ORDER-PICKING OPTIMIZATION FOR AUTOMATED PICKING SYSTEM WITH PARALLEL DISPENSERS

Based on the characteristics of parallel dispensers in automated picking system, an order-picking optimization problem is presented. Firstly, the working principle of parallel dispensers is introduced, which implies the time cost of picking each order is influenced by the order-picking sequence. So the order-picking optimization problem can be classified as a dynamic traveling salesman problem （TSP）. Then a mathematical model of the problem is established and an improved max-min ant system （MMAS） is adopted to solve the model. The improvement includes two aspects. One is that the initial assignment of ants depends on a probabilistic formula instead of a random deployment; the other is that the heuristic factor is expressed by the extra picking time of each order instead of the total. At last, an actual simulation is made on an automated picking system with parallel dispensers. The simulation results proved the optimization value and the validity of improvement on MMAS.

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

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 effectively.