Abstract
This paper presents the optimal design of in-plant logistics in manufacturing. Owing to rising energy costs and the supply-demand imbalance leading to price competition, resulting in a sharp decline in profit, business management is facing a severe test, especially heavy industry. In practice, the important decision-making in C Company has relied on expert experience and existing knowledge, the lack of systematic thinking, and technology applications, resulting in a waste of logistics costs. Therefore, a study on the application of genetic algorithms to optimize steel mill factory by-product transport and logistics is presented herein. The aim is to solve the bottleneck of traditional decision-making in order to achieve the goal of optimizing transportation logistics decision-making via artificial intelligence. In defining the problem, the model of the steel mill factory by-products transport and logistics is constructed through in-plant route information, vehicle routing systematization and consideration of the transport demand frequency. The modified variable length chromosome ending technique and bi-level genetic algorithm are used to effectively solve the problem of different zoning transportation on double layer genetic algorithm application. The results show that the total transport time is slightly better than the existing results.
Highlights
Steelmaking is an essential industry in many advanced and developing countries
Artificial zoning subarea transport path by the pilot according to experience means a long time trial and error mode work, similar to the Ant Colony Optimization solution mode; a shorter path means more vehicles running, the equivalent of ants in the path of residual pheromones content will be more numerous, and we will follow the residual pheromone content of the best path to travel, so the total transport time is higher than the double-layer genetic algorithms (DLGA) partition, it is still quite competitive
A straight line passing through these two points is drawn on the basis of the total transit times of one and two subzones, with which the total transit time required under each of the subzones can be predicted; the solution shows that the total transport time of three to six districts located on the right side of the line, that is, under the number of partitions, obtained by Artificial intelligence (AI) is lower than the expected total transport time, indicating that AI can really obtain the better forecast solution
Summary
Steelmaking is an essential industry in many advanced and developing countries. Several fundamental steel products, including steel plates, steel strips, rolled steels, etc., made by the steelmaking manufacturers have been utilized in the creation of various basic infrastructures in modern cities. Shipments of by-products are not affected by the temporary cancelation of the demand and are still visiting each loading location on the original planned route, which can be categorized as Fixed Routes described in the study by Sungur[13] with poor transport efficiency; the analysis of the geographical distribution of the plant site and the loading demand point are limited, covering a total area of only about 3 Â 3 km. During the process of solving the optimal arrangement for each subarea partition, we can increase the number 0’s of foam genes to fill vacated sites of loci when the genes numbered di of whole uploading location were assigned less than were required in a single checkerboard chromosome. That is a suitable approach to keep the total length of chromosome fixed for solving the optimal arrangement of the subarea transporting operation; the uploading location could even be arbitrarily adjusted. Each kind of recycled by-product must be dumped into its designated recycling farm
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