A warehouse-in and warehouse-out scheduling method for linear reciprocating multi-RGV cooperative operation

Abstract

The application discloses a kind of in-out warehouse scheduling method of linear reciprocating multi-RGV collaborative operation, it is related to RGV scheduling control technical field.Collect and arrange the historical in-out warehouse task data of RGV in system, and linear track is gridded, extract linear track serial number, in-out warehouse platform and RGV relevant data;Arrange the buffer zone position that can be selected in system on linear track, determine the current buffer zone position serial number, task distribution is carried out to RGV;With the maximum time consumed by RGV to complete task as objective function, establish RGV in-out warehouse scheduling mathematical model;The buffer zone position and optimal time that RGV executes task are solved by genetic algorithm.Through setting buffer zone on linear track, RGV collision is avoided, and the total time that RGV completes task is minimized by optimizing buffer zone position, and the in-out warehouse operation scheduling scheme of RGV is solved using intelligent algorithm, improve operation efficiency and the adaptability and flexibility of system.

Description

Technical Field

[0001] This invention relates to the field of RGV scheduling and control technology, specifically a method for scheduling inbound and outbound operations of multiple RGVs working together in a linear reciprocating manner. Background Technology

[0002] The linear reciprocating RGV inbound / outbound scheduling system is divided into single RGV mode and multi-RGV collaborative operation mode according to different operating modes. With the increasing demands for efficiency and safety in the logistics industry, the traditional single RGV mode can no longer meet the growing logistics task requirements, while the multi-RGV collaborative operation mode increases the risk of vehicle collisions. However, existing systems lack the ability to quickly respond and adjust routes in emergency situations or task changes, reducing the system's adaptability. Therefore, there is an urgent need to design an inbound / outbound scheduling method for linear reciprocating multi-RGV collaborative operations to adapt to constantly changing task requirements and complex working environments. Summary of the Invention

[0003] To address the shortcomings of the prior art, this invention provides a method for scheduling inbound and outbound operations of multiple RGVs working collaboratively on a straight track. This method avoids RGV collisions by setting buffers on the straight track and minimizes the total time for RGVs to complete tasks by optimizing the buffer positions. It also employs intelligent algorithms to solve the RGV inbound and outbound operation scheduling scheme, thereby improving operational efficiency and the system's adaptability and flexibility.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a method for scheduling inbound and outbound operations of linear reciprocating multi-RGV collaborative operations, comprising the following steps:

[0005] Step 1: Collect and organize historical RGV inbound and outbound task data within the system, including the set of RGVs N = {1, 2, ..., n}, and the task content M = {1, 2, ..., m}. Denote each task as [task number, starting coordinates, ending coordinates, task type], where the task type is either an outbound task or an inbound task. Rasterize the straight tracks and extract the set of straight track numbers L = {0, 1, 2, ..., l} and the set of outbound platforms S. chu 、Inbound platform set S ru and the average velocity v of RGV;

[0006] Step 2: Organize the selectable buffer positions on the straight track and determine the current buffer position number XP. The buffer divides the straight track into two regions, A and B. Define RGV in region A as RGV1 and RGV in region B as RGV2. For tasks within the same region where both the start and end points are in region A or region B, assign the task to RGV1 or RGV2 within that region to complete independently. For cross-region tasks where the start and end points are in region A and region B respectively, assign the task to RGV1 and RGV2 to complete collaboratively. Mark the task type according to the position of the start and end points to determine whether the assigned task is an outbound task or an inbound task.

[0007] Step 3: Using minimizing the maximum time consumed by the RGV to complete the task as the objective function, the total time to complete the task is divided into the adjustment time required for the RGV to move from its current position to the task start point, the transportation and loading / unloading time required to execute the task, and the waiting time required next to the buffer when executing cross-regional tasks. A mathematical model for RGV inbound and outbound scheduling is established.

[0008] Step 4: Use a genetic algorithm to determine the buffer location and optimal time for RGV to execute tasks.

[0009] Furthermore, in step two, when selecting the buffer position, the index x of the middle position of the straight track is calculated based on the total length of the straight track, and the buffer position index is...

[0010] Furthermore, the mathematical model for RGV inbound / outbound scheduling in step three is as follows:

[0011] Objective function:

[0012] min(max{T j ,j=1,2})

[0013] T j =T tj +T fj +T wj

[0014]

[0015]

[0016] In the formula, T j T represents the total time it takes for the j-th RGV to complete its assigned task. tj T represents the adjustment time required for the j-th RGV to travel from its current position to the task start point. fj T represents the transportation and loading / unloading time required for the j-th RGV to perform its task. wj R1 represents the waiting time required next to the buffer when the j-th RGV performs a cross-region task.i and R2 i Both are binary variables, representing whether the i-th task executed by RGV1 and RGV2 is a task within the same region or a task across regions, respectively. If R1 i and R2 i A value of 0 indicates that the task is in the same region. If R1 i and R2 i A value of 1 indicates a cross-region task, X ij This is a binary variable; if the j-th RGV executes the i-th task, it takes the value 1; otherwise, it takes the value 0. iu This is a binary variable. If the u-th task is executed immediately followed by the i-th task, the value is 1; otherwise, the value is 0. XE uj XS represents the endpoint position number of the j-th RGV executing the u-th task. ij XE represents the starting position index of the j-th RGV executing the i-th task. ij The position index of the endpoint of the j-th RGV executing the i-th task is indicated, d represents the length of a unit grid on the straight track, and T represents the position of the endpoint. z Indicates the time of loading and unloading goods;

[0017] Constraints:

[0018]

[0019]

[0020] In the formula, TS uj TE represents the start time of the u-th task. ij This indicates the end time of the i-th task.

[0021] Furthermore, step four specifically includes:

[0022] 4.1 Encoding Method

[0023] Determine the RGV, task start point, and task end point. Generate a chromosome using real number encoding. The chromosome length is equal to the total number of tasks m. It consists of a number sequence composed of randomly generated and non-repeating numbers from 1 to m. Each number corresponds to a task number. The number sequence represents the task execution order.

[0024] 4.2 Population Initialization

[0025] An initial population is created by randomly generating multiple chromosomes through encoding.

[0026] 4.3 Calculation of Task Completion Time

[0027] Determine if the current task is a task within the same region. If it is, first calculate the adjustment time of RGV1 or RGV2 from its current location to the task start point, then calculate the transportation and loading / unloading time of RGV1 or RGV2 from the task start point to the task end point. Summing these times yields the total time to complete the task. If the current task is a cross-region task, first calculate the adjustment time of RGV1 or RGV2 from its current location to the task start point within the task start point region, then calculate the transportation and loading / unloading time of RGV1 or RGV2 from the task start point to the buffer zone. Next, calculate the adjustment time of RGV2 or RGV1 from its current location to the buffer zone within the task end point region, then calculate the transportation and loading / unloading time of RGV2 or RGV1 from the buffer zone to the task end point. In addition, based on the arrival times of RGV1 and RGV2 at the buffer zone, calculate the waiting time required near the buffer zone. Summing these times yields the total time to complete the task. Finally, determine if the task is fully completed. If yes, proceed to step 4.4; otherwise, repeat step 4.3.

[0028] 4.4 Solving the fitness function

[0029] For each chromosome in the population, calculate the maximum time Z for RGV1 and RGV2 to complete all their respective tasks using the fitness function, and set the initial maximum task time threshold Z for the iteration. max When Z is greater than Z during the iteration process max When Z max Update to Z, and calculate the fitness of each chromosome as follows:

[0030]

[0031] 4.5. Selection of Binary Tournaments

[0032] Each time, two chromosomes are randomly selected from the population for comparison, and the chromosome with higher fitness is selected to enter the next generation of the population;

[0033] 4.6 Crossover

[0034] Two crossover points are randomly selected from the parent chromosomes, and the numerical sequence between these two crossover points is copied to the corresponding offspring. For the regions outside the two crossover points, the sequence is filled according to the other parent. If the number has already appeared in the sequence between the crossover points, the number is skipped and the next number is filled. If the number has not appeared before, the number is filled directly.

[0035] 4.7 Variation

[0036] The mutation probability is set to determine whether each chromosome will mutate. If a chromosome mutates, two numbers are randomly selected from the chromosome and their positions are swapped to change the original task order. If a chromosome does not mutate, the original order is maintained. Each mutated chromosome is added to the new population until the number of chromosomes reaches the maximum population size.

[0037] 4.8. Set the maximum number of iterations

[0038] A new population is generated iteratively. It is then determined whether the maximum number of iterations has been reached. If not, step 4.3 is continued using the current population. If so, the process is stopped and the scheduling result and total time of RGV under this buffer are output.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: Based on the consideration of task sequence, the method of the present invention sets up a buffer zone on the straight track, so that each RGV runs independently in its own area to avoid collisions. By optimizing the position of the buffer zone, the total time for the RGV to complete the task is minimized, thereby improving the efficiency of inbound and outbound operations. By determining the buffer zone and simultaneously performing obstacle avoidance and scheduling, and using intelligent algorithms to solve the RGV inbound and outbound operation scheduling scheme, it is easier to adapt to different operating conditions and environmental changes, thereby improving the adaptability and flexibility of the system. Compared with the traditional straight reciprocating RGV system, it can improve the system's conveying capacity without increasing system complexity and meet the needs of larger-scale inbound and outbound operations. Attached Figure Description

[0040] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0041] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, a method for scheduling inbound and outbound operations of linear reciprocating multi-RGV collaborative operations includes the following steps:

[0043] Step 1: Collect and organize historical RGV entry and exit task data within the system, and rasterize the linear tracks, extracting the linear track serial number, entry and exit platforms, and RGV-related data. Specifically:

[0044] 1.1 Collect historical order data within the system and organize the task content of RGVs. Let N = {1,2,...,n} represent the set of all RGVs, where n is the total number of RGVs. Let M = {1,2,...,m} represent the task content, where m is the total number of tasks. Let each task = [task number, starting coordinates, ending coordinates, task type]. The task type is either outbound task or inbound task. Generate an Excel data table from the task content M.

[0045] 1.2. Rasterize the straight track and extract the set of straight track numbers L = {0, 1, 2, ..., l} and the set of outbound platforms S. chu 、Inbound platform set S ru And the average velocity v of RGV.

[0046] Step Two: Organize the selectable buffer positions on the straight track in the system, determine the current buffer position number XP, and assign tasks to RGV. Specifically:

[0047] 2.1 The buffer zone divides the straight track into two regions, A and B. RGV1 is defined within region A, and RGV2 is defined within region B. RGV1 operates within the [0, XP] region of the straight track, and RGV2 operates within the (XP, l] region, ensuring that RGV1 and RGV2 operate within their respective fixed regions A and B, thus avoiding collisions.

[0048] 2.2. Calculate the index of the middle position of the straight track based on the total length of the straight track, and set it as x. To prevent uneven task distribution, the buffer position index is...

[0049] 2.3. Based on the region where the task originates, assign the task to the RGV within that region. For tasks within the same region (i.e., tasks with both origin and destination in region A or region B), assign the task to RGV1 or RGV2 within that region for independent completion. For cross-region tasks (i.e., tasks with origin and destination in regions A and B respectively), assign the task to RGV1 and RGV2 for collaborative completion. If the task originates in region A or region B and the task destination is in region B or region A, firstly, RGV1 or RGV2 loads the goods at the task origin and transports them to the buffer zone. Subsequently, RGV2 or RGV1 transports the goods from the buffer zone to the task destination and unloads them. Furthermore, classify the task according to the locations of the task origin and destination to determine whether the assigned task is an outbound or inbound task.

[0050] Step 3: Establish a mathematical model for RGV inbound / outbound scheduling, with the objective function being to minimize the maximum time consumed by RGV to complete the task. Specifically:

[0051] 3.1 Design an objective function that breaks down the total time to complete the task into the adjustment time required for the RGV to travel from the current position to the task starting point, the transportation and loading / unloading time required to execute the task, and the waiting time required near the buffer zone when executing cross-regional tasks. This improves the transparency and accuracy of the measurement of each time component. The objective function is expressed as follows:

[0052] min(max{T j ,j=1,2})

[0053] T j =T tj +T fj +T wj

[0054] In the formula, T j T represents the total time it takes for the j-th RGV to complete its assigned task. tj T represents the adjustment time required for the j-th RGV to travel from its current position to the task start point. fj T represents the transportation and loading / unloading time required for the j-th RGV to perform its task. wj This represents the waiting time required next to the buffer when the j-th RGV performs a cross-region task;

[0055] 3.2 Calculate the adjustment time between RGV1 and RGV2 after completing the u-th task and immediately moving to the starting point of the i-th task. The calculation formula is as follows:

[0056]

[0057] In the formula, R1 i and R2 i Both are binary variables, representing whether the i-th task executed by RGV1 and RGV2 is a task within the same region or a task across regions, respectively. If R1 i and R2 i A value of 0 indicates that the task is in the same region. If R1 i and R2 i A value of 1 indicates a cross-region task, X ij This is a binary variable; if the j-th RGV executes the i-th task, it takes the value 1; otherwise, it takes the value 0. iu This is a binary variable. If the u-th task is executed immediately followed by the i-th task, the value is 1; otherwise, the value is 0. XE uj XS represents the endpoint position number of the j-th RGV executing the u-th task. ij d represents the starting position number of the j-th RGV executing the i-th task, and d represents the length of a unit grid on the straight track;

[0058] 3.3 Calculate the transportation and loading / unloading time of RGV1 and RGV2 from the start point to the end point of the i-th task, respectively. The calculation formula is as follows:

[0059]

[0060] In the formula, XE ij T represents the endpoint position number of the j-th RGV executing the i-th task. z Indicates the time of loading and unloading goods;

[0061] 3.4. Set constraints, including:

[0062] To ensure that RGV1 and RGV2 have tasks that need to be executed in the same region, the following conditions must be met:

[0063]

[0064] To ensure that RGV1 and RGV2 have cross-regional tasks that need to be executed, the following conditions must be met:

[0065]

[0066] To ensure all tasks are completed, the following conditions must be met:

[0067]

[0068] To ensure that the same RGV does not start another task before completing the previous one, the following conditions must be met:

[0069]

[0070] In the formula, TS uj TE represents the start time of the u-th task. ij This indicates the end time of the i-th task.

[0071] Step 4: Use a genetic algorithm to determine the buffer location and optimal time for RGV task execution. Specifically:

[0072] 4.1 Encoding Method

[0073] Determine the RGV, task start point, and task end point. Generate a chromosome using real number encoding. The chromosome length is equal to the total number of tasks m. It consists of a number sequence composed of randomly generated and non-repeating numbers from 1 to m. Each number corresponds to a task number. The number sequence represents the task execution order.

[0074] 4.2 Population Initialization

[0075] An initial population is created by randomly generating multiple chromosomes through encoding.

[0076] 4.3 Calculation of Task Completion Time

[0077] Determine if the current task is a task within the same region. If it is, first calculate the adjustment time of RGV1 or RGV2 from its current location to the task start point, then calculate the transportation and loading / unloading time of RGV1 or RGV2 from the task start point to the task end point. Summing these times yields the total time to complete the task. If the current task is a cross-region task, first calculate the adjustment time of RGV1 or RGV2 from its current location to the task start point within the task start point region, then calculate the transportation and loading / unloading time of RGV1 or RGV2 from the task start point to the buffer zone. Next, calculate the adjustment time of RGV2 or RGV1 from its current location to the buffer zone within the task end point region, then calculate the transportation and loading / unloading time of RGV2 or RGV1 from the buffer zone to the task end point. In addition, based on the arrival times of RGV1 and RGV2 at the buffer zone, calculate the waiting time required near the buffer zone. Summing these times yields the total time to complete the task. Finally, determine if the task is fully completed. If yes, proceed to step 4.4; otherwise, repeat step 4.3.

[0078] 4.4 Solving the fitness function

[0079] For each chromosome in the population, calculate the maximum time Z for RGV1 and RGV2 to complete all their respective tasks using the fitness function, and set the initial maximum task time threshold Z for the iteration. max When Z is greater than Z during the iteration process max When Z max Update to Z, and calculate the fitness of each chromosome as follows:

[0080]

[0081] 4.5. Selection of Binary Tournaments

[0082] The goal of survival of the fittest is achieved by directly comparing chromosomes, so as to guide the population to evolve in a better direction. Each time, two pairs of chromosomes are randomly selected from the population for comparison, and the chromosome with higher fitness is selected to enter the next generation of the population. Random sampling enhances the diversity of the population and prevents the algorithm from getting trapped in local optima too early during the search process, thereby maintaining the ability to explore the global optimal solution.

[0083] 4.6 Crossover

[0084] Two crossover points are randomly selected from the parent chromosomes, and the numerical sequence between these two crossover points is copied to the corresponding offspring. For the regions outside the two crossover points, the sequence is filled according to the other parent. If the number has already appeared in the sequence between the crossover points, the number is skipped and the next number is filled. If the number has not appeared before, the number is filled directly.

[0085] 4.7 Variation

[0086] The mutation probability is set to determine whether each chromosome will mutate. If a chromosome mutates, two numbers are randomly selected from the chromosome and their positions are swapped to change the original task order. If a chromosome does not mutate, the original order is maintained. Each mutated chromosome is added to the new population until the number of chromosomes reaches the maximum population size.

[0087] 4.8. Set the maximum number of iterations

[0088] A new population is generated iteratively. It is then determined whether the maximum number of iterations has been reached. If not, step 4.3 is continued using the current population. If so, the process is stopped and the scheduling result and total time of RGV under this buffer are output.

[0089] Implement the genetic algorithm described above using programming software (such as MATLAB or Python). Input data such as RGV, coordinates of the entry and exit platforms of the straight track, and task orders to solve for the optimal time and buffer position for RGV to complete the task.

[0090] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0091] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for scheduling inbound and outbound operations of linear reciprocating multi-RGV collaborative operations, characterized in that: Includes the following steps: Step 1: Collect and organize historical RGV inbound and outbound task data within the system, including the set of RGVs N = {1, 2, ..., n}, and the task content M = {1, 2, ..., m}. Denote each task as [task number, starting coordinates, ending coordinates, task type], where the task type is either an outbound task or an inbound task. Rasterize the straight tracks and extract the set of straight track numbers L = {0, 1, 2, ..., l} and the set of outbound platforms S. chu 、Inbound platform set S ru and the average velocity v of RGV; Step 2: Organize the selectable buffer positions on the straight track and determine the current buffer position number XP. The buffer divides the straight track into two regions, A and B. Define RGV in region A as RGV1 and RGV in region B as RGV2. For tasks within the same region where both the start and end points are in region A or region B, assign the task to RGV1 or RGV2 within that region to complete independently. For cross-region tasks where the start and end points are in region A and region B respectively, assign the task to RGV1 and RGV2 to complete collaboratively. Mark the task type according to the position of the start and end points to determine whether the assigned task is an outbound task or an inbound task. Step 3: Using minimizing the maximum time consumed by the RGV to complete the task as the objective function, the total time to complete the task is divided into the adjustment time required for the RGV to move from its current position to the task start point, the transportation and loading / unloading time required to execute the task, and the waiting time required next to the buffer when executing cross-regional tasks. A mathematical model for RGV inbound and outbound scheduling is established. Step 4: Use a genetic algorithm to determine the buffer location and optimal time for RGV to execute tasks.

2. The inbound / outbound scheduling method for linear reciprocating multi-RGV collaborative operation according to claim 1, characterized in that: In step two, when selecting the buffer position, the index x of the middle position of the straight track is calculated based on the total length of the straight track, and the buffer position index is...

3. The inbound / outbound scheduling method for linear reciprocating multi-RGV collaborative operation according to claim 1, characterized in that: The mathematical model for RGV inbound / outbound scheduling in step three is as follows: Objective function: min(max{T j ,j=1,2}) T j =T tj +T fj +T wj In the formula, T j T represents the total time it takes for the j-th RGV to complete its assigned task. tj T represents the adjustment time required for the j-th RGV to travel from its current position to the task start point. fj T represents the transportation and loading / unloading time required for the j-th RGV to perform its task. wj R1 represents the waiting time required next to the buffer when the j-th RGV performs a cross-region task. i and R2 i Both are binary variables, representing whether the i-th task executed by RGV1 and RGV2 is a task within the same region or a task across regions, respectively. If R1 i and R2 i A value of 0 indicates that the task is in the same region. If R1 i and R2 i A value of 1 indicates a cross-region task, X ij This is a binary variable; if the j-th RGV executes the i-th task, it takes the value 1; otherwise, it takes the value 0. iu This is a binary variable. If the u-th task is executed immediately followed by the i-th task, the value is 1; otherwise, the value is 0. XE uj XS represents the endpoint position number of the j-th RGV executing the u-th task. ij XE represents the starting position index of the j-th RGV executing the i-th task. ij The position index of the endpoint of the j-th RGV executing the i-th task is indicated, d represents the length of a unit grid on the straight track, and T represents the position of the endpoint. z Indicates the time of loading and unloading goods; Constraints: In the formula, TS uj TE represents the start time of the u-th task. ij This indicates the end time of the i-th task.

4. The inbound / outbound scheduling method for linear reciprocating multi-RGV collaborative operation according to claim 3, characterized in that: step four specifically includes: 4.1 Encoding Method Determine the RGV, task start point, and task end point. Generate a chromosome using real number encoding. The chromosome length is equal to the total number of tasks m. It consists of a number sequence composed of randomly generated and non-repeating numbers from 1 to m. Each number corresponds to a task number. The number sequence represents the task execution order. 4.2 Population Initialization An initial population is created by randomly generating multiple chromosomes through encoding. 4.3 Calculation of Task Completion Time Determine if the current task is a task within the same region. If it is, first calculate the adjustment time of RGV1 or RGV2 from its current location to the task start point, then calculate the transportation and loading / unloading time of RGV1 or RGV2 from the task start point to the task end point. Summing these times yields the total time to complete the task. If the current task is a cross-region task, first calculate the adjustment time of RGV1 or RGV2 from its current location to the task start point within the task start point region, then calculate the transportation and loading / unloading time of RGV1 or RGV2 from the task start point to the buffer zone. Next, calculate the adjustment time of RGV2 or RGV1 from its current location to the buffer zone within the task end point region, then calculate the transportation and loading / unloading time of RGV2 or RGV1 from the buffer zone to the task end point. In addition, based on the arrival times of RGV1 and RGV2 at the buffer zone, calculate the waiting time required near the buffer zone. Summing these times yields the total time to complete the task. Finally, determine if the task is fully completed. If yes, proceed to step 4.4; otherwise, repeat step 4.

3. 4.4 Solving the fitness function For each chromosome in the population, calculate the maximum time Z for RGV1 and RGV2 to complete all their respective tasks using the fitness function, and set the initial maximum task time threshold Z for the iteration. max When Z is greater than Z during the iteration process max When Z max Update to Z, and calculate the fitness of each chromosome as follows: 4.

5. Selection of Binary Tournaments Each time, two chromosomes are randomly selected from the population for comparison, and the chromosome with higher fitness is selected to enter the next generation of the population; 4.6 Crossover Two crossover points are randomly selected from the parent chromosomes, and the numerical sequence between these two crossover points is copied to the corresponding offspring. For the regions outside the two crossover points, the sequence is filled according to the other parent. If the number has already appeared in the sequence between the crossover points, the number is skipped and the next number is filled. If the number has not appeared before, the number is filled directly. 4.7 Variation The mutation probability is set to determine whether each chromosome will mutate. If a chromosome mutates, two numbers are randomly selected from the chromosome and their positions are swapped to change the original task order. If a chromosome does not mutate, the original order is maintained. Each mutated chromosome is added to the new population until the number of chromosomes reaches the maximum population size. 4.

8. Set the maximum number of iterations A new population is generated iteratively. It is then determined whether the maximum number of iterations has been reached. If not, step 4.3 is continued using the current population. If so, the process is stopped and the scheduling result and total time of RGV under this buffer are output.

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