Yahong Yang

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

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

Application of A Hybrid Particle Swarm Optimization Algorithm to Dynamic Holon Reconfiguring Problem

In this paper, dynamic re-configuration and task optimization of holonic manufacturing system (HMS) model is put forward. The concept of dynamic virtual clustering is extended to the control process of a holarchy or holonic organization. A task-oriented clustering mechanism and a corresponding optimization algorithm are presented as an efficient approach to the holonic control in HMS domain. A hybrid algorithm based on PSO for holon task allocation is described to support the optimum organization of a holarchy. Convergence property of PSO is analyzed to guide the parameter selection of it. The simulation results show that the proposed model and algorithm are effective

Timed Petri-Net(TPN) based scheduling Holon and its solution with a hybrid PSO-GA based evolutionary algorithm(HPGA)

Modern manufacturing systems have to cope with dynamic changes and uncertainties such as machine break down, hot orders and other kinds of disturbances. Holonic manufacturing systems (HMS) provide a flexible and decentralized manufacturing environment to accommodate changes dynamically. In this paper, A new class of Time Petri Nets(TPN), Buffer-nets, for defining a Scheduling Holon is proposed, which enhances the modeling techniques for manufacturing systems with features that are considered difficult to model. The proposed novel GA algorithm performs the population alternation according to the features of the evolution of the populations in natural. Simulation results show that the proposed GA is more efficient than standard GAs. The proposed HPGA synthesizes the merits in both PSO and GA. The simulation results of the example show that the methods to scheduling holon are effective for fulfilling the scheduling problem.

Petri Net Modeling Method to Scheduling Problem of Holonic Manufacturing System (HMS) and Its Solution with a Hybrid PSO Algorithm

Holonic manufacturing is a highly distributed control paradigm based on a kind of autonomous and cooperative entity called “holon”. It can both guarantee performance stability, predictability and global optimization of hierarchical control, and provide flexibility and adaptability of heterarchical control. In this paper, A new class of Time Petri Nets(TPN), Buffer-nets, for defining a Scheduling Holon is proposed, A TPN represents a set of established contracts among the agents in HMS to fulfill an order. To complete processing of orders, liveness of TPNs must be maintained. As different orders may compete for limited resources, conflicts must be resolved by coordination among TPNs. A liveness condition for a set of TPNs is provided to facilitate feasibility test of commitments. which enhances the modeling techniques for manufacturing systems with features that are considered difficult to model. 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 are effective for the optimization problems.

A database realization method to product information model based on STEP

Product data exchange and interfacing between different CAD/CAM systems are very important to the development of concurrent integrated design. In this paper, an integrated STEP-based production information model for mechanical systems is presented. First, an architecture of this system is established using a three-layer architecture (application, logic and physical layers) recommended by STEP, and an information model using database implementation approach is defined using STEPs EXPRESS language. This model includes GIM (general information model), SIM (structure information model), SCM (shape construction model), PFM (physical feature model), and MIM (management information model). Then, the database implementation method to information model is analyzed thoroughly, which include how to build data dictionary, how to transfer EXPRESS entity to base table in relational database and how to access product data in database. The information model and its application can make heterogeneous CAD system integration more convenient.

An information modeling approach in conceptual design for cooperative design

Information modeling is critical to the integration of conceptual design and process planning. An information model for conceptual design is provided in this paper. Conceptual design is a key activity in early product development. It determines product functions, form, and the basic structure. Since major manufacturing cost is determined in early design, it is critical to be able to assess manufacturability and cost as early as possible in the design process. A literature review of the current status of computer-aided design (CAD) and computer-aided process planning (CAPP) software technologies, reveals the lack of interface standards to enable the integration of these systems. In order to develop interface standards, information models have to be first developed to define the interfaces. An initial information model for conceptual process planning has been developed. This model includes an activity model and an object model for manufacturing process selection, resource selection, and cost and time estimation. The activity model sets the context in which the objects are used for information sharing and exchange. The object model defines classes used in conceptual process planning. The main purpose of developing this model is to initiate the development of standard interface specifications that are necessary for design and process planning integration.