Optimization for cooperative task planning of heterogeneous multi-robot systems in an order picking warehouse
Zilong Zhuang, Zizhao Huang, Yan‐Ning Sun, Wei Qin
Abstract
This article investigates the cooperative task planning problem of heterogeneous multi-robot systems in an order picking warehouse, with challenging characteristics such as heterogeneous agents, interrelated utilities and complex intertask dependencies. These characteristics bring great challenges for modelling and optimization of the problem. This study establishes a novel mapping mechanism to describe the problem as open shop scheduling with sequence-dependent set-up and transportation times, and develops an efficient mixed-integer linear programming model to obtain the optimal task planning scheme for small-size problems. To increase the size of solvable problems and improve the performance of the solution, an effective hybrid artificial bee colony algorithm is proposed, which introduces some concepts from the tabu search algorithm and the artificial immune algorithm. The effectiveness of the proposed method is confirmed by simulation experiments.