首页|New Robotics Study Results from Shanghai Jiao Tong University Described (Task Scheduling for Heterogeneous Agents Pickup and Delivery Using Recurrent Open Shop Scheduling Models)
New Robotics Study Results from Shanghai Jiao Tong University Described (Task Scheduling for Heterogeneous Agents Pickup and Delivery Using Recurrent Open Shop Scheduling Models)
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NETL
NSTL
Elsevier
Research findings on Robotics are discussed in a new report. According to newsreporting originating from Shanghai, People’s Republic of China, by NewsRx editors, the research stated,“We study the transport-pick agents task scheduling (TPTS) problem in heterogeneous agents pickupand delivery (HAPD). Two functionally heterogeneous agent types, transport agents and pick agents,collaborate to execute multi-goal tasks subjecting to complex-schedule dependency.”Funders for this research include National Key R&D Program of China, National Natural ScienceFoundation of China (NSFC), Science & Technology Commission of Shanghai Municipality (STCSM).Our news editors obtained a quote from the research from Shanghai Jiao Tong University, “The objectiveis to plan a collective time-extended task schedule with the minimization of total completion time. To bridgethe 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 modelsare 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 aredesigned accordingly. Theoretically, we rigorously analyze sequence and schedule structures, and algorithmicproperties; 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 ofthe proposed methodology is discussed along with a baseline algorithm TEAMWISE.”
ShanghaiPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsShanghai Jiao Tong University
NETL
NSTL
Elsevier
