Design And Analysis Of Order Picking Routing In Multiple-blocks Warehouse

The high effective order picking should be able to complete the picking service with the the least cost and the shortest time. At the same time, the order picking optimization not only improves the customer satisfaction, but also improves the service level of the entire supply chain. Recently, the application of new production and operation mode such as VMI, and new information technologies such as RFID has brought new opportunities and challenges to order picking.Thus, we need new order picking strategies to adapt to these development.Most related studies on these strategies are conducted with simple, static, determinis-tic assumption, which ignore the complex factors in picking operation. For example, most strategies are designed for one order picker, which ignore the impact of multiple order pick-ers; while most studies assume the details of orders are known in advance, orders often generate over time; most studies assume the information and circumstance of order picking is deterministic in spite of the fact that order picking usually is carried out in nondetermin-istic circumstance. As the strategy in operation Level, routing method has a great impact on picking efficiency and service level. Thus, it is necessary to investigate new routing method with complex, dynamic, nondeterministic assumption.Based on the meta-heuristics and information technologies, this thesis aims at the rout-ing problems in order picking, which are caused by the complex operation environment and nondeterministic factors, which are found in the project practice. The contributions are listed as follows:This thesis develops a bias ratio based routing method for multiple block warehouse. This method decides the access mode of the current aisle containing picks, depending on the spatial distribution of all the picks in current block. The method remedies a defect of existing heuristics only concerning the spatial distribution of the picks in current aisle. A simulation experiment is conducted to evaluate the effectiveness of the method in contrast with other heuristics, the impact of warehouse layout and pick density is analyzed as well. It proves that all the routing methods only can perform well under a suitable pick density. When the pick density is too high, order picker will have to walk through all the pick aisles, no matter which routing method is applied.When multiple order pickers work in the warehouse simultaneously, congestion will happen if they want to access one pick at the same time. To deal with congestion, an ACO-based routing method for two order pickers with congestion consideration is developed in this thesis, which is named as A-TOP. Using the tabu list and logic travel distance, the second order picker can form a picking route which shorten the order service time including waiting. In a simulation experiment, two routing methods are compared with A-TOP. The result confirms that the route forms on the basis of ACO and the mechanism for dealing with congestion are key points which make A-TOP perform best.At present, most related studies on designing or analyzing the picking policies ignore or assume the picking time of one pick is constant. However, the quantity of items to be picked in a pick location is determined by order. Moreover, due to the variance on item size and weight, in addition to operate miss, the picking time of one pick is nondeterministic. Some related literature points out the nondeterministic picking time has an effect on congestion and order service time. Here, by altering the congestion judge rules and algorithm flow, this thesis extends the A-TOP in two ways: the first is A-MOP, which can be applied in warehouse with more than two order pickers; the second is A-MOP-N, which aims at dealing with the congestion in nondeterministic picking time circumstance.The development in e-business makes the online order gradually become the main source of demand. In contrast to off-line order, the details of online order cannot be known in advance, and online order has a higher demand in response and service level. In this thesis, different from the existing order batching policies, the new method spreads the online order to the working order pickers, which cuts down the waiting time of order for service, which is named as Green-Area. The experiment proves the effectiveness of Green-Area. Moreover, Green-Area does not subject to the optimal batch size, which is affected by order arrival rate. It means that Green-Area can be applied in the e-business circumstance, in which order arrival rate changes frequently.