Qiuyu Zhang

A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems

Modern manufacturing systems have to cope with dynamic changes and uncertainties such as machine breakdown, hot orders and other kinds of disturbances. Holonic manufacturing systems (HMS) provide a flexible and decentralised manufacturing environment to accommodate changes dynamically. HMS is based on the notion of holon, an autonomous, co-operative and intelligent entity which is able to collaborate with other holons to complete the tasks. HMS requires a robust coordination and collaboration mechanism to allocate available resources to achieve the production goals. In this paper, a basic integrated process planning and scheduling system, which is applicable to the holonic manufacturing systems is presented. A basic architecture of holonic manufacturing system is proposed from the viewpoint of the process planning and the scheduling systems. Here, the process planning is defined as a process to select suitable machining sequences of machining features and suitable operation sequences of machining equipments, taking into consideration the short-term and long-term capacities of machining equipments. A fuzzy inference system (FIS), in choosing alternative machines for integrated process planning and scheduling of a job shop in HMS, is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machines capacity. The mean time for failure (MTF) values are input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalised based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilisation machines, sometimes faced by unreliable machine, the hybrid particle swarm optimisation (PSO) with differential evolution (DE) has been applied to balance the load for all the machines. Simulation studies show that the proposed system can be used as an effective way of choosing machines in integrated process planning and scheduling.

A hybrid algorithm based on PSO and simulated annealing and its applications for partner selection in virtual enterprise

Partner selection is a very popular problem in the research of virtual organization and supply chain management, the key step in the formation of virtual enterprise is the decision making on partner selection. In this paper, a activity network based multi-objective partner selection model is put forward. Then a new heuristic algorithm based on particle swarm optimization(PSO) and simulated annealing(SA) is proposed to solve the multi-objective problem. PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search(by self experience) and global search(by neighboring experience), possessing high search efficiency. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum of SA. We compare the hybrid algorithm to both the standard PSO and SA models, the simulation results show that the proposed model and algorithm are effective.

A Hybrid Self-Adaptive Pso Algorithm and its Applications for Partner Selection in Holonic Manufacturing System (HMS)

Partner selection is a very popular problem in the research of HMS, the key step in the formation of HMS is the decision making on partner selection. In this paper, collaboration process between holons is modeling with contract net protocol; and an activity network based multi-objective partner selection model is put forward. Then a new hybrid self-adaptive PSO (HAMPSO) algorithm based on particle swarm optimization (PSO) and genetic algorithm (GA) is proposed to solve the multi-objective problem. PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. GA provides the optimization parameter of PSO to get a good performance during the hybrid search process. HAMPSO implements easily and reserves the generality of PSO and GA. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum by velocity mutation. We compare the hybrid algorithm to both the standard PSO and GA model. The simulation results show that the proposed model and algorithm are effective. Moreover, such HAMPSO can be applied to many combinatorial optimization problems by simple modification

A Novel Genetic Algorithm to Optimize QoS Multicast Routing

Multicast routing service is becoming a key requirement of computer networks supporting multimedia applications. And multicast routing problem has been demonstrated technically as a NP-complete. This paper proposes a novel QoS-based multicast routing algorithm using the genetic algorithms (GA), which has the following characteristics: the preprocessing mechanism, the tree structure coding method, novel heuristic algorithms for creation of random individuals crossover, and the instructional mutation process. The result of simulation shows that the proposed GA-based algorithm has the advantage over the conventional algorithms in efficiency.

A Scheduling Holon Modeling Method with Petri Net and its Optimization with a Novel PSO-GA Algorithm

Holonic manufacturing systems (HMS) provide a flexible and decentralized manufacturing environment to accommodate changes dynamically. This paper presents a framework to model and control HMS based on Petri net and MAS theory. A time Petri net (TPN) model was proposed to achieve this goal. A TPN represents a set of established contracts among the agents in HMS to fulfil an order. A scheduling architecture which integrates TPN models and AI techniques is proposed. By introducing dynamic individuals into the reproducing pool randomly according to their fitness, a variable population-size genetic algorithm is presented to enhance the convergence speed of GA. Based on the novel GA and the particle swarm optimization (PSO) algorithms, a hybrid PSO-GA algorithm (HPGA) is also proposed in this paper. Simulation results show that the proposed method is effective for the optimization problems

A multi-agent model for the order driven agile manufacturing systems and order form selection algorithm

The manufacturing industry is entering a stage of change which is driven by the competitive environment of continuous and unpredictable events. Efficient and effective manufacturing enterprise organization methods are highly desired to be responsive to the physical and software disturbances. A multi-agent model for the order driven agile manufacturing systems and order form selection algorithm are provided in this paper. The information of component agent and detailed schedule plan in the architecture are presented. Shop floor activity and its corresponding control flow are given to support the interaction for the agent community. Particle Swarm Optimization (PSO) algorithm for the order form selection for the order driven system is used to intelligently select the suitable order for the system. Simulation results show that the architecture model and its optimization algorithm are effective to the problem.

An Improved Particle Swarm Optimization-Based Approach for Production Scheduling Problems

Job-shop scheduling problem (JSSP) is very common in a discrete manufacturing environment. It deals with multi-operation models, which are different from the flow shop models. It is usually very hard to find its optimal solution. In this paper, a new hybrid approach in dealing with this job-shop scheduling problem based on particle swarm optimization (PSO) and simulated annealing (SA) technique is presented. PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum of SA. We compare the hybrid algorithm to both the standard PSO and SA models, computer simulations have shown that the proposed hybrid approach is of high speed and efficiency

Study on Universal Steganalysis for BMP Images Based on Multi-Domain Features

Steganslysis is an important research issue in information security. Aimed at the most commonly used cover media, digital images, the paper proposed a new method of universal steganalysis method based on multi-domain features. Features were extracted from spatial domain and DWT domain to overcome the drawbacks of steganslysis algorithm for specific steganography, such as high complexity and low correct detecting ratio. Experiment results show that the proposed algorithm can solve the low detecting problem and achieve a better reliability on low embedding rates.

An Improved Particle Swarm Optimization(PSO) Algorithm and Fuzzy Inference Systems Based Approach to Process Planning and Production Scheduling Integration in Holonic Manufacturing System (HMS)

New paradigms for manufacturing system control are required that provide manufacturers with the adaptability and responsiveness required to compete in todays market. In this paper, an integrated process planning and scheduling system, which is applicable to the holonic manufacturing system is presented. Basic architecture of the target holonic manufacturing system is discussed from the viewpoint of the process planning and the scheduling systems. Process planning are proposed to select suitable machining sequences of machining features and suitable sequences of machining equipment, taking into consideration of future schedules of machining equipment. A fuzzy inference system (FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop in HMS is presented. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, an improved particle swarm optimization (PSO) have been used to balance the load for all the machines. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling

A Novel Task Allocation Problem Solution with PSO Algorithm for Holonic Manufacturing System

The dynamic re-organization of holons is a key element of current research on HMS. Dynamic intelligent reconfiguration is important for holonic control. This paper extends the mechanism of virtual clustering to the reorganization of holons and uses contract net-based task allocation protocol to efficiently deal with the communication and coordination problems during task-oriented clustering. The PSO-based virtual clustering optimization algorithm described in this paper can solve the optimization problem of task allocation on the basis of global optimization. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum of SA. We compare the PSO algorithm to both GA and SA models, the simulation results show that the proposed model and algorithm are effective