Zhong Yuguang

A PSO algorithm for multi-objective hull assembly line balancing using the stratified optimization strategy

Hull assembly line (HAL) is a mixed-model assembly line on which different hull blocks can be assembled at the same time. Aiming at the balance problem of HAL, minimizing the cycle time, minimizing the static load balancing between workstations, minimizing the dynamic load balancing in all workstations, and minimizing the multi-station associated complexity are considered as optimization objectives. An improved discrete particle swarm optimization (IDPSO) algorithm based on the stratified optimization idea is developed for scheduling of the multi-objective problem. In the proposed algorithm, particles were coded by a two-dimensional task-oriented representation method, and then collaboration and competition of particle individuals are simulated by crossover and mutation operators in the genetic algorithm (GA). The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Finally, a practical case is used to analyze the effectiveness and feasibility of the stratified scheduling strategy. 2

Clustering and group selection of interim product in shipbuilding

Cellular manufacturing of the interim products based on group technology (GT) is a useful way to increase the productivity and improving the process flows of shipbuilding. In this paper, we analyze the similarity relationship on interim product to form product families and establish requirement mode of similarity measurement based on shipbuilding GT. The classifier of ART2 artificial neural network is proposed on the basis of the adaptive resonance theory. It can classify and identify automatically the input data through analyzing the characteristics of interim product in shipbuilding. With the modified algorithm, the interim product can easily and efficiently be formed product families and controlled to group related interim product into families in reason by similar coefficient. Meanwhile, through analyzing the production constraint to form assembly cell, the set of evaluating indexes is founded whose weights are decided by the entropy weight method and the comprehensive assessment values of product families design schemes can be computed to judge the final optimization scheme through the grey correlation analysis. The formation of sub-assembly interim product family in a bulk carrier is taken as an example to verify the proposed method, and the results show that it is an effective method for solving the group manufacturing problem in shipbuilding activities.

Optimisation of block erection scheduling based on a Petri net and discrete PSO

The shipyard block erection system (SBES) is a typical discrete-event dynamic system. To model multiprocessing paths and a concurrent assembly procedure, a timed Petri net (TPN) is proposed. The definition of a Petri net is extended to accord with the real-world SBES organisation. The basic TPN modules are presented to model the corresponding variable structures in the SBES, and then the scheduling model of the whole SBES is easily constructed. A modified discrete particle swarm optimisation (PSO) based on the reachability analysis of Petri nets is developed for scheduling of the SBES. In the proposed algorithm, particles are coded by welding transitions and selecting places of the TPN model, and then the collaboration and competition of particle individuals is simulated by crossover and mutation operators in a genetic algorithm. Numerical simulation suggests that the proposed TPN–PSO scheduler can provide an improvement over the conventional scheduling method. Finally, a case study of the optimisation of a back block erection process is provided to illustrate the effectiveness of the method.

Application of neural network method to process planning in ship pipe machining

Based on artificial neural network for process planning decision in ship pipe manufacturing, a novel method is established by analyzing process characteristics of the ship pipe machining. The process knowledge of pipe machining is shifted from the expression of the external rules to the description of the internal net weight value in order for the net inferring engine to decide the process route of pipe machining rapidly and rightly. Simulation shows that the method can resolve problems of process decision, and overcome the drawbacks of “matching difficulty” and “combination explosion” in traditional intelligent CAPP based on symbol reasoning.