Distributed control of industrial robot systems using Petri net based multitask processing
In this paper, the methods of the modeling of the discrete event robotic manufacturing systems using Petri nets are considered, and a methodology of decomposition and coordination is presented for hierarchical and distributed control, where the cooperation of each controller is implemented so that the behavior of the overall system is the same as the centralized control system and the task specification is completely satisfied. First, a task specification is defined as a Petri net model at the conceptual level, and then transformed to the detailed Petri net representation of the subtasks. Finally, the overall Petri net is decomposed and the constituent subnets are assigned to local Petri net based controllers. The controllers are coordinated so that the decomposed transitions fire at the same time by the coordinator. System coordination algorithm through communication between the coordinator and the controllers, is presented. By the proposed method, modeling, simulation and control of large and complex manufacturing systems can be performed consistently using Petri nets.
A Multi-objective constraint programming for centralized production-distribution planning
This research deals with the production-distribution planning system in a supply chain environment. We consider multi-objectives for the plan including minimization of production cost, transformation cost, backorder cost and inventory holding cost. Also we consider realistic constraints such as the capacity of factory, distribution center, inventory and transportation vehicle, balance of inventory, backorder, etc. We propose a method that transforms the supply chain planning problem into constraint programming so that is can handle multiple objectives. A small example is presented to illustrate the proposed algorithm.
Distributed constrained heuristic search
A model of decentralized problem solving, called distributed constrained heuristic search (DCHS), that provides both structure and focus in individual agent search spaces to optimize decisions in the global space, is presented. The model achieves this by integrating decentralized constraint satisfaction and heuristic search. It is a formalism suitable for describing a large set of distributed artificial intelligence problems. The notion of textures that allow agents to operate in an asynchronous concurrent manner is introduced. The use of textures coupled with distributed asynchronous backjumping, a type of distributed dependency-directed backtracking that the authors have developed, enables agents to instantiate variables in such a way as to substantially reduce backtracking. The approach has been tested experimentally in the domain of decentralized job-shop scheduling. A formulation of distributed job-shop scheduling as a DCHS and experimental results are presented. >