Abstract

When simple automated guided vehicles (AGVs) having no random access load transfer mechanism are used for carrying multiple loads between workstations, the loads cannot be handled independently. This paper considers the case when loads are placed in flat pallets and each new picked up pallet is loaded on the top of batch of pallets already carried by the AGV. To avoid use of excessive space and time needed to reorder pallets in the batch, the loading–unloading procedures should be performed in accordance with last-in-first-out (LIFO) rule. In this paper we formulate the condition of existence of AGV routes in which, it visits each workstation only once and meets LIFO constraint. We also suggest an algorithm for finding the shortest one among such routes. Examples are provided to illustrate the performance of the algorithm.

Scope and purpose

The loads transfer task is defined by an oriented graph G=(V,E) in which vertices v_i (1iN) correspond to loading/unloading nodes in a network. Arc (v_i,v_j)E corresponds to existence of load to be transferred from node v_i to node v_j. The distance between each pair of nodes is defined by a matrix D={d(v_i,v_j), 1iN, 1jN}. An autonomous agent has to deliver loads in accordance with graph G. The loads are stored in the stack of the agent and are loaded and unloaded using last-in-first-out (LIFO) rule. To fulfill the transfer task the agent has to pass through all the existing vertices in some order that ensures that for each arc (v_i,v_j)E vertex v_i is visited before vertex v_j. Conditions of existence of a sequence of vertices which allow all the loads to be transferred without violating LIFO rule and visiting each vertex only once are formulated. An algorithm for finding the shortest tour meeting LIFO constraints is developed. The possible applications of the algorithm are scheduling automated guided multiple load vehicles or routing autonomous intelligent agents with stack memory organization in computer networks.

Author Keywords: Optimal routing; Autonomous agent; Last-in-first-out rule; Automated guided vehicle

Article Outline

1. Introduction

2. Problem formulation

3. Building minimal length PP for a feasible graph

4. Illustrative example 1

5. Building minimal length PP for a group of unconnected feasible graphs

6. Illustrative example 2

7. Computational effort

8. Conclusions

References