Article Placement Optimization Using Picking-Time and Demand Data
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Solution Overview
Problem
Existing systems fail to optimally arrange articles based on demand prediction, neglecting the time-related aspects of picking, which hinders efficient article placement.
Innovation Solution
An article management system that includes an acquisition, prediction, evaluation, and determination processing circuit to determine optimal arrangement positions by considering individual picking processing times and future demand, evaluating combination picking processing times, and selecting the most efficient arrangement position combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If articles are arranged based on demand prediction only, then future demand satisfaction is improved, but picking time efficiency deteriorates
Solution Approach 1:
The system changes the arrangement parameters from solely demand-based to a composite parameter system that includes both demand prediction and picking time. The evaluation value combines multiple factors (demand, picking time, travel time) to determine optimal arrangement positions, transforming the single-parameter approach into a multi-parameter optimization problem.
Solution Approach 2:
The system performs preliminary calculation of picking times and travel times for various arrangement combinations before finalizing the arrangement. By pre-evaluating multiple arrangement options using the evaluation value calculation, the system prepares optimal arrangement decisions in advance, considering both future demand and time efficiency factors.
2Manufacturing precision
If multiple arrangement position combinations are evaluated, then optimal position selection is improved, but calculation complexity increases
Solution Approach 1:
The system extracts only the essential factors needed for arrangement optimization (demand prediction, picking time, travel time) and excludes irrelevant information. By focusing on these key parameters, the system reduces calculation complexity while maintaining arrangement optimization accuracy, avoiding unnecessary computational overhead.
Solution Approach 2:
The system transforms the arrangement optimization problem into a parameter-based evaluation model where complex multi-factor optimization is converted into calculating evaluation values based on defined parameters. This parameterization approach simplifies the decision-making process by providing a standardized method to compare different arrangement combinations.
Data Source
AI summary
The management server includes an acquisition processing circuit that acquires an individual picking processing time when each of a plurality of articles is arranged at each of a plurality of arrangement positions, a prediction processing circuit that acquires picking prediction information of the article, an evaluation processing circuit that evaluates a combination picking processing time corresponding to a plurality of arrangement position combinations based on the individual picking processing time and the picking prediction information, and a determination processing circuit that determines one arrangement position combination from the plurality of arrangement position combinations based on the combination picking processing time.