To improve the throughput performance of the autonomous guide vehicle (AGV) unmanned storage system, a two-stage mathematical model was established. The model also considers the equipment configuration of the AGV unmanned storage system and the AGV-picking stations dual resource coordination scheduling problem. In the equipment-task scheduling phase, the model aims at the shortest order completion time, while the equipment configuration and layout model aims at the minimum equipment configuration and operating cost. To solve the two-stage model, a two-layer genetic algorithm was designed. The inner layer algorithm was used to optimize the task scheduling order of AGVs and picking stations. The results of the inner layer algorithm are fed back to the outer model to optimize configuration of the equipment and the picking station's layout. The inner and outer loops are combined to obtain the optimal equipment configuration scheme. Through the simulation study of an enterprise AGV unmanned storage case, the optimal equipment configuration combination and picking stations layout scheme are obtained. Compared with the equipment configuration scheme based on the principle the task scheduling in operation is another key link that affects the picking efficiency of an unmanned warehouse of random task scheduling and the principle of shortest job time first; The model can improve the efficiency of warehouse retrieval and minimize the number of equipment configurations. Finally, the improved genetic algorithm is used to solve the model, and the performance is compared with that of LINGO to verify the effectiveness of the improved algorithm.
Facility layout is not only the premise of production, but also a breakthrough for manufacturing industry to realize energy saving, environmental protection, and low entropy development. On the one hand, considering the interaction between product process routes and facility layout, a joint optimization model is proposed. The model aims to minimize the total logistics cost and consider the global optimization of facility layout and process route planning. On the other hand, considering the application of low entropy concept in facility layout, the analytic network process (ANP) is used to evaluate the low entropy layout. In the choice of the final facility layout, the algorithm results and expert knowledge are considered comprehensively to make up for the shortcomings of the model in the design of qualitative indicators. The algorithm innovation of this paper is to use genetic algorithm (GA) and particle swarm optimization (PSO) to search the solution of product process routes and facility layout simultaneously, to ensure the overall optimal solution of the two decision variables. Finally, an example is given to compare the joint optimization results with the independent optimization results, and the effectiveness of the joint optimization method is verified.
The unequal area facility layout problem (UA-FLP) refers to the reasonable arrangement of a certain number of facilities in a given layout area. The facility layout should satisfy given layout constraints and optimize given optimization objectives as far as possible. In this paper, a method combining improved lowest horizontal line method and particle swarm optimization algorithm is proposed to solve UA-FLP, which achieves multi-objective optimization of material handling cost, the adjacent value and the utilization rate of floor shop. On the one hand, the algorithm formulates facility packing rules through the improved lowest horizontal line method, which simplifies the legalization of facility layout. On the other hand, a modified particle swarm optimization (PSO) algorithm combining objective space division method (OSD) and niche technology is used for multi-objective optimization. The proposed algorithm overcomes the shortcomings of previous facility layout methods such as complex overlapping interference process, large amount of calculation and long time for multi-objective optimization. Compared with the comparison methods, the results show that material handling cost is reduced by 1%-6% and the utilization ratio of floor shop is increased by 2%-7%.
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