This paper presents a new multi-attribute decision-making (MADM) method, named as R-method, for ranking of Pareto-optimal solutions and selecting the best solution in multi- and many-objective optimization problems. The compromise among the optimization objectives is different for each Pareto-optimal solution and, hence, the solution that has the best compromise among the objectives can be considered as the best solution. The proposed R-method is used to identify such best compromise solution. The method ranks the objectives based on their importance for the given optimization problem and ranks the alternative solutions (i.e. Pareto-optimal solutions) based on their data corresponding to the objectives. The ranks assigned to the objectives and the ranks assigned to the alternative solutions with respect to each of the objectives are converted to appropriate weights and the final composite scores of the alternative solutions are computed using these weights. The final ranking of alternative solutions is done based on the composite scores. The steps of the proposed method are described along with a pseudocode. Three examples are considered to demonstrate and validate the proposed method. The first example contains 4-objectives and 50 alternative solutions, the second example contains 6-objectives and 30 alternative solutions, and the third example contains 3-objectives and 25 alternative solutions. The results of the proposed method are compared with those of the other widely used MADM methods for the three examples considered. Also, the proposed method is compared with four well-known ranking methods to demonstrate its rationality in assigning weights to the ranks of the objectives and the alternative solutions. The proposed method is comparatively easier, more logical, and can be used for choosing the best compromise solution in multi- and many-objective optimization problems.

Ranking of Pareto-optimal solutions and selecting the best solution in multi- and many-objective optimization problems using R-method

Energy-efficient scheduling of distributed production systems has become a common practice among large companies with the advancement of economic globalization and green manufacturing. Nevertheless, energy-efficient scheduling of distributed permutation flow-shop problem with limited buffers (DPFSP-LB) does not receive adequate attention in the relevant literature. This paper is therefore the first attempt to study this DPFSP-LB with objectives of minimizing makespan and total energy consumption ( T E C ). To solve this energy-efficient DPFSP-LB, a Pareto-based collaborative multi-objective optimization algorithm (CMOA) is proposed. In the proposed CMOA, first, the speed scaling strategy based on problem property is designed to reduce T E C . Second, a collaborative initialization strategy is presented to generate a high-quality initial population. Third, three properties of DPFSP-LB are utilized to develop a collaborative search operator and a knowledge-based local search operator. Finally, we verify the effectiveness of each improvement component of CMOA and compare it against other well-known multi-objective optimization algorithms on instances. Experiment results demonstrate the effectiveness of CMOA in solving this energy-efficient DPFSP-LB. Especially, the CMOA is able to obtain excellent results on all problems regarding the comprehensive metric, and is also competitive to its rivals regarding the convergence metric.

A Pareto-based collaborative multi-objective optimization algorithm for energy-efficient scheduling of distributed permutation flow-shop with limited buffers

Energy-efficient scheduling of distributed production systems has become a common practice among large companies with the advancement of economic globalization and green manufacturing. Nevertheless, energy-efficient scheduling of distributed permutation flow-shop problem with limited buffers (DPFSP-LB) does not receive adequate attention in the relevant literature. This paper is therefore the first attempt to study this DPFSP-LB with objectives of minimizing makespan and total energy consumption ( T E C ). To solve this energy-efficient DPFSP-LB, a Pareto-based collaborative multi-objective optimization algorithm (CMOA) is proposed. In the proposed CMOA, first, the speed scaling strategy based on problem property is designed to reduce T E C . Second, a collaborative initialization strategy is presented to generate a high-quality initial population. Third, three properties of DPFSP-LB are utilized to develop a collaborative search operator and a knowledge-based local search operator. Finally, we verify the effectiveness of each improvement component of CMOA and compare it against other well-known multi-objective optimization algorithms on instances. Experiment results demonstrate the effectiveness of CMOA in solving this energy-efficient DPFSP-LB. Especially, the CMOA is able to obtain excellent results on all problems regarding the comprehensive metric, and is also competitive to its rivals regarding the convergence metric. • A green criterion is considered in the studied problem. • A new constraint of the limited buffers is introduced into this problem. • A multi-objective optimization algorithm is presented to solve this problem. • An effective energy saving strategy is proposed. • An initialization strategy and local search strategy are proposed. 

With the boosting development of the advanced manufacturing industry in the world, the original production pattern transformed from the traditional industries into the intelligence mode is completed with the least delay possible, which are still facing new challenges. The timeliness, stability, and reliability of them are significantly restricted due to the lack of real-time communication. Therefore, a model framework of intelligent workshop manufacturing system based on a digital twin is proposed in this paper, driving the deep information integration among the physical entity, data collection, and information decision-making. The traditional digital twin of conceptualization and fuzziness needs to be refined, optimized, and upgraded on the basis of the four-dimension collaborative model thinking. The model framework of a refined nine-layer intelligent digital twin is established. Firstly, the physical evaluation is refined into entity layer, auxiliary layer, and interface layer, scientific managing the physical resources and the instrument, and coordinating the overall system. Secondly, dividing the data evaluation into the data layer and the processing layer can greatly improve the flexible response-ability and ensure the synchronization of the real-time data. Finally, the system evaluation is subdivided into information layer, algorithm layer, scheduling layer, and functional layer, developing flexible manufacturing plan more reasonably, shortening the production cycle, and reducing logistics cost. Simultaneously, combining SLP and artificial bee colonies is applied to investigate the production system optimization of the textile workshop. The results indicate that the production efficiency of the optimized production system is increased by 34.46%.

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Construction of Intelligent Integrated Model Framework for the Workshop Manufacturing System Via Digital Twin

Process parameters have a significant effect on surface integrity, which determines the service performance of the parts. To improve surface integrity, the process parameters are determined: 1) by experienced engineers directly, 2) based on the Pareto frontier automatically constructed by swarm intelligence algorithms. However, as the Pareto frontier contains many non-dominated solutions, the final parameters are still determined by experienced engineers, which reduces the intelligence level. Therefore, an intelligent process parameters determination method based on multi-algorithm fusion is proposed towards minimal surface residual stress in feed or transverse direction (Rsf, Rst) and surface roughness (Ra) in five-axis milling. Firstly, the Improved Generalized Regression Neural Network (IGRNN), which enhances the nonlinear mapping capability even in dealing with a small batch of experiments, is proposed to predict the Rsf, Rst, and Ra with certain inputs (including lead angle, tilt angle, cutting depth, feed speed, and spindle speed). Then based on the proposed model, the Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II), which improves the uniformity of the Pareto frontier, is used to obtain a series of non-dominated process parameters. Finally, the optimal parameters are determined by the Principal Component Analysis (PCA) without manual weight assignment for Rs and Ra. By comparing with the second-best one, although the Rsf decreases by 0.33%, which is still able to obtain negative residual stress, the Rst and Ra are greatly improved by 9.3% and 47.94%, respectively. The proposed method could improve the intelligent level of process parameters determination and the service performance of the parts. Furthermore, it lays a foundation for the realization of intelligent manufacturing. 

An intelligent process parameters determination method based on multi-algorithm fusion: a case study in five-axis milling

With the intensification of globalization, the competition among various manufacturing enterprises has become increasingly fierce, enterprises are developing in the direction of the product diversification, zero inventory or low inventory, and scheduling in production management has become more complicated. In this paper, machine and workpiece were as objects to study the problem of workshop scheduling in intelligent manufacturing environment. The resource scheduling model of intelligent manufacturing workshop was established with the goal of minimizing the maximum completion time, tardiness, machine load and energy consumption. The Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm was improved, and the evaluation function was established based on ranking level and crowding degree, then the competition mechanism was introduced. Random mutation strategy and crossover method based on process and machine was adopted to generate a new generation of populations. The elitist retention strategy was improved, the variable proportion method was designed to determine the probability, and the optimal solution is determined by the Analytic Hierarchy Process (AHP). The benchmark cases and practical production and processing problems were tested to verify the superiority and effectiveness of the improved algorithm.

Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm

In view of the fact that traditional job shop scheduling only considers a single factor, which affects the effect of resource allocation, the dual-resource integrated scheduling problem between AGV and machine in intelligent manufacturing job shop environment was studied. The dual-resource integrated scheduling model of AGV and machine was established by comprehensively considering constraints of machines, workpieces and AGVs. The bidirectional single path fixed guidance system based on topological map was determined, and the AGV transportation task model was defined. The improved A* path optimization algorithm was used to determine the optimal path, and the path conflict elimination mechanism was described. The improved NSGA-II algorithm was used to determine the machining workpiece sequence, and the competition mechanism was introduced to allocate AGV transportation tasks. The proposed model and method were verified by a workshop production example, the results showed that the dual resource integrated scheduling strategy of AGV and machine is effective.

Dual-resource integrated scheduling method of AGV and machine in intelligent manufacturing job shop

The invention discloses a workshop dual-resource integrated scheduling method and system in an intelligent manufacturing environment. The workshop dual-resource integrated scheduling method comprisesthe following steps: establishing a topological model of an AGV driving map; establishing a bidirectional single-path guiding system according to the topological model; generating a driving path fromthe bidirectional single-path guide system by adopting an improved A * path search algorithm; obtaining a machining workpiece sequence of the machine tool according to an improved NSGA-II algorithm; establishing a path conflict elimination strategy based on an improved time window method; and according to the driving path, the machining workpiece sequence and the path conflict elimination strategy, completing AGV scheduling, and realizing transportation of the workpieces from the warehouse to the machine, from the machine to the machine and from the machine to the warehouse. According to the workshop dual-resource integrated scheduling method in the intelligent manufacturing environment, the resource allocation efficiency and accuracy are improved, and the production cost of an enterpriseis reduced.

Workshop dual-resource integrated scheduling method and system in intelligent manufacturing environment

The invention discloses a flattening and shaping conveyor. The flattening and shaping conveyor comprises a conveying mechanism and a bag-pressing and shaping mechanism. The conveying mechanism comprises a conveyor frame, a driving roller, a driven roller, a conveyor belt and a gear motor. The conveyor frame comprises a conveying frame and a bracket. The flattening and shaping conveyor has the advantages of simple and compact structure, flexible operation, small occupied area and the like.

Flattening and shaping conveyor

The invention discloses a bionic fire-fighting robot, and belongs to the technical field of machinery. The bionic fire-fighting robot comprises a robot body, mechanical legs and an operating mechanism; the mechanical legs are symmetrically arranged on the two sides of the robot body, and the robot body is rotatably connected with the mechanical legs; the operating mechanism comprises a bottom plate, a high-pressure spraying gun and a cutting device; the high-pressure spraying gun and the cutting device are rotatably connected with the bottom plate; and the bottom plate is fixed to the robot body. The bionic fire-fighting robot can well adapt to a fire environment and has diverse functions.

Li Yadong

Papers

Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm

Dual-resource integrated scheduling method of AGV and machine in intelligent manufacturing job shop

Workshop dual-resource integrated scheduling method and system in intelligent manufacturing environment

Flattening and shaping conveyor

Bionic fire-fighting robot