metaheuristics for a new minlp model with reduced response time for on-line order batching

Companies are looking for effective strategies to improve warehouse performance quality due to customers dissatisfaction of service. the order picking process is one of the main warehouse management strategies. as the inventory of stored items and the number of orders increased, the picking process and response time became more important. effective coordination between order batching and order picking process is essential to improve the eciency of the warehouse management system. in this paper, a novel mixed- integer nonlinear programming (minlp) model for on-line order batching is proposed for improving the warehouse performance, which in turn results in the reduction of the response and idle times. the proposed method takes aim at the investigation of order classification for the first time in the picker-to-part system as a manual picking system and an online order batching system, with the intent of minimizing the turnover time and idle time. besides, an order batching model in a blocked warehouse using a zoning system is proposed which is called online order batching in blocked warehouse with one picker for each block (oobbwopb). the mentioned model is solved by two algorithms: artificial bee- colony (abc) algorithm and ant-colony (aco) algorithm. two numerical case studies are defined and analyzed using matlab software. according to the results compared with the results of zhang et al. (2017) the proposed model shows better performance and the average customer order response time is significantly reduced (2017) and the aco yields better results than abc.