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

Info

Publication number
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
Authority
CN
China
Prior art keywords
robot
time
medical waste
collection point
collection
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202111057473.1A
Other languages
Chinese (zh)
Other versions
CN113762780B (en
Inventor
欧嘉俊
曾伟良
黄永慧
孙为军
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong University of Technology
Original Assignee
Guangdong University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong University of Technology filed Critical Guangdong University of Technology
Priority to CN202111057473.1A priority Critical patent/CN113762780B/en
Publication of CN113762780A publication Critical patent/CN113762780A/en
Application granted granted Critical
Publication of CN113762780B publication Critical patent/CN113762780B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Operations Research (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Tourism & Hospitality (AREA)
  • Quality & Reliability (AREA)
  • Marketing (AREA)
  • Evolutionary Biology (AREA)
  • Artificial Intelligence (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Software Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • General Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Genetics & Genomics (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Geometry (AREA)
  • Physiology (AREA)
  • Computer Hardware Design (AREA)
  • Biomedical Technology (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)

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.
Drawings
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.
CN202111057473.1A 2021-09-09 2021-09-09 Method for treating medical waste collection problem by using improved genetic algorithm Active CN113762780B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111057473.1A CN113762780B (en) 2021-09-09 2021-09-09 Method for treating medical waste collection problem by using improved genetic algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111057473.1A CN113762780B (en) 2021-09-09 2021-09-09 Method for treating medical waste collection problem by using improved genetic algorithm

Publications (2)

Publication Number Publication Date
CN113762780A true CN113762780A (en) 2021-12-07
CN113762780B CN113762780B (en) 2023-08-22

Family

ID=78794405

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111057473.1A Active CN113762780B (en) 2021-09-09 2021-09-09 Method for treating medical waste collection problem by using improved genetic algorithm

Country Status (1)

Country Link
CN (1) CN113762780B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115064252A (en) * 2022-08-17 2022-09-16 南京天溯自动化控制系统有限公司 Medical waste sorting and transferring system
CN115376672A (en) * 2022-10-21 2022-11-22 安徽省伟木软件科技有限公司 Medical waste monitoring method and system
CN117429775A (en) * 2023-10-24 2024-01-23 浙江臻善科技股份有限公司 Intelligent garbage classification system based on artificial intelligence and big data technology

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120170390A1 (en) * 2011-01-03 2012-07-05 Arm Limited Read stability of a semiconductor memory
US20180268705A1 (en) * 2017-03-15 2018-09-20 NEC Laboratories Europe GmbH Method for route optimization for demand responsive transportation
CN112733272A (en) * 2021-01-13 2021-04-30 南昌航空大学 Method for solving vehicle path problem with soft time window

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120170390A1 (en) * 2011-01-03 2012-07-05 Arm Limited Read stability of a semiconductor memory
US20180268705A1 (en) * 2017-03-15 2018-09-20 NEC Laboratories Europe GmbH Method for route optimization for demand responsive transportation
CN112733272A (en) * 2021-01-13 2021-04-30 南昌航空大学 Method for solving vehicle path problem with soft time window

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
何兆成;曾伟良;: "变权系数遗传算法在交叉口信号控制中的应用", 公路交通科技, no. 11 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115064252A (en) * 2022-08-17 2022-09-16 南京天溯自动化控制系统有限公司 Medical waste sorting and transferring system
CN115064252B (en) * 2022-08-17 2022-12-06 南京天溯自动化控制系统有限公司 Medical waste sorting and transferring system
CN115376672A (en) * 2022-10-21 2022-11-22 安徽省伟木软件科技有限公司 Medical waste monitoring method and system
CN115376672B (en) * 2022-10-21 2023-01-31 安徽省伟木软件科技有限公司 Medical waste monitoring method and system
CN117429775A (en) * 2023-10-24 2024-01-23 浙江臻善科技股份有限公司 Intelligent garbage classification system based on artificial intelligence and big data technology

Also Published As

Publication number Publication date
CN113762780B (en) 2023-08-22

Similar Documents

Publication Publication Date Title
CN113762780A (en) Method for processing medical waste collection problem by using improved genetic algorithm
Zhang et al. A hybrid immune simulated annealing algorithm for the job shop scheduling problem
He et al. Fuzzy dispatching model and genetic algorithms for railyards operations
Luh et al. A multi-modal immune algorithm for the job-shop scheduling problem
Zidi et al. A multi-objective simulated annealing for the multi-criteria dial a ride problem
Lin et al. Traffic signal optimization based on fuzzy control and differential evolution algorithm
CN101394345A (en) Co-evolutionary clustering method oriented to data stream sensing by general computation
He et al. An adaptive variable neighborhood search ant colony algorithm for vehicle routing problem with soft time windows
Chaudhry et al. Integrated scheduling of machines and automated guided vehicles (AGVs) in flexible job shop environment using genetic algorithms
Zou et al. A Hybrid Particle Swarm Optimization Algorithm for Multi-Objective Pickup and Delivery Problem with Time Windows.
Liu et al. Research on multi-AGVs path planning and coordination mechanism
Hani et al. Simulation based optimization of a train maintenance facility
Wang Optimal scheduling of flow shop based on genetic algorithm
Xu et al. Design of urban medical waste recycling network considering loading reliability under uncertain conditions
Velimirović et al. Determining the optimal route of electric vehicle using a hybrid algorithm based on fuzzy dynamic programming
Chiu et al. Robot routing using clustering-based parallel genetic algorithm with migration
Bahri et al. Use of electric vehicles in home-health care routing problems: Analysis of a multi-objective approach under uncertainty
CN111415117A (en) Method for optimizing truck path in container yard by considering random driving time
CN104966406B (en) A kind of special vehicle information physical emerging system is selected to method
Hu et al. Municipal solid waste collection and transportation routing optimization based on IAC-SFLA
Mozaffari et al. Coupling Gaussian generalised regression neural network and mutable smart bee algorithm to analyse the characteristics of automotive engine coldstart hydrocarbon emission
Ren et al. Recharge strategies for the electric vehicle routing problem with soft time windows and fast chargers
Guo et al. Design of a reinforcement learning-based intelligent car transfer planning system for parking lots
Zhao et al. A Regional Multi-objective Linear Programming Model for Initial Post-earthquake and Comparison of Three Meta-heuristic Algorithms
Affenzeller et al. Metaheuristic optimization

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant