Task scheduling for heterogeneous agents pickup and delivery using recurrent open shop scheduling models

Abstract

We study the transport-pick agents task scheduling (TPTS) problem in heterogeneous agents pickup and delivery (HAPD). Two functionally heterogeneous agent types, transport agents and pick agents, collaborate to execute multi-goal tasks subjecting to complex-schedule dependency. The objective is to plan a collective time-extended task schedule with the minimization of total completion time. To bridge the gap between robot task scheduling and general scheduling theory, a novel recurrent open shop scheduling (ROSS) problem variant with unique sequence structure is defined. New sequence and schedule models are extended to accommodate for it. Afterwards, the problem-specific append-beam-Christofides (ABC) constructive heuristic, greedy local search (GLS) and simulated annealing (SA) metaheuristic algorithms are designed accordingly. Theoretically, we rigorously analyze sequence and schedule structures, and algorithmic properties; empirically, we study the influence of different algorithm settings on a comprehensive dataset. Design guidelines and parameter settings of these algorithms are provided. The application conditions of the proposed methodology is discussed along with a baseline algorithm TEAMWISE. The proposed methodology could be utilized in various industrial heterogeneous multi-robot or collaborative human–robot systems.