CN116167541B - Path planning method based on self-adaptive distribution strategy under emergency condition - Google Patents
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Abstract
The invention discloses a path planning method based on a self-adaptive distribution strategy under emergency conditions, which comprises the following steps: establishing a sudden emergency degree assessment model, and assessing the emergency degree; establishing a distribution strategy model, formulating an emergency distribution strategy aiming at an emergency situation, and embodying in a distribution strategy influence factor mode; establishing a path planning model, and adaptively adjusting the model based on the distribution strategy influence factors; establishing an emergency distribution request priority calculation formula, and carrying out calculation evaluation on the priority of each emergency distribution request; and solving the path planning model and the data to obtain a path planning result. The invention responds to the sudden emergency delivery request of the customer effectively in time, and prevents the excessive or insufficient emergency measures from occurring by selecting the proper emergency delivery strategy, thereby saving the emergency delivery cost, reducing the influence caused by the sudden emergency situation and ensuring the rights and interests of the parties.
Description
Technical Field
The invention belongs to the technical field of logistics distribution, and particularly relates to a path planning method based on a self-adaptive distribution strategy under emergency conditions.
Background
The traditional path planning model and method generally only consider the distribution service mode and distribution strategy under a single condition, and do not consider the influence of sudden emergency situations with different degrees on logistics distribution. Therefore, the model is often defined to fix the distribution conditions, distribution strategies and optimization targets of the model, such as distribution efficiency, cost optimization, customer satisfaction maximization and the like, and the method has good effect in a fixed distribution environment, but when sudden emergency situations with different degrees occur, the model cannot timely adjust the distribution conditions, distribution strategies and optimization targets, so that the self-adaptability is poor.
Emergency situations are sporadic and difficult to predict, and if not responded to and handled in time, significant losses may be incurred to the parties. Meanwhile, if the adopted emergency distribution strategy is improper, excessive or insufficient conditions may occur, and the emergency condition of the current degree cannot be effectively dealt with, so that the problems of low response speed, high emergency distribution cost, incapability of effectively reducing loss caused by the emergency condition and the like are caused.
In order to solve the above problems, it is necessary to provide a path planning method based on an adaptive distribution strategy in emergency situations.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems.
Therefore, the technical problems solved by the application are as follows: the traditional path planning model and method generally only consider the distribution service mode and distribution strategy under a single condition, and do not consider the influence of sudden emergency situations with different degrees on logistics distribution. Therefore, the model is often defined to fix the distribution conditions, distribution strategies and optimization targets of the model, such as distribution efficiency, cost optimization, customer satisfaction maximization and the like, and the method has good effect in a fixed distribution environment, but when sudden emergency situations with different degrees occur, the model cannot timely adjust the distribution conditions, distribution strategies and optimization targets, so that the self-adaptability is poor.
In order to solve the technical problems, the invention provides the following technical scheme: a path planning method based on self-adaptive distribution strategy under emergency condition comprises the following steps:
establishing a sudden emergency degree assessment model, and assessing the sudden emergency degree in the distribution activity;
establishing a distribution strategy model, formulating corresponding emergency distribution strategies aiming at different emergency situations, and embodying in a distribution strategy influence factor mode;
establishing a path planning model, and adaptively adjusting the distribution conditions, distribution strategies and optimization targets of the model based on the distribution strategy influence factors;
establishing an emergency distribution request priority calculation formula, and carrying out calculation evaluation on the priority of each emergency distribution request;
and solving the obtained path planning model and data to obtain a path planning result meeting the requirement of the current emergency.
As an preferable scheme of the path planning method based on the adaptive distribution strategy under the emergency condition of the burst, the burst emergency degree evaluation model comprises the following steps:
；
wherein the method comprises the steps ofFor emergency degree index>For the total number of requests to be dispatched, < > for >For the total amount of goods to be distributed +.>For the number of emergency delivery requests in burst, < >>Total amount of goods required for all burst emergency delivery requests +.> 、 />Respectively representing the influence coefficients of the two on the emergency degree;
for natural disaster index, for weighted average of the risk levels of natural disaster types in the affected area, +.>Is a weighted average of natural disaster grades, +.>Index of the affected person, i.e. the proportion of the number of affected persons, +.>Index is the affected area, is the proportion of the affected area;
h represents a natural disaster emergency event ifIf no natural disaster emergency event occurs, ifAnd then represents the occurrence of a natural disaster emergency event.
As a preferable scheme of the path planning method based on the self-adaptive distribution strategy under the emergency condition, the emergency degree assessment model further comprises an emergency degree index and an emergency degreeThe corresponding relation formula between:
；
wherein a represents the critical value of the emergency degree index;
if it isNo emergency situation occurs +.> = 0；
If it isRepresenting a low emergency level of the burst +.> ；
If it isIf the emergency degree is high, the emergency degree is high> ；
For emergency situations with different degrees, the emergency degree index is calculated Obtaining the corresponding emergency degree-> 。
As an preferable scheme of the path planning method based on the adaptive distribution strategy under the emergency condition of emergency, the distribution strategy model includes:
the distribution strategy model aiming at the emergency situations of different degrees is established, corresponding emergency distribution strategies are formulated aiming at the emergency situations of different degrees, and the distribution strategy model is embodied in a mode of distribution strategy influence factors, and the distribution strategy model is concretely as follows:
when (when)When no emergency occurs, the time window policy influence factor is output>Priority policy influence factor-> ；
When (when)Indicating the occurrence of a low-level emergency event, outputting a time window policy influence factor +.>Priority policy influence factor->；
When (when)Description of the embodimentsAt the moment, a high-degree emergency event occurs, and a time window strategy influence factor is outputPriority policy influence factor-> 。
As a preferred scheme of the path planning method based on the self-adaptive distribution strategy under the emergency condition, the invention establishes the corresponding relation between different distribution strategies and distribution strategy influence factors, and outputs the distribution strategy influence factors to embody the distribution strategy selected by the model;
；
Wherein the method comprises the steps ofRepresents the degree of emergency->For the time window policy influencing factor, < >>Is a priority policy impact factor.
As a preferable scheme of the path planning method based on the self-adaptive distribution strategy under the emergency condition, the invention comprises the following steps: the establishing a path planning model comprises the following steps:
when (when)When the method is used, based on a time window strategy influence factor f=1, a time window constraint of a model is activated, a priority strategy influence factor e=0 closes a priority constraint of the model, and the model adaptively establishes a path planning model considering distribution time window and user satisfaction priority;
when (when)When the time window policy influence factor f=1, the time window constraint of the self-adaptive activation model, the priority policy influence factor e=1, the priority constraint of the self-adaptive activation model, on the basis of the user reservation distribution time window, the priority policy is further adopted to carry out priority distribution for the customers with sudden emergency demands, and other customers still carry out distribution according to the time window constraint;
when (when)When the time window policy influence factor f=0, the time window constraint of the self-adaptive closing model, the priority policy influence factor e=1, and the priority constraint of the self-adaptive activating model, the distribution is performed according to the priority of the emergency distribution request of each client, and the distribution is performed to the clients with high priority preferentially.
As an optimal scheme of the path planning method based on the self-adaptive distribution strategy under the emergency condition, the path planning model further comprises a mathematical model, and the method specifically comprises the following steps:
；
expression (1) is an objective function formula requiring minimum total delivery cost, whereIs a distance cost factor, < >>Is a dot->And (4) point->Distance between (I) and (II)>Is the time window penalty cost,/->Is a correlation coefficient if the vehicle is->Completion Point->Continue to complete the point->Distribution of->Otherwise 0, & gt>Is a correlation coefficient, if the distribution point +.>By vehicle->Responsible for delivery, then->Otherwise, 0;
；
the constraint function (2) indicates that the amount of cargo that the delivery vehicle can carry cannot exceed the maximum load of the delivery vehicle, whereinIs client Point->Is a required amount of (a);
；
the constraint function (3) indicates that each delivery point is serviced only once and is only serviced by one delivery vehicle;
；
the constraint function (4) indicates that all delivery vehicles from the delivery center will eventually return to the delivery center after all delivery tasks are completed, whereinIs a correlation coefficient representing vehicle->Whether or not to go from the distribution center to the customer point->Dispensing, if yes, then- >Otherwise-> ， />Is a correlation coefficient representing vehicle->For customer point->Whether the delivery center is returned after delivery, if yes, the method comprises the steps of +.>Otherwise->；
；
The constraint function (5) indicates that the delivery vehicle arrives at the delivery point and departs from the delivery point;
；
the constraint function (6) is a priority constraint,representing customer points->Priority of->Is a priority policy influencing factor, the expression requires that if client point + ->Is dispatched by the same dispatching vehicle as customer point j and +.>When the priority of (a) is higher than the client point j, then the +.> ；
；
Expression (7) is a time window constraint that will be penalized when the vehicle does not reach the specified delivery point within the specified time window, whereinIs the time the delivery vehicle arrives at the customer point, +.>Is client Point->Left time window of the specified time window, +.>Is client Point->Right time window of the specified time window, +.>For a penalty factor for vehicles that arrive earlier than the delivery time,penalty factors for vehicles arriving later than delivery time;
；
expression (8) represents the correlation coefficient QUOTE 、 />The value of (2) can only be 0 or 1.
As an preferable scheme of the path planning method based on the adaptive distribution strategy under the emergency condition of emergency, the establishing an emergency distribution request priority calculation formula includes:
；
Wherein,,priority for emergency delivery request +.>、 />、 />For dynamic autonomous decision coefficient, respectively reflect emergency delivery requestsThe calculated cargo value, the potential economic loss which is brought if the cargo value cannot be met in time, and the relative importance of the additional effective profit which is brought if the request is met in time under the emergency condition of the burst;
freight value for the present emergency delivery request, +.>Represents the loss caused if the emergency distribution request cannot be satisfied in time, and specifically comprises cost increase loss, production data damage loss and later potential service loss, < >>Representing the effective profit that the emergency delivery request will bring additionally if it is satisfied in time,/>The time required for completing the emergency delivery service.
The method for planning the path based on the self-adaptive distribution strategy under the emergency condition of the invention is an optimal scheme, wherein the establishing an emergency distribution request priority calculation formula further comprises the following steps:
；
wherein,,for normalizing the processed cargo value, +.>For potential loss after normalization treatment, +.>For the normalized required service time, +.>The influence degree of the emergency treatment on the emergency event is normalized for the request after the processing; 、/>、/>、/>The weight of each index is required to be determined according to specific conditions so as to meet the emergency distribution requirement under the emergency event of natural disasters.
The method for planning the path based on the self-adaptive distribution strategy under the emergency condition of the invention is a preferable scheme, wherein the method for solving the obtained path planning model and data specifically comprises the following steps:
and solving the obtained path planning model and data by applying an improved genetic algorithm based on the damage repair thought.
The invention has the beneficial effects that: the invention provides a path planning method based on a self-adaptive distribution strategy under emergency conditions, which aims to solve the problem of single distribution mode of a traditional path planning model and a traditional path planning method when coping with emergency conditions of different degrees. The method can adaptively adjust the distribution strategy to cope with emergent conditions of different degrees, improves the response speed and distribution efficiency of distribution service, reduces the distribution cost and effectively reduces the loss caused by emergent conditions.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flowchart illustrating steps of a path planning method based on an adaptive distribution strategy in emergency situations according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a distribution strategy model for emergency situations with different degrees according to a path planning method based on an adaptive distribution strategy under emergency situations provided in an embodiment of the present invention;
FIG. 3 is a detailed implementation step diagram of a path planning method based on an adaptive distribution strategy in emergency situations according to an embodiment of the present invention;
fig. 4 is a comparison diagram of a path planning scheme of a path planning method based on an adaptive distribution strategy in emergency situations according to embodiment 2 of the present invention;
fig. 5 is a comparison diagram of a path planning scheme of a path planning method based on an adaptive distribution strategy in emergency situations according to embodiment 3 of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1-3, a first embodiment of the present invention provides a path planning method based on an adaptive distribution strategy in emergency situations, including:
s1: establishing a sudden emergency degree assessment model, and assessing the sudden emergency degree in the distribution activity;
further, a sudden emergency degree assessment model is established, and sudden emergency degree in the distribution activities is assessed by calculating an emergency degree index;
it should be noted that in daily logistics distribution activities, the influence caused by emergent conditions of different degrees is often different, the emergent conditions have sporadic property, and the emergent conditions are often urgent when occurring, if the emergent conditions cannot be properly treated in time, the emergent conditions bring about serious losses to the parties;
The emergency degree assessment is carried out by calculating the emergency degree indexes of different types of emergency events, and the specific steps are as follows:
s11: aiming at different types of emergency situations, an emergency degree index calculation formula is provided, and the specific steps are as follows:
s111: in the event of a conventional emergency event, the ratio of the emergency degree index to the amount of the emergency delivery request cargo to the total delivery request cargo and the ratio of the emergency delivery request amount to the total delivery request amount are correlated, the effects of both are additive, 、 />representing the influence coefficients of the two on the emergency degree index respectively. The formula is as follows:
；
wherein the method comprises the steps ofFor emergency degree index>For the total number of requests to be dispatched, < > for>For the total amount of goods to be distributed +.>For the number of emergency delivery requests in burst, < >>The total amount of cargo required for all burst emergency delivery requests. />Represents a natural disaster emergency event, if +.>If no natural disaster emergency occurs, if +.>And then represents the occurrence of a natural disaster emergency event.
S112: in the case of a natural disaster emergency event, factors such as the type, grade, number of affected areas, number of people and the like of the natural disaster event should be fully considered when calculating the emergency degree index. Based on comprehensively considering the type of natural disaster event, the index of affected area, the number of people affected, the index of natural disaster grade and the like, an emergency degree index calculation formula under the occurrence of natural disaster emergency event is provided, and the calculation formula is specifically as follows:
；
Wherein the method comprises the steps ofFor natural disaster index, for weighted average of the risk levels of natural disaster types in the affected area, +.>Is a weighted average of natural disaster grades, +.>For the affected person index, i.e. the proportion of the number of persons affected,index is the affected area, is the proportion of the affected area;
s113: in summary, the emergency degree index calculation formula under the emergency condition is as follows:
；
s12: for emergency situations with different degrees, the emergency degree index is providedA corresponding relation formula with the emergency degree r;
the corresponding relation formula of the emergency degree index and the emergency degree is shown as follows, whereinIs an index of the degree of emergency,is the degree of emergency>Is a critical value of the emergency degree index, and the dividing standard of the critical value is usually determined according to the types, the influence ranges and the possible losses of different emergency events, and the critical value of the emergency degree index is determined by combining actual conditions and expert experience to carry out comprehensive evaluation. In general, emergency events caused by emergency situations such as natural disasters have higher emergency degrees, so that the emergency degree index critical value is generally lower than that of conventional emergency events;
When (when)When no emergency situation occurs, the emergency degree r=0 is 0, and the distribution activity can be normally developed without adopting an additional emergency distribution strategy;
when (when)In this case, only slight emergency occurs, so that the emergency degree is +.>For the level 1, certain measures should be taken to respond to sudden emergency delivery requests of clients preferentially so as to fully ensure the rights and interests of the clients;
when (when)At this time, a serious emergency situation occurs, so the emergency degree is +.>The level is 2, and the requirement of emergency is fully ensured to protect the rights and interests of the parties;
；
s2: establishing a distribution strategy model, formulating corresponding emergency distribution strategies aiming at different emergency situations, and embodying in a distribution strategy influence factor mode;
further, a distribution strategy model aiming at emergency conditions with different degrees is established, corresponding distribution strategies are adaptively selected according to the current emergency degree, and distribution strategy influence factors are output
It should be noted that in case of sudden emergency, different degrees of emergency affect the logistics distribution to different extents. If the adopted emergency distribution strategy is improper, the emergency situation of the current degree can not be effectively dealt with, the situation that the adopted emergency distribution strategy is excessive or insufficient appears, and the emergency distribution effect is further affected, so that the problems of low response speed, low distribution efficiency, high distribution cost, untimely distribution and the like exist when the emergency distribution request is dealt with;
Therefore, this section proposes a distribution strategy model for emergency situations of different degrees, aiming at adopting corresponding distribution strategies according to different degrees for emergency situations of different degrees and outputting distribution strategy influence factors, and specifically comprises the following steps:
s21: when (when)When the user satisfaction degree priority distribution strategy considering the distribution time window is adopted;
when (when)When no emergency occurs, the customer satisfaction degree of the delivery service is mainly dependent on the time of the delivery service, so that the delivery strategy taking the priority of the customer satisfaction degree of the delivery time window into consideration is adopted, and the time window strategy influence factor ∈ ->Priority policy influence factor-> ；
S22: when (when)When the distribution strategy combining the distribution time window with the client priority is adopted;
when (when)In order to cope with the part of customers, the distribution service should be provided preferentially, and the distribution to other customers is completed as much as possible within a specified time window, so that the distribution strategy of taking the distribution time window into consideration and combining the customer priority is adopted, and a time window strategy influencing factor is output>Priority policy influence factor- > ；
S23: when (when)When the distribution strategy considering the distribution service priority is adopted;
when (when)The emergency distribution system has the advantages that the emergency distribution system shows that serious emergency events occur at the moment, emergency distribution demands are generated for users and cargoes with high proportion, customer benefits are guaranteed, loss is reduced, and distribution services are provided for all customers according to the emergency degree of the distribution demands at the moment. In this case, a delivery policy considering the priority of delivery service should be adopted and a time window policy influence factor +.>Priority policy influence factor-> ；
S24: establishing corresponding relations between different delivery strategies and delivery strategy influence factors, and outputting the delivery strategy influence factors to embody the delivery strategies selected by the model;
；
s3: establishing a path planning model, and adaptively adjusting the distribution conditions, distribution strategies and optimization targets of the model based on the distribution strategy influence factors;
further, a path planning model based on the emergency self-adaptive distribution strategy is established, and the model can be based on distribution conditions, distribution strategies and optimization targets of a distribution strategy influence factor self-adaptive adjustment model;
and establishing a path planning model based on the self-adaptive distribution strategy under the emergency conditions based on the distribution strategy model established by the parts aiming at the emergency conditions with different degrees. The method comprises the following specific steps:
S31: when (when)Adaptive set-upA path planning model that considers delivery time windows and user satisfaction;
when (when)When no emergency occurs at this time, the satisfaction degree of the customer to the delivery service is mainly influenced by the delivery service time, the time window constraint of the model is activated based on the time window policy influence factor f=1, the priority policy influence factor e=0 closes the priority constraint of the model, and the model adaptively establishes a path planning model considering the priority of the delivery time window and the user satisfaction degree;
the detailed time window strategy punishment function formula is shown in the following (7), if the delivery vehicle arrives earlier than the earliest time required by a customer, the delivery vehicle needs to accept a certain waiting time punishment, if the delivery vehicle arrives just within the time window reserved by the customer, the delivery can be directly completed without accepting punishment, if the arrival time of the delivery vehicle is later than the latest time window reserved by the customer, the soft time window constraint is used, and the unmanned delivery vehicle can still complete the delivery, but the waiting time punishment of the customer is required to be accepted;
s32: when (when)When the distribution time window is combined with the client priority, a path planning model is built in a self-adaptive mode;
when (when)When only a small proportion of customers and cargoes have emergency delivery requests, the emergency degree is low, a delivery strategy which considers the delivery time window and combines the priority of the customers is adopted, the time window strategy influence factor f=1, the time window constraint of the self-adaptive activation model, the priority strategy influence factor e=1, the priority constraint of the self-adaptive activation model, the priority strategy is further adopted on the basis of the user reservation delivery time window, the priority delivery is carried out for a small number of customers with sudden emergency demands, and the other customers still carry out the delivery according to the time window constraint;
The detailed priority policy formula is as follows(6) Shown therein, whereinRepresenting customer points->Priority of->Is a priority policy influencing factor, the expression requires that if client point + ->Is dispatched by the same dispatching vehicle as customer point j and +.>When the priority of (a) is higher than the client point j, then the +.>. The detailed time window policy penalty function formula is shown in (7) below;
s33: when (when)When the distribution service priority is met, a path planning model considering distribution service priority is built in a self-adaptive mode;
when (when)When emergency delivery requests occur to customers and cargoes with higher proportion, the emergency degree is higher, a delivery strategy considering the priority of delivery service is adopted, a time window strategy influence factor f=0, the time window constraint of the self-adaptive closing model, a priority strategy influence factor e=1 and the priority constraint of the self-adaptive activating model, at the moment, delivery is carried out according to the priority of the emergency delivery requests of all customers, and the customers with high priority are delivered preferentially. The detailed priority policy formula is shown in (6) below, wherein +.>Representing customer points->Priority of->Is a priority policy influencing factor, the expression requires that if client point + ->Is dispatched by the same dispatching vehicle as customer point j and +. >When the priority of (a) is higher than the client point j, then the distribution is prioritized ；
S34: in summary, a path planning model based on an adaptive distribution strategy under emergency conditions is specifically expressed as follows:
；
expression (1) is an objective function formula requiring minimum total delivery cost, whereIs a distance cost factor, < >>Is a dot->And (4) point->Distance between (I) and (II)>Is the time window penalty cost,/->Is a correlation coefficient, if the vehicle k completes the distribution of the point i, the point is continuously completed/>Distribution of->Otherwise 0, & gt>Is a correlation coefficient, if the distribution point +.>The vehicle k is responsible for delivery, then->Otherwise, 0;
；
the constraint function (2) indicates that the amount of cargo that the delivery vehicle can carry cannot exceed the maximum load of the delivery vehicle, whereinIs client Point->Is a required amount of (a);
；
the constraint function (3) indicates that each delivery point is serviced only once and is only serviced by one delivery vehicle;
；
the constraint function (4) indicates that all delivery vehicles from the delivery center will eventually return to the delivery center after all delivery tasks are completed, whereinIs thatCorrelation coefficient indicating whether vehicle k is going from distribution center to customer point +.>Dispensing, if yes, then->Otherwise- > ， />Is a correlation coefficient indicating whether the vehicle k returns to the delivery center after delivering to the customer point j, if yes +.>Otherwise-> ；
；
The constraint function (5) indicates that the delivery vehicle arrives at the delivery point and departs from the delivery point;
；
the constraint function (6) is a priority constraint,representing customer points->E is a priority policy influencing factor, the expression requires if client point + ->Is dispatched by the same dispatching vehicle as customer point j and +.>When the priority of (a) is higher than the client point j, then the +.> ；
；
Expression (7) is a time window constraint that will be penalized when the vehicle does not arrive at a given delivery point within a prescribed time window, where t is the time the delivery vehicle arrives at the customer point,is client Point->The left time window of the time window is specified,is client Point->Right time window of the specified time window, +.>For the penalty factor when the vehicle arrives earlier than the delivery time,/->Penalty factors for vehicles arriving later than delivery time;
；
expression (8) represents the correlation coefficient QUOTE 、 />The value of (2) can only be 0 or 1; />
The specific meaning of each parameter in the model is shown in table 1 below:
TABLE 1 model parameter meaning Table
S4: establishing an emergency distribution request priority calculation formula, and carrying out calculation evaluation on the priority of each emergency distribution request;
An emergency delivery request priority calculation formula is presented for evaluating and determining the priority of each request. The method comprises the following specific steps:
s41: for emergency delivery requests in a conventional emergency event, priority calculation should be based on economic factors such as the cargo value of the emergency delivery request, potential losses that may be incurred, and additional effective profits that would be incurred if the request were satisfied in time. Based on the above, an emergency distribution request priority calculation formula under a conventional emergency event is provided, as follows:
；/>
wherein,,priority for emergency delivery request +.>、 />、 />The dynamic autonomous decision coefficient reflects the cargo value of the emergency delivery request, the potential economic loss which is caused if the request cannot be timely satisfied, and the relative importance of the additional effective profit which is caused if the request is timely satisfied under the emergency condition of the emergency.
For the cargo value of the emergency delivery request +.>Represents the loss caused if the emergency distribution request cannot be satisfied in time, and specifically comprises cost increase loss, production data damage loss and later potential service loss, < >>If the emergency distribution request is timely satisfied, the effective profit brought by the extra is represented, and the time required by the emergency distribution service is completed by T;
S42, aiming at the emergency distribution request in the natural disaster emergency event, the specificity and the complexity of the emergency distribution request need to be comprehensively considered. In addition to economic factors such as cargo value, potential loss, time cost, etc., the extent of impact of the request on emergency handling of the emergency event is also considered. Since the magnitude of the different indicators may vary widely, normalization is required to more accurately calculate the priority of each emergency delivery request. Taking the normalization of the required service time T as an example, this is denoted as， />Is the minimum value of the required service time in the emergency delivery request, +.>The specific formula is as follows, for the maximum value of the required service time:
；
other factors considered are normalized in the same manner as described above. The detailed priority calculation formula is as follows:
；
wherein,,for normalizing the processed cargo value, +.>For potential loss after normalization treatment, +.>For the normalized required service time, +.>To normalize the extent to which the processed request affects emergency handling of the emergency event. />、/>、 />、 />The weight of each index is determined according to specific conditions so as to meet the emergency distribution requirement under the emergency event of natural disasters;
s43: in summary, an emergency delivery request priority calculation formula is as follows:
；
S44: calculating priorities of different emergency delivery requests by using an emergency delivery request priority calculation formula;
s5: solving the obtained path planning model and data to obtain a path planning result meeting the requirement of the current emergency;
solving the obtained path planning model and data by using an improved genetic algorithm based on a damage repair idea, so as to obtain a path planning result meeting the requirement of the current emergency;
the algorithm can generate new individuals in a mode of destroying individual structures according to the characteristics and constraint conditions of the problems, and changes the new individuals into feasible solutions again through a repair strategy so as to improve the global searching capability and the quality of the solution of the algorithm. The method comprises the following specific steps:
s51: initializing a population: randomly generating a group of initialized individuals as a population;
s52: evaluating fitness: for each individual, calculating an fitness value according to an objective function of the problem;
s53: selection operation: selecting individuals from the population using a roulette algorithm as a selection operator so that they can participate in the reproduction of the next generation;
s54: cross mutation operation: for selected individuals, crossing by using a crossing operator, combining genes of the selected individuals into new individuals, and for the crossed individuals, performing mutation operation by using a mutation operator, and randomly changing certain gene positions;
S55: a damage repair operation: first, random removal is used to randomly remove any gene locus in the current individual, and disruption operation is performed on the gene locus. Then, for each destroyed individual, repairing the individual bit by using a greedy repair method, attempting to insert the gene bit into all possible positions in the individual, and selecting the effective and optimal individual, thereby recovering the genome of the individual;
s56: evaluating fitness: recalculating fitness values of all newly generated individuals;
s57: updating the population: combining the newly generated individuals with the original individuals to form a new population;
s58: check stop criteria: if the stopping criterion is reached, if the maximum iteration number is reached or the optimal solution is found, stopping the algorithm, otherwise returning to S53, and continuing the iteration.
Example 2
Example 2 an unmanned automated tanker is used to provide daily oil delivery to 11 points of fueling in area a. In the conventional case, the oil truck will deliver the oil filling points in sequence according to a predetermined delivery time to ensure that the oil shortage in the oil filling points does not occur. Daily distribution data of each oiling point are shown in the following table, wherein the point 0 is the starting point of the oil truck, and the points 1-11 are the oiling points;
However, immediately after the unmanned automatic oil vehicle starts, the oil filling points No. 10 and No. 11 are short of oil due to the sudden peak of oil consumption on holidays, and the oil needs to be supplemented. Preliminary estimation shows that the oil gap of the No. 10 oil filling point is about 6 tons, the oil gap of the No. 11 oil filling point is about 4 tons, the values of the oil gap of the No. 11 oil filling point are about 63630 yuan and 42420 yuan respectively, if the oil cannot be timely supplemented to the No. 10 oil filling point and the No. 11 oil filling point, cost losses about 4200 yuan and 3500 yuan are expected to be generated respectively, and if the oil can be timely supplemented to the No. 10 oil filling point and the No. 11 oil filling point, effective profits of 6618 yuan and 4412 yuan are additionally brought;
table 2 example 2 delivery data table
Under the above background, if the path planning method based on the self-adaptive distribution strategy under the emergency condition provided by the invention is applied, the main steps are as follows:
s1: performing emergency degree assessment by using the established emergency degree assessment model;
s11, the total number of points to be distributed at the moment isThe individual, QUOTE->The total amount of goods to be distributed is 29 tons, i.e. +.>The number of the burst emergency requests is 2, namely s=2, and the total quantity of goods required by the burst emergency delivery requestsIs 10 tons, i.e.)>And h=0, i.e. no natural disaster emergency occurs at this time. In view of the fact that the current emergency situation is a conventional emergency event, the influence range and the possible loss are small, and a=0.3 is taken as an emergency degree index critical value, and the emergency degree index critical value is- >0.5%>Preferably 0.2, the current emergency level index is mainly influenced by the total amount of goods required for emergency delivery requests, and is calculated by the following emergency level index formula to be available, +.> = 0.173。
；
S12: using the emergency degree index shown belowThe corresponding relation formula of the emergency degree r shows that the current emergency degree is lower and is 1 level, namely r=1;
；
s2: using the established distribution strategy model aiming at the emergency conditions of different degrees to adaptively select an emergency distribution strategy;
s21: transmitting the obtained emergency degree r=1 into the established distribution strategy model;
s22, outputting a distribution strategy influence factor QUOTE by the model according to the following corresponding relation formula of the emergency degree and the distribution strategyAdaptively selecting a delivery strategy considering a user time window and a client priority;
；
s3: the established path planning model based on the self-adaptive distribution strategy under the emergency condition adaptively adjusts distribution conditions, distribution strategies and optimization targets according to the distribution strategy influence factors obtained above;
s31: input QUOTERespectively activating time window strategies and priority strategies of the model;
s32: after the model adaptively adjusts the distribution conditions, distribution strategies and optimization targets, the obtained expression is as follows:
；
；
；/>
；
;
;
;
;
S4: calculating the priority of each emergency delivery request by using the proposed emergency delivery request priority calculation formula;
s41: for emergency requests under a conventional emergency event, the priority of each emergency delivery request is calculated by using an emergency delivery request priority calculation formula shown below. Wherein,,and->Is an autonomous decision coefficient, and is advisable according to the current emergency and cargo property> 0.3， /> ，/> ， />Representing the expected loss caused by the fact that the current emergency distribution request cannot be timely satisfied, and the emergency distribution request is ++>The cargo value of the emergency delivery request is given, and T is the time required for completing the emergency delivery service;
;
s42: according to the proposed emergency delivery request priority calculation formula, calculating the priority of emergency delivery requests of the No. 10 oiling point and the No. 11 oiling point to obtain=1.56， />=0.88, so the number 10 fuel filler point has a higher priority than the number 11 fuel filler point;
s5: solving the obtained model and data by utilizing an improved genetic algorithm based on a damage repair idea, so as to obtain a path planning scheme of the current distribution;
as shown in fig. 4 (right), the present solution adaptively adjusts the distribution conditions, distribution strategies, and distribution targets in consideration of the current emergency situation. The distribution route obtained by the solution was 0- > 10- > 11- > 1- > 2- > 3- > 4- > 5- > 6- > 7- > 8- > 9- > 0, and the total travel distance was 59.48 km. The scheme can meet the priority distribution requirements of the No. 10 oiling point and the No. 11 oiling point, and meanwhile, the priority of the No. 10 oiling point is higher than that of the No. 11 oiling point, so that the No. 10 oiling point is preferentially distributed during distribution, the potential total loss of about 18730 yuan possibly caused by emergent conditions is effectively avoided, the emergent distribution cost is relatively low, and the relative distribution sequence and time window requirements of other oiling points are not influenced;
If the present solution is not adopted, but the delivery is performed according to the conventional time window strategy with the shortest travel distance as the target, the delivery scheme is as shown in fig. 4 (left), the delivery route is 0→1→2→3→4→5→6→7→8→9→10→11→0, and the total travel distance is 58.41 km. Although this solution results in a shorter delivery route, the solution does not take into account the emergency delivery requirements of the number 10 and number 11 fuel filler points at all, and the number 10 and number 11 fuel filler points will be delivered last, which may result in economic losses for the number 10 and number 11 fuel filler points totaling approximately 18730 yuan.
Example 3
Example 3 taking the example where an unmanned irrigation vehicle is responsible for emergency irrigation of 8 irrigation points in region B due to sudden high temperature weather, conventionally, the unmanned irrigation vehicle would irrigate 8 irrigation points in the region with the shortest total travel distance as the goal to reduce delivery costs. However, due to sudden high-temperature weather, 8 irrigation points in the area B are drought conditions with different degrees, if the irrigation points cannot be irrigated as soon as possible, great loss is brought to economic property of local farmers, and specific data are shown in the following table, wherein the No. 0 is the initial position of an irrigation vehicle, and the No. 1-8 points are drought points;
Table 3 example 3 delivery data table
Under the above background, if the path planning method based on the self-adaptive distribution strategy under the emergency condition provided by the invention is applied, the main steps are as follows:
s1: performing emergency degree assessment by using the established emergency degree assessment model;
s11: at this time, the total number of points to be distributedThe number of sudden emergency requests s=8, and h=1, a natural disaster emergency event occurs. In view of the fact that the current emergency is a natural disaster emergency, the influence range and possible loss are large, so a=0.2 can be taken as an emergency degree index critical value, and the emergency degree index critical value can be calculated by using the following emergency degree index formula to obtain the emergency degree index> = 0.5。
；
S12: using the emergency degree index as described belowThe corresponding relation formula of the emergency degree r shows that the current emergency degree is higher and is of level 2, namely r=2;
；
s2: using the established distribution strategy models aiming at different emergency conditions to adaptively select an emergency distribution strategy;
s21: the obtained emergency degree r=2 is transmitted into the established distribution strategy model;
s22: outputting a distribution strategy influence factor f=0, e=1 according to the following corresponding relation formula of the emergency degree and the distribution strategy, and adaptively selecting the distribution strategy considering the distribution service priority;
；
S3: the established path planning model based on the self-adaptive distribution strategy under the emergency condition automatically adjusts the distribution conditions, distribution strategies and optimization targets according to the distribution strategy influence factors obtained above;
s31: inputting f=0, closing a time window strategy of the model, inputting e=1, and activating a priority strategy of the model;
s32: after the model adaptively adjusts the distribution conditions, distribution strategies and optimization targets, the obtained expression is as follows:
；
；
；
；
；
；
；
s4: calculating the priority of each emergency delivery request by using the proposed emergency delivery request priority calculation formula;
s41, aiming at an emergency distribution request in a natural disaster emergency event, the specificity and complexity of the emergency distribution request need to be comprehensively considered, economic factors such as cargo value, potential loss, time cost and the like are also considered, the influence of the request on the emergency event needs to be considered, and a detailed calculation formula is as follows:
；
wherein,,for normalizing the processed cargo value, +.>For potential loss after normalization treatment, +.>For the normalized required service time, +.>For the normalization of the potential effect after treatment, the drought degree at each drought-affected point was calculated here as potential effect, +.>、 />、 />、 />Is the weight of each index, and is preferable according to the current situation 、 />、 /> 、 />To meet the emergency distribution requirement under the emergency event of natural disasters;
s42: according to the proposed emergency delivery request priority calculation formula, calculating the priority of the emergency delivery request of No. 1-8 drought points to obtain = 0.10，/> =0.27， /> = 0.33，/> = 0.34， />= 0.71，/> = 0.38， /> = 0.70， />The priority of each drought-affected point is 8 # point, 5 # point, 7 # point, 6 # point, 4 # point, 3 # point, 2 # point, 1 # point from top to bottom in sequence;
s5: solving the obtained model and data by utilizing an improved genetic algorithm based on a damage repair idea, so as to obtain a path planning scheme of the current distribution;
as shown in fig. 5 (right), after the scheme is applied, the model adaptively adjusts the distribution conditions, distribution strategies and distribution targets by considering the current emergency situation, and the obtained distribution route is 0-8-5-7-6-4-3-2-1-0, and the total travel distance is 102.41 km. The distribution scheme can effectively distribute according to the emergency degree of each drought-enduring point according to the priority of each drought-enduring point, and can preferentially meet the irrigation requirements of 8 #, 5 #, 7 # and 6 # drought-enduring points with higher emergency degree and larger potential loss, and then meet the irrigation requirements of 4 #, 3 #, 2 # and 1 # points with lighter drought-enduring degree. The scheme can reduce the loss of sudden natural disasters to farmers as much as possible, and the expected loss of the sudden emergency situation is 104000 yuan after the scheme is applied, so that a good effect is achieved.
If the scheme is not adopted, the unmanned irrigation vehicle aims at the shortest driving distance and the lowest delivery cost according to the previous delivery strategy, and the current sudden natural disaster condition, the emergency degree and the priority of each drought-affected point are not considered. In this case, as shown in fig. 5 (left), the distribution route is 0→1→2→3→4→5→6→7→8→0, and irrigation points with low drought tolerance such as point 1, point 2, point 3 and point 4 are sequentially irrigated, and finally irrigation is considered to be carried out on point 8, point 5, point 7 and point 6 with more serious drought tolerance. The total travel distance is 95.88 km, but even though the delivery distance is shorter, high priority drought-affected points cannot be irrigated in time due to the lack of priority and urgency of each drought-affected point, which would result in economic losses up to 211000 yuan.
In summary, when the path planning is performed, the influence of the emergency situations with different degrees on the distribution process is fully considered, and the path planning result meeting the current emergency situation requirement is obtained by adaptively selecting the corresponding distribution strategy and calculating the priority of each emergency distribution request. The invention not only can timely and effectively respond to the emergency delivery request of the customer, improve the emergency delivery efficiency and reduce the loss and influence caused by emergency conditions, but also can effectively avoid the problem of excessive or insufficient emergency delivery strategy adopted and save the emergency delivery cost.
The above embodiments and the accompanying drawings are for illustrative purposes only and are not intended to limit the invention, and various modifications and improvements will readily occur to those skilled in the art. Any equivalent substitutions, modifications and improvements made within the spirit and scope of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. A path planning method based on an adaptive distribution strategy under emergency conditions is characterized by comprising the following steps:
establishing a sudden emergency degree assessment model, and assessing sudden natural disaster emergency degree in the distribution activities;
establishing a distribution strategy model, formulating corresponding emergency distribution strategies aiming at different emergency situations, and embodying in a distribution strategy influence factor mode;
establishing a path planning model, and adaptively adjusting the distribution conditions, distribution strategies and optimization targets of the model based on the distribution strategy influence factors;
establishing an emergency distribution request priority calculation formula, and carrying out calculation evaluation on the priority of each emergency distribution request;
solving the obtained path planning model and data to obtain a path planning result meeting the requirement of the current emergency;
The sudden emergency degree assessment model comprises the following steps:
wherein E is f As an emergency degree index, S t To be distributed request total number, U t S is the total quantity of goods to be distributed and s is the sudden emergency distributionThe request sending number u is the total cargo quantity required by all burst emergency delivery requests, and alpha and beta respectively represent the influence coefficients of the two to the emergency degree;
D e for natural disaster index, for weighted average of the risk levels of natural disaster types in affected area, D l Is a weighted average of natural disaster grades, P a For the index of the affected persons, i.e. the proportion of the number of persons affected, C a Index is the affected area, is the proportion of the affected area;
h represents a natural disaster emergency event, if h=0, it represents that no natural disaster emergency event occurs, and if h=1, it represents that a natural disaster emergency event occurs.
2. The path planning method based on an adaptive distribution strategy in emergency situations according to claim 1, wherein the emergency degree evaluation model further comprises a correspondence formula between an emergency degree index and an emergency degree r:
wherein a represents the critical value of the emergency degree index;
if E f If the ratio is=0, no emergency occurs, and r=0;
If 0 < E f A is less than, the emergency degree of the burst is low, and r=1;
if a is less than or equal to E f Less than or equal to 1, the emergency degree is high, and r=2;
for emergency situations with different degrees, the emergency degree index E is calculated f The corresponding emergency degree r is obtained.
3. The path planning method based on an adaptive distribution strategy in emergency situations according to claim 2, wherein the distribution strategy model comprises:
the distribution strategy model aiming at the emergency situations of different degrees is established, corresponding emergency distribution strategies are formulated aiming at the emergency situations of different degrees, and the distribution strategy model is embodied in a mode of distribution strategy influence factors, and the distribution strategy model is concretely as follows:
when r=0, indicating that no emergency occurs, outputting a time window policy influence factor f=1 and a priority policy influence factor e=0;
when r=1, indicating that a low-level emergency occurs, outputting a time window policy influence factor f=1 and a priority policy influence factor e=1;
when r=2, it indicates that a high degree of emergency occurs, the time window policy influence factor f=0 is output, and the priority policy influence factor e=1.
4. The path planning method based on the self-adaptive distribution strategy under the emergency condition of the sudden emergency according to claim 3, wherein the corresponding relation between different distribution strategies and distribution strategy influence factors is established, and the distribution strategy influence factors are output to embody the distribution strategy selected by the model;
Where r represents the degree of emergency, f is the time window policy influence factor, and e is the priority policy influence factor.
5. The path planning method based on adaptive distribution strategy in emergency situation according to claim 4, wherein said establishing a path planning model comprises:
when r=0, based on a time window policy influence factor f=1, activating a time window constraint of the model, and closing a priority constraint of the model by a priority policy influence factor e=0, and adaptively establishing a path planning model taking the distribution time window and the user satisfaction into consideration;
when r=1, the time window policy influence factor f=1, the time window constraint of the self-adaptive activation model, the priority policy influence factor e=1, the priority constraint of the self-adaptive activation model, on the basis of the user reservation distribution time window, the priority policy is further adopted to carry out priority distribution for the customers with sudden emergency demands, and other customers still carry out distribution according to the time window constraint;
when r=2, the time window policy influence factor f=0, the time window constraint of the self-adaptive closing model, the priority policy influence factor e=1, and the priority constraint of the self-adaptive activating model, at this time, the distribution is performed according to the priority of the emergency distribution request of each client, and the distribution is performed preferentially for the clients with high priority.
6. The path planning method based on the adaptive distribution strategy in emergency according to claim 5, wherein the path planning model further comprises a mathematical model, specifically expressed as follows:
expression (1) is an objective function formula requiring minimum total delivery cost, where λ is a distance cost coefficient, d ij Is the distance between point i and point j, c i Is the time window penalty cost, x ijk Is a correlation coefficient, if the vehicle k continues to finish the distribution of the point j after finishing the distribution of the point i, x ijk =1, otherwise 0, y ik Is a correlation coefficient, if the delivery point i is responsible for delivery by the vehicle k, y ik =1, otherwise 0;
the constraint function (2) indicates that the amount of cargo that the delivery vehicle can carry cannot exceed the maximum load of the delivery vehicle, where q i Is the demand of client point i;
the constraint function (3) indicates that each delivery point is serviced only once and is only serviced by one delivery vehicle;
constraint function (4) indicates that all delivery vehicles from the delivery center will eventually return to the delivery center after all delivery tasks are completed, where x 0ik Is a correlation coefficient indicating whether the vehicle k is delivered from the delivery center to the client point i, if so, x 0ik =1, otherwise x 0ik ＝0，x j0k Is a correlation coefficient indicating whether the vehicle k returns to the distribution center after distributing the customer point j, if yes, x j0k =1, otherwise x j0k ＝0；
The constraint function (5) indicates that the delivery vehicle arrives at the delivery point and departs from the delivery point;
constraint function (6) is a priority constraint, p i The priority of the representative client point i, e is a priority policy influence factor, and the expression requires that if the client point i and the client point j are distributed by the same distribution vehicle and the priority of i is higher than that of the client point j, the i is distributed preferentially;
expression (7) is a time window constraint that penalizes when the vehicle does not reach the specified delivery point within the specified time window, where T is the time the delivery vehicle reaches the customer point, T li Is the left time window of the time window designated by the client point i, T ri Is the time designated by the client point iThe right time window of the window, gamma is the punishment coefficient when the vehicle arrives earlier than the delivery time, delta is the punishment coefficient when the vehicle arrives later than the delivery time;
x 0ik ，x j0k ，x ijk ，y ik ∈{0,1} (8)
expression (8) represents the correlation coefficient x 0ik 、x j0k 、x ijk 、y ik The value of (2) can only be 0 or 1.
7. The path planning method based on an adaptive distribution strategy in emergency situations as claimed in claim 6, wherein said creating an emergency distribution request priority calculation formula comprises:
Wherein P is D For priority of emergency delivery requests ε 1 、ε 2 、ε 3 The dynamic autonomous decision coefficient respectively reflects the cargo value of the emergency delivery request, the potential economic loss which is caused if the request cannot be satisfied in time, and the relative importance of the additional effective profit which is caused if the request is satisfied in time under the emergency condition of the emergency;
D price for the cargo value of the emergency delivery request, D loss Represents the loss caused if the emergency distribution request cannot be satisfied in time, and specifically comprises the cost increase loss, the production data damage loss and the later potential service loss, D profit And if the emergency distribution request is timely satisfied, the effective profit is additionally brought, and T is the time required for completing the emergency distribution service.
8. The method for path planning based on adaptive distribution strategy in emergency according to claim 7, wherein said creating an emergency distribution request priority calculation formula further comprises:
P D ＝w 1 *P G +w 2 *L G +w 3 *(1-T G )+w 4 *I G
wherein P is G To normalize the processed cargo value, L G To normalize potential loss after processing, T G I for normalized required service time G The influence degree of the emergency treatment on the emergency event is normalized for the request after the processing; w (w) 1 、w 2 、w 3 、w 4 The weight of each index is required to be determined according to specific conditions so as to meet the emergency distribution requirement under the emergency event of natural disasters.
9. The path planning method based on the adaptive distribution strategy in emergency according to claim 8, wherein the solving the obtained path planning model and data specifically comprises:
and solving the obtained path planning model and data by applying an improved genetic algorithm based on the damage repair thought.
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| 任世科 ; 袁治平 ; 徐渝 ; .突发事件应急救援动态调度优化:以KX井喷事故为例.运筹与管理.2012,(03),1-7. * |
| 基于资源约束的突发事件应急救援鲁棒性调度优化;胡信布;何正文;徐渝;;运筹与管理(02);72-79 * |
| 湖北省重大自然灾害应急物资储备节点布局研究;刘钢;党小红;游攀;;物流技术(23);318-321 * |
| 突发事件下多目标应急救援物资配送路径规划;康斌;刘权;黄健;龚建兴;;科学技术与工程(04);1521-1527 * |
| 突发事件应急救援动态调度优化:以KX井喷事故为例;任世科;袁治平;徐渝;;运筹与管理(03);1-7 * |
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