Picking Movement Path Optimization via Server-Mediated Algorithm
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Solution Overview
Problem
In logistics warehouses, order picking tasks are labor-intensive and often require skilled workers to calculate optimal movement paths, while unskilled workers lack necessary experiential knowledge, and small to medium-sized enterprises face challenges in sharing work knowledge due to aging workforces and lack of new personnel.
Innovation Solution
A method for optimizing picking movement paths using a path search algorithm, which collects and processes reference information from skilled workers, forms a grid unit graph, identifies obstacle and path areas, and applies algorithms like Dijkstra, TSP, or ACO to calculate the most efficient path, storing the optimized paths in a business support system for new workers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If path search algorithms are applied to calculate optimized picking movement paths, then productivity is improved, but device complexity increases
Solution Approach 1:
A server acts as an intermediary between the business support system and workers. The server receives work information including product location and worker location from the business support system, processes this data through path search algorithms (Dijkstra, TSP, or ACO algorithms), and provides optimized picking movement paths to workers. This intermediary approach centralizes the computational complexity in the server while keeping the business support system and worker interfaces relatively simple.
Solution Approach 2:
The patent replaces manual path calculation methods (mechanical/systematic human calculation) with automated path search algorithms. Instead of workers manually calculating optimal paths or using simple heuristics, the system employs mathematical algorithms (Dijkstra's algorithm for shortest path, TSP for traveling salesman problem, ACO for ant colony optimization) to automatically compute optimized picking movement paths based on product locations, worker locations, and warehouse constraints.
2Reliability
If work knowledge is shared through formalized systems, then reliability is improved, but device complexity increases
Solution Approach 1:
The system extracts and formalizes work knowledge from skilled workers by creating digital representations of their expertise. Path search algorithms and optimization rules are copied from skilled workers' implicit knowledge into explicit system rules. The server stores and manages these formalized knowledge patterns, allowing reliable reproduction and application of expert knowledge across all workers without requiring direct transmission of personal expertise.
Solution Approach 2:
The business support system is enhanced to serve multiple functions: it not only manages basic warehouse operations but also collects work information, processes it through path optimization algorithms, stores formalized knowledge patterns, and distributes optimized paths to workers. This multi-functional approach consolidates knowledge management capabilities within an existing system rather than requiring separate specialized systems.
Data Source
AI summary
One embodiment of a method for optimizing a picking movement path comprises the steps of collecting reference information on a product and worker who performs picking; processing the reference information; searching for a picking movement path of the product and the worker based on the collected processed reference information; selecting, by the worker, the searched picking movement path; and storing the reference information and the picking movement path selected by the worker in a business support system.