AgentTime: A Distributed Multi-agent Software System for University’s Timetabling

In the course of researching timetabling problems for complex distributed systems this article applies the multi-agent paradigm of computations and presents a correspondent mathematical model for university’s timetabling problem solution. The model takes into account dynamic nature of this problem and individual preferences of different remote users for time and location of classes. In the framework of that model authors propose an original problem-oriented algorithm of multi-agent communication. Developed algorithm is used as a foundation for the distributed software system AgentTime. Based on multi-agent JADE platform AgentTime provides friendly graphical interface for online design of time tables for universities.

[1]  Frederick E. Petry,et al.  Genetic Algorithms , 1992 .

[2]  Xavier Défago,et al.  Agent-based approach to dynamic meeting scheduling problems , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[3]  Kuldeep Singh Sandhu,et al.  Automating Class Schedule Generation in the Context of a University Timetabling Information System , 2003 .

[4]  Takayuki Ito,et al.  A new distributed approach to solve meeting scheduling problems , 2003, IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003..

[5]  Michael P. Wellman,et al.  Auction Protocols for Decentralized Scheduling , 2001, Games Econ. Behav..

[6]  Clive ″Max″ Maxfield.  Chapter 22 – Genetic Algorithms: “Programs that boggle the mind” , 1998 .

[7]  Makoto Yokoo,et al.  Distributed constraint satisfaction algorithm for complex local problems , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[8]  K. Kaplan.  H. Haken, Synergetics. An Introduction. Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology (2nd Edition). XI + 355 S., 152 Abb. Berlin—Heidelberg—New York 1978. Springer-Verlag. DM 66,00 , 1980 .

[9]  Makoto Yokoo,et al.  On Market-Inspired Approaches to Propositional Satisfiability , 2001, IJCAI.

[10]  Andrea Schaerf,et al.  Local search techniques for large high school timetabling problems , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[11]  Francesca Rossi,et al.  Multi-agent meeting scheduling with preferences: efficiency, privacy loss, and solution quality , 2002 .

[12]  Michael P. Wellman,et al.  The WALRAS Algorithm: A Convergent Distributed Implementation of General Equilibrium Outcomes , 1998 .

[13]  Fabio Bellifemine,et al.  Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology) , 2007 .

[14]  Ali Nosary,et al.  Constraint Programming and Multi-Agent System Mixing Approach for Agricultural Decision Support System , 2006 .

[15]  David Abramson,et al.  Constructing school timetables using simulated annealing: sequential and parallel algorithms , 1991 .

[16]  Elie Sanchez,et al.  Soft computing perspectives , 1994, Proceedings of 24th International Symposium on Multiple-Valued Logic (ISMVL'94).

[17]  Panagiotis Miliotis,et al.  Implementation of a university course and examination timetabling system , 2001, Eur. J. Oper. Res..

[18]  Takanori Shibata,et al.  Genetic Algorithms And Fuzzy Logic Systems Soft Computing Perspectives , 1997 .

[19]  Katia Sycara,et al.  Multi-Agent Meeting Scheduling: Preliminary Experimental Results , 1996 .

[20]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[21]  D. Fogel.  Evolutionary algorithms in theory and practice , 1997, Complex..

[22]  Makoto Yokoo,et al.  Algorithms for Distributed Constraint Satisfaction: A Review , 2000, Autonomous Agents and Multi-Agent Systems.

[23]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[24]  Agostino Poggi,et al.  Developing Multi-agent Systems with JADE , 2007, ATAL.

[25]  D. de Werra,et al.  An introduction to timetabling , 1985 .

[26]  E. A. Akkoyunlu.  A Linear Algorithm for Computing the Optimum University Timetable , 1973, Comput. J..

[27]  H. Haken.  Synergetics: an Introduction, Nonequilibrium Phase Transitions and Self-organization in Physics, Chemistry, and Biology , 1977 .

[28]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[29]  Dharmendra Sharma,et al.  An Evolutionary Approach to Constraint-Based Timetabling , 2000, PRICAI Workshops.