A computational study on design and performance issues of multi-agent intelligent systems for dynamic scheduling environments

Abstract Multi-Agent Intelligent Systems (MAIS) are loosely-coupled network of problem solving systems that, whenever needed, work together with each other to dynamically solve problems that none of the system can individually solve. Among the advantages of the MAIS, when compared to the centralized systems, are increased reliability, faster problem solving, decreased communication, and more flexibility. Learning to coordinate the actions is one of the most important task in MAIS. In the current research, we use a widely reported dynamic job shop scheduling simulation model that uses distributed genetic learning of job scheduling strategies (Pendharkar, P.C., 1997. Doctoral Dissertation, Graduate School, Southern Illinois University at Carbondale; Pendharkar, P.C., 1998. Distributed learning of objectives for adaptive scheduling (in review); Pendharkar, P.C., Bhattacharyya, S., 1997. Multi-agent learning in distributed artificial intelligence. Proc. 2nd INFORMS Conference on Information Systems and Technology. San Diego, CA, p.156–163; Bhattacharyya, S., Koehler, G.J., 1997. Learning by objectives for adaptive shop-floor learning. Decision Sciences (to appear). Aytug, H., Koehler, G.J., Snowdon, J.L., 1994. Genetic learning of dynamic scheduling within a simulation environment, Computers and Operations Research, 21 (8), 909–925; Aytug, H., Bhattacharyya, S., Koehler, G.J., Snowdon, J.L., 1994. A review of machine learning in scheduling, IEEE Transactions on Engineering Management 41 (2) ) and study the performance and design issues in multi-agents information systems for dynamic scheduling in manufacturing. Among the design issue and performance issues considered in this research are coordination between agents, number of agents, and frequency of learning. Our results indicate that coordination between agents, and learning frequency play a significant role in the performance of multi-agent intelligent systems.

[1]  Gerhard Weiß,et al.  Adaptation and Learning in Multi-Agent Systems: Some Remarks and a Bibliography , 1995, Adaption and Learning in Multi-Agent Systems.

[2]  Heimo H. Adelsberger,et al.  Expert systems in production scheduling , 1987 .

[3]  Sati S. Sian,et al.  Extending Learning to Multiple Agents: Issues and a Model for Multi-Agent Machine Learning (MA-ML) , 1991, EWSL.

[4]  Darrell Whitley,et al.  Scheduling problems and traveling salesman: the genetic edge recombination , 1989 .

[5]  Takao Terano,et al.  A computational model for distributed knowledge systems with learning mechanisms , 1996 .

[6]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[7]  Siddhartha Bhattacharyya,et al.  A review of machine learning in scheduling , 1994 .

[8]  Siddhartha Bhattacharyya,et al.  Multi-agent learning for adaptive scheduling , 1997 .

[9]  Darrell Whitley,et al.  The Travelling Salesman and Sequence Scheduling: Quality Solutions using Genetic Edge Recombination , 1990 .

[10]  Gary J. Koehler,et al.  Genetic learning of dynamic scheduling within a simulation environment , 1994, Comput. Oper. Res..

[11]  Süleyman Tüfekci,et al.  A genetic algorithm for the talent scheduling problem , 1994, Comput. Oper. Res..

[12]  Candace Arai Yano,et al.  Algorithms for a class of single-machine weighted tardiness and earliness problems , 1991 .

[13]  Mark S. Fox,et al.  An investigation into distributed constraint-directed factory scheduling , 1990, Sixth Conference on Artificial Intelligence for Applications.

[14]  Moshe Tennenholtz,et al.  Adaptive Load Balancing: A Study in Multi-Agent Learning , 1994, J. Artif. Intell. Res..

[15]  B. R. Fox,et al.  Genetic Operators for Sequencing Problems , 1990, FOGA.

[16]  Andrew B. Whinston,et al.  A comparison of three information gathering strategies in DAI systems under noisy conditions , 1996 .

[17]  D. J. Smith,et al.  A Study of Permutation Crossover Operators on the Traveling Salesman Problem , 1987, ICGA.

[18]  Colin R. Reeves,et al.  A genetic algorithm for flowshop sequencing , 1995, Comput. Oper. Res..

[19]  Larry Bull,et al.  Evolution in Multi-agent Systems: Evolving Communicating Classifier Systems for Gait in a Quadrupedal Robot , 1995, ICGA.

[20]  Alan H. Bond,et al.  Readings in Distributed Artificial Intelligence , 1988 .

[21]  David E. Goldberg,et al.  Alleles, loci and the traveling salesman problem , 1985 .

[22]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[23]  Sandip Sen,et al.  Strongly Typed Genetic Programming in Evolving Cooperation Strategies , 1995, ICGA.

[24]  Brahim Chaib-draa,et al.  An overview of distributed artificial intelligence , 1996 .

[25]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[26]  Jae Young Choi,et al.  A genetic algorithm for job sequencing problems with distinct due dates and general early-tardy penalty weights , 1995, Comput. Oper. Res..

[27]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[28]  Stephen C. Graves,et al.  A Review of Production Scheduling , 1981, Oper. Res..

[29]  Khosrow Hadavi,et al.  An Architecture for Real-Time Distributed Scheduling , 1992, AI Mag..

[30]  Paul Skarek,et al.  Multi-agent cooperation for particle accelerator control , 1996 .

[31]  Michael J. Shaw,et al.  Learning and Adaptation In Distributed Artificial Intelligence Systems , 1989, Distributed Artificial Intelligence.

[32]  Gunar E. Liepins,et al.  Learning Decision Rules for scheduling Problems: A Classifier Hybrid Approach , 1989, ML.

[33]  Gerhard Weiß,et al.  Action selection and learning in multi-agent environments , 1993, Forschungsberichte, TU Munich.

[34]  Gunar E. Liepins,et al.  Machine learning applications to job shop scheduling , 1988, IEA/AIE '88.

[35]  Gerhard Weiss,et al.  Learning to Coordinate Actions in Multi-Agent-Systems , 1993, IJCAI.

[36]  FEDERICO DELLA CROCE,et al.  A genetic algorithm for the job shop problem , 1995, Comput. Oper. Res..

[37]  ANTHONY WREN,et al.  A genetic algorithm for public transport driver scheduling , 1995, Comput. Oper. Res..