Intelligent Simulation Modeling of a Flexible Manufacturing System with Automated Guided Vehicles

Although simulation is a very flexible and cost effective problem solving technique, it has been traditionally limited to building models which are merely descriptive of the system under study. Relatively new approaches combine improvement heuristics and artificial intelligence with simulation to provide prescriptive power in simulation modeling. This study demonstrates the synergy obtained by bringing together the "learning automata theory" and simulation analysis. Intelligent objects are embedded in the simulation model of a Flexible Manufacturing System (FMS), in which Automated Guided Vehicles (AGVs) serve as the material handling system between four unique workcenters. The objective of the study is to find satisfactory AGV routing patterns along available paths to minimize the mean time spent by different kinds of parts in the system. System parameters such as different part routing and processing time requirements, arrivals distribution, number of palettes, available paths between workcenters, number and speed of AGVs can be defined by the user. The network of learning automata acts as the decision maker driving the simulation, and the FMS model acts as the training environment for the automata network; providing realistic, yet cost-effective and risk-free feedback. Object oriented design and implementation of the simulation model with a process oriented world view, graphical animation and visually interactive simulation (using GUI objects such as windows, menus, dialog boxes; mouse sensitive dynamic automaton trace charts and dynamic graphical statistical monitoring) are other issues dealt with in the study

An SDS Modeling Approach for Simulation-Based Control

We initiate a study of mathematical models for specifying (discrete) simulation-based control systems. It is desirable to specify simulation-based control systems using a model that is intuitive, succinct, expressive, and whose state space properties are relatively easy computationally. We compare automata-based models for specifying control systems and find that all systems that are currently used (such as finite state machines, communicating hierarchical finite state machines (FSM), communicating finite state machines, and Turing machines) lack at least one of the abovementioned features. We propose using sequential dynamical systems (SDS) - a formalism for representing discrete simulations - to specify simulation-based control systems. We show how to adapt the standard SDS model to specify cell-level controllers for a generic cell. For reasonable flexible manufacturing cells, the SDS-based specification has size polynomial in the size of the cell, while in the worst case the FSM-based specification has size exponential in the size of the cell

An SDS Modeling Approach for Simulation-Based Control

We initiate a study of mathematical models for specifying (discrete) simulation-based control systems. It is desirable to specify simulation-based control systems using a model that is intuitive, succinct, expressive, and whose state space properties are relatively easy computationally. We compare automata-based models for specifying control systems and find that all systems that are currently used (such as finite state machines, communicating hierarchical finite state machines (FSM), communicating finite state machines, and Turing machines) lack at least one of the abovementioned features. We propose using sequential dynamical systems (SDS) - a formalism for representing discrete simulations - to specify simulation-based control systems. We show how to adapt the standard SDS model to specify cell-level controllers for a generic cell. For reasonable flexible manufacturing cells, the SDS-based specification has size polynomial in the size of the cell, while in the worst case the FSM-based specification has size exponential in the size of the cell

Intelligent Product Agents for Multi-Agent Control of Manufacturing Systems

The current manufacturing paradigm is shifting toward more flexible manufacturing systems that produce highly personalized products, adapt to unexpected disturbances in the system, and readily integrate new manufacturing system technology. However, to achieve this type of flexibility, new system-level control strategies must be developed, tested, and integrated to coordinate the components on the shop floor. One strategy that has been previously proposed to coordinate the resources and parts in a manufacturing system is multi-agent control. The manufacturing multi-agent control strategy consists of agents that interface with the various components on the shop floor and continuously interact with each other to drive the behavior of the manufacturing system. Two of the most important decision-making agents for this type of control strategy are product agents and resource agents. A product agent represents a single product and a resource agent represents a single resource on the plant floor. The objective of a product agent is to make decisions for an individual product and request operations from the resource agents based on manufacturer and customer specifications. A resource agent is the high-level controller for a resource on the shop floor (e.g., machines, material-handling robots, etc.). A resource agent communicates with other product and resource agents in the system, fulfills product agent requests, and interfaces with the associated resource on the plant floor. While both product agents and resource agents are important to ensure effective performance of the manufacturing system, the work presented in this dissertation improves the intelligence and capabilities of product agents by providing a standardized product agent architecture, models to capture the dynamics and constraints of the manufacturing environment, and methods to make improved decisions in a dynamic system. New methods to explore the manufacturing system and cooperate with other agents in the system are provided. The proposed architecture, models, and methods are tested in a simulated manufacturing environment and in several manufacturing testbeds with physical components. The results of these experiments showcase the improved flexibility and adaptability of this approach. In these experiments, the model-based product agent effectively makes decisions to meet its production requirements, while responding to unexpected disturbances in the system, such as machine failures or new customer orders. The model-based product agent proposed in this dissertation pushes the fields of manufacturing and system-level control closer to realizing the goals of increased personalized production and improved manufacturing system flexibility.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162893/1/ikoval_1.pd

Microgrid design, control, and performance evaluation for sustainable energy management in manufacturing

This research studies the capacity sizing, control strategies, and performance evaluation of the microgrids with hybrid renewable sources for manufacturing end use customers towards a distributed sustainable energy system paradigm. Microgrid technology has been widely investigated and applied in commercial and residential sector, while for manufacturers, it has been less explored and utilized. To fill the gap, the dissertation first proposes a cost-effective sizing model to identify the capacities as well as control strategies of the components in microgrids considering a commonly used energy tariff, i.e., Time of Use (TOU). Then, the sizing model is extended by integrating control strategies for both microgrid components and manufacturing systems considering a typical demand response program, i.e., Critical Peak Pricing (CPP), where customer side load adjustment is highly encouraged. After that, the control strategy of the manufacturers in an overgeneration mitigation-oriented demand response program is further investigated based on the identified optimal size of onsite microgrid to minimize the energy cost. Later, the system is analyzed from its higher level of abstraction where a prosumer community is developed by aggregating such manufacturers with onsite microgrid system. To enhance the reliable energy operation in the community, the performance of the microgrid is investigated through the estimation of the lifetime of Battery Energy Storage System (BESS), a critical design parameter the architecture. Finally, conclusions are presented and future research on real-time joint control strategy for both microgrids and manufacturing systems and identification as well as optimal energy management of the controllable loads in manufacturing system are discussed --Abstract, page iii

Compositional optimization of large-scale discrete event systems

Optimization of industrial processes such as manufacturing cells can have great impact on their performance. Finding optimal solutions to these large-scale systems is, however, a complex problem. They typically include multiple subsystems, and the search space generally grows exponentially with each subsystem. This is usually referred to as the state explosion problem and is a well-known problem within the control and optimization of automation systems.This thesis proposes a new method of solving these optimization problems using a compositional optimization approach. This integrates optimization with techniques from compositional supervisory control, dividing the optimization of subsystems into separate sub-problems. The key to this approach is the identification of local behavior in subsystems, behavior that is independent of all other subsystems. It is proven in this thesis that this local behavior can be optimized individually without affecting the global optimal solution. This is used by the approach, to reduce the state space in each subsystem, and then to utilize these reduced models compositionally when the global optimal solution is computed.Results in this thesis show that compositional optimization efficiently can generate global optimal solutions to large-scale optimization problems, too big to solve based on traditional monolithic models. It is also shown that these techniques can be applied to several industrial applications, e.g. in logistics, manufacturing etc