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Scheduling and Routing Algorithms for AGVs: A Survey by Ling Qiu, Wen-Jing Hsu, Shell-Ying Huang and Han Wang. Emrah Zarifoğlu 97021730. AGVs. AGVs becoming popular in Automatic materials-handling systems FMS Container handling applications in seaports
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Scheduling and Routing Algorithms for AGVs: A Surveyby Ling Qiu, Wen-Jing Hsu, Shell-Ying Huang and Han Wang Emrah Zarifoğlu 97021730
AGVs • AGVs becoming popular in • Automatic materials-handling systems • FMS • Container handling applications in seaports • Scheduling and Routing has considerable attraction
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
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
Recent Problem Scheduling and Routing
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
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
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
Other Differences from VRP • AGVs inferior to human drivers • Sensory and decision making capabilities • Algorithms handle collision-free property • Appropriate and effective algorithms required
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
Similarity with Routing Electronic Data in a Network • AGVs ↔ data packets • paths↔ data links • Traffic control devices↔ routers • Also some distinctions
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
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
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
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)
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)
Path Optimization • Optimization of path layout or distribution of P/D stations integer programming formulation
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
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
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
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
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)
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
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
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
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
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
Important Notice • AGV systems are parallel and distributed systems