CN117789955B - Medical service distribution and path planning method, system, equipment and medium - Google Patents
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Abstract
The invention belongs to the field of medical management allocation, and provides a medical service allocation and path planning method, a system, equipment and a medium, which comprise a desired service time window considering a demand point, and constructing a medical detection service allocation and path planning model by taking the maximum duration minimization of a medical group and the maximum degree of satisfaction of users as targets; and constructing a state transition equation based on the state variables of the medical subgroups, and solving by utilizing a dynamic programming algorithm to obtain the service distribution relation between each medical subgroup and each demand point, the service path of each medical subgroup and the time for reaching each demand point. The invention tracks the service path and the arrival time of each medical team, can realize the path-service allocation integral optimization under the limitation of time window requirements, can fully utilize medical resources, ensures that the workload of medical staff is not overloaded and balanced, and ensures the maximum service efficiency of the system; the user difference and the self demand are considered, and the user-friendly method has better humanized consideration.
Description
Technical Field
The invention belongs to the technical field of medical management distribution, and particularly relates to a medical service distribution and path planning method, a system, equipment and a medium.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Sudden public health events seriously jeopardize the life health safety of the masses. Under the condition of limited medical resources, how to reasonably dispatch the detection group, reduce the waiting time of the group to be detected and improve the service satisfaction is an important scientific problem and a practical problem.
Improving satisfaction of users or served groups is part of the humanization consideration in the optimization modeling process, and is increasingly included in various optimization models. In the prior art, a health institution dynamically adjusts a response scheme according to the demands of demand points so as to minimize the unmet demands on resources, delivery time and transportation cost, and a mixed integer programming model is constructed with the minimum of the unmet demands as a goal. For medical resource transportation problems with time window restrictions, the penalty cost caused by violating delivery time is included, and the delivery time of materials is ensured to be within an ideal time range of a demand point under the condition of pursuing as little carbon emission as possible. When the hard time window and the soft time window coexist simultaneously, aiming at the satisfaction quantization of the demand point under the mixed time window to the on-time arrival rate of the medical resource, a multi-target cold chain logistics transportation path optimization model with the mixed time window is established. Aiming at the service life limitation of perishable products, and considering the green transportation and customer satisfaction problems of the perishable products, the prior art establishes a green vehicle path VRP distribution model of the perishable products, optimizes the carbon emission in the resource distribution process, and promotes the low-carbon economic development; synchronously reducing the damage cost of resources, improving the utilization rate of the resources and enhancing the satisfaction degree of clients.
Another important sub-problem in emergency medical service is a path optimization problem, and an optimal service path is determined, so that the running time of a service vehicle can be shortened, the distance cost is reduced, and the requirement of receiving medical service time at a demand point can be met to the greatest extent. Aiming at the problem of multi-target vehicle paths with hard time windows, the prior art builds a multi-target vehicle path optimization model, determines the optimal number configuration of vehicles and minimizes the total service time and the total expenditure cost. And a multi-objective stochastic programming model for solving the problem of integrated optimization of pre-disaster vehicle site selection decision, post-disaster vehicle path planning and resource allocation decision is also constructed by considering the contradictory relation between disaster prediction accuracy and cost expenditure. For the situation of insufficient own vehicles in public health events, intermodal modes of combination of own vehicles and rented vehicles are also proposed, and a multi-objective emergency path optimization model with time window for mixed vehicle path problems is established. On the basis of considering the problem of the soft time window of the user, the punishment cost is taken into the lower-layer target, so that the optimal running path of the vehicle is obtained. A mixed integer programming model targeting the minimum total cost is then constructed based on the two-layer programming approach. In the prior art, a vehicle path optimization model which takes the total cost and the timeliness of medical resources into consideration is provided, and under the limitation of the resource quality guarantee period, the demand point time window and the maximum cargo capacity of the vehicle, the optimal service sequence of the user and the optimal transportation path of the resource are determined. And an integer linear path planning model for the mixed fleet is constructed for the passenger and parcel transport service problems in emergency situations, which aims to design an optimal driving route for the mixed taxi fleet which meets the passenger and parcel requests simultaneously. And a vehicle path planning model in a disaster response stage is also constructed, and an optimal evacuation mode from a disaster point to a refuge is explored, so that the shortest total time spent in the vehicle transportation process is ensured.
In summary, although the prior art has conducted a lot of researches on problems such as user satisfaction and path optimization, in the medical service distribution and path planning research, the service paths of the medical team, the service distribution decisions, the balance of the workload distribution of the service system, the satisfaction measure for the demand point time window requirement and the like are not put into the same modeling framework for comprehensive consideration, which obviously greatly increases the complexity of the technology and challenges the solution of the decision problem.
Disclosure of Invention
In order to solve the problems, the invention provides a medical service distribution and path planning method, a system, equipment and a medium. And solving by adopting a dynamic programming algorithm, and obtaining the service distribution relation of each medical group and each demand point, the service path of each medical group and the time for reaching each demand point by solving and analyzing, thereby calculating the overall satisfaction degree of all the demand points.
According to some embodiments, a first aspect of the present invention provides a medical service allocation and path planning method, which adopts the following technical scheme:
A medical service distribution and path planning method, comprising:
Taking an expected service time window of a demand point into consideration, and constructing a medical detection service distribution and path planning model with the aim of minimizing the maximum duration of a medical group and maximizing the overall satisfaction of users;
And constructing a state transition equation based on the state variables of the medical subgroups, and solving by utilizing a dynamic programming algorithm to obtain the service distribution relation between each medical subgroup and each demand point, the service path of each medical subgroup and the time for reaching each demand point.
Further, the medical detection service distribution and path planning model specifically comprises the following steps:
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s.t.
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；
；
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Wherein, Is a set of demand points,/>，/>Is a medical team collection,/>，/>Is a number large enough,/>Is the point of need/>The number of people to be detected,/>Is at/>Service time of point, wherein/>；/>Is the medical team arrival time,/>Is the distance time from point i to point j,/>Is the distance time from point j to point i,/>Is the service time of unit person time,/>Is the maximum working capacity of group g,/>Is the urgency of the demand point i,/>Is the urgency of the demand point j,/>Is the service time of the medical team at point j,/>Representing whether there is a path of medical team g from point i to point j,/>Is whether the medical team g has a path from point j to point i,/>Is the time of arrival of medical team g at demand point j,/>Is whether the demand point j is responsible for by the medical team g,/>Defines the time from the starting point of each medical team as 0,/>Is a satisfaction evaluation function.
Further, the state transition equation is constructed based on the state variables of the medical group, specifically:
numbering the groups and the current points of the groups ) The team can bear the workload (/ >)) Current maximum working time (temp/>) in all teamsElapsed time (/ >)) Urgency (/ >)) Non-access point set (/ >)) Constructing a state transition equation for the recursive variable;
In the state transition equation, consider whether the group is the last group, whether the group is at the starting point and whether the demand point demand can meet the maximum working capacity problem, and finally return to the group path containing n points and the path target value.
Further, assume the medical teamIs/>The specific logic of the demand point is as follows:
when only the last subgroup is left, i.e =/>When (1):
No unassigned demand points, no demand points assigned to the last subgroup, the medical subgroup being in an idle state, defining the solution as an infeasible solution;
there is only one unallocated demand point, and the number of people to be detected at the demand point is checked first ) Whether or not to exceed medical team/>Maximum working capacity of (2); if not, call state transition equation/>Calculate two target values (/ >) after the inclusion of the point) ; And then compare/>And temp/>If/>Let/>Otherwise, determining that the solution is not a feasible solution;
A plurality of unassigned demand points are arranged, the rest demand points are traversed in sequence, and when the number of people to be detected at the demand points is in a group When within the affordable range, the point is included in the service path list for the group and the maximum working capacity (/ >) of the group is updated) Time to reach this point/>Working time (/ >)) An unassigned set of demand points (/ >) Then call state transition equation/>Calculating two target values; if all the demand points are not in the working capacity range of the group, judging the solution to be an infeasible solution, otherwise, outputting an optimal result.
Further, assume the medical teamIs/>The specific logic of the demand point is as follows:
There are multiple subgroups:
Firstly, checking whether the number of the remaining subgroups is more than the number of the unallocated demand points, if so, proving that the subgroups are in an idle state, and judging that the solution is an infeasible solution;
otherwise, respectively calculating:
Results of changing panelist cases, if panelist Not at the starting point, change to medical team/>Updating the current point as a starting point, setting the arrival time and the working time to 0, and calling a state transition equation/>Calculating two target values at the starting point; if the solution is not feasible, outputting a very poor value; otherwise, storing two target values;
otherwise, define as a infeasible solution;
The result of the condition of not changing the group is that the rest demand points are traversed in sequence, when the number of people to be detected at the demand points is in the group When the point is within the affordable range, the point is included in the service path list of the subgroup, the maximum working capacity of the subgroup, the time to reach the point, the working time and the unassigned set of demand points are updated, and then a state transition equation/>, is calledCalculating two target values; and then compare/>And temp/>If/>Let/>；
If the set of solution results is empty without changing the team, it is stated that the team cannot serve any one of the demand points: if the subgroup is not at the starting point, outputting a result of replacing the subgroup; otherwise, define this solution as an infeasible solution;
And if the solution set is not empty under the condition of not replacing the subgroup, comparing the solution results of the replacement subgroup and the non-replacement subgroup, and outputting an optimal solution.
According to some embodiments, a second aspect of the present invention provides a medical service distribution and path planning system, which adopts the following technical solutions:
A medical service distribution system, comprising:
The model construction module is configured to take the expected service time window of the demand point into consideration, and constructs a medical detection service distribution and path planning model with the aim of minimizing the maximum duration of the medical group and maximizing the overall satisfaction of the user;
And the objective function solving module is configured to construct a state transition equation based on the state variables of the medical subgroups, and solve the state transition equation by utilizing a dynamic programming algorithm to obtain the service distribution relation between each medical subgroup and each demand point, the service path of each medical subgroup and the time for reaching each demand point.
According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method of medical service allocation and path planning as described in the first aspect above.
According to some embodiments, a fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in a medical service allocation and path planning method as described in the first aspect above when the program is executed.
Compared with the prior art, the invention has the beneficial effects that:
According to the invention, an ideal time window for receiving detection of each demand point is considered, meanwhile, research on the aspect of bringing the demand urgency into relevant service strategies is further needed to be deepened, four indexes of total number, regional diagnosis number, suspected case number and simultaneous blank number of each demand point are combined, the urgency of waiting for sampling service of each demand point is firstly clarified by utilizing an entropy weight method, a medical detection service distribution and path planning model aiming at minimizing the maximum duration of medical groups and maximizing the total satisfaction of users is established, and an optimal medical detection service strategy is searched under the mode of 'sending a plurality of medical groups to a plurality of continuous detection points/demand points for detection service'; the accurate algorithm dynamic programming is adopted to solve, so that the solved is ensured to be an optimal solution, namely the obtained service allocation scheme and the path planning strategy are both optimal; the service distribution relation between each medical group and each demand point, the service path of each medical group and the time for reaching each demand point are obtained through solving and analyzing, so that the total satisfaction degree of all demand points is calculated, and finally, the obtained service distribution scheme and path planning strategy can simultaneously consider two indexes of working time and satisfaction degree, so that the service demands of the demand points are considered, the working strength balance among the medical groups is considered, and an auxiliary decision reference is provided for decision makers under the 'benefit' of both parties.
The invention comprehensively considers the operation efficiency of the medical service system, the satisfaction of the group to be detected and the balance of the working capacity of the medical group, establishes a multi-objective planning model, ensures the efficient utilization of medical resources, and can better consider the individual demands of the group to be detected and the interests of medical staff, thereby obtaining a more efficient and humanized medical service distribution scheme. Aiming at the differences of individual demands of the group to be detected (such as the time window demands which want to accept detection) and the group category differences (such as diagnosis confirmation, suspected, simultaneous time and space, normal state and the like) in reality, the method of measuring satisfaction degree in a sectional way (early arrival, arrival in the time window and late arrival of a medical team) is adopted, and the final medical service distribution scheme can ensure the benefit demands of the group to be detected to the greatest extent by minimizing the loss of own benefits born by the group to be detected.
According to the invention, the total detection time of each medical subgroup reaching a plurality of continuous demand points/groups to be detected is measured, and the minimum maximization problem is introduced to realize the minimum maximum time of the medical subgroup, so that the optimal medical service allocation scheme can fully utilize medical resources under the condition of limited working capacity of each medical subgroup, and meanwhile, the working capacity of each medical subgroup can be balanced; on the basis of the classical path-service allocation problem, further carefully capturing the time of arrival of a behavior subject (i.e., a medical team) at each network node, which is the resultant manifestation of a series of service allocation decisions by upstream nodes; the service allocation decision of the current network node also directly affects the arrival time calculation of the downstream node, thereby affecting the matching condition of the arrival time of the medical team and the time window requirement of the group to be inspected on the node and the satisfaction degree result of the group to be inspected. The technical difficulties are included in the same modeling framework, the service path and arrival time of each medical team are tracked, and the path-service allocation overall optimization under the limitation of time window requirements can be realized.
The invention establishes a double-target nonlinear structure model, adopts an accurate algorithm dynamic programming to solve the model so as to achieve the aim of solving the model into an optimal solution, i.e. explores an optimal service distribution relation between a medical team and a demand point and an optimal service path of the medical team; the dynamic programming optimization algorithm is adopted to solve the model, so that a reliable accurate solution (compared with a classical NSGA-II algorithm) can be obtained finally, and the method has high operation efficiency (compared with a Ɛ -constraint algorithm).
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a flowchart of a medical service distribution and path planning method according to an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1
As shown in fig. 1, the present embodiment provides a medical service distribution and path planning method, and the present embodiment is applied to a server for illustration by using the method, and it can be understood that the method may also be applied to a terminal, and may also be applied to a system and a terminal, and implemented through interaction between the terminal and the server. The server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, security services CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the present application is not limited herein. In this embodiment, the method includes the steps of:
Taking an expected service time window of a demand point into consideration, and constructing a medical detection service distribution and path planning model with the aim of minimizing the maximum duration of a medical group and maximizing the overall satisfaction of users;
constructing a state transition equation based on state variables of the medical subgroups, and solving by utilizing a dynamic programming algorithm to obtain service distribution relations between each medical subgroup and each demand point, service paths of each medical subgroup and time for reaching each demand point;
And determining a final service distribution scheme and a path planning strategy according to the service distribution relation between each medical group and each demand point, the service path of each medical group and the time of reaching each demand point.
In this embodiment, in combination with the service time window limitation of each point to be inspected/demand point in reality, a plurality of medical groups are dispatched from a medical institution to each demand point to provide medical services, and an optimal medical service allocation and path planning scheme is formulated by optimizing the overall working time balance of each medical group and maximizing the service time window satisfaction of the demand point. For a medical institution, the scheme can fully utilize medical resources, the workload of medical staff is not overloaded and balanced, and meanwhile, the maximum service efficiency of the system is ensured; for points to be detected/demand points, the scheme gives consideration to the difference of users and the demand of the users, and has better humanized consideration.
Specifically, assuming that the information such as the position, the detection number, the time window and the like of each demand point is known, sending a medical team to provide detection service for each demand point on the basis that the maximum working capacity of the medical team is not exceeded, and planning an optimal service path of each medical team at the same time so as to ensure that each demand point can finish detection within the expected time window. The parameters and variables required by the model are defined as follows:
set of demand points,/> ；
Medical team set,/>；
A number large enough;
average detection time of each person;
demand Point/> The number of people to be detected;
At/> Service time of point, wherein/>；
Demand Point/>Acceptable earliest service time;
demand Point/> Acceptable latest service time;
Medical team/> Maximum working capacity;
，/>；
，/>；
Medical team/> Reach the demand Point/>Time of (2);
Suppose a point of demand The expected service time window is [/>,/>If the medical team is at/> ]Before reaching the point of needThen demand points/>Is/>; If medical team arrival time/>Is at the point of need/>Time window [/>),/>Between the demand points/>Is affected to some extent by the service satisfaction; if the medical team is later than/>And if so, the satisfaction is 0. Demand Point/>The service satisfaction of (2) may be expressed as follows:
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the text model thus constructed is:
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（6）；
（7）；
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（9）；
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（11）；
In the model, the formulas (1) and (2) are objective functions, and the formula (1) is that the duration of the minimum group with the longest working time is minimized; maximizing the total satisfaction of the formula (2); the formula (3) ensures that the service group can only go from one demand point to another demand point and finally return to the detection mechanism from the detection mechanism; equation (4) ensures that the number of subgroups that reach a certain point of demand is equal to the number of subgroups that leave from that point; the formula (5) ensures that each demand point has and only one group of people serves the demand point; equation (6) ensures that only when a certain point of demand is assigned to a group When, team/>To perform the task; the formula (7) ensures that the sum of the number of people to be detected at the service demand point does not exceed the maximum working capacity of each group; formula (8) specifies the service team's time transitivity at each point of demand; equation (9) is the cancellation sub-path; the service time at the start point of formula (10); equation (11) defines the time each subgroup starts at the start point.
In this embodiment, an ideal time window for receiving detection of each demand point is considered, and meanwhile, research on incorporating the demand urgency into related service strategies is further needed, and four indexes of total number, regional diagnosis number, suspected case number and simultaneous blank number of each demand point are combined, firstly, the urgency of waiting for sampling service of each demand point is clarified by using an entropy weight method, so that a medical detection service distribution and path planning model aiming at minimizing the maximum duration of a medical group and maximizing the overall satisfaction of users is established. The urgency of waiting for sampling service at each demand point is calculated and obtained by using four indexes of total number, regional diagnosis number, suspected case number and simultaneous blank number of each demand point, and the existing entropy weight method can be adopted, including but not limited to the entropy weight method.
Dynamic programming is to break down a problem into smaller sub-problems and solve each sub-problem only once. The solution of the former sub-problem provides useful information for the solution of the latter sub-problem, and the transfer of this information is accomplished through a state transfer equation. The state transition equation derives the state of the stage according to the decision and state of the previous stage.
The key idea of dynamic programming is to use the results of the sub-problems to avoid repeated computations. Solutions to sub-problems are stored in memory and used to solve larger sub-problems if necessary. This reduces the time complexity of the algorithm making it more efficient.
State transition equation: the number (g) of the group is used for counting the current point of the group) The team can bear the workload (/ >)) Current maximum working time (temp/>) in all teamsElapsed time (/ >)) Urgency (/ >)) Non-access point set (/ >)) And a path time (time [ p ] [ point ]) between two points (p, point) to construct a state transition equation for the recursive variableIn this function, consider whether the group is the last group, whether the group is at the start point and whether the demand point demand can meet its maximum working capacity, and finally return to the group path and path target value containing n points.
Assuming medical teamIs/>The specific logic of the demand point is as follows:
When only the last subgroup (i.e =/>）；
There are no unassigned demand points;
No demand points can be assigned to the last subgroup, which is in an idle state, defining the solution as an infeasible solution.
With only one unassigned demand point；
Firstly checking the number of people to be detected at the demand point) Whether or not to exceed medical team/>Maximum working capacity of (2); if not, update team/>Affordable workload/>Working timeSimultaneously remove/>, the point in the list of non-access-required pointsCall state transition equation/>Calculate two target values (/ >) after the inclusion of the point) ; And then compare/>And temp/>If/>Let/>. Otherwise, it is determined that this solution is not a viable solution.
It should be noted that f2 does not need to be compared, f2 is a process of accumulating from the beginning, f2=0 is assigned first, the satisfaction (i.e. objective function two) of each subgroup is accumulated successively from the first subgroup until the task is completed for the last subgroup, and the final f2 is the sum of the satisfaction obtained by all subgroups.
There are a plurality of unassigned demand points;
Traversing the rest demand points in turn, when the demand points are The number of people to be detected is in the group/>If the point is within the affordable range, the point is included in the service path list of the group, and the remaining bearer capabilities (/ >) of the group are updated as well) Time to reach this point/>Unassigned set of demand points/>)，
Then call the state transition equationCalculating two target values; if all the demand points are not within the working capacity range of the group, the solution is judged to be an infeasible solution. Otherwise, outputting the optimal result.
There are multiple subgroups;
It is first checked whether the remaining group number is more than the unallocated demand point. If yes, the group is proved to be in an idle state, and the solution is judged to be an infeasible solution. Otherwise, respectively calculating:
Changing the results of the panelist case;
If group of Not at the starting point, change to medical team/>Updating the current point as a starting point, resetting the working time to 0, and calling a state transition equation/>Computing the assignment of non-accessed demand points to groups/>The latter two target values. If the solution is not feasible, outputting a very poor value; otherwise, two target values are stored.
Otherwise, define an infeasible solution.
The results of the panelist case were not changed;
Traversing the rest demand points in turn, when the demand points are The number of people to be detected is in the group/>When the point is within the affordable range, the point is included in the service path list of the group, the remaining working bearing capacity of the group, the time to reach the point, the working time and the unassigned set of demand points are updated, and then the state transition equation is calledComputing points of need/>Assigned to group/>The latter two target values. And then compare/>And temp/>If/>Let/>。
If the set of solution results is empty without changing the team, it is stated that the team cannot serve any one of the demand points: if the subgroup is not at the starting point, outputting a result of replacing the subgroup; otherwise, define this solution as an infeasible solution.
And if the solution set is not empty under the condition of not replacing the subgroup, comparing the solution results of the replacement subgroup and the non-replacement subgroup, and outputting an optimal solution.
The final service distribution scheme and the path planning strategy are determined according to the service distribution relation between each medical group and each demand point, the service path of each medical group and the time of reaching each demand point, and specifically comprise the following steps:
For example, each number may represent a demand point first, and the solution generated last is a set of "randomly" arranged data containing all demand points, and the service tasks of each group are separated based on the rules set by us. For example, there are a total of 8 demand points, 3 medical subgroups, the number of demand points is 1,2, …,8, a "baffle" is added to separate the service tasks of each subgroup, 8 demand points are allocated to 3 subgroups, then only 2 "baffles" are needed to be inserted between 1-8 of the 8 numbers, the baffles are denoted by numerals 9 and 10, the final generated solution is 3,2,6,9,5,4,1,10,7,8, the first subgroup needs to serve the demand points 3,2,6, the second subgroup is 5,4,1, and the third subgroup is 7,8.
Decision making: the whole equation solving process can be described by an optimal decision table, and the optimal selection (namely, the shortest time and the greatest satisfaction) under a certain state at a certain stage is determined, so that the optimal value is calculated in a bottom-up mode.
In the embodiment, an ideal time window of each demand point is considered, and a medical detection service distribution and path planning model aiming at minimizing the maximum duration of a medical group and maximizing the overall satisfaction of users is established. And solving by adopting a dynamic programming algorithm. Through solving and analyzing, the service distribution relation between each medical group and each demand point, the service path of each medical group and the time for reaching each demand point are obtained, so that the overall satisfaction degree of all the demand points is calculated. And finally, the obtained service allocation scheme and the path planning strategy provide auxiliary decision reference for a decision maker in the measurement of two indexes of the working time length and the satisfaction.
Example two
The present embodiment provides a medical service distribution system, including:
The model construction module is configured to take the expected service time window of the demand point into consideration, and constructs a medical detection service distribution and path planning model with the aim of minimizing the maximum duration of the medical group and maximizing the overall satisfaction of the user;
And the objective function solving module is configured to construct a state transition equation based on the state variables of the medical subgroups, and solve the state transition equation by utilizing a dynamic programming algorithm to obtain the service distribution relation between each medical subgroup and each demand point, the service path of each medical subgroup and the time for reaching each demand point.
The above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the first embodiment. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
The foregoing embodiments are directed to various embodiments, and details of one embodiment may be found in the related description of another embodiment.
The proposed system may be implemented in other ways. For example, the system embodiments described above are merely illustrative, such as the division of the modules described above, are merely a logical function division, and may be implemented in other manners, such as multiple modules may be combined or integrated into another system, or some features may be omitted, or not performed.
Example III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a medical service allocation and path planning method as described in the above embodiment one.
Example IV
The present embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps in a medical service distribution and path planning method according to the above embodiment.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random-access Memory (Random Access Memory, RAM), or the like.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (4)
1. A medical service distribution and path planning method, comprising:
Taking an expected service time window of a demand point into consideration, and constructing a medical detection service distribution and path planning model with the aim of minimizing the maximum duration of a medical group and maximizing the overall satisfaction of users;
constructing a state transition equation based on state variables of the medical subgroups, and solving by utilizing a dynamic programming algorithm to obtain service distribution relations between each medical subgroup and each demand point, service paths of each medical subgroup and time for reaching each demand point;
Suppose a point of demand The expected service time window is [/>,/>If the medical team is at/> ]Before reaching the demand point/>Then demand points/>Is/>; If medical team arrival time/>Is at the point of need/>Time window [/>),/>Between the demand points/>Is affected to some extent by the service satisfaction; if the medical team is later than/>When the user arrives, the satisfaction degree is 0; demand Point/>The service satisfaction of (2) may be expressed as follows:
；
Wherein, T Ei is the earliest service time acceptable to the demand point i, and T Li is the latest service time acceptable to the demand point i;
the medical detection service distribution and path planning model specifically comprises the following steps:
；
；
s.t.
；
；
；
；
；
；
；
；
；
Wherein, Is a set of demand points,/>，/>Is a medical team collection,/>，/>Is a number large enough,/>Is the point of need/>The number of people to be detected,/>Is at/>Service time of point, wherein/>；/>Is the medical team arrival time,/>Is the distance time from point j to point i,/>Is the distance time from point i to point j,/>Is the service time of unit person time,/>Is the maximum working capacity of group g,/>Is the urgency of the demand point i,/>Is the urgency of the demand point j,/>Is the service time of the medical team at point j,/>Representing whether there is a path of medical team g from point i to point j,/>Is whether the medical team g has a path from point j to point i,/>Is the time of arrival of medical team g at demand point j,/>Is whether the demand point i is responsible for by the medical team g,/>Defines the time from the starting point of each medical team as 0,/>Is a satisfaction evaluation function;
The state transition equation is constructed based on the state variables of the medical group, specifically:
the method is characterized by a group number (g), a current point (p) of the group, a workload (p g) of the group, a current maximum working time (tempF 1) of all groups, a used time (t u) and urgency ) The set of non-access points (U p) and the path time (timp [ p ] [ point ]) between two points (p, point) construct a state transition equation/>, for the recursive variables；
Solving a state transition equation by considering whether the subgroup is the last subgroup, whether the subgroup is at a starting point and whether the requirement of a requirement point can meet the maximum working capacity problem, and finally returning subgroup paths containing n points and path target values;
The solving of the state transition equation includes:
Assuming medical team Is/>The specific logic of the demand point is as follows:
when only the last subgroup is left, i.e =/>When (1):
No unassigned demand points, no demand points assigned to the last subgroup, the medical subgroup being in an idle state, defining the solution as an infeasible solution;
there is only one unallocated demand point (point), and the number of people to be detected at the demand point is checked first ) Whether or not to exceed medical team/>Maximum working capacity of (2); if not, the update team g can bear the workload (p g-qpoint) and the working time (t u + timp [ p ] [ point ]), and the point is removed from the list of non-access requirement points (U p -point), and a state transition equation/>, is calledCalculate two target values (/ >) after the inclusion of the point) ; And then compare/>And temp/>If/>Let/>Otherwise, determining that the solution is not a feasible solution;
When a plurality of unallocated demand points exist, traversing the rest demand points in sequence, and when the number of people to be detected at the demand points is in a group When the point is within the affordable range, the point is included in the service path list of the group, the remaining carrying capacity (p g-qpoint) of the group, the time to reach the point (t u + timp [ p ] [ point ]) and the set of non-access points (U p -point) are updated, and then the state transition equation/>, is calledCalculating two target values; if all the demand points are not in the working capacity range of the group, judging the solution to be an infeasible solution, otherwise, outputting an optimal result;
The solving the state transition equation further includes:
Assuming medical team Is/>The specific logic of the demand point is as follows:
There are multiple subgroups:
Firstly, checking whether the number of the remaining subgroups is more than the number of the unallocated demand points, if so, proving that the subgroups are in an idle state, and judging that the solution is an infeasible solution;
otherwise, respectively calculating:
Results of changing panelist cases, if panelist Not at the starting point, change to medical team/>Updating the current point as a starting point, resetting the working time to 0, and calling a state transition equation/>Calculating two target values after the non-accessed demand points are allocated to the subgroup g+1; if the solution is not feasible, outputting a very poor value; otherwise, storing two target values;
otherwise, define as a infeasible solution;
The solving the state transition equation further includes:
The result of the condition of not changing the group is that the rest demand points are traversed in sequence, when the number of people to be detected at the demand points is in the group When the point is within the affordable range, the point is included in the service path list of the group, the remaining working bearing capacity of the group, the time to reach the point, the working time and the unassigned set of demand points are updated, and then the state transition equation is calledCalculating two target values after the demand points are allocated to the group g; and then compare/>And temp/>If/>Let/>；
If the set of solution results is empty without changing the team, it is stated that the team cannot serve any one of the demand points: if the subgroup is not at the starting point, outputting a result of replacing the subgroup; otherwise, define this solution as an infeasible solution;
And if the solution set is not empty under the condition of not replacing the subgroup, comparing the solution results of the replacement subgroup and the non-replacement subgroup, and outputting an optimal solution.
2. A medical service distribution and path planning system, comprising:
The model construction module is configured to take the expected service time window of the demand point into consideration, and constructs a medical detection service distribution and path planning model with the aim of minimizing the maximum duration of the medical group and maximizing the overall satisfaction of the user;
The objective function solving module is configured to construct a state transition equation based on the state variables of the medical subgroups, and solve the state transition equation by utilizing a dynamic programming algorithm to obtain service distribution relations between each medical subgroup and each demand point, service paths of each medical subgroup and time for reaching each demand point;
Suppose a point of demand The expected service time window is [/>,/>If the medical team is at/> ]Before reaching the demand point/>Then demand points/>Is/>; If medical team arrival time/>Is at the point of need/>Time window [/>),/>Between the demand points/>Is affected to some extent by the service satisfaction; if the medical team is later than/>When the user arrives, the satisfaction degree is 0; demand Point/>The service satisfaction of (2) may be expressed as follows:
；
Wherein, T Ei is the earliest service time acceptable to the demand point i, and T Li is the latest service time acceptable to the demand point i;
the medical detection service distribution and path planning model specifically comprises the following steps:
；
；
s.t.
；
；
；
；
；
；
；
；
；
Wherein, Is a set of demand points,/>，/>Is a medical team collection,/>，/>Is a number large enough,/>Is the point of need/>The number of people to be detected,/>Is at/>Service time of point, wherein/>；/>Is the medical team arrival time,/>Is the distance time from point j to point i,/>Is the service time of unit person time,/>Is the maximum working capacity of group g,/>Is the urgency of the demand point i,/>Representing whether there is a path of medical team g from point i to point j,/>Is whether the demand point i is responsible for by the medical team g,/>Is a satisfaction evaluation function;
The state transition equation is constructed based on the state variables of the medical group, specifically:
the method is characterized by a group number (g), a current point (p) of the group, a workload (p g) of the group, a current maximum working time (tempF 1) of all groups, a used time (t u) and urgency ) The set of non-access points (U p) and the path time (timp [ p ] [ point ]) between two points (p, point) construct a state transition equation/>, for the recursive variables；
Solving a state transition equation by considering whether the subgroup is the last subgroup, whether the subgroup is at a starting point and whether the requirement of a requirement point can meet the maximum working capacity problem, and finally returning subgroup paths containing n points and path target values;
The solving of the state transition equation includes:
Assuming medical team Is/>The specific logic of the demand point is as follows:
when only the last subgroup is left, i.e =/>When (1):
No unassigned demand points, no demand points assigned to the last subgroup, the medical subgroup being in an idle state, defining the solution as an infeasible solution;
there is only one unallocated demand point (point), and the number of people to be detected at the demand point is checked first ) Whether or not to exceed medical team/>Maximum working capacity of (2); if not, the update team g can bear the workload (p g-qpoint) and the working time (t u + timp [ p ] [ point ]), and the point is removed from the list of non-access requirement points (U p -point), and a state transition equation/>, is called Calculate two target values (/ >) after the inclusion of the point) ; And then compare/>And temp/>If/>Let/>Otherwise, determining that the solution is not a feasible solution;
When a plurality of unallocated demand points exist, traversing the rest demand points in sequence, and when the number of people to be detected at the demand points is in a group When the point is within the affordable range, the point is included in the service path list of the group, the remaining carrying capacity (p g-qpoint) of the group, the time to reach the point (t u + timp [ p ] [ point ]) and the set of non-access points (U p -point) are updated, and then the state transition equation/>, is calledCalculating two target values; if all the demand points are not in the working capacity range of the group, judging the solution to be an infeasible solution, otherwise, outputting an optimal result;
The solving the state transition equation further includes:
Assuming medical team Is/>The specific logic of the demand point is as follows:
There are multiple subgroups:
Firstly, checking whether the number of the remaining subgroups is more than the number of the unallocated demand points, if so, proving that the subgroups are in an idle state, and judging that the solution is an infeasible solution;
otherwise, respectively calculating:
Results of changing panelist cases, if panelist Not at the starting point, change to medical team/>Updating the current point as a starting point, resetting the working time to 0, and calling a state transition equation/> Assigning the non-accessed demand points to the two target values after the subgroup g+1; if the solution is not feasible, outputting a very poor value; otherwise, storing two target values;
otherwise, define as a infeasible solution;
The solving the state transition equation further includes:
The result of the condition of not changing the group is that the rest demand points are traversed in sequence, when the number of people to be detected at the demand points is in the group When the point is within the affordable range, the point is included in the service path list of the group, the remaining working bearing capacity of the group, the time to reach the point, the working time and the unassigned set of demand points are updated, and then the state transition equation is called The demand points are distributed to two target values after the group g; and then compareAnd temp/>If/>Let/>；
If the set of solution results is empty without changing the team, it is stated that the team cannot serve any one of the demand points: if the subgroup is not at the starting point, outputting a result of replacing the subgroup; otherwise, define this solution as an infeasible solution;
And if the solution set is not empty under the condition of not replacing the subgroup, comparing the solution results of the replacement subgroup and the non-replacement subgroup, and outputting an optimal solution.
3. A computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of a medical service allocation and path planning method according to claim 1.
4. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of a medical service allocation and path planning method according to claim 1 when the program is executed by the processor.
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