Integrated tasks assignment and routing for the estimation of the optimal number of AGVS

Abstract

A fundamental problem in the management of an automated guided vehicle system (AGVS) is the determination of the load to be transported and the vehicle to transport it. The time for the loading and unloading of pallets must be specified as soon as possible. Typical objectives are minimization of travel times and costs by the reduction of the number of vehicles required to fulfill a given transportation order. This article presents a methodology for the estimation the minimum number of AGVs (considering all the available ones at the shop floor level) required to execute a given transportation order within a specific time-window. A comparison is made between the algorithms Shortest Job First and meta-heuristic Tabu Search (applied to an initial solution) for a task assignment. An enhanced Dijkstra algorithm is used for the conflict-free routing task. The number of vehicles is estimated so as to provide an efficient distribution of tasks and reduce the operational costs of the materials handling system. Simulation results of two typical industrial warehouse shop floor scenarios are provided. Although the study focuses on pre-planning of order fulfillment of materials handling, the proposed methodology can also be utilized as an important tool for investment analysis of the warehouse layout design and for estimating the ideal number of AGVs.