首页|New Findings from Thiagarajar College of Engineering Describe Advances in Roboti cs (Simultaneous Allocation and Sequencing of Orders for Robotic Mobile Fulfillm ent System Using Reinforcement Learning Algorithm)
New Findings from Thiagarajar College of Engineering Describe Advances in Roboti cs (Simultaneous Allocation and Sequencing of Orders for Robotic Mobile Fulfillm ent System Using Reinforcement Learning Algorithm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in Robotics. Accordin g to news originating from Madurai,India,by NewsRx correspondents,research st ated,"Robotic Mobile Fulfillment Systems (RMFS) can benefit large e-commerce wa rehouse operations significantly. To fulfill the orders received,RMFS deploys m obile robots to carry shelves back and forth from the storage area to the pickin g station." Our news journalists obtained a quote from the research from the Thiagarajar Col lege of Engineering,"Order allocation and sequencing for mobile robots is a com plex yet critical task as it influences the distance traveled by mobile robots i n fulfilling the orders,i.e.,appropriately allocating and sequencing the order s. In this paper,a Simultaneous Allocation and Sequencing of Orders Reinforceme nt Learning (SASORL) algorithm is proposed to minimize the distance traveled by mobile robots. Unlike existing methods,the SASORL algorithm optimizes order all ocation and sequencing concurrently,significantly reducing mobile robot travel distance. The proposed SASORL algorithm encompasses three sets,namely state,ac tion,and reward/penalty. The state set comprises the orders fulfilled,whereas the action set contains the orders yet to be fulfilled. The distance traveled by the mobile robot as a result of the orders allocated and sequenced is taken as the penalty for the proposed SASORL algorithm. As the proposed SASORL algorithm simultaneously allocates and sequences the orders to the mobile robots,the acti on set depletes,the state set enlarges,and the penalty updates until the actio n set becomes null. Each episode restarts with the learned experience of the pri or episodes,and after completing a few episodes,the SASORL algorithm is capabl e of generating an optimal order allocation and sequence that commits the minimu m travel distance to the mobile robots. SASORL algorithm is superior to widely a dopted soft computing techniques when orders are randomly distributed. This supe riority is evidenced by a 26% reduction in the maximum distance tr aveled by all mobile robots,a 54% reduction in the standard devia tion of the distance traveled by the mobile robots,and a marginal increase of 7 % in the total distance traveled by all mobile robots."
MaduraiIndiaAsiaAlgorithmsEmergi ng TechnologiesMachine LearningNano-robotReinforcement LearningRobotRo boticsRobotsThiagarajar College of Engineering
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