Research Of Optimization Methods On Automated Storage And Retrieval Systems

Automated storage and retrieval system (AS/RS) is an important tache between the modern factory logistics and the CMS. The requirement to AS/RS becomes higher with the development of modern industry manufacture. In order to further improve the intelligence management level and overall efficiency of AS/RS, the dissertation integrates modern intelligence optimization theory into AS/RS application. The research of optimization methods on AS/RS has important academic significance and practical value to enhance the enterprise competition and the circulation quality of the state economic.The AS/RS is a discrete, stochastic, dynamic, multi-factors and multi-objective system. The intelligence management of AS/RS will lead to complicating system optimization problems. To solve the optimization problems by traditional methods needs long solving time, higher cost and is difficult to search the optimum solutions. This dissertation investigates some optimization problems of AS/RS based on Genetic Algorithm (GA) and Ant Colony Algorithm (ACO), proposes corresponding improved algorithms and carries out to validate the algorithms with a practical example. The main contents of the dissertation are as follows:1. In the practical running conditions of AS/RS transportation system, to deal with conflict of equipments and assignment problem of Automated Guided Vehicle (AGV), a mathematical model with multiple complex constraints is constructed to AGV dispatching optimization problem. A hybrid genetic algorithm combining with local search technique is proposed to solve the multi-parameter model. The algorithm solved the technical hard problem of searching efficacious adjacent structure. Tests demonstrate it can search global solution and overcome the phenomena of decklock in transportation process.2. To solve the problem of storage/retrieval frequently and dynamic change storage locations, a multi-objective mathematical model is formulated for storage location assignment of the fixed rack system. Beause all objectives in the model are conflicting and the sole optimm solution does not exist, an improved GA with Pareto optimization and Niche Technology is developed. The approach adds Pareto solution sets and Niche technology besides traditional operators. It can search the optimum solution sets that distribute uniformly. The approach ensures storage location assignment optimization and offers an optimization decision making scheme for AS/RS. 3. To further improve the efficiency of AS/RS, it needs solving the path planning problems of large scale storage/retrieval tasks. First a mathematical model of the path planning problem is given for a single-picking station multi-carousel system of AS/RS. Next according to the fixed rack system under different equipment configurations, Single-Aisle Order Picking Problem (SAOPP) and Multi-Aisle Order Picking Problem (MAOPP) are defined; the mathematical model of SAOPP and the multi-objective mathematical model with uncertain task times of MAOPP are constructed for path planning problem. The stagety of dynamic change on algorithm parameters is proposed to overcome the stagnation in ACO algorithm. The stageties of awaiting nodes sets, interpolation operator and selection operator are presented to solve slow constringency and getting int local optimum solutions. Tests validate the novel improved ACO algorithm can fast search the better solutions when it is applied to deal with large scale problems.4. To validate the proposed model and optimization methods, system design and application analysis are carried out for a practical objective. The result shows that the proposed model and optimization methods can be applied to the practical AS/RS, it reduces the cost of enterprise logistics and improves the overall efficiency of the warehouse.This dissertation is supported by the NSFC of China, Program for LNET and LNIG.