Heuristic rule method for arranging cruise ship production design task plan

Abstract

The invention provides a heuristic rule method for arranging a cruise ship production design task plan. The heuristic rule method comprises the steps of 1, quantifying a parallel dependency relationship between tasks based on task logic and maturity between the tasks; 2, designing a task man-hour-people number relation function fused with the learning curve; 3, constructing a plan arrangement model; and 4, proposing a design plan arrangement rule for arranging the plan. The invention belongs to the field of ship design technology management. According to the method, task execution logic models between specialties and between different processes in the specialties of cruise ship production design are established, a task maturity concept is defined, and a task logic directed acyclic graph (DAG) is drawn by using execution logic and maturity between tasks so as to clearly express the parallel dependency relationship between the logic between the tasks and quantitative tasks. And constructing a task man-hour-people number relation function fused with the learning curve, and designing a heuristic rule method to solve and obtain a cruise ship production design task execution plan.

Description

Technical Field

[0001] The invention relates to a heuristic rule method for arranging a cruise ship production design task plan, and belongs to the field of ship design technology management. Background Art

[0002] Cruise production design is an important stage for undertaking detailed design and starting on-site construction. From a professional perspective, the cruise production design business stage can be divided into: structure, outfitting, piping, duct, electrical, and hotel engineering. From a task perspective, each professional task is different, and the profession can be divided into pre-preparation, modeling, and drawing tasks. From the perspective of production design process, the design process in the cruise production design process is complex. The shipyard divides the professional tasks into the main process-sub-process-design rhythm dimensions, and there is a 1-N task quantity relationship between different dimensions. For example, in tasks such as pre-preparation of ducts, it can be subdivided into multiple processes such as component library definition and modeling rule definition. There are complex execution logics between different professions and within the same profession. For example, the modeling tasks of each profession can only be started after the construction of the component library and material library is completed, and the drawing task can only be started after the model is available.

[0003] In general, the processes and management within the cruise production design profession are clear, the management between professions is relatively independent, and the potential connections between professions are complex, so the cross-professional processes are more complicated; the processes are complex and changeable, the design processes of various professions are not unified, and there are intersections and overlaps between different design processes; the degree of task parallelism is high, and there is a high degree of dependence between tasks.

[0004] Therefore, the execution logic of cruise ship production design tasks varies due to different professional business processes, the parallel dependencies between tasks are difficult to quantify, and the rough division of planning nodes makes the constraints loose. In order to reduce the gaps and delays between plans, meet the requirements of planning nodes, and realize the orderly development of tasks, it is necessary to carry out research on task planning scheduling methods that consider multi-professional design parallel collaboration.

[0005] Task scheduling includes task sorting and planning. Sorting represents the task execution sequence, and planning represents the specific time arrangement of task execution. Commonly used methods in task sorting problems include priority constraints, graph networks, and design structure matrices, but they are mainly based on 0 and 1 sorting, and rarely involve situations with different degrees of parallelism between tasks. The quantitative relationship between the parallel dependency relationship and the plan of complex tasks with multiple disciplines and processes is not clear.

[0006] Commonly used methods in planning problems include linear programming, nonlinear programming, dynamic programming, heuristic algorithms, meta-heuristic algorithms, etc. Generally, the background is enterprise production. Because most enterprises adopt the business characteristics of assembly line production, most planning methods do not consider the impact of data dependency between tasks on the plan. Therefore, the application effect of planning for cruise production design tasks with multi-professional, multi-process and complex task parallel dependencies is poor. At the same time, the cruise design task process needs to match the cruise construction and loading process. Therefore, it is sometimes not the best choice to prepare a task plan for cruise production design with the goal of improving the execution speed of the plan and minimizing the execution cost. On the contrary, when arranging the plan, it is necessary to control the design process, balance the relationship between the plan and the personnel, and ensure the continuity of task execution, the balance of number changes, and the stability of single-person working hours.

[0007] As a type of heuristic algorithm, heuristic rule algorithm guides the problem solving or decision-making process through a series of rules. The results obtained by this type of algorithm may not be the optimal solution, but usually a good approximate solution can be found within a reasonable time. Compared with meta-heuristic algorithm, meta-heuristic algorithm is the product of the combination of random algorithm and local search algorithm. It depends on the population size, parameter selection and fitness function establishment. It needs multiple adjustments to avoid the algorithm from falling into the local optimum. Therefore, this type of algorithm is sometimes less efficient.

[0008] In the cruise ship production design task scheduling problem, when using meta-heuristic algorithms to conduct problem research, it is impossible to accurately establish a planning model with complex task execution logic and parallel dependencies, and it is difficult to solve the optimal value. Therefore, the heuristic rule method is more suitable for the cruise ship production design task scheduling problem. Summary of the invention

[0009] In order to solve the impact of execution logic and degree of parallelism between quantitative parallel tasks on the plan, the present invention focuses on the planning requirements of task execution continuity, number of people change balance, and single-person working time stability, and further proposes a heuristic rule method for scheduling cruise ship production design task plans.

[0010] The technical solution adopted by the present invention to solve the above problems is: the steps of the present invention include:

[0011] Step 1: Quantify the parallel dependency between tasks based on task logic and task maturity;

[0012] Step 2: Design a task-hour-number-of-people relationship function that integrates the learning curve;

[0013] Step 3: Construct a planning model;

[0014] Step 4: Propose design plan scheduling rules for scheduling the plan.

[0015] Furthermore, the parallel dependency relationship between tasks in step 1 is quantified based on task logic and task maturity:

[0016] task Xi represents the design task i in major X, task Xi The start time is Completion time is The working time for the mth execution is but

[0017] Since tasks have execution logic and business process time sequence requirements, tasks can be used as vertices and the execution logic between tasks as directed edges to form a task logic directed acyclic graph (DAG).

[0018] Mission Maturity Represents two adjacent tasks in the task logic DAG connected by a directed edge Xi With task Xj The maturity relationship between the previous tasks Xi At least after completing a certain degree, you can start the subsequent task tasj Xj That is, the subsequent task tasj Xj The earliest start time is

[0019] Define two types of task execution logic, Indicates tasj Xj In TAsj Xj After a while, it started. Indicates tasj Xj In TAsj Xj After completion, start

[0020] Therefore, through the task execution logic and the maturity relationship between tasks, a task logic DAG graph can be constructed to quantify the parallel dependency relationship between tasks.

[0021] Furthermore, the task-hour-number-of-people relationship function of the fusion learning curve designed in step 2 is:

[0022]

[0023] The learning curve is a type of mathematical model that describes the learning effect of personnel. It was first proposed by Wright and further summarized and named as the learning curve by Yelle. Currently, the learning curve can be divided into logarithmic learning curve, exponential learning curve, and hyperbolic learning curve. Considering the application of machine resources such as design modeling software, calculation software, and drawing software in the design process, as well as some prior factors such as design plans and rules before the start of the design, the S learning curve model proposed by Nembhard is selected.

[0024] Among them, N Xi represents the number of people who have completed task Xi . NT X represents the number of designers in specialty X, 1 ≤ N Xi ≤ NT X , N Xi ∈ Z, NT = ∑ X NT X . represents the man-hour of the m-th execution of design task task Xi , represents the average man-hour of a single designer for the first time to complete design task task Xi . tr is the adjustment parameter of the task man-hour change rate, 0 < tr < 1. M represents the non-compressible factor in the design process, that is, the time for repeating the design task cannot be reduced, 0 < M < 1. 1 - M represents the compressible factor in the design process. B is the prior factor existing in the design process, representing prior experience. If sufficient preparation is made before the first business and there is a detailed document for reference, the business completion time will be greatly reduced. Generally, the experience level is divided into 10 equal parts, 0 < B < 10. b represents the learning index, that is, the learning ability in the design process, 0 < b < 1.

[0025] Furthermore, the construction of the plan scheduling model in step 3 includes:

[0026] Step 301, define the plan node constraints;

[0027] Step 302, construct the plan scheduling model.

[0028] Furthermore, the definition of the plan node constraints in step 301 is:

[0029] tepn1, tepn2…tepnm represent the plan nodes that need to be satisfied during the task process.

[0030] λ represents a step size, that is, the length of a directed edge in the task logic DAG. represents the connection relationship between any two tasks in the task logic DAG, Indicates the sequential connection relationship with a step length of 1 in the task logic DAG, that is, from task Xj To task Xj . Indicates the reverse connection relationship with a step length of 1 in the task logic DAG, that is, from task Xj To task Xi .set up

[0031] Plan node constraint 1:

[0032] Plan node constraint 2:

[0033] Plan node constraint 3:

[0034]

[0035] in, Indicates the latest start time constraint of the task, that is, the start time of the task needs to satisfy the inequality Represents the latest completion time constraint of the task, that is, the completion time of the task needs to satisfy the inequality

[0036] Two tasks connected by a directed edge on the task logic DAG Xi and task Xj There is a direct execution logic between tasks, so Xi and task Xj The completion time needs to satisfy the inequality Therefore, on the task logic DAG, the completion time of two adjacent tasks with a step size of 1 and a sequential connection relationship must meet

[0037] Furthermore, in step 302, the planning model is constructed as follows:

[0038]

[0039] The goals are as follows:

[0040]

[0041] Among them, Δt X represents the planned idle time of all tasks in specialty X, represents the standard deviation of the number of people in all tasks in professional X, It represents the standard deviation of the change in the single-person working time of all tasks in specialty X.

[0042] The planning model constraints are as follows:

[0043]

[0044] 1≤Z Xi ≤NT X ,N Xi ∈Z

[0045]

[0046] Furthermore, the design plan arrangement rules for arranging the plan proposed in step 4 include:

[0047] Step 401, task planning and arranging sequence rules;

[0048] Step 402, planning unit rules;

[0049] Step 403: Adjustment rules for task headcount and working hours.

[0050] Furthermore, the task planning sequence rule proposed in step 401 is:

[0051] Ind and outd are important concepts in DAG. Ind reflects the degree of influence of a vertex by its previous vertex, while outd reflects the degree of influence of the vertex on the subsequent vertex. The larger the ind and outd are, the more critical the vertex is in DAG. Therefore, the task scheduling order rules are designed using the concepts of ind and outd, including: initial vertex task initial Selection rules, anchor point AnchorP X Selection rules, anchor point traversal order rules, and other vertex traversal order rules.

[0052] Rule 1 (initial vertex selection rule):

[0053]

[0054] Rule 2 (Anchor Point AnchorP X Select the rule):

[0055] AnchorP X ={task Xi |Outd(task Xi )>1or Ind(task Xi )>1for the first time

[0056] Rule 3 (anchor point traversal order rule): Let Indicates the traversal order of tasks, Indicates descending order.

[0057]

[0058] Rule 4 (other vertex traversal order rules): Indicates ascending order.

[0059]

[0060] When arranging task plans, according to the above rules, we can traverse all vertices on the DAG in a fixed and ordered order through the criticality of each vertex on the task logic DAG and its influence on other vertices, providing a sequential basis for plan arrangement.

[0061] In this traversal order context, there are two types of situations when scheduling tasks.

[0062] Case 1: When calculating the plan of the previous vertex through the subsequent vertex, apply the formula in reverse order to solve the start time of the previous vertex.

[0063] Case 2: When calculating the vertex plan with indegree Ind ≥ 2, it is necessary to compare the vertex start times calculated by different predecessor tasks and take the best one.

[0064] Furthermore, the planning arrangement unit rule proposed in step 402 is:

[0065] Rule 5 (Task Planning Unit Rules):

[0066] On the task logic DAG, according to the task planning sequence rules, select the task with the smallest step distance and satisfying Start time constraint or The two vertices of the completion time constraint are taken as the front and back endpoints of the Unit. The two endpoints and their middle vertex on the task logic DAG are taken as a complete Unit.

[0067] Under the combined effect of rules 1 to 5, the task logic DAG will be divided into Unit combination,

[0068] A combination of two units is obtained according to the unit rules of the task plan.

[0069] Assume that Unit2 contains task task Xi ,task Xj ,task Xn ,task Xl . Through Unit1 task task Xo The plan can calculate the task in Unit2 Xi Start time And through The right endpoint task can be calculated step by step XlThe start time and completion time.

[0070]

[0071] The planned time of the task is the number of people N who complete the task Xi The function f(N Xi ).

[0072] Based on endpoint task Xl The constraints that need to be met are different, the constructor Contains tasks Xi ,task Xj ,task Xn ,task Xl , if the right endpoint task task Xl Need to satisfy the start time constraint, let If the right endpoint task Xl Need to meet the completion time constraint, let

[0073] Furthermore, the adjustment rules for the number of people and working hours for a task proposed in step 403 are:

[0074] The left and right endpoints of a unit must either meet the start time constraint or the finish time constraint. In a task logic DAG built with task logic, the planned times of tasks affect each other. Therefore, there are two situations when arranging task plans.

[0075] Case 1: When When the left endpoint of does not satisfy the plan node, by adjusting the previous Task plan to achieve adjustment The purpose of the left endpoint plan.

[0076] Case 2: When When the right endpoint of does not satisfy the planned node, by adjusting Task plan to achieve adjustment The purpose of the right endpoint plan.

[0077] exist First, the total number of people required for the endpoint task to meet the node constraints is calculated; secondly, the planning function of the endpoint task Find the number of tasks N respectively Xi ,N Xj ,N Xn ,N Xl Partial derivatives, record the absolute value of each partial derivative coefficient Φ Xi ,Φ Xj ,Φ Xn,Φ Xl ; Finally, Φ Xi ,Φ Xj ,Φ Xn ,Φ Xl The total number of tasks is allocated in proportion to the number of people, and the calculation is recalculated. At the same time, for two tasks with a step size of 1, if the completion time of the predecessor task is greater than the completion time of the successor task, the personnel allocation will continue to be adjusted based on the number of people in the task adjusted above until the task plan meets the constraints.

[0078] The specific steps are as follows:

[0079] (1) By The task time is calculated The left endpoint task task Xi The start time of the

[0080] ·like Then continue with step (2).

[0081] ·like make return make Adjustment task plan.

[0082] (2) Calculation Right endpoint task Xl plan.

[0083] (3) Let the number of people on the task be N Xi ,N Xj ,…=1,calculate

[0084] ·like Then task Xl Satisfy node constraints. Number of people in the task N Xi ,N Xj ,…=1, execute step (8).

[0085] ·like Execute step (4) to adjust the task plan.

[0086] (4) Unified function variable N Xi ,N Xj ,…is N X ,N X ∈Z, calculate and set The minimum total number of tasks is N X The value of .

[0087] (5) Determine N X Whether the number of people is met.

[0088] ·like Continue with step (6).

[0089] ·like make return make Adjustment task plan.

[0090] (6) Treated as N Xi ,N Xj ...a multivariate function, 1≤N Xi ,N Xj …≤NT X . Find N Xi ,N Xj ...partial derivatives, record the absolute value of the coefficient of each partial derivative Φ Xi ,Φ Xj ….

[0091] To Find N Xi For example, the partial derivative of is calculated in the same way as the other partial derivatives. For the convenience of calculation, calculate the absolute value of the partial derivative Where Π=M+(1-M)(m+B) -b Is a fixed value.

[0092]

[0093] (c)Φ X =Φ Xi +Φ Xj +…

[0094] (7) Φ Xi ,Φ Xj The total number of tasks N is allocated in proportion to … X . Set the ceiling sign Indicates the smallest integer greater than or equal to itself. At the same time, according to rule 3, the number of tasks needs to satisfy 1≤N Xi ,N Xj …≤NT X .

[0095]

[0096] (8) Apply the number of tasks obtained in the above steps to calculate the start time and completion time of the tasks in the unit.

[0097] (9) Compare the completion time of adjacent predecessor tasks and successor tasks in sequence.

[0098] Compare tasks Xi ,task Xj The completion time, if The number of people on the mission remains the same as planned. Then N Xi ++1,1≤N Xi ≤NT X ; N Xj --1,1≤N Xj ≤NT X , until Final update task number N Xi With N Xj and recalculate Task plan within.

[0099] Gradually compare and adjust the number of people and completion time of adjacent previous and subsequent tasks until The internal task plan has been completed.

[0100] (10) The internal task calculation is completed. At the same time, it can be calculated The start time of the left endpoint task, continue to loop task plan.

[0101] The beneficial effects of the present invention are as follows: the present invention solves the impact of the execution logic and degree of parallelism between quantitative parallel tasks on the plan, while focusing on the planning requirements of task execution continuity, number of people change balance, and single-person working time stability. By establishing a task execution logic model between different disciplines and different processes within a discipline in cruise ship production design, defining the concept of task maturity, and using the execution logic and maturity between tasks to draw a task logic directed acyclic graph (DAG), it is possible to clearly express the logic between tasks and quantify the parallel dependency of tasks. Construct a task working time-number relationship function that integrates the learning curve, and design a heuristic rule method to solve the cruise ship production design task execution plan. It avoids the problem that the meta-heuristic algorithm cannot accurately establish a planning model with complex task execution logic and parallel dependency, and it is difficult to solve the optimal value. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 is a flow chart of the present invention;

[0103] Figure 2 It is an example diagram of the task logic DAG of the present invention;

[0104] Figure 3 is an example of a combination of two Units;

[0105] Figure 4 This is an example of the first adjustment method when arranging a task plan in the present invention;

[0106] Figure 5 This is an example of the second adjustment method when arranging a task plan in the present invention;

[0107] Figure 6 It is a plan for 6 professional tasks of cruise ship production design arranged by the present invention. DETAILED DESCRIPTION

[0108] Combination Figure 1 To illustrate this embodiment, the steps of a heuristic rule method for arranging a cruise ship production design task plan described in this embodiment include:

[0109] Step 1: Quantify the parallel dependency between tasks based on task logic and task maturity;

[0110] task Xi represents the design task i in major X, task Xi The start time is Completion time is The working time for the mth execution is but

[0111] Since tasks have execution logic and business process time sequence requirements, tasks can be used as vertices and the execution logic between tasks as directed edges to form a task logic directed acyclic graph (DAG).

[0112] Mission Maturity Represents two adjacent tasks in the task logic DAG connected by a directed edge Xi With task Xj The maturity relationship between the previous tasks Xi After completing at least a certain level, you can start the subsequent task. Xj . That is, the subsequent task task Xj The earliest start time is

[0113] Define two types of task execution logic, Represents task Xj In task Xi After a while, it started. Represents task Xj In task Xi After completion, start

[0114] Therefore, through the task execution logic and the maturity relationship between tasks, a task logic DAG graph can be constructed. The originally discrete tasks are connected into a DAG network through the DAG directed edges, and any two task vertices in this DAG network can be connected by the DAG directed edges. The construction basis of the task logic DAG is the execution logic between tasks. Only tasks with data interaction can be connected through task logic, and task maturity is the time quantization result of task logic. At the same time, it is also the time quantization result of the priority constraint between two adjacent tasks connected by directed edges on the task logic DAG. The task logic DAG graph obtained by quantifying the parallel dependence relationship between tasks based on task logic and task maturity in Step 1 is as Figure 2 shown.

[0115] Step 2: Design a task man-hour - number relationship function that incorporates the learning curve;

[0116] The learning curve is a type of mathematical model that describes the learning effect of personnel. It was first proposed by Wright and further summarized and named the learning curve by Yelle. Currently, the learning curve can be divided into the logarithmic learning curve, the exponential learning curve, and the hyperbolic learning curve. Considering the application of machine resources such as design modeling software, calculation software, and drawing software in the design process, as well as some prior factors such as plans and rules before the start of the design, the S learning curve model proposed by Nembhard is selected.

[0117] t m = C1[M+(1 - M)(m + B) -b

[0118] C1 represents the time to produce the first product, M represents the non-compressible factor in the design process, that is, the time for the repetitive design task that cannot be reduced, 0 < M < 1. 1 - M represents the compressible factor in the design process. B is the prior factor existing in the design process, representing prior experience. If sufficient preparation is made before the first business and there are detailed documents for reference, then the business completion time will be greatly reduced. Generally, the experience level is divided into 10 equal parts, 0 < B < 10. b represents the learning exponent, that is, the learning ability in the design process, 0 < b < 1.

[0119] Task man-hours are closely related to the number of task personnel. As the number of personnel increases, the task man-hours gradually decrease. According to the characteristics of the change relationship between task man-hours and the number of personnel, the task man-hour formula based on the personnel learning effect is rewritten to obtain formula (1).

[0120]

[0121] Among them, N Xi represents the number of people completing the task Xi NT​X represents the number of designers in specialty X, 1≤N Xi ≤NT X ,N Xi ∈Z,NT=∑ X NT X . Indicates the mth execution of the design task task Xi of working hours, Indicates that a single designer completes a design task for the first time Xi tr is the task time change rate adjustment parameter, 0 <tr<1。

[0122] Step 3: Construct a planning model, including:

[0123] Step 301, define planning node constraints;

[0124] Step 302: Construct a planning model.

[0125] Specific:

[0126] The plan node constraints are defined in step 301 as:

[0127] tepn1, tepn2…tepnm represent the requirements that need to be met during the task. planning nodes.

[0128] λ represents a step size, that is, the length of a directed edge in the task logic DAG. Indicates the connection relationship between any two tasks in the task logic DAG. Indicates the sequential connection relationship with a step length of 1 in the task logic DAG, that is, from task Xi To task Xj . Indicates the reverse connection relationship with a step length of 1 in the task logic DAG, that is, from task Xj To task Xi .set up

[0129] Plan node constraint 1:

[0130] Plan node constraint 2:

[0131] Plan node constraint 3:

[0132]

[0133] in, Indicates the latest start time constraint of the task, that is, the start time of the task needs to satisfy the inequality Represents the latest completion time constraint of the task, that is, the completion time of the task needs to satisfy the inequality

[0134] Two tasks connected by a directed edge on the task logic DAG Xi and task Xj There is a direct execution logic between tasks, so Xi and task Xj The completion time needs to satisfy the inequality Therefore, on the task logic DAG, the completion time of two adjacent tasks with a step size of 1 and a sequential connection relationship must meet

[0135] The planning model constructed in step 302 is:

[0136]

[0137] The goals are as follows:

[0138]

[0139] Among them, Δt X represents the planned idle time of all tasks in specialty X, represents the standard deviation of the number of people in all tasks in professional X, It represents the standard deviation of the change in the single-person working time of all tasks in specialty X.

[0140] The planning model constraints are as follows:

[0141] task Xi ,task Xj ∈D(task Xi ,task Xj )

[0142] 1≤N Xi ≤NT X ,N Xi ∈Z

[0143]

[0144] Step 4: Propose design plan arrangement rules for arranging the plan. Including:

[0145] Step 401, task planning sequence rules;

[0146] Step 402, planning unit rules;

[0147] Step 403: Adjustment rules for task headcount and working hours.

[0148] Specifically: the task plan arrangement order rule in step 401 is:

[0149] In-degree & and out-degree Outd are important concepts in DAG. In-degree reflects the degree of influence of a vertex by its previous vertex, while out-degree reflects the degree of influence of the vertex on the subsequent vertex. The larger the in-degree and out-degree are, the more critical the vertex is in DAG. Therefore, the concept of in-degree and out-degree is used to design the order rules of task scheduling, including: initial vertex task inital Selection rules, anchor point AnchorP X Selection rules, anchor point traversal order rules, and other vertex traversal order rules.

[0150] Rule 1 (initial vertex selection rule):

[0151]

[0152] Rule 2 (Anchor Point AnchorP X Select the rule):

[0153] AnchorP X ={task Xi |Outd(task Xi )>1orInd(task Xi )>1for the first time}

[0154] Rule 3 (anchor point traversal order rule): Let Indicates the traversal order of tasks, Indicates descending order.

[0155]

[0156] Rule 4 (other vertex traversal order rules): Indicates ascending order.

[0157]

[0158] When arranging task plans, according to the above rules, we can traverse all vertices on the DAG in a fixed and ordered order through the criticality of each vertex on the task logic DAG and its influence on other vertices, providing a sequential basis for plan arrangement.

[0159] In this traversal order context, there are two types of situations when scheduling tasks.

[0160] Case 1: When calculating the plan of the previous vertex through the subsequent vertex, apply the formula in reverse order to solve the start time of the previous vertex.

[0161] Case 2: When calculating the vertex plan with indegree Ind ≥ 2, it is necessary to compare the vertex start times calculated by different predecessor tasks and take the best one.

[0162] The planning arrangement unit rule in step 402 is:

[0163] Rule 5 (Task Planning Unit Rules):

[0164] On the task logic DAG, according to the task planning sequence rules, select the task with the smallest step distance and satisfying Start time constraint or The two vertices of the completion time constraint are taken as the front and back endpoints of the Unit. The two endpoints and their middle vertex on the task logic DAG are taken as a complete Unit.

[0165] Under the combined effect of rules 1 to 5, the task logic DAG will be divided into Unit combination,

[0166] An example of a combination of two units obtained by arranging the unit rules according to the task plan is as follows Figure 3 shown.

[0167] Assume that Unit2 contains task task Xi ,task Xj ,task Xn ,task Xl ,like Figure 3 As shown. Through Unit1 task task Xo The plan can calculate the task in Unit2 Xi Start time And through The right endpoint task can be calculated step by step Xl The start time and completion time.

[0168]

[0169] The planned time of the task is the number of people N who complete the task Xi The function f(N Xi ).

[0170] Based on endpoint task Xl The constraints that need to be met are different, the constructor Contains tasks Xi ,task Xj ,task Xn ,taskXl , if the right endpoint task task Xl Need to satisfy the start time constraint, let If the right endpoint task Xl Need to meet the completion time constraint, let

[0171] The rules for adjusting the number of people and working hours in the task in step 403 are:

[0172] The left and right endpoints of a unit must either meet the start time constraint or the finish time constraint. In a task logic DAG built with task logic, the scheduled times of tasks affect each other. Therefore, there are two situations when scheduling tasks, such as Figure 4 and Figure 5 shown.

[0173] Case 1: When When the left endpoint of does not satisfy the plan node, by adjusting the previous Task plan to achieve adjustment The purpose of the left endpoint plan.

[0174] Case 2: When When the right endpoint of does not satisfy the planned node, by adjusting Task plan to achieve adjustment The purpose of the right endpoint plan.

[0175] Rule 6 (Task Number and Working Hours Adjustment Rules):

[0176] exist First, the total number of people required for the endpoint task to meet the node constraints is calculated; secondly, the planning function of the endpoint task Find the number of tasks N respectively Xi ,N Xj ,N Xn ,N Xl Partial derivatives, record the absolute value of each partial derivative coefficient Φ Xi ,Φ Xj ,Φ Xn ,Φ Xl ; Finally, Φ Xi ,Φ Xj ,Φ Xn ,Φ Xl The total number of tasks is allocated in proportion to the number of people, and the calculation is recalculated. At the same time, for two tasks with a step size of 1, if the completion time of the predecessor task is greater than the completion time of the successor task, the personnel allocation will continue to be adjusted based on the number of people in the task adjusted above until the task plan meets the constraints.

[0177] The specific steps are as follows:

[0178] (1) By The task time is calculated The left endpoint task Xi The start time of the

[0179] ·like Then continue with step (2).

[0180] ·like make return make Adjustment task plan.

[0181] (2) Calculation Right endpoint task Xl plan.

[0182] (3) Let the number of people on the task be N Xi ,N Xj ,…=1,calculate

[0183] ·like Then task Xl Satisfy node constraints. Number of people in the task N Xi ,N Xj ,…=1, execute step (8).

[0184] ·like Execute step (4) to adjust the task plan.

[0185] (4) Unified function variable N Xi ,N Xj ,…is N X ,N X ∈Z, calculate and set The minimum total number of tasks is N X The value of .

[0186] (5) Determine N X Whether the number of people is met.

[0187] ·like Continue with step (6).

[0188] ·like make return make Adjustment task plan.

[0189] (6) Treated as N Xi,N Xj ...a multivariate function, 1≤N Xi ,N Xj …≤NT X . Find N Xi ,N Xj ...partial derivatives, record the absolute value of the coefficient of each partial derivative Φ Xi ,Φ Xj ….

[0190] To Find N Xi For example, the partial derivative of is calculated in the same way as the other partial derivatives. For the convenience of calculation, calculate the absolute value of the partial derivative Where Π=M+(1-M)(m+B) -b Is a fixed value.

[0191]

[0192] (c)Φ X =Φ Xi +Φ Xj +…

[0193] (7) Φ Xi ,Φ Xj The total number of tasks N is allocated in proportion to … X . Set the ceiling sign Indicates the smallest integer greater than or equal to itself. At the same time, according to rule 3, the number of tasks needs to satisfy 1≤N Xi ,N Xj …≤NT X .

[0194]

[0195] (8) Apply the number of tasks obtained in the above steps to calculate the start time and completion time of the tasks in the unit.

[0196] (9) Compare the completion time of adjacent predecessor tasks and successor tasks in sequence.

[0197] Compare tasks Xi ,task Xj The completion time, if The number of people on the mission remains the same as planned. Then N Xi ++1,1≤N Xi ≤NT X ; N Xj --1,1≤N Xj ≤NT X , until Final update task number N Xi With N Xj and recalculate Task plan within.

[0198] Gradually compare and adjust the number of people and completion time of adjacent previous and subsequent tasks until The internal task plan has been completed.

[0199] (10) The internal task calculation is completed. At the same time, it can be calculated The start time of the left endpoint task, continue to loop task plan.

[0200] In this implementation, after the heuristic rule method for cruise ship production design task planning is designed, the heuristic rule method is used to solve the task plans of the six specialties of cruise ship production design. The specific steps are as follows:

[0201] Step 1: Analyze the standard task execution logic in the six disciplines of cruise ship production design;

[0202] Step 2: Obtain expert weights and expert scores based on the AHP hierarchical analysis method to solve the maturity of tasks;

[0203] Step 3: Draw the task logic DAG based on the task execution logic and the maturity of tasks;

[0204] Step 4: Set planning node constraints;

[0205] Step 5: Determine the order of the cruise ship production design task plan according to the task plan arrangement order rules;

[0206] Step 6: Determine the cruise ship production design task plan scheduling unit according to the task plan scheduling Unit rule;

[0207] Step 7: Arrange the cruise ship production design task plan according to the number of tasks and working hours adjustment rules;

[0208] After iterating the above steps, we can get the plan of the six professional tasks of cruise ship production design and the number of people required to complete each task. The six professional task sets of cruise ship production design are shown in Table 1. X ={task Xi |X=A,B,C,D,E,F} respectively represent the six professional task sets of hull structure, outfitting, piping, duct, electrical engineering, and hotel engineering.

[0209] Table 1 Six professional task sets for cruise production design

[0210]

[0211]

[0212] Cruise production design 6 professional task planning schemes such as Figure 6 The rated number of people required to complete each task is shown in Table 2.

[0213] Table 2 The number of people required to complete each task

[0214]

[0215] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments with equivalent changes without departing from the scope of the technical solution of the present invention.

Claims

1. A heuristic rule method for arranging a cruise ship production design task plan, characterized in that the steps include: Step 1: Quantify the parallel dependency between tasks based on task logic and task maturity; Step 2: Design a task-hour-number-of-people relationship function that integrates the learning curve; Step 3: Construct a planning model; Step 4: Propose design plan scheduling rules for scheduling the plan.

2. A heuristic rule method for arranging a cruise ship production design task plan according to claim 1, characterized in that: Step 1: task Xi represents the design task i in major X, task Xi The start time is Completion time is The working time for the mth execution is but Tasks have execution logic and time sequence requirements for business processes. Tasks are used as vertices and the execution logic between tasks is used as directed edges to form a task logic directed acyclic graph (DAG). Mission Maturity Represents two adjacent tasks in the task logic DAG connected by a directed edge Xi With task Xj The maturity relationship between the previous tasks Xi After completing at least a certain degree, the subsequent tasks can be started Xj ; That is, the subsequent task task Xj The earliest start time is Define two types of task execution logic, Represents task Xj In task Xi After a while, it started. Represents task Xj In task Xi After completion, start A task logic DAG graph is constructed based on the task execution logic and the maturity relationship between tasks to quantify the parallel dependency between tasks.

3. A heuristic rule method for arranging a cruise ship production design task plan according to claim 1, characterized in that: In step 2, the task-hour-number relationship function of the integrated learning curve is designed: Among them, N Xi represents the number of people who have completed task Xi ; NT X represents the number of designers in specialty X, 1 ≤ N Xi ≤ NT X , N Xi ∈ Z, NT = ∑ X NT X ; represents the man - hour of the m - th execution of design task task Xi ; represents the average man - hour for a single designer to complete design task task Xi for the first time; tr is the adjustment parameter of the task man - hour change rate, 0 < tr < 1; M represents the incompressible factor in the design process, that is, the time for repeated design tasks cannot be reduced, 0 < M < 1; 1 - M represents the compressible factor in the design process; B is the prior factor existing in the design process, representing prior experience, and the experience level is divided into 10 equal parts, 0 < B < 10; b represents the learning index, that is, the learning ability in the design process, 0 < b < 1.

4. A heuristic rule method for arranging a cruise ship production design task plan according to claim 1, characterized in that: The planning model constructed in step 3 includes: Step 301, define planning node constraints; Step 302: Construct a planning model.

5. A heuristic rule method for arranging a cruise ship production design task plan according to claim 4, characterized in that: In step 301, the planning node constraints are defined as: Indicates the requirements that need to be met during the task planning nodes; λ represents a step length, i.e., the length of a directed edge in the task logic DAG; Indicates the connection relationship between any two tasks in the task logic DAG. Indicates the sequential connection relationship with a step length of 1 in the task logic DAG, that is, from task Xi To task Xj ; Indicates the reverse connection relationship with a step length of 1 in the task logic DAG, that is, from task Xj To task Xi ;set up Plan node constraint 1: Plan node constraint 2: Plan node constraint 3: in, Indicates the latest start time constraint of the task, that is, the start time of the task needs to satisfy the inequality Represents the latest completion time constraint of the task, that is, the completion time of the task needs to satisfy the inequality Two tasks connected by a directed edge on the task logic DAG Xi and task Xj There is a direct execution logic between tasks. Xi and task Xj The completion time needs to satisfy the inequality On the task logic DAG, the completion time of two adjacent tasks with a step size of 1 and a sequential connection relationship must meet 6. A heuristic rule method for arranging a cruise ship production design task plan according to claim 4, characterized in that: The planning model constructed in step 302 is: The goals are as follows: Among them, Δt X represents the planned idle time of all tasks in specialty X, represents the standard deviation of the number of people in all tasks in professional X, represents the standard deviation of the change in the single-person working time for all tasks in specialty X; The planning model constraints are as follows:

7. A heuristic rule method for arranging a cruise ship production design task plan according to claim 1, characterized in that: The design plan arrangement rules proposed in step 4 for arranging the plan include: Step 401, task planning sequence rules; Step 402, planning unit rules; Step 403: Adjustment rules for task headcount and working hours.

8. A heuristic rule method for arranging a cruise ship production design task plan according to claim 7, characterized in that: The task plan arrangement order rule proposed in step 401 is: Ind and Outd are important concepts in DAG. Ind reflects the degree to which a vertex is influenced by its previous vertex, while Outd reflects the degree to which the vertex influences its subsequent vertex. The larger the ind and outd, the more critical the vertex is in DAG. The concepts of in-degree and out-degree are used to design the order rules for task scheduling, including: initial vertex task initial Selection rules, anchor point AnchorP X Selection rules, anchor point traversal order rules, and other vertex traversal order rules; Rule 1, initial vertex selection rule: Rule 2, Anchor Point AnchorP X Selection Rules: AnchorP X ={task Xi |Outd(task Xi )}>1 or Ind(task Xi )>1 for the first time} Rule 3, anchor point traversal order rule: Let Indicates the traversal order of tasks, Indicates descending order; Rule 4, other vertex traversal order rules: Indicates ascending order; When arranging task plans, according to the above rules, we can traverse all vertices on the DAG in a fixed and orderly order through the criticality of each vertex on the task logic DAG and its influence on other vertices, providing a sequential basis for plan arrangement; In this traversal order context, there are two types of situations when scheduling tasks; Case 1: When calculating the plan of the previous vertex through the subsequent vertex, the formula is applied in reverse order to solve the start time of the previous vertex; Case 2: When calculating the vertex plan with indegree Ind ≥ 2, compare the vertex start times calculated by different predecessor tasks and take the best one.

9. A heuristic rule method for arranging a cruise ship production design task plan according to claim 7, characterized in that: The planning arrangement unit rule proposed in step 402 is: Rule 5, Task Planning Unit Rules: On the task logic DAG, according to the task planning sequence rules, select the task with the smallest step distance and satisfying Start time constraint or The two vertices of the completion time constraint are taken as the front and back endpoints of the Unit; the two endpoints and the middle vertex on the task logic DAG are taken as a complete Unit; Under the combined effect of rules 1 to 5, the task logic DAG will be divided into Unit combination, A combination of two units is obtained according to the unit rules of the task plan; Let Unit2 contain task task Xi ,task Xj ,task Xn ,task Xl , through Unit1's task task Xo The plan calculates the task in Unit2 Xi Start time And through The right endpoint task can be calculated step by step Xl The start and finish time of the The planned time of the task is the number of people N who complete the task Xi The function f(N Xi ); Based on endpoint task Xl The constraints that need to be met are different, the constructor Contains tasks Xi ,task Xj ,task Xn ,task Xl , if the right endpoint task task Xl Need to satisfy the start time constraint, let If the right endpoint task Xl Need to meet the completion time constraint, let 10. A heuristic rule method for arranging a cruise ship production design task plan according to claim 7, characterized in that: The adjustment rules for the number of people and working hours for tasks proposed in step 403 are: The left and right endpoints of a unit must either meet the start time constraint or the finish time constraint. In the task logic DAG built with task logic, the planned times of tasks affect each other. There are two situations when arranging task plans: Case 1: When When the left endpoint of does not satisfy the plan node, by adjusting the previous Task plan to achieve adjustment The purpose of the left endpoint plan; Case 2: When When the right endpoint of does not satisfy the planned node, by adjusting Task plan to achieve adjustment The purpose of the Right Endpoint Program; Rule 6 Task headcount and working hours adjustment rules: exist First, the total number of people required for the endpoint task to meet the node constraints is calculated; secondly, the planning function of the endpoint task Find the number of tasks N respectively Xi ,N Xj ,N Xn ,N Xl Partial derivatives, record the absolute value of each partial derivative coefficient Φ Xi ,Φ Xj ,Φ Xn ,Φ Xl ; Finally, Φ Xi ,Φ Xj ,Φ Xn ,Φ Xl The total number of tasks is allocated in proportion to the number of people, and the calculation is recalculated. At the same time, for two tasks with a step size of 1, if the completion time of the predecessor task is greater than the completion time of the successor task, the personnel allocation will continue to be adjusted based on the number of people in the tasks adjusted above until the task plan meets the constraints; The specific steps are as follows: (1) By The task time is calculated The left endpoint task Xi The start time of ·like Then continue with step (2); ·like make return make Adjustment Mission plan; (2) Calculation Right endpoint task Xl plans; (3) Let the number of people on the task be N Xi ,N Xj ,…=1,calculate ·like Then task Xl Satisfy node constraints; number of people in the task N Xi ,N Xj ,…=1, execute step (8); ·like Execute step (4) to adjust the task plan; (4) Unified function variable N Xi ,N Xj ,…is N X ,N X ∈Z, calculate and set The minimum total number of tasks is N X The value of (5) Determine N X Whether the number of people is limited; ·like Continue to step (6); ·like make return make Adjustment Mission plan; (6) Treated as N Xi ,N Xj ...a multivariate function, 1≤N Xi ,N Xj …≤NT X ; respectively Find N Xi ,N Xj ...partial derivatives, record the absolute value of the coefficient of each partial derivative Φ Xi ,Φ Xj …; To Find N Xi Take the partial derivative of as an example, and the calculation of other partial derivatives is similar; for the convenience of calculation, calculate the absolute value of the partial derivative Where Π=M+(1-M)(m+B) -b is a fixed value; (a) (b) (c)F X =Φ Xi +F Xj +… (7) Φ Xi ,Φ Xj The total number of tasks N is allocated in proportion to … X ; Set the rounding symbol Represents the smallest integer greater than or equal to itself; at the same time, according to rule 3, the number of tasks needs to satisfy 1≤N Xi ,N Xj …≤NT X ; (8) Apply the number of people in the task obtained in the above steps to calculate the start time and completion time of the task in the unit; (9) Compare the completion time of adjacent predecessor tasks and successor tasks in sequence; Compare tasks Xi ,task Xj The completion time, if The number of people on the mission remains the same as planned; like Then N Xi ++1,1≤N Xi ≤NT X ; N Xj --1,1≤N Xj ≤NT X , until Final update task number N Xi With N Xj and recalculate Mission plan within Gradually compare and adjust the number of people and completion time of adjacent previous and subsequent tasks until The internal task plan has been completed; (10) The internal task calculation is completed; at the same time, it can be calculated The start time of the left endpoint task, continue to loop task plan.

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