Research On Optimization Of Cargo Space Allocation In Company D Based On Improved Simulated Annealing Algorithm

The labor division has become an increasingly visible phenomenon in company as a result of the fast expansion of business globalization.Many industrial enterprises and Internet behemoths seek to outsource services like transportation and inventories to third-party organizations so that they can focus on their core technology.Various kinds of operating systems,such as outsourcing factories and third-party distribution firms,have steadily evolved in response to these demands,providing employers with expert logistics,handling,and other services.Logistics management and control strength is not only a crucial signal for customer evaluation,but also a significant strength for companies competing for market share,as they gradually become a business-oriented third-party distribution organization.Company D is a logistical service provider.Because the lack of intelligent storage system makes the warehousing process time-consuming,labor-intensive,and inefficient,this paper develops a multi-objective cargo space allocation optimization model for warehousing with the goal of increasing Company D’s inventory management efficiency.The objective function is solved by using the improved simulated annealing algorithm,which takes into account the efficiency of storage,safety of shelves,and the time-consuming of unpacking,and the optimization results of the simulated annealing algorithm before and after the improvement are compared.The efficiency and effect of finding the optimal result have both improved by using the enhanced simulated annealing technique.It is effective for Company D in optimizing the storage space allocation problem and reduce cost.This article begins by identifying the company’s current challenges based on the warehouse’s storage strategy,the zoning concept,and the D company’s real operating procedure.Second,all kinds of items and all cargo spaces in Company D are partitioned and matched by the quality,and in a single partition,according to the concept of cargo space optimization,the goals are preventing energy loss,enhancing the shelves and reducing the time-consuming of unpacking.The AHP converts the model into a single-objective result model based on the three goals.In the new result generation step,the simulated annealing algorithm replaces the exponential cooling approach with a Doppler cooling function and incorporates a crossover operator and a mutation operator.For simulation analysis,Company D’s data is given parameters,and Python is utilized to solve the problem.Finally,the effects of the crossover and mutation operators on the outcomes are addressed.When compared to the random storage technique,the approach of assigning storage based on the findings of the enhanced simulated annealing algorithm provides a considerable gain in efficiency.The improved simulated annealing algorithm effectively solves the problem that ordinary three-dimensional warehouses cannot be assigned storage,and the improved algorithm has a significant improvement in the account and effect of calculations when compared to the results of the simulated annealing algorithm.The enhanced cargo space allocation algorithm can not only simplify and intelligentize the warehouse’s operation process,but it can also increase D company’s efficiency,warehousing management,and competitiveness.