Network model and effective evolutionary approach for AGV dispatching in manufacturing system

Automated guided vehicles (AGVs), are the state-of-the-art, and are often used to facilitate automatic storage and retrieval systems (AS/RS). In this paper, we focus on the dispatching of AGVs in a flexible manufacturing system (FMS). A FMS environment requires a flexible and adaptable material handling system. We model an AGV system by using network structure. This network model of an AGV dispatching has simplexes decision variables with considering most AGV problem’s constraints, for example capacity of AGVs, precedence constraints among the processes, deadlock control. Furthermore, these problems can be solved by using a lot of heuristic algorithms as network optimization problems. We are also proposed an effective evolutionary approach for solving a kind of AGV’s problems in which minimizing time required to complete all jobs (i.e. makespan) and minimizing the number of AGVs, simultaneously. For applying an evolutionary approach for this multicriteria case of AGV problem, priority-based encoding method and Interactive Adaptive-weight GA (i-awGA) were proposed. Numerical analyses for case study show the effectiveness of proposed approach.

[1]  Mitsuo Gen,et al.  Network-based hybrid genetic algorithm for scheduling in FMS environments , 2004, Artificial Life and Robotics.

[2]  Iris F. A. Vis,et al.  Survey of research in the design and control of automated guided vehicle systems , 2006, Eur. J. Oper. Res..

[3]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[4]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[5]  Xiaolan Xie,et al.  Optimization of the number of transportation devices in a flexible manufacturing system using event graphs , 1997, IEEE Trans. Ind. Electron..

[6]  Mitsuo Gen,et al.  An integrated model for the design of end-of-aisle order picking system and the determination of unit load sizes of AGVs , 2002 .

[7]  Charles E. Taylor Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Complex Adaptive Systems.John H. Holland , 1994 .

[8]  Ling Qiu,et al.  Scheduling and routing algorithms for AGVs: A survey , 2002 .

[9]  Zbigniew Michalewicz,et al.  Genetic Algorithms Plus Data Structures Equals Evolution Programs , 1994 .

[10]  Charles V. Stewart,et al.  Reducing the search time of a steady state genetic algorithm using the immigration operator , 1991, [Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91.

[11]  Hark Hwang,et al.  A DISPATCHING ALGORITHM FOR MULTIPLE-LOAD AGVS USING A FUZZY DECISION-MAKING METHOD IN A JOB SHOP ENVIRONMENT , 2001 .

[12]  Mitsuo Gen,et al.  Network design techniques using adapted genetic algorithms , 2001 .

[13]  Hark Hwang,et al.  An adaptive dispatching algorithm for automated guided vehicles based on an evolutionary process , 1999 .

[14]  Hark Hwang,et al.  Determination of unit load sizes of AGV in multi-product multi-line assembly production systems , 1999 .

[15]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[16]  David Naso,et al.  Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  René M. B. M. de Koster,et al.  A review of design and control of automated guided vehicle systems , 2006, Eur. J. Oper. Res..

[18]  Jian-Bo Yang,et al.  GA-based discrete dynamic programming approach for scheduling in FMS environments , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[19]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.