CN113762780A - Method for processing medical waste collection problem by using improved genetic algorithm - Google Patents

Method for processing medical waste collection problem by using improved genetic algorithm Download PDF

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CN113762780A
CN113762780A CN202111057473.1A CN202111057473A CN113762780A CN 113762780 A CN113762780 A CN 113762780A CN 202111057473 A CN202111057473 A CN 202111057473A CN 113762780 A CN113762780 A CN 113762780A
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欧嘉俊
曾伟良
黄永慧
孙为军
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Abstract

The invention discloses a method for processing medical waste collection problems by using an improved genetic algorithm, which finishes medical waste collection work by a plurality of robots together, and a scheduling scheme of the plurality of robots is obtained by the following steps: s1, determining the positions of each collection point and the robot workstation in the map according to the plan of the hospital, calculating the distance between the collection point and the workstation and the distance between the collection point and the collection point to obtain a distance matrix, and determining the amount of medical waste of each collection point; s2, establishing a robot scheduling model for medical waste collection; and S3, solving the robot scheduling model for medical waste collection established in the step S2 through a modified genetic algorithm, thereby obtaining scheduling schemes of a plurality of robots. The invention has the advantages of improving the efficiency of medical waste collection, improving the safety and the usability in the collection process, avoiding the algorithm from falling into an extreme value trap and the like.

Description

Method for processing medical waste collection problem by using improved genetic algorithm
Technical Field
The invention relates to the technical field of computers, medical waste collection and robot path planning, in particular to a method for processing medical waste collection problems by using an improved genetic algorithm.
Background
With the continuous development of artificial intelligence technology, more and more work can be done by robots. For large medical institutions, a large amount of Infectious Health Care Waste (IHCW) is generated every day, and a robot is used for collecting medical waste, so that the treatment capacity can be improved, and the risk of infection of treatment personnel can be avoided. Inside a hospital, after a robot workstation is arranged, the amount of infectious medical waste generated by each collection point is different, a driving path from the workstation to each scattered collection point needs to be searched, and certain constraint is met. The Problem can be translated into a classical Vehicle scheduling Problem (VRP).
The logistics transportation scheduling problem has been a hot research problem for logistics distribution, and is widely applied to the fields such as transportation, industrial management, logistics transportation and the like. The scholars at home and abroad respectively develop researches on the path structure, local search, mathematical programming and the like. The method for solving the logistics distribution route optimization problem effectively at present mainly comprises an ant colony algorithm, a tabu search algorithm, a simulated annealing algorithm and a genetic algorithm. The ant colony algorithm is complex, needs long search time and is easy to generate stagnation. The tabu search algorithm is a single operation, only one initial solution of the search process is available, and the tabu search algorithm has strong dependence on the initial solution. The simulated annealing algorithm has low convergence speed, long execution time and great relation between the performance and the initial value.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for processing the medical waste collection problem by using an improved genetic algorithm, which not only improves the efficiency of medical waste collection and the safety and the usability in the collection process, but also has the advantages of avoiding the algorithm from falling into an extreme value trap and the like.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a method for dealing with medical waste collection problems using improved genetic algorithms, wherein medical waste collection is performed by a plurality of robots together, and a scheduling scheme for the plurality of robots is obtained by:
s1, determining the positions of each collection point and the robot workstation in the map according to the plan of the hospital, calculating the distance between the collection point and the workstation and the distance between the collection point and the collection point to obtain a distance matrix, and determining the amount of medical waste of each collection point;
s2, establishing a robot scheduling model for medical waste collection;
and S3, solving the robot scheduling model for medical waste collection established in the step S2 through a modified genetic algorithm, thereby obtaining scheduling schemes of a plurality of robots.
Further, in step S2, the robot scheduling model for medical waste collection is established by considering the upper half soft time window, and the establishment process is as follows:
s2-1, defining a semi-soft time window, wherein the robot can reach the outside of the time window, and the method specifically comprises the following conditions:
when the robot is in the designated time window [ a ]i，bi]When the collection point is reached, punishment cannot be carried out;
if the robot arrives earlier than aiNo penalty is received, thereby shortening the stay time in the waste bin, but if the robot is more than biLate, it will be punished;
let allowable arrival time be b'i(b′i＞bi)：
b′i＝b0-ti0 (1)
In the formula (1), b0Is a constant, representing the time for the robot to eventually return to the workstation; t is ti0Representing the travel time from the collection point i to the workstation; the formula stipulates that enough time is reserved from a collection point i to a workstation;
s2-2, definition of overtime punishment, when the robot reaches the time interval [ bi，b′i]Time of day, i.e. service start time siLanding anglebi，b′i]It will be penalized by a penalty cdelay×(si-bi)；
The penalty for each collection point i is calculated as follows:
Figure BDA0003255155010000021
in the formula (2), siService time for the beginning of collection point i, cdelayUnit cost for delay time;
s2-3, defining the total travel time M of each route for time limitation:
Figure BDA0003255155010000031
wherein ,
Figure BDA0003255155010000032
is a boolean variable, which is 1 if the robot k moves directly from the collection point i to the collection point j in the time wheel h, otherwise it is 0; t is tijIs the travel time from collection point i to collection point j;
s2-4, setting an objective function to minimize the total cost, including the starting cost, the driving distance cost and the delay time cost of the robot:
Figure BDA0003255155010000033
in the formula (3), the reaction mixture is,
Figure BDA0003255155010000034
is a boolean variable that is 1 if robot k moves directly from workstation 0 to collection point i in time wheel h; otherwise, the value is 0; c. CstartRepresents the starting cost of the robot; c. CijIs the travel distance from collection point i to collection point j;
Figure BDA0003255155010000035
is a boolean variable whose value is 1 if the collection point i is served by the robot k in the time wheel h, otherwise it is 0;
constraint conditions are as follows:
Figure BDA0003255155010000036
Figure BDA0003255155010000037
Figure BDA0003255155010000038
Figure BDA0003255155010000039
Figure BDA00032551550100000310
Figure BDA00032551550100000311
Figure BDA0003255155010000041
Figure BDA0003255155010000042
Figure BDA0003255155010000043
(Ti-1)M≤si-bi≤TiM (13)
Figure BDA0003255155010000044
Figure BDA0003255155010000045
Figure BDA0003255155010000046
formulas (4) and (5) are degree constraints, and the constraint (4) ensures that each acquisition point can only be accessed once; constraints (5) ensure that the number of incoming arcs equals the number of node outgoing arcs; constraints (6) ensure that each acquisition point can only be assigned to one robot; constraint (7) indicates that distance should be a non-negative variable; constraining (8) to ensure that the total volume per route does not exceed the capacity of the robot, wherein the quantity of medical waste at the ith medical waste collection point, and Q represents the capacity of the robot to store medical waste; constraint (9) calculating the service start time of each acquisition point in the route; the constraint (10) limits the service start time to be between its earliest and latest arrival times; constraining (11) the service start time to be less than the total time of each route; the constraint (12) indicates that the service time should be a non-negative variable; the constraint (13) determines whether the service start time of an acquisition Point is within its corresponding penalty interval, i.e. (b)i，b′i](ii) a Constraints (14) - (16) are integrity constraints.
Further, the specific process of step S3 is as follows:
s3-1, input parameters: medical waste collection requirements, trip distance, trip time, time window;
s3-2, generating a plurality of paths according to the number of the robots and the medical waste collection point initialization solution, and setting the iteration times, wherein k is 0;
s3-3, calculating the fitness of each current path;
s3-4, selecting an initial path with high fitness according to the fitness;
s3-5, dividing the initial path obtained in the step S3-4 into two groups of nodes, wherein the first group is placed in front, and the starting point and the end point of the first group are both 0;
s3-6, arranging the nodes of the first group in sequence, wherein the second group of nodes is the nodes which are not accessed;
s3-7, randomly dividing the second group of nodes into 1 to n-1 paths by inserting 0 for multiple times, and calculating the objective function of each path;
s3-8, sorting the paths from small to large according to the target function to form a list;
s3-9, selecting the route with the lowest total cost, namely the smallest objective function, and judging whether the route is feasible or not, and the route cannot be directly deleted; adding feasible paths to the back of the first group of paths to form a brand new path to obtain a candidate solution; (Steps S3-5 to S3-9 can refer to the example given in FIG. 3)
S3-10, inverting the bit to the complementary bit by the mutation operator;
s3-11, judging whether the current candidate scheme is feasible, if so, calculating the objective function, otherwise, returning to the step S3-10;
s3-12, selecting a candidate solution with the lowest total cost, namely the smallest objective function;
s3-13, judging whether k is larger than the set iteration number, if yes, the candidate solution obtained in the step S3-12 is the final optimal solution, otherwise, returning to the step S3-3.
Further, in the step S3-3, the calculated fitness is used to evaluate the current solution, and the calculation formula is as follows:
Figure BDA0003255155010000051
in the formula (17), f is a fitness value, and Z is the total cost.
Compared with the prior art, the principle and the advantages of the scheme are as follows:
1. the medical waste collection model of the medical institution is represented by an improved vehicle scheduling problem with a semi-soft time window, and the problem needing attention in medical waste treatment is subjected to modeling constraint, so that the safety and the usability in the collection process are improved.
2. The improved genetic algorithm is used for solving the robot scheduling model, and the algorithm is further optimized by combining the sequential crossing strategy with the cross mutation strategy, so that the population diversity of the algorithm is effectively increased, and the algorithm is prevented from falling into an extreme value trap.
3. The mode that a plurality of robots finish work jointly is adopted to improve the efficiency of medical waste collection.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the services required for the embodiments or the technical solutions in the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of problem solving in a method of processing medical waste collection problems using an improved genetic algorithm of the present invention;
FIG. 2 is a schematic illustration of a medical waste collection environment in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a genetic algorithm sequence crossing and feasibility detection principle;
FIG. 4 is a schematic diagram of a distance matrix involved in an embodiment of the present invention;
FIG. 5 is a statistical map of medical waste distribution at various collection points in an embodiment of the present invention;
FIG. 6 is a graph of the output of the improved genetic algorithm in an embodiment of the present invention;
FIG. 7 is a diagram showing the optimization effect of the improved genetic algorithm in the embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples:
in the method for processing the medical waste collection problem by using the improved genetic algorithm, the medical waste collection work is completed by a plurality of robots together, and the scheduling scheme of the plurality of robots is obtained by the following steps:
s1, according to the plan view of the hospital shown in figure 2, determining the positions of each collection point and the robot workstation in the drawing, calculating the distance between the collection point and the workstation and the distance between the collection point and the collection point to obtain a distance matrix shown in figure 4, and determining the amount of medical waste of each collection point shown in figure 5;
s2, the established robot scheduling model for medical waste collection is a robot scheduling model considering the upper half soft time window, and the establishment process is as follows:
s2-1, defining a semi-soft time window, wherein the robot can reach the outside of the time window, and the method specifically comprises the following conditions:
when the robot is in the designated time window [ a ]i，bi]When the collection point is reached, punishment cannot be carried out;
if the robot arrives earlier than aiNo penalty is received, thereby shortening the stay time in the waste bin, but if the robot is more than biLate, it will be punished;
let allowable arrival time be b'i(b′i＞bi)：
b′i＝b0-ti0 (1)
In the formula (1), b0Is a constant, representing the time for the robot to eventually return to the workstation; t is ti0Representing the travel time from the collection point i to the workstation; the formula stipulates that enough time is reserved from a collection point i to a workstation;
s2-2, definition of overtime punishment, when the robot reaches the time interval [ bi，b′i]Time of day, i.e. service start time siFall in [ b ]i，b′i]It will be penalized by a penalty cdelay×(si-bi)；
The penalty for each collection point i is calculated as follows:
Figure BDA0003255155010000071
in the formula (2), siService time for the beginning of collection point i, cdelayUnit cost for delay time;
s2-3, defining the total travel time M of each route for time limitation:
Figure BDA0003255155010000072
wherein ,
Figure BDA0003255155010000073
is a boolean variable, which is 1 if the robot k moves directly from the collection point i to the collection point j in the time wheel h, otherwise it is 0; t is tijIs the travel time from collection point i to collection point j;
s2-4, setting an objective function to minimize the total cost, including the starting cost, the driving distance cost and the delay time cost of the robot:
Figure BDA0003255155010000074
in the formula (3), the reaction mixture is,
Figure BDA0003255155010000075
is a boolean variable that is 1 if robot k moves directly from workstation 0 to collection point i in time wheel h; otherwise, the value is 0; c. CstartRepresents the starting cost of the robot; c. CijIs the travel distance from collection point i to collection point j;
Figure BDA0003255155010000081
is a boolean variable whose value is 1 if the collection point i is served by the robot k in the time wheel h, otherwise it is 0;
constraint conditions are as follows:
Figure BDA0003255155010000082
Figure BDA0003255155010000083
Figure BDA0003255155010000084
Figure BDA0003255155010000085
Figure BDA0003255155010000086
Figure BDA0003255155010000087
Figure BDA0003255155010000088
Figure BDA0003255155010000089
Figure BDA00032551550100000810
(Ti-1)M≤si-bi≤TiM (13)
Figure BDA00032551550100000811
Figure BDA00032551550100000812
Figure BDA00032551550100000813
formulas (4) and (5) are degree constraints, and the constraint (4) ensures that each acquisition point can only be accessed once; constraints (5) ensure that the number of incoming arcs equals the number of node outgoing arcs; constraints (6) ensure that each acquisition point can only be assigned to one robot; constraint (7) indicates that distance should be a non-negative variable; constraining (8) to ensure that the total volume per route does not exceed the capacity of the robot, wherein the quantity of medical waste at the ith medical waste collection point, and Q represents the capacity of the robot to store medical waste; constraint (9) calculating the service start time of each acquisition point in the route; the constraint (10) limits the service start time to be between its earliest and latest arrival times; constraining (11) the service start time to be less than the total time of each route; the constraint (12) indicates that the service time should be a non-negative variable; the constraint (13) determines whether the service start time of an acquisition Point is within its corresponding penalty interval, i.e. (b)i，bi](ii) a Constraints (14) - (16) are integrity constraints.
And S3, solving the robot scheduling model for medical waste collection established in the step S2 through a modified genetic algorithm, thereby obtaining scheduling schemes of a plurality of robots.
As shown in fig. 1, the specific process is as follows:
s3-1, input parameters: medical waste collection requirements, trip distance, trip time, time window;
s3-2, generating a plurality of paths according to the number of the robots and the medical waste collection point initialization solution, and setting the iteration times, wherein k is 0;
s3-3, calculating the fitness of each current path;
the calculated fitness is used to evaluate the current solution, and the calculation formula is as follows:
Figure BDA0003255155010000091
in the formula (17), f is a fitness value, and Z is the total cost.
S3-4, selecting an initial path with high fitness according to the fitness;
s3-5, dividing the initial path obtained in the step S3-4 into two groups of nodes, wherein the first group is placed in front, and the starting point and the end point of the first group are both 0;
s3-6, arranging the nodes of the first group in sequence, wherein the second group of nodes is the nodes which are not accessed;
s3-7, randomly dividing the second group of nodes into 1 to n-1 paths by inserting 0 for multiple times, and calculating the objective function of each path;
s3-8, sorting the paths from small to large according to the target function to form a list;
s3-9, selecting the route with the lowest total cost, namely the smallest objective function, and judging whether the route is feasible or not, and the route cannot be directly deleted; adding feasible paths to the back of the first group of paths to form a brand new path to obtain a candidate solution;
s3-10, inverting the bit to the complementary bit by the mutation operator;
s3-11, judging whether the current candidate scheme is feasible, if so, calculating the objective function, otherwise, returning to the step S3-10;
s3-12, selecting a candidate solution with the lowest total cost, namely the smallest objective function;
s3-13, judging whether k is larger than the set iteration number, if yes, the candidate solution obtained in the step S3-12 is the final optimal solution, otherwise, returning to the step S3-3.
As shown in fig. 6, the final output results are as follows:
route of robot 1: 0-2-6-1-11-19-22-25-21-27-0;
route of robot 2: 0-7-10-13-9-15-5-0;
route of robot 3: 0-26-23-24-14-8-20-17-0;
route of robot 4: 0-12-3-4-28-18-16-0.
The optimization effect is shown in fig. 7.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that variations based on the shape and principle of the present invention should be covered within the scope of the present invention.

Claims (4)

1. A method for dealing with medical waste collection problems using improved genetic algorithms, wherein medical waste collection is performed by a plurality of robots together, and a scheduling scheme for the plurality of robots is obtained by:
s1, determining the positions of each collection point and the robot workstation in the map according to the plan of the hospital, calculating the distance between the collection point and the workstation and the distance between the collection point and the collection point to obtain a distance matrix, and determining the amount of medical waste of each collection point;
s2, establishing a robot scheduling model for medical waste collection;
and S3, solving the robot scheduling model for medical waste collection established in the step S2 through a modified genetic algorithm, thereby obtaining scheduling schemes of a plurality of robots.
2. The method for processing medical waste collection problem using improved genetic algorithm as claimed in claim 1, wherein the robot scheduling model for medical waste collection established in step S2 is a robot scheduling model considering the upper half soft time window, and the establishment process is as follows:
s2-1, defining a semi-soft time window, wherein the robot can reach the outside of the time window, and the method specifically comprises the following conditions:
when the robot is in the designated time window [ a ]i，bi]When the collection point is reached, punishment cannot be carried out;
if the robot arrives earlier than aiNo penalty is received, thereby shortening the stay time in the waste bin, but if the robot is more than biLate, it will be punished;
let allowable arrival time be b'i(b′i＞bi)：
b′i＝b0-ti0 (1)
In the formula (1), b0Is a constant, representing the time for the robot to eventually return to the workstation; t is ti0Representing the travel time from the collection point i to the workstation; the formula stipulates that enough time is reserved from a collection point i to a workstation;
s2-2, definition of overtime punishment, when the robot reaches the time interval [ bi，b′i]Time of day, i.e. service start time siFall in [ b ]i，b′i]It will be penalized by a penalty cdelay×(si-bi)；
The penalty for each collection point i is calculated as follows:
Figure FDA0003255154000000021
in the formula (2), siService time for the beginning of collection point i, cdelayUnit cost for delay time;
s2-3, defining the total travel time M of each route for time limitation:
Figure FDA0003255154000000022
wherein ,
Figure FDA0003255154000000023
is a boolean variable, which is 1 if the robot k moves directly from the collection point i to the collection point j in the time wheel h, otherwise it is 0; t is tijIs the travel time from collection point i to collection point j;
s2-4, setting an objective function to minimize the total cost, including the starting cost, the driving distance cost and the delay time cost of the robot:
Figure FDA0003255154000000024
in the formula (3), the reaction mixture is,
Figure FDA0003255154000000025
is a boolean variable that is 1 if robot k moves directly from workstation 0 to collection point i in time wheel h; otherwise, the value is 0; c. CstartRepresents the starting cost of the robot; c. CijIs the travel distance from collection point i to collection point j;
Figure FDA0003255154000000026
is a boolean variable whose value is 1 if the collection point i is served by the robot k in the time wheel h, otherwise it is 0;
constraint conditions are as follows:
Figure FDA0003255154000000027
Figure FDA0003255154000000028
Figure FDA0003255154000000029
Figure FDA00032551540000000210
Figure FDA0003255154000000031
Figure FDA0003255154000000032
Figure FDA0003255154000000033
Figure FDA0003255154000000034
Figure FDA0003255154000000035
(Ti-1)M≤si-bi≤TiM (13)
Figure FDA0003255154000000036
Figure FDA0003255154000000037
Figure FDA0003255154000000038
formulas (4) and (5) are degree constraints, and the constraint (4) ensures that each acquisition point can only be accessed once; constraints (5) ensure that the number of incoming arcs equals the number of node outgoing arcs; constraints (6) ensure that each acquisition point can only be assigned to one robot; constraint (7) indicates that distance should be a non-negative variable; constraining (8) to ensure that the total volume per route does not exceed the capacity of the robot, wherein the quantity of medical waste at the ith medical waste collection point, and Q represents the capacity of the robot to store medical waste; constraint (9) calculating the service start time of each acquisition point in the route; the constraint (10) limits the service start time to be between its earliest and latest arrival times; constraining (11) the service start time to be less than the total time of each route; the constraint (12) indicates that the service time should be a non-negative variable; the constraint (13) determines whether the service start time of an acquisition Point is within its corresponding penalty interval, i.e. (b)i，b′i](ii) a Constraints (14) - (16) are integrity constraints.
3. The method for processing medical waste collection problem using improved genetic algorithm as claimed in claim 2, wherein the specific process of step S3 is as follows:
s3-1, input parameters: medical waste collection requirements, trip distance, trip time, time window;
s3-2, generating a plurality of paths according to the number of the robots and the medical waste collection point initialization solution, and setting the iteration times, wherein k is 0;
s3-3, calculating the fitness of each current path;
s3-4, selecting an initial path with high fitness according to the fitness;
s3-5, dividing the initial path obtained in the step S3-4 into two groups of nodes, wherein the first group is placed in front, and the starting point and the end point of the first group are both 0;
s3-6, arranging the nodes of the first group in sequence, wherein the second group of nodes is the nodes which are not accessed;
s3-7, randomly dividing the second group of nodes into 1 to n-1 paths by inserting 0 for multiple times, and calculating the objective function of each path;
s3-8, sorting the paths from small to large according to the target function to form a list;
s3-9, selecting the route with the lowest total cost, namely the smallest objective function, and judging whether the route is feasible or not, and the route cannot be directly deleted; adding feasible paths to the back of the first group of paths to form a brand new path to obtain a candidate solution;
s3-10, inverting the bit to the complementary bit by the mutation operator;
s3-11, judging whether the current candidate scheme is feasible, if so, calculating the objective function, otherwise, returning to the step S3-10;
s3-12, selecting a candidate solution with the lowest total cost, namely the smallest objective function;
s3-13, judging whether k is larger than the set iteration number, if yes, the candidate solution obtained in the step S3-12 is the final optimal solution, otherwise, returning to the step S3-3.
4. The method for processing a medical waste collection problem using an improved genetic algorithm as claimed in claim 3, wherein the fitness calculated in step S3-3 is used to evaluate the current solution, and the calculation formula is as follows:
Figure FDA0003255154000000041
in the formula (17), f is a fitness value, and Z is the total cost.
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