Scheduling method and system for ship main power equipment maintenance projects considering employee characteristics

Abstract

The present invention provides a method and system for scheduling ship main power equipment maintenance projects that take employee characteristics into consideration, and relates to the technical field of project scheduling. With the goal of minimizing order duration, the present invention, based on the real situation of multi-skill, multi-mode project scheduling with team collaboration and employee changes, studies the number of teams employees should form, the type of teams they should form, the task model, team selection, and task allocation. The system uses the Intelligent Fruit Fly Optimization Empire Competition Algorithm (IFOICA) to solve the problem, ultimately obtaining a project scheduling result that takes into consideration employee characteristics such as team collaboration and employee changes. The present invention's method for scheduling ship main power equipment maintenance projects that takes employee characteristics into consideration produces more accurate and efficient solutions.

Description

Technical Field

[0001] The present invention relates to the technical field of project scheduling, and in particular to a method and system for scheduling ship main power equipment maintenance projects taking employee characteristics into consideration. Background Art

[0002] During the maintenance process of ship main propulsion equipment, task scheduling and personnel allocation during the repair order generation process have always been a significant concern. Maintenance project management typically encompasses processes such as cylinder overhaul, cylinder head overhaul, piston overhaul, bearing overhaul, and crankshaft overhaul, divided into numerous tasks based on functional business and process. Furthermore, projects often involve a large number of personnel in testing, maintenance, and inspection, who possess diverse skills and perform a variety of tasks. Maintenance project management is challenging due to its complex functions, wide range of technical requirements, and diverse team members. Consequently, a single maintenance project is often broken down into multiple tasks and assigned to different maintenance teams, who then collaborate to complete them. Scientifically and rationally allocating tasks, coordinating team members, arranging specific work for each employee, and assigning appropriate tools (i.e., resource models) to each task directly impact the overall maintenance project progress and ultimate completion time. Therefore, optimizing the scheduling of ship main propulsion equipment maintenance project management is of great significance.

[0003] In summary, the ship main power equipment maintenance project scheduling problem is a typical multi-mode multi-skill resource-constrained project scheduling (MRCPSP-MS) problem. Currently, many existing methods for the MRCPSP-MS problem have been developed, but these methods do not consider employee characteristics (employee characteristics refer to employee human resource characteristics, such as skills, work efficiency, teamwork, and resignation). Given the complex workload and high employee turnover in current project management across various industries, optimizing scheduling according to traditional project scheduling schemes will make it difficult to achieve the original task objectives if employee changes (resignation, transfer, job hopping, dismissal) occur. Furthermore, traditional algorithms are prone to premature failure when solving the MRCPSP-MS problem, and are unable to accurately obtain global and local optimal solutions.

[0004] It can be seen that the existing project scheduling methods do not take into account the realities of team collaboration and employee changes, and the traditional algorithms for solving MRCPSP-MS problems themselves have defects, resulting in inaccurate optimization scheduling results. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides a method and system for scheduling ship main power equipment maintenance projects that take employee characteristics into consideration, solving the problem that the existing method for scheduling ship main power equipment maintenance projects has inaccurate optimization scheduling results due to the failure to consider employee characteristics and other realities.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] In a first aspect, the present invention first proposes a method for scheduling ship main power equipment maintenance projects taking into account employee characteristics, the method comprising:

[0010] S1. Setting and initializing the operating parameters of the Fruit Fly-Empire competition algorithm; the operating parameters include the maximum number of maintenance teams Team_Num, the current number of teams Team; the number of generations of civilization development N1 in the Fruit Fly-Empire competition algorithm, the algebraic interval N2-N1 during which world wars occur; the current number of environmental upheavals j, and the maximum number of environmental upheavals UPH;

[0011] S2. Considering the constraints of teamwork and employee turnover, several initial feasible scheduling schemes are generated as national leader individuals based on the serial progress generation mechanism. Based on these leader individuals, the minibatch K-means algorithm is used to cluster to form several civilizations. The neighboring operator is used to expand the country to generate several individuals, countries, and civilizations.

[0012] S3. Calculate the fitness of each individual, country, and civilization and select the best individual;

[0013] S4. Determine whether the current multiplication algebra satisfies the algebra for the occurrence of world war (N2-N1). If not, execute S5; if so, execute S6.

[0014] S5. Implement civilization development, with individuals moving towards the optimal state within their civilization;

[0015] S6. Implement world war and move towards the optimal civilization;

[0016] S7: Execute individual rebellion and national flag change according to the large-scale mutation strategy, and execute S3;

[0017] S8. Determine whether the termination condition has been met. If so, execute S9; if not, execute S4. The termination condition includes: when the optimal fitness of all civilizations is the same and the fitness remains unchanged for C generations, or when the current number of civilizations is not greater than the preset minimum number of civilizations;

[0018] S9. Determine whether the current number of environmental drastic changes j is greater than the preset upper limit of environmental drastic changes UPH. If so, execute S10; if not, j=j+1, and update the team sequence P before executing S3;

[0019] S10. Record the optimal individual and team sequence;

[0020] S11. Determine whether the current team number Team>Team_Num is established. If not, update the team sequence P and set Team=Team+1, j=0, and execute S2. If so, execute S12.

[0021] S12. The algorithm is executed and the scheduling result of the ship main power equipment maintenance project is outputted, which takes into account the characteristics of the employees. The project scheduling result includes the optimal number of teams, team members, task arrangement and mode arrangement.

[0022] Preferably, the fitness calculation formula is:

[0023]

[0024] in, represents the fitness of each individual; T i represents the task sequence of the i-th individual; T best is the best individual in the country; f(T) represents the objective function value under the current feasible solution.

[0025] Preferably, the civilization development includes genetic manipulation of individuals according to the civilization development strategy:

[0026] For each country, a portion of individuals in the country are randomly selected to use a two-point crossover operator on the pattern sequence to explore the results of different pattern combinations under the same task sequence;

[0027] Use the subpath crossover operator on the task sequences of individuals in another part of the country to explore the results of different sequences under the same mode combination;

[0028] Said world war involves genetic manipulation of individuals according to the world war strategy:

[0029] The optimal value of the best individual in each civilization is obtained as the civilization fitness, and the ratio of the individual to the fitness of its civilization is defined as the survival value;

[0030] Then, the SEX operator is used to move individuals with greater survival value closer to the center of their civilization, while individuals with lower survival value are eliminated; and new individuals are generated around the optimal individuals of the civilization with the highest fitness;

[0031] Finally, a mutation operation will be performed on each civilization in a roulette-style manner to determine whether it will be destroyed.

[0032] Preferably, the execution of individual rebellion and national flag change according to the large-scale mutation strategy includes: calculating the optimal individual fitness of each country and each civilization, and obtaining the mutation value of each individual and country, and then deciding whether the country and the individual change flag and immigrate in a roulette manner.

[0033] Preferably, the calculation formula for the variation value from country to civilization is:

[0034]

[0035] Among them, CountryR ij The mutation value from country i to civilization j, FitCoun i is the fitness of the i-th country; FitCivil j is the fitness of the jth civilization; DIS ij is the average Euclidean distance of the three sequences from the best individual in the i-th country to the best individual in the j-th civilization;

[0036] The calculation formula for the variation value from individual to civilization is:

[0037]

[0038] Among them, PeoR ij The mutation value of individual i to civilization j, dis ij is the average Euclidean distance of the three sequences from individual i to the optimal individual of the jth civilization.

[0039] In a second aspect, the present invention further proposes a ship main power equipment maintenance project scheduling system that takes into account employee characteristics, the system comprising:

[0040] A processing unit is configured to perform the following steps:

[0041] S1. Setting and initializing the operating parameters of the Fruit Fly-Empire competition algorithm; the operating parameters include the maximum number of maintenance teams Team_Num, the current number of teams Team; the number of generations of civilization development N1 in the Fruit Fly-Empire competition algorithm, the algebraic interval N2-N1 during which world wars occur; the current number of environmental upheavals j, and the maximum number of environmental upheavals UPH;

[0042] S2. Considering the constraints of teamwork and employee turnover, several initial feasible scheduling schemes are generated as national leader individuals based on the serial progress generation mechanism. Based on these leader individuals, the minibatch K-means algorithm is used to cluster to form several civilizations. The neighboring operator is used to expand the country to generate several individuals, countries, and civilizations.

[0043] S3. Calculate the fitness of each individual, country, and civilization and select the best individual;

[0044] S4. Determine whether the current multiplication algebra satisfies the algebra for the occurrence of world war (N2-N1). If not, execute S5; if so, execute S6.

[0045] S5. Implement civilization development, with individuals moving towards the optimal state within their civilization;

[0046] S6. Implement world war and move towards the optimal civilization;

[0047] S7: Execute individual rebellion and national flag change according to the large-scale mutation strategy, and execute S3;

[0048] S8. Determine whether the termination condition has been met. If so, execute S9; if not, execute S4. The termination condition includes: when the optimal fitness of all civilizations is the same and the fitness remains unchanged for C generations, or when the current number of civilizations is not greater than the preset minimum number of civilizations;

[0049] S9. Determine whether the current number of environmental drastic changes j is greater than the preset upper limit of environmental drastic changes UPH. If so, execute S10; if not, j=j+1, and update the team sequence P before executing S3;

[0050] S10. Record the optimal individual and team sequence;

[0051] S11. Determine whether the current team number Team>Team_Num is established. If not, update the team sequence P and set Team=Team+1, j=0, and execute S2. If so, execute S12.

[0052] S12, algorithm execution ends;

[0053] The output unit is used to output the ship main power equipment maintenance project scheduling results taking into account employee characteristics, and the project scheduling results include the optimal team number, team members, task arrangement and mode arrangement.

[0054] Preferably, the fitness calculation formula in S3 is:

[0055]

[0056] in, represents the fitness of each individual; T i represents the task sequence of the i-th individual; T best is the best individual in the country; f(T) represents the objective function value under the current feasible solution.

[0057] Preferably, the civilization development includes genetic manipulation of individuals according to the civilization development strategy:

[0058] For each country, a portion of individuals in the country are randomly selected to use a two-point crossover operator on the pattern sequence to explore the results of different pattern combinations under the same task sequence;

[0059] Use the subpath crossover operator on the task sequences of individuals in another part of the country to explore the results of different sequences under the same mode combination;

[0060] Said world war involves genetic manipulation of individuals according to the world war strategy:

[0061] The optimal value of the best individual in each civilization is obtained as the civilization fitness, and the ratio of the individual to the fitness of its civilization is defined as the survival value;

[0062] Then, the SEX operator is used to move individuals with greater survival value closer to the center of their civilization, while individuals with lower survival value are eliminated; and new individuals are generated around the optimal individuals of the civilization with the highest fitness;

[0063] Finally, a mutation operation will be performed on each civilization in a roulette-style manner to determine whether it will be destroyed.

[0064] Preferably, the execution of individual rebellion and national flag change according to the large-scale mutation strategy includes: calculating the optimal individual fitness of each country and each civilization, and obtaining the mutation value of each individual and country, and then deciding whether the country and the individual change flag and immigrate in a roulette manner.

[0065] Preferably, the calculation formula for the variation value from country to civilization is:

[0066]

[0067] Among them, CountryR ij The mutation value from country i to civilization j, FitCoun i is the fitness of the i-th country; FitCivil j is the fitness of the jth civilization; DIS ij is the average Euclidean distance of the three sequences from the best individual in the i-th country to the best individual in the j-th civilization;

[0068] The calculation formula for the variation value from individual to civilization is:

[0069]

[0070] Among them, PeoR ij The mutation value of individual i to civilization j, dis ij is the average Euclidean distance of the three sequences from individual i to the optimal individual of the jth civilization.

[0071] (3) Beneficial effects

[0072] The present invention provides a method and system for scheduling ship main power equipment maintenance projects that takes employee characteristics into consideration. Compared with existing technologies, it has the following advantages:

[0073] 1. The present invention is based on the actual situation of the combination of human resource characteristics (team collaboration and employee changes) of ship main power equipment maintenance orders and project scheduling. With the ultimate goal of minimizing the total project duration during maintenance project optimization scheduling, it studies how many teams employees should form, what kind of teams they should form, the task model and team selection and task allocation issues, and uses the Intelligent Fruit Fly Optimization Empire Competition Algorithm (IFOICA) to solve the problem. Finally, the scheduling results of ship main power equipment maintenance projects that take employee characteristics into consideration are obtained. The ship main power equipment maintenance project scheduling method that takes employee characteristics into consideration of the present invention has more accurate solution results and higher solution efficiency. This reduces enterprise costs, improves resource utilization, provides decision-making theoretical support for production managers, and has certain practical reference significance.

[0074] 2. Compared with traditional related algorithms that are prone to "premature maturity" and the inability to accurately obtain global optimal solutions and local optimal solutions when solving MRCPSP-MS problems, the IFOICA proposed in the present invention has the flexibility and excellent global search capabilities of traditional FOA and the rapid convergence ability of ICA. It is more effective in solving large-scale project scheduling problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0076] Figure 1 This is a flow chart of a method for scheduling ship main power equipment maintenance projects that takes employee characteristics into consideration according to the present invention;

[0077] Figure 2 Schematic diagram of coding for each alternative solution in the embodiment of the present invention;

[0078] Figure 3 Schematic diagram of two different genetic strategies for implementing civilization development in an embodiment of the present invention. DETAILED DESCRIPTION

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0080] The embodiments of the present application provide a method and system for scheduling ship main power equipment maintenance projects that take employee characteristics into consideration, thereby solving the problem that the existing ship main power equipment maintenance project scheduling methods have inaccurate optimization scheduling results due to the failure to consider actual conditions such as employee characteristics, thereby achieving the goal of reducing enterprise costs and improving resource utilization through precise scheduling.

[0081] The technical solution in the embodiments of the present application is to solve the above technical problems, and the overall idea is as follows:

[0082] In order to solve the problem that the existing project scheduling methods do not take into account the realities of team collaboration and employee changes, and the related algorithms for solving the MRCPSP-MS problem (such as the traditional ICA algorithm and FOA algorithm) themselves have defects such as "premature maturity" and poor solving performance, resulting in inaccurate optimization scheduling results, the present invention designs an intelligent fruit fly empire competition algorithm (IFOICA) with NP-hard properties to optimize the completion time by reasonably determining the number of teams and members, selecting the project execution mode, and sorting the projects. The IFOICA algorithm is then used to solve the multi-skill, multi-mode, resource-constrained project scheduling problem that takes into account team collaboration and employee changes.

[0083] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0084] In the process of generating ship main power maintenance orders, each maintenance project consists of n tasks, each task does not allow preemption, and its priority relationship is given. The first J0 and the last J n+1 The task is a virtual task, that is, a task with a duration of 0 and no resource consumption. Each maintenance task has u modes to choose from, and each task can only select one mode and cannot be changed in the middle. At the beginning of each project, d maintenance teams need to be formed, and each team selects a number of employees, and the teams cannot be reorganized during the execution of the task. The number of teams needs to be decided before the start of the project, and then the team composition is determined before the start of each task. After the project starts, each task needs to decide the execution order, the mode used and the execution team, and the actual duration is determined based on whether the employee leaves. The goal of project optimization scheduling is to minimize the total duration of the project. Based on this, we propose the technical solution of this application.

[0085] Example 1:

[0086] In a first aspect, the present invention first proposes a method for scheduling ship main power equipment maintenance projects taking into account employee characteristics, the method comprising:

[0087] S1. Setting and initializing the operating parameters of the Fruit Fly-Empire competition algorithm; the operating parameters include the maximum number of maintenance teams Team_Num, the current number of teams Team; the number of generations of civilization development N1 in the Fruit Fly-Empire competition algorithm, the algebraic interval N2-N1 during which world wars occur; the current number of environmental upheavals j, and the maximum number of environmental upheavals UPH;

[0088] S2. Considering the constraints of teamwork and employee turnover, several initial feasible scheduling schemes are generated as national leader individuals based on the serial progress generation mechanism. Based on these leader individuals, the minibatch K-means algorithm is used to cluster to form several civilizations. The neighboring operator is used to expand the country to generate several individuals, countries, and civilizations.

[0089] S3. Calculate the fitness of each individual, country, and civilization and select the best individual;

[0090] S4. Determine whether the current multiplication algebra satisfies the algebra for the occurrence of world war (N2-N1). If not, execute S5; if so, execute S6.

[0091] S5. Implement civilization development, with individuals moving towards the optimal state within their civilization;

[0092] S6. Implement world war and move towards the optimal civilization;

[0093] S7: Execute individual rebellion and national flag change according to the large-scale mutation strategy, and execute S3;

[0094] S8. Determine whether the termination condition has been met. If so, execute S9; if not, execute S4. The termination condition includes: when the optimal fitness of all civilizations is the same and the fitness remains unchanged for C generations, or when the current number of civilizations is not greater than the preset minimum number of civilizations;

[0095] S9. Determine whether the current number of environmental drastic changes j is greater than the preset upper limit of environmental drastic changes UPH. If so, execute S10; if not, j=j+1, and update the team sequence P before executing S3;

[0096] S10. Record the optimal individual and team sequence;

[0097] S11. Determine whether the current team number Team>Team_Num is established. If not, update the team sequence P and set Team=Team+1, j=0, and execute S2. If so, execute S12.

[0098] S12. The algorithm is executed and the scheduling result of the ship main power equipment maintenance project is outputted, which takes into account the characteristics of the employees. The project scheduling result includes the optimal number of teams, team members, task arrangement and mode arrangement.

[0099] As can be seen, this embodiment combines the human resource characteristics (teamwork and employee turnover) of ship main power equipment maintenance orders with the real-world situation of project scheduling. With the ultimate goal of minimizing the total project duration during maintenance project optimization scheduling, it examines the number of teams employees should form, the types of teams they should form, the task model, and team selection and task allocation. It then uses the Intelligent Fruit Fly Optimization Empire Competition Algorithm (IFOICA) to solve the problem, ultimately obtaining a scheduling result for ship main power equipment maintenance projects that considers employee characteristics. This embodiment's method for scheduling ship main power equipment maintenance projects that considers employee characteristics yields more accurate and efficient solutions.

[0100] The following is combined with Figure 1-3 , and explanations of specific steps S1-S12 are provided to describe in detail the implementation process of an embodiment of the present invention.

[0101] S1. Set and initialize the operating parameters of the Fruit Fly-Empire competition algorithm; the operating parameters include the maximum number of maintenance teams Team_Num, the current number of teams Team; the number of generations of civilization development in the Fruit Fly-Empire competition algorithm, N1, the algebraic interval N2-N1 within which world war occurs; the current number of environmental upheavals, j, and the maximum number of environmental upheavals, UPH.

[0102] When solving the typical multi-mode multi-skill resource-constrained project scheduling (MRCPSP-MS) problem, existing commercial solvers are unable to represent the turnover probability caused by workload and are unable to solve large-scale problems within a limited time. For example, the traditional imperial competition algorithm (ICA) suffers from a continuous decrease in the number of empires, resulting in a decrease in population diversity, which affects the global search capability of the ICA algorithm and is prone to "premature maturity". In addition, the ICA algorithm has shortcomings such as long running time due to its complex rules. The traditional fruit fly optimization algorithm (FOA) relies on the initial population and exploration direction, and its performance is poor when processing high-dimensional nonlinear functions or functions that are difficult to obtain global optimal solutions and local optimal solutions. Based on this, this embodiment combines the characteristics of the ICA algorithm and the FOA algorithm, proposes an idea of combining heuristic factors, ICA and FOA, and proposes the fruit fly-imperial competition algorithm (IFOICA) for solving the NP-hard MRCPSP-MS problem.

[0103] When using IFOICA to solve the ship main power equipment maintenance project scheduling problem considering employee characteristics, the first step is to set and initialize the operating parameters of the Fruit Fly-Empire competition algorithm. Specifically, the parameters set and initialized for the Fruit Fly-Empire competition algorithm include the maximum number of maintenance teams Team_Num, the initial current team number Team = 1, the algebraic interval N1 of civilization development, the algebraic interval N2 of world war, the initial current environmental upheaval number j = 1, and the maximum environmental upheaval number UPH.

[0104] S2. Considering the constraints of teamwork and employee turnover, a serial progress generation mechanism is used to generate several initial feasible scheduling schemes as national leader individuals. These leader individuals are then clustered using the minibatch K-means algorithm to form several civilizations. Finally, neighborhood operators (swap, insert) are used to expand the countries, ultimately generating several individuals, countries, and civilizations.

[0105] Since the number of maintenance teams is unknown, we first determine the upper limit of the number of teams Team_Num, and then search for each maintenance team number (denoted as Team). Each task has a unique number i=1...N, where N is the total number of all tasks. In addition, this embodiment also assumes that the first task (i=0) and the last activity (i=N+1) are virtual tasks, which have no duration and resource consumption. The alternative plan X is represented by four parts X=(T, M, G, P), as follows Figure 2 shown.

[0106] Among them, the sequence T = (0, ..., i, ..., N+1) determines the scheduling order of tasks. In the solution, tasks must satisfy the priority relationship constraints. For example, T = ((0.1.2.3) means that task 0 precedes task 1, task 1 precedes 2, and task 2 is executed before 3. The sequence M = (0, ..., m, ..., 0) is the mode selected for each task i. For example, M = (0.2.3, 0) means that virtual tasks 0 and task 3 do not require a mode, while tasks 1 and task 2 select mode 2 and mode 3 respectively. The sequence G = (0, ..., h, ..., 0) is the team assigned to each task i. For example, G = ((0, 2, 4, 0) means that virtual tasks 0 and task 3 do not require a team, while tasks 1 and task 2 are assigned to teams 2 and 4 respectively. The sequence P = (w 1j ,...,w hj) is the number of employees assigned to each team h. For example, P = ([1, 2, 3], [2, 4], [4]) means that team 1 consists of employees 1, 2, and 3, team 2 consists of employees 2 and 4, and team 3 consists of employee 4. In the initial generation, the team employee sequence is determined based on the critical path priority rule (prioritizing high-efficiency models and high-skilled teams on the critical path) and the high-skilled employee rule (prioritizing highly skilled employees in team selection).

[0107] When decoding a solution vector, the Serial Schedule Generation Scheme (SSGS) that integrates the human resource constraint operator is used to obtain the earliest task start time that satisfies the model resource and human resource constraints. Specifically, the decoding process using SSGS is shown in the following table:

[0108] Table 1 Pseudo code of SSGS generating initial feasible scheduling solution

[0109]

[0110]

[0111] When using SSGS to form a scheduling plan, considering the overtime factor and the turnover factor will cause the duration to fluctuate within a certain range, and the oscillation effect has a bullwhip effect. The following is an explanation. In the process of using SSGS decoding, because the start time and end time must be determined first to determine whether to work overtime and thus update the task duration, it is necessary to first obtain a complete scheduling plan, and then update the overtime factor and the turnover factor according to the scheduling plan and calculate the duration. Therefore, the algorithm first calculates the duration without considering the overtime factor and the turnover factor. This state is the time lower bound of the scheduling plan, recorded as t min Then, the task duration is updated by taking into account the overtime factor and the turnover factor. After the update, SSGS is used to reschedule the task and obtain a scheduling plan. The total duration of the scheduling plan is recorded as t max For the new scheduling scheme, since SSGS requires minimizing the task start time, the original overtime items may lose their original overtime relationship during the reshoot process, which will cause the new task duration to continue to change (shorten). Therefore, the total duration of the actual scheduling project will be between the two. The actual duration during the scheduling process is recorded as t real , then t real ∈[t min ,t max ]. Since the task duration does not decrease after considering the two factors, some tasks increase the parallel relationship due to the extension of the duration, causing the resources that originally did not reach the upper limit to exceed the resource upper limit and cannot be carried out between the two tasks. This phenomenon will greatly increase the total project duration. Therefore, the impact of the two factors on their task duration has a bullwhip effect. To solve this problem, this embodiment will adopt the mean method, that is, treal =(t min +t max ) / 2 to calculate the actual duration.

[0112] After the initial feasible scheduling plan is formed by SSGS, the initial feasible scheduling plan is used as the national leader individual, and individuals, countries and civilizations are generated based on the leader individual. Specifically,

[0113] Each initial feasible scheduling solution is a matrix in IFOICA, referred to as a leader in this embodiment. In this problem, a leader is defined as the aforementioned X matrix. First, the SSGS algorithm is used to generate a task list that meets the timing constraints. Then, patterns and teams are randomly assigned to it, and repeated encoding is used to obtain N unique leaders. In traditional ICA, the influence of each colony is calculated based on a cost function, and K empires are selected from them. The remaining NK countries are then divided into empires by calculating the sphere of influence of each empire, forming a group of countries (hereinafter referred to as civilizations) with empires as the optimal solution. The minibatch K-means algorithm is then used to improve the selected empires and their spheres of influence, and the civilizations are divided based on this improved empire division process. The process of dividing civilizations using the minibatch K-means algorithm is shown in Table 2 below.

[0114] Table 2 Pseudocode for dividing civilizations using the minibatch K-means algorithm

[0115]

[0116]

[0117] Then, using two neighborhood operators (swap and insert) that guarantee time constraints, M new individuals are generated around each leader within the civilization, and this is called a country. Ultimately, we get K civilizations, N countries, and N*M individuals.

[0118] S3. Calculate the fitness of each individual, country, and civilization and select the best individual from them.

[0119] After the initial generation or war, each civilization undergoes a self-update process to obtain a better local optimal solution. To obtain this local optimal solution, the Fruit Fly Optimization Algorithm (FOA) is first used to explore the individuals of each civilization. The fitness formula of the FOA algorithm is as follows:

[0120]

[0121] in, is the fitness of each individual; T i is the i-th individual task sequence, T best The best individuals in the country, that is, the best mission sequence, the first generation of T best is the origin, and in other algebras it is the project number of the optimal individual.

[0122] S4. Determine whether the current multiplication algebra satisfies the algebra for the occurrence of world war (N2-N1). If not, execute S5; if so, execute S6.

[0123] In this embodiment, we pre-set two special reproduction periods, N2 and N1, where N2 is greater than N1. During each reproduction process, civilization development for generation N1 occurs first, followed by a world war for generation (N2-N1), and then civilization development for generation N1, repeating the cycle.

[0124] Following the algebraic iteration process of reproduction, if the current generation is N1, the civilization development strategy (S5) is executed. If the current generation is (N2-N1), the world war strategy (S6) is executed. After the world war strategy is executed, the civilization development strategy is executed again at generation N1. At generation (N2-N1), the world war strategy is executed again, and this cycle continues.

[0125] S5. Implement civilization development, and each individual moves to the optimal country within his or her civilization.

[0126] The civilization development strategy is to execute the civilization development strategy if the current reproduction generation is at every N1 generation, so that all individuals in each country move to the center of the country (the best individual in the country). The best individuals in each country are obtained as the parent individuals of the genetic strategy within each country. The specific genetic strategy is: for each country, a part of the individuals in the country are randomly selected to use the two-points crossover (TPX) operator on the pattern sequence to explore the results of different pattern combinations under the same task sequence; the subtour exchange crossover (SEX) operator is used on the task sequence of another part of the individuals in the country to explore the results of different sequences under the same pattern combination. The inheritance methods under the two different strategies are as follows Figure 3 (a) and Figure 3 (b) shown. Figure 3 (a) is the genetic strategy of TPX operator based on pattern sequence; Figure 3 (b) is the genetic strategy of the SEX operator based on item sequence.

[0127] Among them, the mother generation is the best individual in the country where the individual is located, the father generation is the current individual, and the child generation is the updated current individual.

[0128] Then, we determine whether the individual above implements the mutation strategy. There are two conditions for judgment, which must be met at the same time. They are as follows:

[0129] First, the individual fitness of the above selection is better, that is, it satisfies

[0130] Second, the individuals selected above are non-leader individuals.

[0131] For individuals that meet both of the above conditions, a critical path variation strategy is implemented as follows: first, find the tasks on the critical path of the individual, then use the roulette operator to select some of the tasks, and replace the mode and team of the task with the mode that minimizes its duration and the team with the current highest skill level.

[0132] S6. Carry out world war and move towards the optimal civilization.

[0133] After reproduction, when the current reproduction number is every (N2-N1) generations, each civilization will engage in a full-scale world war, eliminating a large number of inferior countries and individuals to accelerate its convergence and increase its randomness. Within every (N2-N1) generation, the genetic strategy and mutation strategy are changed.

[0134] First, the optimal individual and optimal value of each civilization are obtained, the optimal value is used as the fitness of the civilization, and the ratio of the individual to the fitness of the civilization to which it belongs is defined as the survival value, i.e., AliveR i =Fitness i / F it C ivi l j Then different genetic strategies are implemented for individuals with different survival values. Individuals with larger survival values, namely AliveR i ≥AliveRate, it will use the best individual of the civilization as the mother body and use the above-mentioned SEX operator to move closer to the center of the civilization to which it belongs; individuals with poor survival value will be eliminated, and new individuals will be generated around the best individual of the civilization with the highest fitness, by fixing their pattern sequence and team sequence, and using the swap operator and insert operator of the above-mentioned neighborhood search on the task sequence.

[0135] Finally, the mutation operation is performed, and the mutation only occurs at the civilization level. Each civilization will decide whether to destroy in a roulette manner, that is, a number (0,1) is randomly generated. If the number is less than the combat power value of the civilization, the civilization is defeated. Then its combat power value DestR i The calculation formula is:

[0136]

[0137] Among them, FitCivili is the fitness of the i-th civilization.

[0138] Once a civilization is defeated, all individuals within that civilization mutate, using the SEX operator for the task sequence and the TPX operator for the pattern sequence, and randomly move toward the surviving civilization.

[0139] S7. Carry out individual rebellion and national flag change according to the large-scale mutation strategy, and execute S3.

[0140] After reproduction, both nations and individuals possess self-awareness and can potentially migrate to other civilizations. The former means all individuals within a nation join another civilization, while the latter means a single individual joins another civilization. First, calculate the optimal individual fitness for each nation and civilization, and use the following formula to obtain the variation value for each individual and nation. Roulette is then used to determine whether the nation or individual changes flags or migrates. j≤UPH

[0141]

[0142] Among them, CountryR ij The mutation value from country i to civilization j, FitCoun i is the fitness of the i-th country, that is, the fitness of the best individual in the country, FitCivi lj is the fitness of the jth civilization, DIS ij is the average Euclidean distance of the three sequences from the best individual in the i-th country to the best individual in the j-th civilization.

[0143]

[0144] Among them, PeoR ij The mutation value of individual i to civilization j, dis ij is the average Euclidean distance of the three sequences from individual i to the optimal individual of the jth civilization.

[0145] S8. Determine whether the termination condition has been met. If so, execute S9; if not, execute S4. The termination condition includes: when the optimal fitness of all civilizations is the same and the fitness remains unchanged for C generations or the current number of civilizations is not greater than the preset minimum number of civilizations.

[0146] There are two pre-set termination conditions; the process terminates if either condition is met. The termination conditions are: if the optimal fitness of all civilizations remains unchanged for C generations, or if the current number of civilizations is no greater than the preset minimum number. In this example, the minimum number of civilizations is set to 2, meaning the process terminates if the current number of civilizations is no greater than 2.

[0147] S9. Determine whether the current number of environmental drastic changes j is greater than the preset upper limit of environmental drastic changes UPH. If so, execute S10; if not, j=j+1, and update the team sequence P and then execute S3.

[0148] Repeat the above process UpH times, and take the best solution from the optimal solutions under UpH different team combinations, which is the optimal solution when the number of teams is Team. Change the number of teams and repeat the above process to obtain the optimal solution under different teams, until the number of teams reaches the upper limit.

[0149] S10. Record the optimal individual and team sequence, and set the current team number Team = Team + 1.

[0150] S11. Determine whether the current team number Team>Team_Num is established. If not, update the team sequence P and set Team=Team+1, j=0, and execute S2; if so, execute S12.

[0151] The optimal solution is recorded and the team's employee composition is changed, that is, the sequence P is changed to obtain the optimal solution under different team combinations. The sequence P is changed by randomly selecting multiple teams and randomly changing different employees. After completion, the civilization continues to reproduce and fight.

[0152] S12. The algorithm is executed and the scheduling result of the ship main power equipment maintenance project is outputted, which takes into account the characteristics of the employees. The project scheduling result includes the optimal number of teams, team members, task arrangement and mode arrangement.

[0153] Example 2:

[0154] In a second aspect, the present invention further provides a system for scheduling ship main power equipment maintenance projects taking into account employee characteristics, the system comprising:

[0155] A processing unit is configured to perform the following steps:

[0156] S1. Setting and initializing the operating parameters of the Fruit Fly-Empire competition algorithm; the operating parameters include the maximum number of maintenance teams Team_Num, the current number of teams Team; the number of generations of civilization development N1 in the Fruit Fly-Empire competition algorithm, the algebraic interval N2-N1 during which world wars occur; the current number of environmental upheavals j, and the maximum number of environmental upheavals UPH;

[0157] S2. Considering the constraints of teamwork and employee turnover, several initial feasible scheduling schemes are generated as national leader individuals based on the serial progress generation mechanism. Based on these leader individuals, the minibatch K-means algorithm is used to cluster to form several civilizations. The neighboring operator is used to expand the country to generate several individuals, countries, and civilizations.

[0158] S3. Calculate the fitness of each individual, country, and civilization and select the best individual;

[0159] S4. Determine whether the current multiplication algebra satisfies the algebra for the occurrence of world war (N2-N1). If not, execute S5; if so, execute S6.

[0160] S5. Implement civilization development, with individuals moving towards the optimal state within their civilization;

[0161] S6. Implement world war and move towards the optimal civilization;

[0162] S7: Execute individual rebellion and national flag change according to the large-scale mutation strategy, and execute S3;

[0163] S8. Determine whether the termination condition has been met. If so, execute S9; if not, execute S4. The termination condition includes: when the optimal fitness of all civilizations is the same and the fitness remains unchanged for C generations, or when the current number of civilizations is not greater than the preset minimum number of civilizations;

[0164] S9. Determine whether the current number of environmental drastic changes j is greater than the preset upper limit of environmental drastic changes UPH. If so, execute S10; if not, j=j+1, and update the team sequence P before executing S3;

[0165] S10. Record the optimal individual and team sequence;

[0166] S11. Determine whether the current team number Team>Team_Num is established. If not, update the team sequence P and set Team=Team+1, j=0, and execute S2. If so, execute S12.

[0167] S12, algorithm execution ends;

[0168] The output unit is used to output the ship main power equipment maintenance project scheduling results taking into account employee characteristics, and the project scheduling results include the optimal team number, team members, task arrangement and mode arrangement.

[0169] Optionally, the fitness calculation formula in S3 is:

[0170]

[0171] in, represents the fitness of each individual; T i represents the task sequence of the i-th individual; T best is the best individual in the country; f(T) represents the objective function value under the current feasible solution.

[0172] Optionally, the civilization development includes performing genetic manipulation on individuals according to the civilization development strategy:

[0173] For each country, a portion of individuals in the country are randomly selected to use a two-point crossover operator on the pattern sequence to explore the results of different pattern combinations under the same task sequence;

[0174] Use the subpath crossover operator on the task sequences of individuals in another part of the country to explore the results of different sequences under the same mode combination;

[0175] Said world war involves genetic manipulation of individuals according to the world war strategy:

[0176] The optimal value of the best individual in each civilization is obtained as the civilization fitness, and the ratio of the individual to the fitness of its civilization is defined as the survival value;

[0177] Then, the SEX operator is used to move individuals with greater survival value closer to the center of their civilization, while individuals with lower survival value are eliminated; and new individuals are generated around the optimal individuals of the civilization with the highest fitness;

[0178] Finally, a mutation operation will be performed on each civilization in a roulette-style manner to determine whether it will be destroyed.

[0179] Optionally, executing individual rebellion and national flag change according to the large-scale mutation strategy includes: calculating the optimal individual fitness of each country and each civilization, and obtaining the mutation value of each individual and country, and then deciding whether the country and the individual change flag and immigrate in a roulette manner.

[0180] Optionally, the formula for calculating the mutation value from country to civilization is:

[0181]

[0182] Among them, CountryR ij The mutation value from country i to civilization j, FitCoun i is the fitness of the i-th country; FitCivil j is the fitness of the jth civilization; DIS ij is the average Euclidean distance of the three sequences from the best individual in the i-th country to the best individual in the j-th civilization;

[0183] The calculation formula for the variation value from individual to civilization is:

[0184]

[0185] Among them, PeoR ij The mutation value of individual i to civilization j, dis ijis the average Euclidean distance of the three sequences from individual i to the optimal individual of the jth civilization.

[0186] It can be understood that the ship main power equipment maintenance project scheduling system considering employee characteristics provided by the embodiment of the present invention corresponds to the above-mentioned ship main power equipment maintenance project scheduling method considering employee characteristics. The explanations, examples, beneficial effects, etc. of the relevant contents can refer to the corresponding contents in the ship main power equipment maintenance project scheduling method considering employee characteristics, and will not be repeated here.

[0187] In summary, compared with the existing technology, the present invention has the following beneficial effects:

[0188] 1. The present invention is based on the actual situation of the combination of human resource characteristics (team collaboration and employee changes) of ship main power equipment maintenance orders and project scheduling. With the ultimate goal of minimizing the total project duration during maintenance project optimization scheduling, it studies how many teams employees should form, what kind of teams they should form, the task model and team selection and task allocation issues, and uses the Intelligent Fruit Fly Optimization Empire Competition Algorithm (IFOICA) to solve the problem. Finally, the scheduling results of ship main power equipment maintenance projects that take employee characteristics into consideration are obtained. The ship main power equipment maintenance project scheduling method that takes employee characteristics into consideration of the present invention has more accurate solution results and higher solution efficiency. This reduces enterprise costs, improves resource utilization, provides decision-making theoretical support for production managers, and has certain practical reference significance.

[0189] 2. Compared with traditional related algorithms that are prone to "premature maturity" and the inability to accurately obtain global optimal solutions and local optimal solutions when solving MRCPSP-MS problems, the IFOICA proposed in the present invention has the flexibility and excellent global search capabilities of traditional FOA and the rapid convergence ability of ICA. It is more effective in solving large-scale project scheduling problems.

[0190] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0191] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for scheduling ship main power equipment maintenance projects considering employee characteristics, characterized in that: The method comprises: S1. Setting and initializing the operating parameters of the Fruit Fly-Empire competition algorithm; the operating parameters include the maximum number of maintenance teams Team_Num, the current number of teams Team; the number of generations of civilization development N1 in the Fruit Fly-Empire competition algorithm, the algebraic interval N2-N1 during which world wars occur; the current number of environmental upheavals j, and the maximum number of environmental upheavals UPH; S2. Considering the constraints of teamwork and employee turnover, several initial feasible scheduling schemes are generated as national leader individuals based on the serial progress generation mechanism. Based on these leader individuals, a minibatch K-means algorithm is used to cluster them into several civilizations. The countries are then expanded using the neighborhood operator to generate several individuals, countries, and civilizations. S3. Calculate the fitness of each individual, country, and civilization and select the best individual; S4. Determine whether the current multiplication algebra satisfies the algebra for the occurrence of world war (N2-N1). If not, execute S5; if so, execute S6. S5. Implement civilization development, with individuals moving towards the optimal state within their civilization; S6. Implement world war and move towards the optimal civilization; S7: Execute individual rebellion and national flag change according to the large-scale mutation strategy, and execute S3; S8. Determine whether the termination condition has been met. If so, execute S9; if not, execute S4. The termination condition includes: when the optimal fitness of all civilizations is the same and the fitness remains unchanged for C generations, or when the current number of civilizations is not greater than the preset minimum number of civilizations; S9. Determine whether the current number of environmental drastic changes j is greater than the preset upper limit of environmental drastic changes UPH. If so, execute S10; if not, j=j+1, and update the team sequence P before executing S3; S10. Record the optimal individual and team sequence; S11. Determine whether the current team number Team>Team_Num is established. If not, update the team sequence P and set Team=Team+1, j=0, and execute S2. If so, execute S12. S12. The algorithm is executed and the scheduling result of the ship main power equipment maintenance project is outputted, which takes into account the characteristics of the employees. The project scheduling result includes the optimal number of teams, team members, task arrangement and mode arrangement.

2. The method according to claim 1, wherein The fitness calculation formula is: in, represents the fitness of each individual; T i represents the task sequence of the i-th individual; T best is the best individual in the country; f(T) represents the objective function value under the current feasible solution.

3. The method according to claim 1, wherein Said civilization development includes genetic manipulation of individuals according to the civilization development strategy: For each country, a portion of individuals in the country are randomly selected to use a two-point crossover operator on the pattern sequence to explore the results of different pattern combinations under the same task sequence; Use the subpath crossover operator on the task sequences of individuals in another part of the country to explore the results of different sequences under the same mode combination; Said world war involves genetic manipulation of individuals according to the world war strategy: The optimal value of the best individual in each civilization is obtained as the civilization fitness, and the ratio of the individual to the fitness of its civilization is defined as the survival value; Then, the SEX operator is used to move individuals with greater survival value closer to the center of their civilization, while individuals with lower survival value are eliminated; and new individuals are generated around the optimal individuals of the civilization with the highest fitness; Finally, a mutation operation will be performed on each civilization in a roulette-style manner to determine whether it will be destroyed.

4. The method according to claim 1, wherein The execution of individual rebellion and national flag change according to the large-scale mutation strategy includes: calculating the optimal individual fitness of each country and each civilization, and obtaining the mutation value of each individual and country, and then deciding whether the country and the individual should change flag and immigrate in a roulette manner.

5. The method according to claim 1, wherein The formula for calculating the mutation value from country to civilization is: Among them, CountryR ij The mutation value from country i to civilization j, FitCoun i is the fitness of the i-th country; FitCivil j is the fitness of the jth civilization; DIS ij is the average Euclidean distance of the three sequences from the best individual in the i-th country to the best individual in the j-th civilization; The calculation formula for the variation value from individual to civilization is: Among them, PeoR ij The mutation value of individual i to civilization j, dis ij is the average Euclidean distance of the three sequences from individual i to the optimal individual of the jth civilization.

6. A ship main power equipment maintenance project scheduling system considering employee characteristics, characterized by: The system comprises: A processing unit is configured to perform the following steps: S1. Setting and initializing the operating parameters of the Fruit Fly-Empire competition algorithm; the operating parameters include the maximum number of maintenance teams Team_Num, the current number of teams Team; the number of generations of civilization development N1 in the Fruit Fly-Empire competition algorithm, the algebraic interval N2-N1 during which world wars occur; the current number of environmental upheavals j, and the maximum number of environmental upheavals UPH; S2. Considering the constraints of teamwork and employee turnover, several initial feasible scheduling schemes are generated as national leader individuals based on the serial progress generation mechanism. Based on these leader individuals, a minibatch K-means algorithm is used to cluster them into several civilizations. The countries are then expanded using the neighborhood operator to generate several individuals, countries, and civilizations. S3. Calculate the fitness of each individual, country, and civilization and select the best individual; S4. Determine whether the current multiplication algebra satisfies the algebra for the occurrence of world war (N2-N1). If not, execute S5; if so, execute S6. S5. Implement civilization development, with individuals moving towards the optimal state within their civilization; S6. Implement world war and move towards the optimal civilization; S7: Execute individual rebellion and national flag change according to the large-scale mutation strategy, and execute S3; S8. Determine whether the termination condition has been met. If so, execute S9; if not, execute S4. The termination condition includes: when the optimal fitness of all civilizations is the same and the fitness remains unchanged for C generations, or when the current number of civilizations is not greater than the preset minimum number of civilizations; S9. Determine whether the current number of environmental drastic changes j is greater than the preset upper limit of environmental drastic changes UPH. If so, execute S10; if not, j=j+1, and update the team sequence P before executing S3; S10. Record the optimal individual and team sequence; S11. Determine whether the current team number Team>Team_Num is established. If not, update the team sequence P and set Team=Team+1, j=0, and execute S2. If so, execute S12. S12, algorithm execution ends; The output unit is used to output the ship main power equipment maintenance project scheduling results taking into account employee characteristics, and the project scheduling results include the optimal team number, team members, task arrangement and mode arrangement.

7. The system according to claim 6, wherein: The fitness calculation formula in S3 is: in, represents the fitness of each individual; T i represents the task sequence of the i-th individual; T best is the best individual in the country; f(T) represents the objective function value under the current feasible solution.

8. The system according to claim 6, wherein: Said civilization development includes genetic manipulation of individuals according to the civilization development strategy: For each country, a portion of individuals in the country are randomly selected to use a two-point crossover operator on the pattern sequence to explore the results of different pattern combinations under the same task sequence; Use the subpath crossover operator on the task sequences of individuals in another part of the country to explore the results of different sequences under the same mode combination; Said world war involves genetic manipulation of individuals according to the world war strategy: The optimal value of the best individual in each civilization is obtained as the civilization fitness, and the ratio of the individual to the fitness of its civilization is defined as the survival value; Then, the SEX operator is used to move individuals with greater survival value closer to the center of their civilization, while individuals with lower survival value are eliminated; and new individuals are generated around the optimal individuals of the civilization with the highest fitness; Finally, a mutation operation will be performed on each civilization in a roulette-style manner to determine whether it will be destroyed.

9. The system according to claim 6, wherein: The execution of individual rebellion and national flag change according to the large-scale mutation strategy includes: calculating the optimal individual fitness of each country and each civilization, and obtaining the mutation value of each individual and country, and then deciding whether the country and the individual should change flag and immigrate in a roulette manner.

10. The system according to claim 6, wherein: The formula for calculating the mutation value from country to civilization is: Among them, CountryR ij The mutation value from country i to civilization j, FitCoun i is the fitness of the i-th country; FitCivil j is the fitness of the jth civilization; DIS ij is the average Euclidean distance of the three sequences from the best individual in the i-th country to the best individual in the j-th civilization; The calculation formula for the variation value from individual to civilization is: Among them, PeoR ij The mutation value of individual i to civilization j, dis ij is the average Euclidean distance of the three sequences from individual i to the optimal individual of the jth civilization.

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