Fluid-based Slotting Optimization for Automated Order Picking System with Multiple Dispenser Types

作     者：LIU Peng WU Yaohua ZHOU Chen XU Na 

作者机构：School of Control Science andEngineering Shandong University Jinan 250061 China School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA 30332 USA Business School Shandong Jianzhu University Jinan 250101 China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2011年第24卷第4期

页      面：529-538页

核心收录：

学科分类：07[理学] 08[工学] 070204[理学-等离子体物理] 0822[工学-轻工技术与工程] 0702[理学-物理学] 

基　　金：supported by China Scholarship Council (Grant No.2007102074) National Natural Science Foundation of China (Grant No.50175064) Georgia Institute of Technology Visiting Research EngineerProgram of the United States (Grant No. 2401247) Graduate InnovationFoundation of Shandong University, China (Grant No. yzc09066) Costal International Logistics Company of the United States (Project No.20080727) 

主　　题：slotting complex automated order picking system restocking cost dispenser 

摘      要：Slotting strategy heavily influences the throughput and operational cost of automated order picking system with multiple dispenser types, which is called the complex automated order picking system （CAOPS）. Existing research either focuses on one aspect of the slotting optimization problem or only considers one part of CAOPS, such as the Low-volume Dispensers, to develop corresponding slotting strategies. In order to provide a comprehensive and systemic approach, a fluid-based slotting strategy is proposed in this paper. The configuration of CAOPS is presented with specific reference to its fast-picking and restocking subsystems. Based on extended fluid model, a nonlinear mathematical programming model is developed to determine the optimal volume allotted to each stock keeping unit （SKU） in a certain mode by minimize the restocking cost of that mode. Conclusion from the allocation model is specified for the storage modules of high-volume dispensers and low-volume dispensers. Optimal allocation of storage resources in the fast-picking area of CAOPS is then discussed with the aim of identifying the optimal space of each picking mode. The SKU assignment problem referring to the total restocking cost of CAOPS is analyzed and a greedy heuristic with low time complexity is developed according to the characteristics of CAOPS. Real life application from the tobacco industry is presented in order to exemplify the proposed slotting strategy and assess the effectiveness of the developed methodology. Entry-item-quantity （EIQ） based experiential solutions and proposed-model-based near-optimal solutions are compared. The comparison results show that the proposed strategy generates a savings of over 18% referring to the total restocking cost over one-year period. The strategy proposed in this paper, which can handle the multiple dispenser types, provides a practical quantitative slotting method for CAOPS and can help picking-system-designers make slotting decisions efficiently and