Part Supply Method for Mixed-Model Assembly Lines with Decentralized Supermarkets

In-house part supply affects the efficiency of mixed-model assembly lines considerably. Hence, we propose a reliable Just-In-Time part supply strategy with the use of decentralized supermarkets. For a given production sequence and line layout, the proposed strategy schedules tow train routing and delivery problems jointly to minimize the number of employed town trains and the traveling time, while ensuring that stations never run out of parts. To solve this problem, a mathematical formulation is proposed for each sub-problem aiming at minimizing supply cost. Then, a dynamic programming algorithm for routing and a greedy algorithm for delivery are developed, both of which are of polynomial runtime. Finally, a computational study is implemented to validate the effectiveness of the strategy, and to investigate the effects of the delivery capacity of tow trains and storage capacity of stations on supply cost.

Real-Time Control for CPS of Digital Airplane Assembly with Robust H-Infinity Theory

This study introduces a real-time controller design method under the effects of network time delay and external disturbance. The study first introduces the digital, virtual, intelligent trend of airplane assembly and reveals the status and problems of digital airplane assembly studies. The Cyber-Physical System （CPS） structure is then proposed for digital airplane assembly, and the real-time control issues are discussed. Then, the question of real- time control undertaken by a parallel robot is simplified to a control question with bounded time delay and complex interference, and a mathematical description is presented. Next, a robust Hoo controller with a disturbance degree of decay 7 is designed according to the mathematical description. Finally, a simulation is conducted. All of the experiment results show the feasibility of the above proposed methods.

Synergic Motion Trajectory Planning for Airplane Docking Based on6PURU Parallel Mechanism

Motion planning issues encountered in assembling airplanes by employing the 6PURU parallel mechanism are analyzed in this paper. A sine curve of the change rate of acceleration, which is called as jerk in the following text, is proposed for uniaxial flexible acceleration and deceleration planning based on the optimal time and velocity and acceleration constraints. Compared with other curves, the proposed curve can realize a continuous n-order derivative and the smooth change of the speed and acceleration. The method is computational^y simple and suitable for programming. In addition, a multiaxial coordinated movement scheme is proposed. The motion trajectory is no longer simply split into many single-direction trajectories nor are all single-direction planning trajectories combined directly. The multiaxial coordinated movement scheme aims to achieve synergic movement in multiple directions to ensure smoothness of the movement in the event of a kinematic error when maintaining a stable value. If the movement fails to achieve this goal, driving force mutations will deteriorate the effect of synergic movement. A physical model of the parallel mechanism is developed in simMechanics, and a holistic system model is completed in SIMULINK. The feasibility of the new planning algorithm is simulated and tested, and then, the multiaxial synergic movement planning method is proposed and verified.

Data fusion and simulation-based planning and control in cyber physical system for digital assembly of aeroplane

This paper introduces a CPS application for intelligent aeroplane assembly.At first,the CPS structure is presented,which acquires the characteristics of general CPS and enables“simulation-based planning and control”to achieve high level intelligent assembly.Then the paper puts forward data fusion estimation algorithm under synchronous and asynchronous sampling,respectively.The experiment shows that global optimal distributed fusion estimation under synchronized sampling proves to be closer to the actual value compared with ordinary weighted estimation,and multi-scale distributed fusion estimation algorithm of wavelet under asynchronous sampling does not need time registration,it can also directly link to data,and the error is smaller.This paper presents hybrid control strategy under the circumstance of joint action of the inner and outer loop to address the problems caused by the less controllable feature of the parallel mechanism when undertaking online process simulation and control.A robust adaptive sliding mode controller is designed based on disturbance observer to restrain inner interference and maintain robustness.At the same time,an outer collaborative trajectory planning is also designed.All the experiment results show the feasibility of above proposed methods.

Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

In order to deal with the dynamic production environment with frequent fluctuation of processing time,robotic cell needs an efficient scheduling strategy which meets the real-time requirements.This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process.The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler,which can adjust the scheduling rules according to the current production status.In the process of establishing scheduler,how to choose essential attributes is the main difficulty.In order to solve the low performance and low efficiency problem of embedded feature selection method,based on the application of Extreme Gradient Boosting model(XGBoost)to obtain the adaptive scheduler,an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization(PSO)is employed to acquire the optimal subset of features.The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules.At the same time,it can meet the demand of real-time scheduling.

Approach of hybrid GA for multi-objective job-shop scheduling

In recent years,scholars have made many research results on job-shop scheduling(JSP)problem,especially in single objective such as the maximum completion time.But most of the actual system scheduling problems are more than one object.Therefore,the research of multi-objective scheduling problem is very important and meaningful.In this paper,we proposed a multi-objective scheduling model which adopts weighted sum method to optimize two important indexes(makespan and total flow time).Genetic algorithm(GA)has diversified global search ability,while simulated annealing(SA)combined with tabu search(TS)have intensified capabilities in local neighborhood search.To overcome the drawback of the GA,we proposed a new hybrid GA(NewHGA)which produces initial solutions by GA firstly,and then take SA operator incorporate TS operator to search in the local space.By adding the novel local search strategy,the diversity of solutions will be improved greatly so that it can ensure the algorithm jump out of the local optimal value.We test this algorithm using the benchmark instances of different sizes taken from the OR-Library,and the results show that the algorithm is efficient than another hybrid algorithm.

A dynamic delivery strategy for material handling in mixed-model assembly lines using decentralized supermarkets

Material handling has become one of the major challenges in modern production management.Consequently,this paper intends to investigate the part delivery of mixedmodel assembly lines with decentralized supermarkets and tow trains.Besides,uncertain exception disturbances,including tow train failures and adjustments of the production sequence,are also considered.To solve this problem,a heuristic-based dynamic delivery strategy is proposed,which dynamically schedules the route,departure time,quantities and types of loaded parts for each tour.To evaluate the performance of this strategy,it is used to solve an instance in comparison with the periodic delivery strategy,experimental results are reported and their performances are compared under different metrics.Moreover,a multi-scenario analysis is employed to determinate the long-term decisions,including the number of tow trains and the route layout.Finally,the critical storage is suggested to be set for each station to avoid part starvation resulting from disturbances,and its effect on the delivery performance is investigated.