Multi-Agent Based Beam Search for Real-Time Production Scheduling and Control

2013 ◽  
Author(s):  
Shu Gang Kang ◽  
Shiu Hong Choi
Keyword(s):  
Real Time ◽  
Production Scheduling ◽  
Beam Search ◽  
Agent Based ◽  
Time Production ◽  
Multi Agent ◽  
And Control

Benchmarking and Robust Multi-Agent-Based Production Planning and Control

2003 ◽  
Vol 36 (3) ◽  
pp. 249-254
Author(s):  
Daniel Frey ◽  
Jens Nimis ◽  
Heinz Wörn ◽  
Peter Lockemann
Keyword(s):  
Production Planning ◽  
Production Planning And Control ◽  
Agent Based ◽  
Planning And Control ◽  
Multi Agent ◽  
And Control

scholarly journals Realtime production scheduling and control systems in smart industry

Impact ◽  
2020 ◽  
Vol 2020 (8) ◽  
pp. 60-61
Author(s):  
Wei Weng
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Production Scheduling ◽  
Production Control ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Scheduling Problems ◽  
Smart Industry ◽  
And Control

For a production system, 'scheduling' aims to find out which machine/worker processes which job at what time to produce the best result for user-set objectives, such as minimising the total cost. Finding the optimal solution to a large scheduling problem, however, is extremely time consuming due to the high complexity. To reduce this time to one instance, Dr Wei Weng, from the Institute of Liberal Arts and Science, Kanazawa University in Japan, is leading research projects on developing online scheduling and control systems that provide near-optimal solutions in real time, even for large production systems. In her system, a large scheduling problem will be solved as distributed small problems and information of jobs and machines is collected online to provide results instantly. This will bring two big changes: 1. Large scheduling problems, for which it tends to take days to reach the optimal solution, will be solved instantly by reaching near-optimal solutions; 2. Rescheduling, which is still difficult to be made in real time by optimization algorithms, will be completed instantly in case some urgent jobs arrive or some scheduled jobs need to be changed or cancelled during production. The projects have great potential in raising efficiency of scheduling and production control in future smart industry and enabling achieving lower costs, higher productivity and better customer service.

Agent Support for Design of Aircraft Parts

1998 ◽  
Author(s):  
Krishna N. Jha ◽  
Andrea Morris ◽  
Ed Mytych ◽  
Judith Spering
Keyword(s):  
Preliminary Design ◽  
Formal Representation ◽  
Web Based ◽  
Agent Based ◽  
Multi Agent ◽  
High Level ◽  
Legacy Applications ◽  
The Cost ◽  
And Control ◽  
Task Structures

Abstract Designing aircraft parts requires extensive coordination among multiple distributed design groups. Achieving such a coordination is time-consuming and expensive, but the cost of ignoring or minimizing it is much higher in terms of delayed and inferior quality products. We have built a multi-agent-based system to provide the desired coordination among the design groups, the legacy applications, and other resources during the preliminary design (PD) process. A variety of agents are used to model the various design and control functionalities. The agent-representation includes a formal representation of the task-structures. A web-based user-interface provides high-level interface to the users. The agents collaborate to achieve the design goals.

Identification of Motion-Based Action Potentials in Neural Bundles

2014 ◽  
Vol 2 (2) ◽  
pp. 22-40 ◽  
Author(s):  
Volkhard Klinger ◽  
Arne Klauke
Keyword(s):  
Action Potentials ◽  
Learning Algorithm ◽  
Inverse Kinematic ◽  
Sensory Nerves ◽  
Key Factors ◽  
Agent Based ◽  
Nerve Signal ◽  
Multi Agent ◽  
And Control ◽  
Level Identification

Realizing a nerve signal based prostheses control or limb stimulation is a great challenge in medical technology. It requires a recording and an identification process of the motion-based action potentials of motor and sensory nerves within the corresponding neural bundle. Two additional key factors are used by multi agent-based learning algorithm: The anatomical disposition of the nerves within the neural bundle and the inverse kinematic. In this paper the authors introduce the Smart Modular Biosignal Acquisition, Identification and Control System and its application environment. They present the different process levels and their characteristic identification contribution and they give an overview of the multi-agent based identification framework. The authors show the verification environment and present results regarding the first-level identification procedure.

Sign in / Sign up

Export Citation Format

Share Document