Research On Picking Strategy And Layout Optimization Of Automated Picking System

Order picking operation is the most expensive and time consuming process of all assignments in distribution center, which directly determines the efficiency and level of the distribution center’s service. Traditionally, distribution centers use manual order picking or other auxiliary methods such as electronic tags, voice and etc. With the abundance of product category and the refinement of the market demand, the quantity and frequency of the customers’orders are increased. The conventional picking strategies cannot meet the needs of the distribution centers. In this context efficient and low consumption automated systems have been widely promoted and used. There are different types of automated picking systems, which can be divided into three types, based on the warehousing, based on the transportation and based on the sorting machine. In this paper the automated picking system for many varieties, small batch, and high frequency of FMCG(Fast Moving Consumer Goods) picking based on the sorting machine is chosen as the research object (Not specifically mentioned, the automated picking systems in this paper refer to such kind of picking system.).At present, how to improve the efficiency and accuracy of the automated picking system becomes an important issue for improving the business capability of the distribution centers. Under the premise of the determined order demand characteristics, the efficiency of the automated picking system is mainly influenced by its operation parameters, the structural elements and the selection strategy used. For some time, research on the efficiency of the automated picking system focuses on improving the stand-alone operation efficiency of the sorting machine. However, this improvement is greatly influenced by manufacturing capability and cost. Especially after the single machine efficiency reaches a certain level, further improvement becomes very difficult. Therefore, improving the efficiency of the picking system by optimizing layout elements, strategies and parameter matching becomes the new research hotspot.Foreign literatures on the optimization of automated picking system mainly focus on the automated stereoscopic warehouse system (AS/RS). Little effort has been invested into the research on sorting machine for automated picking system optimization. In recent years, there are many domestic studies on the optimization of automated picking system based on the sorting machine. Besides the technical level such as sorting equipment modifications, more studies focused on the optimization of the macro layout based on partition, parallel and pre-sorting. Studies on the optimization of SKUs assignment and sorting machine configuration are generally based on cost target rather than efficiency target.In order to enhance the efficiency of the picking system, the common model of automated picking system, basic automated picking system, is taken as the main research object. Mathematical modeling, genetic algorithm, clustering analysis and orthogonal experimental design are used for the optimization of picking strategy, structure elements and operation parameters which are the three factors that influence the efficiency of the picking system. The relationship between operation parameters and picking strategy is analyzed, a new type of efficient picking strategy is designed and item assignment is optimized. Based on this, the optimization of item assignment in complex automated picking system is studied. Study on the efficiency optimization of basic automated picking system provides research foundation for the efficiency optimization of the complicated complex automated picking system and also provides the theoretical basis for the design of the automated picking system of distribution centers.The main research contents and innovations of this work are as follows.1. The model of basic automated picking system was constructed. The serial-parallel hybrid picking strategy was proposed based on the usual serial picking strategy. The model of the order-picking time was established.The characteristics of the different types of automated picking systems were analyzed. Based on the common characteristics of the picking systems, basic automated picking system was built. Current commonly used automated picking systems can be obtained by combining or the modifying the basic system. Taking the basic system as the research object, we can apply some analysis results of the basic system directly to the sorting, design and optimization of other automated picking systems. Based on the mathematical model of the order-picking time of commonly used serial picking strategy, a novel serial-parallel hybrid picking strategy was proposed and its operation time model was built. The proposed picking strategy can improve the efficiency of the system by reducing the items’average waiting time. Serial-parallel hybrid picking strategy is to add a parallel sorting link to the conventional serial picking strategy. At the first stage, items that can be picked in parallel are found and chosen. Then some appropriate items not picked are selected to fill in the gaps between the items on the conveying system after parallel picking. Finally, the remaining items are picked in serial. It was shown by experiments that under the same condition, the order-picking time can be reduced by 25% using the proposed hybrid picking strategy compared that using the serial picking strategy. This work has applied for national invention patent.2. The influence of different operation parameters to total order-picking time was analyzed under different picking strategies, which provides a basis for the selection of systems and the design of the parameters.In order to understand the influence of the operation parameters to the picking efficiency for different picking strategies to facilitate the selection of design parameters, the orthogonal experiment design was used to optimize the operation time of the above three kinds of strategies. The influence of single piece picking time, speed of conveying system and space between adjacent sorting machines on picking efficiency was studied, based on which the primary and secondary impact factors were determined. The influence of the parameters with different levels was determined quantitatively using differential analysis method. Optimal combination was found out by searching in different combinations of parameters and parameter levels. Based on this, the exponential regression method was employed to derive the empirical prediction formula of the order-picking time for the three picking strategies. And the influence of different combinations of operation parameters on the total order-picking time was analyzed for the three picking strategies. This study provides a theoretical basis for the design, selection and optimization of the automated picking systems.3. To achieve the shortest order-picking time, the optimization model of item assignment was established. Modified niche genetic design was proposed and its effectiveness was proved by experiments.SKUs assignment is an important structural element in the layout of automated picking system. The influences of different picking strategies on system efficiency are different. The optimization of item assignment was studied for serial-parallel hybrid picking strategy which is sensitive to the item assignment and the serial picking strategy. Optimization was done using conventional genetic algorithm and the modified niche genetic algorithm, respectively. Experimental results show that the order-picking times of the two strategies we used were reduced by 21.4% and 21.4%, respectively, compared with the traditional item assignment method in which the items are sorted in ascending order quantity. The order-picking time of the proposed hybrid picking strategy is shorter than that of the serial picking strategy both before and after optimization. For the two strategies, the effectiveness and the order-picking time of the modified niche genetic algorithm outperform the basic genetic algorithm.4. An optimization model of item assignment for different types of sorting machines in complex automated picking system was established. An improved niche genetic algorithm based on K-means clustering was proposed. The effectiveness of the algorithm was proved by experiments.Based on the study of basic automated picking system, the basic automated picking model for complex picking system was built and the model of the order-picking time was constructed in accordance with the serial picking strategy. In addition the model of item assignment was built for different types of sorting machines targeting on the smallest order-picking time. In this paper, an improved niche genetic algorithm was proposed to solve the problem. In order to enhance the convergence speed, the k-means clustering method was used to optimize the clustering of the items and the result was used as the initial population. The improvement of niche elimination operation and the maintenance of population diversity were obtained by limiting the number of chromosomes for some SKUS assignment in certain positions. Experiments show that the total order-picking time is reduced by 7.5% after the optimization of item assignment compared with the conventional EIQ-ABC method. Contrast experiment on item configuration and sorting in batch order picking area also proved the effectiveness of the proposed method. The empirical guideline for item assignment of complex picking system in the given conditions was summarized, that is, the items belong to the top 10% of order quantity is assigned to batch order picking area and other items is assigned to single order picking area. The items in the picking areas are sorting in ascending order of the quantity.