Abstract
Automation plays an important role in modern transportation and handling systems, e.g., to control the routes of aircraft and ground service equipment in airport aprons, automated guided vehicles in port terminals or in public transportation, handling robots in automated factories, drones in warehouse picking operations, etc. Information technology provides hardware and software (e.g., collision detection sensors, routing and collision avoidance logic) that contribute to safe and efficient operations, with relevant social benefits in terms of improved system performance and reduced accident rates. In this context, we address the design of efficient collision-free routes in a minimum-size routing network. We consider a grid and a set of vehicles, each moving from the bottom of the origin column to the top of the destination column. Smooth nonstop paths are required, without collisions nor deviations from shortest paths, and we investigate the minimum number of horizontal lanes allowing for such routing. The problem is known as fleet quickest routing problem on grids. We propose a mathematical formulation solved, for small instances, through standard solvers. For larger instances, we devise heuristics that, based on known combinatorial properties, define priorities, and design collision-free routes. Experiments on random instances show that our algorithms are able to quickly provide good quality solutions.
Highlights
Modern transportation and handling systems greatly benefit from information technology (IT) and automation, as demonstrated by the consolidated use of sensor-equipped transport networks, automated guided vehicles (AGVs), self-moving robots, as well as the growing adoption of drones, in many industrial, logistic and public transportation environments
We propose the integer linear programming (ILP) formulation and three heuristics (A, B1, and B2) to solve fleet quickest routing problem on grids (FQRP-G)
As observed in [38], dispatching algorithm (DA) returns routing schedules made of simple Manhattan paths on one-way horizontal lanes and, compliant with the hypothesis of Corollary 1: as a consequence, DA cannot provide better solutions than Heuristic A, which is optimal under such restrictions
Summary
Modern transportation and handling systems greatly benefit from information technology (IT) and automation, as demonstrated by the consolidated use of sensor-equipped transport networks, automated guided vehicles (AGVs), self-moving robots, as well as the growing adoption of drones, in many industrial, logistic and public transportation environments. In a similar way, automated warehouses or factories adopt vehicles (like AGVs or drones) to transfer goods or materials from the depot shelves to the delivery docks or between production lines [3], or to perform other inventory, inspection or surveillance operations [4,5] In all of these cases, the traffic load may be relevant and appropriate vehicle routes must be designed and operated, in order to mitigate the risk of collision while preserving the system efficiency in terms of transportation time and cost. The availability of optimization algorithms can be determinant in reducing transportation time, cost and accident rates, with relevant economic as well as social benefits
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