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Abstract
In this paper, the Rail Guide Vehicle (RGV) intelligent scheduling problem is solved by combining simulation and genetic algorithm. The optimal scheduling path under different machine assignment schemes is obtained. The simulation model is used to simulate the running process of RGV trolley, which can fully simulate various problems faced by RGV trolley in the actual operation process, which is of great practical significance. At the same time, using genetic algorithm to solve the simulation model, the optimal scheduling path can be approached step by step, and the optimization of scheduling scheme can be realized. In the solving process, we simulated the actual situation of 8 Computer Number Controller (CNC) and one RGV. 2^8-2 = 254 CNC scheme under the optimal path selection. In the simulated 254 allocation schemes, there were 16 cases with the highest efficiency, which was 23.13 units per hour. There are 2 cases with the lowest efficiency, and the efficiency is 7.10 units per hour.
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Nowadays, Rail-guided Vehicle (RGV) has been widely used in the logistics system, and some large storage systems often use double RGV to operation simultaneously. Then RGV operation policy and influence on the efficiency of the logistics system become interesting problems to designers. This article describes the Path-divided Mode and the Path-integrated Mode of operating policy of double RGV transportation system and logistics planning based on them, and uses AutoMod simulation software to model these two modes of operation. Comparing the simulation data and reach the conclusion: when transport task is closed to full load status, a classification process should be arranged in advance and use the Path-divided Mode to ensure completion of tasks; when transport task is not heavy, the Path-integrated Mode should be chosen to save costs for classification.
A computer simulation model of an Automatic Storage and Retrieval System (AS/RS) consisting of 5 storage conveyors, 5 retrieval conveyors, 2 bi-directional conveyors, 2 order-picking stations, 7 narrow-aisle cranes and 9,310 storage locations, was created in ARENATM version 1.1. Rail-guided vehicles move material to and from the storage racks. The computer simulation model of the AS/RS was used to examine the operational logic of the entire system in order to determine the operational deployment strategy that yields the optimal number of rail-guided vehicles (RGVs), the utilisation of the narrow-aisle crane and also the maximum throughput of the system. This paper reports on the results of running the simulation model. For example, increasing the capacity of the storage and retrieval conveyor from 1 to 2 effectively reduces the number of RGV deadlocks by about 94%. A change of the topology of the RGVs' from 1-zone to 2-zone yielded a slightly better part retrieval time and also a reduction in the optimal number of RGVs from 5 to 4. Under these conditions, the number of storages/retrievals is about 430. The maximum throughput of AS/RS is estimated to be about 33 items per hour. The best retrieval time per part is almost 1205 seconds. As it stands, the system could do with less retrieval stations and narrow-aisle cranes which are under-utilised.
In this paper, we develop a family of solution algorithms based upon computational intelligence for solving the dynamic multi-vehicle pick-up and delivery problem formulated under a hybrid predictive adaptive control scheme. The scheme considers future demand and prediction of expected waiting and travel times experienced by customers.In addition, this work includes an analytical formulation of the proposed prediction models that allow us to search over a reduced feasible space. Predictive models consider relevant state space variables as vehicle load and departure time at stops. A generic expression of the system cost function is used to measure the benefits in dispatching decisions of the proposed scheme when solving for more than two-step ahead under unknown demand. The demand prediction is based on a systematic fuzzy clustering methodology, resulting in appropriate call probabilities for uncertain future.As the dynamic multi-vehicle routing problem considered is NP-hard, we propose the use of genetic algorithms (GA) that provide near-optimal solutions for the three, two and one-step ahead problems. Promising results in terms of computation time and accuracy are presented through a simulated numerical example that includes the analysis of the proposed fuzzy clustering, and the comparison of myopic and new predictive approaches solved with GA.
A Survey on Simulation-based Optimization
- Li Dong
- Wang Ding Wei
Automated Storage and Retrieval System Modeling and Scheduling Optimization Research
- Meng Tang