Emrah Zarifoğlu 97021730

1. Scheduling and Routing Algorithms for AGVs: A Surveyby Ling Qiu, Wen-Jing Hsu, Shell-Ying Huang and Han Wang Emrah Zarifoğlu 97021730

2. AGVs • AGVs becoming popular in • Automatic materials-handling systems • FMS • Container handling applications in seaports • Scheduling and Routing has considerable attraction

3. Agenda • Description of problem • Scheduling • Routing • Common hazards in scheduling and routing of AGVs nad techniques to handle them • Comparison of several similarproblems • Survey of existing major works on AGV scheduling androuting • Classifications • Recommendation of a few fertile areas for further study

4. Problem Origin • Hardware • AGVs • Paths • Controllers • Sensors • Guidance Devices • Software • Approaches or algortihms to manage hardware resources • (!) hardware exceeds software (!) • Hazards due to software • Congestion • Deadlocks

5. Recent Problem Scheduling and Routing

6. Problem Description • Scheduling • Aim  dispatch a set of AGVs to achieve goals for batch of P/D jobs under certain conditions • Goals  related to processing time or utilization of resources • Routing • Mission  find a suitable routefor AGVs from origin to destination based on current traffic situation • Two issues: • existence of a route leading a vehicle from origin to destination • feasibility

7. Problem Description • Relations between scheduling and routing • A few vehicles and jobs  simpler scheduling algorithms • Many jobs  inadequacy of a simple scheduling algorithm to achieve a high system efficiency due to limitations of facility resources • Issues in scheduling and routing • Collisions • Congestion • Livelocks • Deadlocks

8. Path network  metropolitan scale Load capacity of path not considered  assumption of nocollisions or congestion Shortest distance path ↔ shortest time path Path network predefined and unchangeable Not ignorable AGV path occupation High possibility of collusion of congestion due to bad scheduling and routing Not necessarily shortest time path ↔ shortest path Path layout may be revised AGV Scheduling & Routing vs VRP

9. Other Differences from VRP • AGVs inferior to human drivers • Sensory and decision making capabilities • Algorithms handle collision-free property • Appropriate and effective algorithms required

10. Distinction of AGV problems • Different from conventional path problems in graph theory • Shortest path problem • Hamiltonian-type problem • Scheduling problem • Graph theory • Optimal path • AGV problem • Optimal path and when and how (time critical) • System control mechanism and path layout

11. Similarity with Routing Electronic Data in a Network • AGVs ↔ data packets • paths↔ data links • Traffic control devices↔ routers • Also some distinctions

12. Taxonomy of Algorithms • Algorithms for general path topology  treating problem as graph theory • Dijkstra’s shortest path algorithm • Partitioning shortest path algorithm • Algorithms for path layout optimization  focus on optimization of path network • Integer programming • Algorithms for specific path topology  developed to route and control AGVs in specific topologies • Single-loop • Multi-loops • Meshes • Dİspatching or scheduling of AGVswithout consideration of routing

13. Algorithms for General Path Topology • Focus  finding feasible routes for AGVs w/o considering topological characteristic of path layout • Universal routing solutions • Basic  conflict-free and shortest-time routing solutions for AGVs • Method classification • Static methods • Time-window-based methods • Dynamic methods

14. Static Methods • Small scale AGV systems • Advantage  simplicity • Disadvantage  its optimal solutions • Introduction of conflict-free and shortest time AGV routing by Broadbent et al. (1985)  Dijkstra’s shortest path algorithm • Bidirectional path is more advantageous than unidirectional path for utilization of vehicles and potential throughput efficiency by Egbelu and Tanchoco (1986) and Egbelu (1987)  improved productivity and reduced number of AGVs in bidirectional paths • Routing vehicles in bidirectional flowpath ntwork when PSP is applied to find shortest path for an AGV by Daniels (188)  algoithm only suitable for a system with a small path netwprk and a small number of AGVs

15. Time-Window-Based Methods • Aim  to share path network more efficiently • Main contribution  enhancement of path utilization • Labelling algorithm to find shortest time path for routing a single vehicle in a bidirectional path network by Huang et al. (1989)  unacceptably large amount of computation • Conflict-free and shortest timealgorithm for routing AGVs in a bidirectional pathnetwork based on Dijkstra’s algorithm by by Kim and Tanchoco (1991)  more suitable for a small system with few vehicles in the worst case • Operational control of bidirectional path AGV systems for conflict-free and shortest time routing algorithm employing a conservative myopic strategy by Kim and Tanchoco (1993)

16. Dynamic Methods • Aim  to speed up the process of finding routes for AGVs • Incremental route planning by Taghaboni and Tanchoco (1995)  quicker than static algorithm • Algorithm giving an optimal solution for planning dispatching, conflict-free routing and scheduling of AGVs in FMS based on dynamic programming by Langevin et al. (1996)

17. Path Optimization • Optimization of path layout or distribution of P/D stations  integer programming formulation

18. 0-1 Integer Programming Model • Path layout problem as a 0-1 integer programming model with given facility layout and P/D stations byGAskins and Tanchoco (1987)  only considering unidirectional path network whichhas lower utilization than bidirectional ones do by Egbelu and Tanchoco (1986) • 0-1integer programming model and branch-and-bound method by Gaskins and Tanchoco (1990)  reduce computationtime at cost of quality path design

19. Intersection Graph Method • İntersection graph method based on branch-and-bound wherein only a reduced subset of nodes in path network is considered and only intersection nodes are used to find optimal for solving AGV flowpath optimization model by Sİnriech and Tanchoco (1991)  amount f computation greatly reduced

20. Integer Linear Programming Model • İnteger linear programming problem of selecting the pathand location of P/D stations by Goetz and Egbelu (1990)  unidirectional path, low path utilization andsystem throughput

21. Algorithms for Specific Path Topologies • In realistic applications, path topologies  specific and regular • Path layouts  linear, loop or loops, mesh, etc... • Algorithms for specific path topologies better effects than algorithms for general path topologies

22. Linear Topology • Linear path topology  basic type of path layouts • Introductionofascheme to schedule and route a batch of AGVs concurrently on a bidirectional linear path layout amploying the idea of concurrent processing by Qiu and Hsu (2001a)

23. Loop Topology • Loop topology including single-loops, multi-loops, segmented floor topology is commonfor path layout • Few vehicles run in same direction within loop • Simple routing control • But not very high system throughput

24. Loop Topology (Cont’d) • Optimal closed single-loop path layout for AGV system based on integer programming to find optimal single-loop by Tanchoco and Sinriech (1992)  may not be very suitable for large material handling system with a great number of vehicles and stations • Routing AGVs among non-overlapping closed loops within a tandem AGV system by Lin and Dgen  scale of such a system could not be very much • SFT  can be used with oneof three network types (connected, partitioned and split-flow) • Advantage of SFT  lower value of flow x distance compared withother path topologies (single-loop, bidirectional and uni-directional conventional paths,etc..) • Disadvantage of SFT  transferring devices in the buffers are the additional cost of the overall system

25. Mesh Topology • Mesh-like path topology  arrangement into rectangular blocks in the container stacking yards of container shipping andtransportation at container terminals • Analysis of time and space complexities for some basic AGV routing operations in several specific bidirectional path topologies by Hsu and Huang (1994) and Huang and Hsu (1994) • routing operations  single delivery distribution, scattering, accumulation, gathering, sorting, total exchange (shuffling) • Path topologies  linear array, ring, binary-tree, H-tree, star, 2D mesh, n-cube and cube-connected cycles, and complete graph • Different methods to schedule and route AGVs in an n X n mesh-like path topology by Qiu and Hsu (2000a-c)  in all algorithms, freedom of conlictsamong AGVs is provably guaranteed

26. Dedicated Scheduling Algorithms • Scheduling without consideration of routing • Schedule vehicles and jobs in a decision-making hierarchy based on mixed-integer programming by Akturk and Yilmaz (1996) • Micro-opportunistic scheduling algorithm (MOSA) combines job-based and vehicle-based approaches applicable for AGV systems with a small number of jobs and vehicles • A model for scheduling of AGVs for multiple container-cranes to minimize the delay of carrying out all loading unloading operations without consideration of AGV routing by Kim and Bae (1999)  with increase of number of AGVs, congestions or collisions of AGVs might occur at the operating area of container cranes

27. Future Research Directions • Most fertile  development of new scheduling and routing algorithms for specific path topologies • In many applications AGV path metworks areregular graphs (linear array, loop/loops, 2D mesh) • Relatively lower computational complexity compared algorithms for general path topology • More feasible and more efficient

28. Important Notice • AGV systems are parallel and distributed systems