value is 1, and 0 indicates no execution. The optimization objective is to minimize the overall penalty situation.
And->Is a constraint that each task can only be performed once and that each driver can only match one task at a time.
Firstly matching AGVs with all operation instructions, acquiring all possible task matching matrixes and penalty matrixes, respectively considering QC/AGVs, AGVs/ASCs interaction time windows, calculating penalty points of arrival at the earliest or latest, calculating penalty points of no-load distance, calculating penalty points of unbalanced quantity of AGVs distributed by each QC, WSTZ (Waterside Transpoint, sea side interaction area) and the like, and establishing a mixed integer programming model for the penalty matrixes, wherein the constraint condition is that each task can only be executed once, and each driver can only be matched with one task at a time.
In order to reduce the calculated amount, a mixed integer programming model in the method adopts a branch-and-bound method to calculate, and the condition of minimizing total penalty is solved, so that an optimal task matching list is finally provided.
Branch and bound is one of the most common algorithms for solving integer programming problems. The branch-and-bound algorithm is always performed around a search tree, and by considering the minimized penalty situation as a root node, the meaning of a branch is to divide the penalty into penalty intervals, starting from this point.
Specifically, if the AGV matching penalty is minimized, setting the value Z= infinity of the optimal solution (the result of the minimum value); i.e. initializing.
Selecting a node from the nodes (local solutions) which are not yet known according to the branching rule, and dividing the node into a plurality of new nodes in the next level of the node;
calculating a Lower Bound (LB) value of each newly branched node;
and carrying out an insight condition test on each node, wherein if the node meets any one of the following conditions, the node can be in the insight and is not considered any more, and the lower limit value of the node is larger than or equal to the Z value. The feasible solution with the minimum lower limit value in the node is found; if the condition is satisfied, the feasible solution is compared with the Z value, and if the former is smaller, the Z value is updated to be the value of the feasible solution. This node is unlikely to contain a feasible solution;
whether there are any nodes yet to be understood, if yes, step S42 is performed, and if no nodes yet to be understood, the calculation is stopped, and the optimal solution is obtained.
The branching process is a process of adding child nodes to the tree. While delimitation is to check the upper and lower bounds of a sub-problem during branching, if the sub-problem cannot produce a solution that is better than the current optimal solution, then the branch is cut off. The algorithm ends until none of the sub-problems produce a better solution.
Among the penalty factors, the early arrival and the late arrival refer to the arrival target position state of the AGV executing the previous operation instruction, the distance refers to the distance between the AGV and a shore bridge or an automatic track crane, the distance and the penalty are positively correlated, the electric quantity is the residual electric quantity of the AGV, and the electric quantity and the penalty are negatively correlated.
The method achieves AGV high-efficiency operation and low-cost operation by optimizing the number configuration of the devices and coordinating the time between various loading and unloading transportation devices. A mixed integer programming and combined optimization algorithm is utilized to build a model on the basis of real-time operation data, a branch-and-bound method is adopted to calculate, a globally relatively optimal matching decision is made, and timely dispatching is carried out under certain special conditions in the execution process, so that dynamic optimization can be realized.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (4)
1. The AGV scheduling method based on the mixed integer programming algorithm is characterized by comprising the following steps of:
(1) Acquiring a job instruction in a job queue;
(2) Searching all idle AGVs;
(3) Calculating the penalty points matched with each operation instruction by the AGV, wherein the penalty point factors comprise: any combination of early, late, distance, and power;
(4) Inputting the penalty points matched with the AGV and each operation instruction into a mixed integer programming model, and returning an optimal matching result of the AGV and the operation instruction;
(5) According to the optimal matching result, scheduling the AGV to execute a matched operation instruction, wherein the AGV is an automatic guided vehicle;
the method also comprises the following steps before the step (3):
screening the job instructions, screening out job instructions to be scheduled, and updating a job queue;
the operation instructions comprise a ship unloading operation instruction and a ship loading operation instruction, and the dispatch of each AGV is provided with a time window;
screening the job instruction includes:
selecting a job instruction with interaction time within the time window and a job instruction with interaction time earlier than the current time as job instructions to be scheduled;
for a ship unloading operation instruction, the interaction time is QC-AGV interaction time;
for a shipping operation instruction, the interaction time is AGV-ASC interaction time;
the QC-AGV interaction time is the interaction time of the quay crane and the automatic guided vehicle, and the AGV-ASC interaction time is the interaction time of the automatic guided vehicle and the automatic track crane;
the method for estimating the interaction time comprises the following steps:
the estimated time required by the quay crane to execute each operation instruction respectively comprises the following steps:
QC-AGV interaction time = shoreside bridge main trolley operation time + shoreside bridge portal trolley to platform position time;
AGV-ASC interaction time = coastal bridge main trolley operation time-coastal bridge portal trolley operation time-travel time from a box receiving point to a coastal bridge box feeding point-turn-around time-AGV/ASC or bracket box receiving time;
before step (3), the method further comprises:
matching all AGVs with each operation instruction respectively, and if the total number of the operation instructions is less than or equal to the total number of the AGVs, carrying out full matching on all the operation instructions and all the AGVs; if the total number of the operation instructions is greater than the total number of the AGVs, selecting the operation instructions with the same number as the total number of the AGVs, and performing full matching;
if the total number of operation instructions is greater than the total number of AGVs, the selection mode in the operation instruction step with the same number as the total number of AGVs is as follows:
the priority ranking is carried out on the operation instructions according to QC-AGV interaction time or AGV-ASC interaction time, the higher the interaction time is, the higher the priority is, and the operation instructions with the same number as the total number of AGVs are selected from high to low according to the priority;
among the penalty factors, the early arrival and the late arrival refer to the arrival target position state of the AGV executing the previous operation instruction, the distance refers to the distance between the AGV and the shore bridge or the automatic track crane, the distance is positively correlated with the penalty, the electric quantity is the residual electric quantity of the AGV, and the electric quantity is negatively correlated with the penalty.
2. The scheduling method of claim 1 wherein in step (2), the method of locating all idle AGVs comprises:
and searching all AGVs which reach the target position and have no pre-dispatching task, and the AGVs which reach the target position within the set time and have no pre-dispatching task.
3. Scheduling method according to claim 1 or 2, wherein the mixed integer programming model comprises:
wherein m is ij A represents penalty situation, a, for the ith vehicle to execute task j ij = {0,1} indicates whether or not execution is performed, and a value of 1 is performed, and 0 indicates that no execution is performed.
4. A scheduling method according to claim 3 wherein the mixed integer programming model is calculated using branch-and-bound methods.
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