CN113627078A - D-RMS configuration design multi-objective optimization method - Google Patents
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Abstract
The disclosed D-RMS configuration design multi-objective optimization method determines boundary point constraint conditions of D-RMS configuration design multi-objective optimization based on workpiece family information; determining an optimized boundary of the D-RMS configuration design according to the influence of the boundary point of the D-RMS configuration design on a D-RMS optimization target; constructing a D-RMS configuration design multi-objective optimization model according to the D-RMS configuration design multi-objective optimization parameters and the optimization boundary; solving the D-RMS multi-target optimization model by adopting NSGA-II to obtain a pareto frontier; and randomly selecting points in the pareto frontier as an optimization scheme of the D-RMS configuration design, and finishing the D-RMS configuration design based on multi-objective optimization. The invention comprehensively considers the design factor of the D-RMS configuration to optimize the delay reconstruction boundary point, and fully exerts the advantages of the D-RMS so as to improve the reconstruction efficiency and the operation efficiency of the D-RMS.
Description
Technical Field
The invention belongs to the technical field of advanced manufacturing, and particularly relates to a D-RMS (delay-reconstruction-manufacturing-system) configuration design multi-objective optimization method.
Background
In order to alleviate the impact of reconfiguration on the conventional Reconfigurable Manufacturing System (RMS), the concept of deferred reconfiguration is proposed in consideration of the reconfiguration complexity, and an attempt is made to create conditions to postpone the reconfiguration activity to a process at the back end of the manufacturing system. Combining the Delayed reconstruction with the RMS forms a Delayed reconstruction manufacturing system (D-RMS). D-RMS can reduce reconstruction difficulty and downtime loss due to reconstruction, thereby improving the practical capabilities of conventional RMS. D-RMS is a sub-class of RMS that inherits the characteristics of RMS and needs to consider reconfigurability at the beginning of design and further consider the delay reconfiguration characteristics. D-RMS is also a manufacturing system for a specific family of workpieces, which completes configuration design by mapping the functional requirements of the specific family of workpieces to a set of machine tools and the connections between the machine tools. The configuration design determines the implementation of the delay reconstruction feature and the operating efficiency of the corresponding manufacturing system after the construction is completed.
Many researchers are focusing on the design of the configuration. Initially, RMS's have attracted researchers' attention as a low cost, fast responding, capacity scaling. For example, Koren et al analyzed the value creation of RMS scalability and proposed scalability-oriented RMS configuration design methods. Moghaddam et al consider a single product assembly line to perform configuration design, and use a reconfigurable machine tool to cope with demand fluctuations and extend to a workpiece family in subsequent work. In addition to scalability, many factors need to be considered in the RMS configuration design process. Shang et al (2021) propose a key feature based RMS configuration design method that takes into account the effects of online detection. Huang et al (2020) design configurations under the constraint of time-delay reconstruction, taking into account both the formation of the part family and the selection of the machine tool. Gauss et al (2019) attempt to address configuration design issues from the machine hierarchy to the modular machine family based modular machines. In addition, in order to improve the efficiency of configuration design andeffectively, some researchers have also focused on configuration selection/evaluation. Goyal et al (2012) propose a configuration selection method based on reconfigurable machine reconfigurability and reconfigurable machine operation capabilities. Ashraf and Hasan propose a configuration selection framework for reconfigurable manufacturing pipelines that takes into account operational constraints. Pal Singh et al attempts to find a new indicator of RMS product flow configuration selection that involves nine industry-related significant factors. Wang et al propose a profile evaluation method considering RMS key features based on PROTHMEE, emphasizing the importance of key features through mathematical models. Similarly, Benderbal et al propose a configuration evaluation method that takes modularity into account. Puik et al evaluated RMS topography design in terms of resources, lead time, etc. Andersen et al attempted to summarize the general design approach to RMS by analysis and synthesis of existing methods. In addition, the configuration design can also be regarded as an optimization process.
And finding the optimal solution by considering different targets and constraint conditions. The Yelles-Chaouche et al reviewed the configuration design study from an optimization perspective.
And to provide a decision support tool for designing an RMS configuration based on a combinatorial optimization model. Zhang et al propose an optimization method based on demand moment set to support RMS configuration design. Typically, more than one goal of configuration design optimization is achieved. Bensmane et al solved the optimization problem using a non-dominated ranking genetic algorithm (NSGA). Dou provides a reconfigurable pipeline configuration generation and scheduling integrated dual-objective optimization method based on a non-dominated sorting genetic algorithm II (NSGA-II), and the method is expanded to multiple objectives in further research. Bortolini et al have done similar work in configuration design using dual objective optimization. Benderbal et al have studied multi-objective optimization methods for RMS configuration design under unavailable constraints.
However, based on the existing RMS/D-RMS configuration design studies, there are the following technical drawbacks: (1) the conventional configuration design research is basically explored towards the traditional RMS, almost no delay reconstruction characteristic is considered for configuration design research, and the advantage of the D-RMS cannot be fully embodied. (2) The demarcation point decision is the key content of the D-RMS configuration design and directly determines the processing procedures contained by the two subsystems of the D-RMS. The decision-making of the division point based on the subjective model in the only D-RMS configuration design research cannot ensure the optimality of the result.
Disclosure of Invention
The invention overcomes one of the defects of the prior art, provides a D-RMS configuration design multi-objective optimization method, constructs a multi-objective optimization model by fully considering the investment cost, the reconstruction time and the delay reconstruction of a machine tool, and optimizes the boundary points; and an NSGA-II algorithm is introduced to solve the multi-objective optimization model, the advantages of the D-RMS are fully exerted to ensure the high efficiency of the operation and reconstruction of the D-RMS, and the reconstruction efficiency and the operation efficiency of the D-RMS are improved.
According to one aspect of the disclosure, the invention provides a method for multi-objective optimization of D-RMS configuration design, the method comprising:
determining a demarcation point constraint condition of D-RMS configuration design multi-objective optimization based on the workpiece family information;
determining a multi-objective optimization boundary of the D-RMS configuration design according to the boundary point constraint condition and the influence of the boundary point on the optimization target of the D-RMS configuration design;
constructing a D-RMS configuration design multi-objective optimization model according to the D-RMS configuration design multi-objective optimization parameters and the optimization boundary;
solving the D-RMS configuration design multi-objective optimization model by adopting NSGA-II to obtain a pareto frontier;
and randomly selecting points in the pareto frontier as a D-RMS configuration design multi-objective optimization scheme to complete the D-RMS configuration design based on multi-objective optimization.
In one possible implementation, the boundary point constraint is 1< x < N, where x is the location of the boundary point of the D-RMS configuration corresponding to the workpiece family and N is the number of processes of the D-RMS configuration.
In one possible implementation, the multi-objective optimization parameters include machine tool investment cost, machine tool reconstruction time, and delay reconstruction characteristics.
In one possible implementation, the machine tool includes a rigid machine tool, a flexible machine tool, and a reconfigurable machine tool.
In one possible implementation, the demarcation point is used to balance the relationship between the machine tool investment cost, machine tool reconstruction time, and delay reconstruction.
In a possible implementation manner, the solving the multi-objective optimization model for D-RMS configuration design by using NSGA-II to obtain the pareto frontier includes:
step P1: initializing NSGA-II parameters and parameter ranges;
step P2: generating a parent population with Q individuals by adopting binary coding;
step P3: calculating the value of a D-RMS configuration design multi-objective optimization model of each individual in the parent population and the number of dominant solutions of each individual, wherein each individual is distributed to different leading edges according to the number of dominant solutions, and for each leading edge, the individuals on the leading edge are sorted according to the crowdedness;
step P4: when a preset maximum iteration algebra is reached, completing the multi-objective optimization of the D-RMS configuration design and obtaining a pareto frontier, otherwise, executing a step P5;
step P5: selecting, crossing and mutating the parent population by adopting an elite strategy to obtain a offspring population;
step P6: merging the parent population and the offspring population into a new population twice the size of the offspring population, returning to step P3.
In one possible implementation, the step P5 includes:
selecting an individual positioned at the first front edge, and eliminating the individual if the individual does not meet the boundary point constraint condition;
selecting P individuals by using a binary tournament selection method, and carrying out cross and polynomial variation on the P individuals to obtain a progeny population; wherein P is a positive integer.
According to the D-RMS configuration design multi-objective optimization method, a D-RMS configuration design multi-objective optimization model is constructed by fully considering the investment cost, the reconstruction time and the delayed reconstruction of a machine tool, model support is provided for the D-RMS configuration design, the practical process of the D-RMS is guided, and the boundary points are optimized; an NSGA-II algorithm is introduced to solve a multi-objective optimization model, an elite retention strategy is introduced, algorithm solving accuracy and calculation efficiency are considered, and D-RMS reconstruction efficiency and operation efficiency are improved.
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The accompanying drawings are included to provide a further understanding of the technology or prior art of the present application and are incorporated in and constitute a part of this specification. The drawings expressing the embodiments of the present application are used for explaining the technical solutions of the present application, and should not be construed as limiting the technical solutions of the present application.
FIG. 1 illustrates a block diagram of a D-RMS configuration design according to an embodiment of the present disclosure;
FIG. 2 illustrates a flow diagram of a D-RMS configuration design multi-objective optimization method in accordance with an embodiment of the present disclosure;
FIG. 3 illustrates a process flow diagram of an existing family of workpieces according to an embodiment of the present disclosure;
FIG. 4 shows a pareto frontier result diagram for solving an experimented scenario 1 using NSGA-II, according to an embodiment of the present disclosure;
FIG. 5 shows a pareto frontier result diagram for solving an experimented scenario 2 using NSGA-II, according to an embodiment of the present disclosure;
FIG. 6 shows a schematic diagram of a D-RMS configuration design for solving an optimal solution for scenario 1 of a stress test using NSGA-II, according to an embodiment of the present disclosure;
FIG. 7 shows a schematic design of a D-RMS configuration for solving the optimal solution for the situation under test 2 using NSGA-II according to one embodiment of the present disclosure.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the accompanying drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and the features of the embodiments can be combined without conflict, and the technical solutions formed are all within the scope of the present invention.
Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
FIG. 1 shows a block diagram of a D-RMS configuration design according to an embodiment of the present disclosure.
According to Huang et al, D-RMS (delay reconfigurable manufacturing System) is a sub-class of RMS, with more focus on convertibility to accommodate product variety fluctuations. The core idea of D-RMS is to postpone the reconstruction activity as far as possible to the back-end of the manufacturing system. In this case, the reconfiguration activity will only occur at the back end, and the manufacturing system front end can maintain normal production activities during the reconfiguration process. D-RMS can reduce production loss caused by reconfiguration downtime of a manufacturing system, and has great significance. By providing more functionality than is currently required at the D-RMS front-end, part of the production activity during reconstruction is maintained.
As shown in fig. 1, a typical D-RMS configuration generally includes two subsystems (subsystem 1 and subsystem 2), subsystem 1 including a rigid machine tool and a flexible machine tool, and subsystem 2 including a reconfigurable machine tool.
The subsystem 1 remains unchanged during reconfiguration and the subsystem 2 provides accurate functionality through reconfiguration when needed to improve production efficiency. The demarcation point divides subsystem 1 and subsystem 2, similar to the decoupling point of delayed product differentiation. The demarcation point is an abstract concept and can be bound with the last stage of the subsystem 1 to facilitate calculation. Three machine tool options are included in the D-RMS configuration design, namely rigid machine tools, flexible machine tools and reconfigurable machine tools. The rigid machine tool and the flexible machine tool can be selected by the subsystem 1, the rigid machine tool realizes a single function in the subsystem 1, and the flexible machine tool can realize multiple functions so as to eliminate potential reconfiguration activities in the subsystem 1. The reconfigurable machine tool is a core device for performing reconfiguration activities in the subsystem 2 by the D-RMS, and the reconfigurable machine tool can provide a plurality of functions through reconfiguration. Differences in the location of the demarcation point can affect the investment strategy of the machine tool and ultimately affect the production efficiency of the D-RMS. The location of the cut-off point needs to be optimized in the D-RMS structure design.
FIG. 2 illustrates a flow chart of a D-RMS configuration design multi-objective optimization method according to an embodiment of the present disclosure.
As shown in fig. 2, the method may include:
step S1: and determining the boundary point constraint condition of the multi-objective optimization of the D-RMS configuration design based on the workpiece family information.
Wherein the workpiece family information may include: the position constraint of the dividing point, the number of D-RMS procedures, the number of workpieces, the number of functions included in each procedure, etc. The multi-objective optimization parameters may include machine tool investment cost, machine tool reconstruction time, and delay reconstruction characteristics.
The goal of D-RMS configuration design is to optimize the cut-off point, which can be used to balance the relationship between the machine tool investment cost, machine tool reconstruction time, and delay reconstruction, and therefore how to determine the optimal cut-off point for D-RMS is the problem to be solved herein. According to the definition of the division point, the area sizes of the subsystem 1 and the subsystem 2 shown in fig. 1 are changed when the optimal position of the division point is searched. And the different positions of the dividing points lead to different combination modes of the rigid machine tool, the flexible machine tool and the reconfigurable machine tool. In the case of a demarcation point optimization, the influence of different machine tool combinations should be taken into account.
By deferring the cut point as much as possible to the back end of the D-RMS system based on the D-RMS concept, reconstruction activity will be reduced. Compared with a flexible machine tool, the reconfigurable machine tool is relatively low in investment cost and more flexible than a rigid machine tool. In addition, production activities during reconfiguration benefit from additional functional investment in the configuration design process (flexible machines can be selected to achieve multiple functional requirements). In general, the capital cost of flexible machines is much more expensive than rigid and reconfigurable machines.
Therefore, the demarcation point decision optimization problem herein aims to find a balance between additional function investment and reconstruction effect, i.e. a balance between machine tool investment cost, machine tool reconstruction time and delay reconstruction characteristics, thereby obtaining a demarcation point constraint condition as shown in formula (1), 1< x < N formula (1), wherein x is the position of the demarcation point of the D-RMS configuration corresponding to the workpiece family, and N is the number of processes of the D-RMS configuration.
Step S2: and determining a multi-objective optimization boundary of the D-RMS configuration design according to the boundary point constraint condition and the influence of the boundary point on the D-RMS optimization target.
The method comprises the steps of analyzing the influence of a demarcation point on a D-RMS configuration design multi-objective optimization parameter by taking the existing workpiece family as input based on the demarcation point constraint condition 1< x < N, and determining the D-RMS configuration design multi-objective optimization boundary.
For example, to develop a simple and profound D-RMS configuration design multi-objective optimization model, the following assumptions are made:
(1) D-RMS is designed and built around a specific family of parts.
(2) Three machine tools, namely a rigid machine tool, a flexible machine tool and a reconfigurable machine tool, are adopted. The flexible machine tool is the most expensive in investment cost, the rigid machine tool is the least expensive in investment cost, and the investment cost of the reconfigurable machine tool is between that of the flexible machine tool and that of the rigid machine tool.
(3) D-RMS is more concerned about convertibility, and D-RMS configuration design multi-objective optimization ignores scalability-related factors, namely, only considers the machine type of each process in the D-RMS configuration and does not consider the number of machines of each process.
(4) The formation of workpiece families is beyond the scope of this document. It is assumed that the workpiece family information is known and contains delayed reconstruction features. To maintain versatility, all workpieces of the selected workpiece family have the same process path length.
(5) A certain configuration of D-RMS is used to produce only a certain workpiece from a family of workpieces, and other workpieces may be produced by reconstructing into other configurations.
(6) The reconstruction activities only occur in the reconfigurable machine tool in subsystem 2, i.e. only machine tool level reconstruction is considered in the proposed D-RMS configuration design multi-objective optimization model.
(7) For simplicity, one process in the D-RMS configuration is tied to a demarcation point, and processes after the demarcation point are divided into subsystems 2.
The optimization boundary of the D-RMS configuration design multi-objective optimization model can be determined through the assumption, namely the boundary point is bound with the last process and the boundary point of the subsystem 1 shown in FIG. 1, and the processes after the boundary point are divided into the subsystem 2.
Step S3: and constructing a D-RMS configuration design multi-objective optimization model according to the D-RMS configuration design multi-objective optimization parameters and the optimization boundaries.
Based on the definition of the cut-point and the above assumptions, the cut-point optimization process will balance investment cost, reconstruction time, and delayed reconstruction, which is a typical multi-objective optimization problem.
A D-RMS configuration design multi-objective optimization model is shown in formulas (2), (3), (4) and (5), and strives to simultaneously minimize investment cost, reconstruction cost and reconstruction time and maximize delayed reconstruction. The only demarcation point constraint condition of the D-RMS configuration design multi-objective optimization model is shown as a formula (1), wherein a decision variable x is an integer.
max Delayed recognition ═ x formula (5).
The first objective of the D-RMS configuration design multi-objective optimization model is shown as a formula (2) and is used for analyzing the influence of the change of the dividing point on the investment cost of the machine tool. Equation (2) item 1 calculates the investment costs of the rigid machine and the flexible machine in the subsystem 1. The investment cost of the rigid machine tool is determined as the basic investment cost of the machine tool,namely the parameter ICnmN represents the number of steps. MnThe number of functions included in the step n can be calculated from the corresponding workpiece family. For example, for subsystem 1, Mn1 denotes that the process is a rigid machine tool, Mn>1 indicates that the process is a flexible machine tool. Likewise, the second term of equation (2) calculates the investment cost of the reconfigurable machine in subsystem 2, where M isnDivide by 2 rounds represents a moderate investment cost for reconfigurable machines relative to rigid and flexible machines.
The second objective of the D-RMS configuration design multi-objective optimization model is shown in the formula (3) and is used for analyzing the problem of machine tool reconstruction cost. For example, without knowing the specific reconfiguration requirements of the system at the configuration design stage, all reconfiguration activities between workpieces within a workpiece family in the subsystem 2 are calculated in a machine tool reconfiguration cost optimization process. RC (resistor-capacitor) capacitornmm'Is the reconstruction cost between the two functions, i.e. the basic reconstruction cost. The reconstruction cost per process is related to the number of functions and may be based on MnAnd calculating the parameter N as the number of the steps of D-RMS.
The third objective of the D-RMS configuration design multi-objective optimization model (4) is to minimize the reconstruction time during the D-RMS configuration, similar to the reconstruction cost calculation. However, the reconstruction activities between two workpieces within a family of workpieces may be performed simultaneously. Thus, while the reconstruction activity may occur at any stage of the subsystem 2, in reconstructing between two workpieces, only the maximum reconstruction time, i.e., max { RT }, should be calculatednpp'L n }, wherein RTnpp'For the n-th process, the reconstruction time from p work piece to p' work piece.
The fourth objective of the D-RMS configuration design multi-objective optimization model is shown as a formula (5), and is used for analyzing the characteristics of delay reconstruction in the optimization process of the demarcation point, and is also the only objective of pursuing the maximum value, namely, postponing the demarcation point as far as possible, wherein the parameter P is the number of workpieces contained in a workpiece family.
The above equations (2) to (5) are to construct the D-RMS configuration design multi-objective optimization model, and can analyze a plurality of targets of the D-RMS configuration design multi-objective optimization model to optimize the demarcation point, so that four targets (machine tool investment cost, machine tool reconstruction time and delay reconstruction characteristic) of the D-RMS configuration design multi-objective optimization model are balanced. The model integrates the characteristics of machine tool investment cost, machine tool reconstruction time and delay reconstruction, provides model support for D-RMS configuration design, guides the practical process of D-RMS, and improves the reconstruction efficiency and the operation efficiency of D-RMS.
Step S4: and solving the D-RMS configuration design multi-objective optimization model by adopting NSGA-II to obtain the pareto frontier.
In one example, this step may include:
step P1: initializing NSGA-II parameters and parameter ranges;
step P2: binary encoding is used to generate a parent population with Q individuals. The design of a multi-objective optimization model according to the proposed D-RMS configuration is a single decision variable problem, and binary coding can be adopted for the unique decision variable x (x is an integer). For example, if x is 2, the corresponding binary is [0010 ]; if x is 5, the corresponding binary code is [0101 ]. Then, a random parent population with Q individuals is generated based on binary encoding, Q being an integer.
Step P3: calculating a target value of a D-RMS configuration design multi-objective optimization model of each individual in the parent population and the number of dominant solutions of each individual, wherein each individual is distributed to different leading edges according to the number of dominant solutions, and for each leading edge, the individuals on the leading edge are sorted according to the crowdedness.
Step P4: and when a preset maximum iteration algebra is reached, completing the multi-objective optimization of the D-RMS configuration design and obtaining the pareto frontier, otherwise, executing the step P5. Wherein the pareto optimal solution is a point in the pareto frontier.
Step P5: and selecting, crossing and mutating the parent population by adopting an elite strategy to obtain the offspring population.
For example, an individual at the first leading edge may be selected and eliminated if the cut point constraint 1< x < N is not met; selecting P individuals by using a binary tournament selection method, and carrying out cross and polynomial variation on the P individuals to obtain a progeny population; wherein P is a positive integer. The feasibility of individual selection of the offspring population can be ensured.
Step P6: merging the parent population and the offspring population into a new population twice the size of the offspring population, returning to step P3. For example, step P5 obtains P-sized offspring populations, and combining the parent population and the offspring population may form a new population of 2P size. And then returning the new population as a parent population to the step P3 for fast non-dominated sorting again until reaching a preset maximum iteration algebra, finishing the multi-objective optimization of the D-RMS configuration design and obtaining the pareto frontier.
Step S5: and randomly selecting points in the pareto frontier as a D-RMS configuration design multi-objective optimization scheme to complete the D-RMS configuration design based on multi-objective optimization.
For example, in the process route of the existing workpiece family shown in fig. 3, the dividing point (decoupling point) is located after the process step 3.
The values of the relevant parameters of the optimization model in different experimental scenarios (including experimental scenario 1 and experimental scenario 2) are shown in table 1:
TABLE 1
Solving with NSGA-II gives a corresponding solution to the pareto frontier for experimental scenario 1 as shown in fig. 4. As shown in fig. 4, the method comprises three schemes, namely scheme 1, the investment cost of the machine tool is 18, the reconstruction cost of the machine tool is 9, the reconstruction time of the machine tool is 3, and the delay reconstruction characteristic is 3; according to the scheme 2, the investment cost of a machine tool is 20, the reconstruction cost of the machine tool is 6, the reconstruction time of the machine tool is 3, and the delay reconstruction characteristic is 4; and in the scheme 3, the investment cost of the machine tool is 22, the reconstruction cost of the machine tool is 3, the reconstruction time of the machine tool is 3, and the delay reconstruction characteristic is 5.
Solving with NSGA-II gives a corresponding solution to the pareto front for experimental scenario 2 as shown in fig. 5. As shown in fig. 5, the method comprises three schemes, namely scheme 1, the investment cost of the machine tool is 90, the reconstruction cost of the machine tool is 27, the reconstruction time of the machine tool is 9, and the delay reconstruction characteristic is 3; according to the scheme 2, the investment cost of a machine tool is 100, the reconstruction cost of the machine tool is 18, the reconstruction time of the machine tool is 8, and the delay reconstruction characteristic is 4; in the scheme 3, the investment cost of the machine tool is 110, the reconstruction cost of the machine tool is 9, the reconstruction time of the machine tool is 6, and the delay reconstruction characteristic is 5.
The point on the pareto frontier is randomly selected as the optimal solution, for example, taking the solution 2 of the experimental scenario 1 and the solution 3 of the experimental scenario 2 as examples, the corresponding D-RMS configuration designs are shown in fig. 6 and fig. 7, respectively.
The multi-objective optimization method for the D-RMS configuration design determines boundary point constraint conditions of the multi-objective optimization of the D-RMS configuration design based on workpiece family information; determining a D-RMS configuration design multi-objective optimization boundary according to the boundary point constraint condition and the influence of the boundary point on the D-RMS configuration design multi-objective optimization; constructing a D-RMS configuration design multi-objective optimization model according to the D-RMS configuration design multi-objective optimization parameters and the optimization boundary; solving the D-RMS configuration design multi-objective optimization model by adopting NSGA-II to obtain a pareto frontier; and randomly selecting points in the pareto frontier as a D-RMS configuration design multi-objective optimization scheme to complete D-RMS configuration design multi-objective optimization. The invention comprehensively considers the design factor of the D-RMS configuration to optimize the delay reconstruction boundary point, and fully exerts the advantages of the D-RMS so as to improve the reconstruction efficiency and the operation efficiency of the D-RMS.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A method for multi-objective optimization of D-RMS configuration design, the method comprising:
determining a demarcation point constraint condition of multi-objective optimization of D-RMS configuration design based on workpiece family information;
determining a multi-objective optimization boundary of the D-RMS configuration design according to the boundary point constraint condition and the influence of the boundary point on the D-RMS configuration design optimization target;
constructing a D-RMS configuration design multi-objective optimization model according to the D-RMS configuration design multi-objective optimization parameters and the optimization boundary;
solving the D-RMS configuration design multi-objective optimization model by adopting NSGA-II to obtain a pareto frontier;
and randomly selecting points in the pareto frontier as a D-RMS configuration design multi-objective optimization scheme to complete the D-RMS configuration design based on multi-objective optimization.
2. The method of claim 1, wherein the constraint on the boundary point is 1< x < N, where x is the position of the boundary point of the D-RMS configuration corresponding to the workpiece family and N is the number of steps of the D-RMS configuration.
3. The D-RMS configuration design multi-objective optimization method of claim 1, wherein the multi-objective optimization parameters include machine tool investment cost, machine tool reconstruction time, and delay reconstruction characteristics.
4. The D-RMS configuration design multi-objective optimization method according to claim 3, characterized in that said machine tools include rigid, flexible and reconfigurable machine tools.
5. The D-RMS configuration design multi-objective optimization method according to claim 4, wherein said demarcation points are used to balance the relationship between said machine tool investment cost, machine tool reconstruction time and delay reconstruction.
6. The D-RMS configuration design multi-objective optimization method according to claim 1, wherein solving the D-RMS configuration design multi-objective optimization model using NSGA-II to obtain a pareto frontier comprises:
step P1: initializing NSGA-II parameters and parameter ranges;
step P2: generating a parent population with Q individuals by adopting binary coding;
step P3: calculating the value of a D-RMS configuration design multi-objective optimization model of each individual in the parent population and the number of dominant solutions of each individual, wherein each individual is distributed to different leading edges according to the number of dominant solutions, and for each leading edge, the individuals on the leading edge are sorted according to the crowdedness;
step P4: when a preset maximum iteration algebra is reached, completing the multi-objective optimization of the D-RMS configuration design and obtaining a pareto frontier, otherwise, executing a step P5;
step P5: selecting, crossing and mutating the parent population by adopting an elite strategy to obtain a offspring population;
step P6: merging the parent population and the offspring population into a new population twice the size of the offspring population, returning to step P3.
7. The D-RMS configuration design multi-objective optimization method according to claim 6, wherein said step P5 includes:
selecting an individual positioned at the first front edge, and eliminating the individual if the individual does not meet the boundary point constraint condition;
p individuals are selected by a binary tournament selection method, and are subjected to cross and polynomial variation to obtain a progeny population, wherein P is a positive integer.
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