Modular Assembly Transport Scheduling Using Genetic Algorithms
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Solution Overview
Problem
In modular assembly systems, the disorganization of workpiece transport due to varying assembly sequences and lack of comprehensive coordination among multiple workpiece carriers leads to inefficiencies, resulting in suboptimal control and increased production time.
Innovation Solution
A method that determines production data for each workpiece, generates transport data, and uses genetic algorithms to optimize the assignment of transport units, allowing for flexible and efficient planning by recombining allocations between transport units and workpieces, thereby improving the overall scheduling and coordination of transports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple pull principles are used to control workpiece carrier movement, then the control system is simple and easy to implement, but comprehensive coordination between multiple workpiece carriers is lacking and disorganization is not resolved globally
Solution Approach 1:
The patent introduces a central control unit as an intermediary that receives information from all workpiece carriers and assembly locations, processes this data globally, and sends coordinated control signals back. This mediator resolves the disorganization by centrally managing the transport sequences and timing, enabling comprehensive coordination without requiring complex decentralized decision-making at each carrier level.
Solution Approach 2:
The control system implements feedback mechanisms where workpiece carriers and assembly locations continuously report their status (position, occupancy, completion) to the central controller. The controller uses this feedback information to dynamically adjust transport sequences, optimize carrier allocation, and resolve disorganization in real-time, thereby improving system productivity while maintaining manageable control complexity.
2Ease of operation
If no comprehensive coordination is implemented among multiple workpiece carriers, then the control mechanism remains simple, but optimal control is prevented and disorganization persists
Solution Approach 1:
The control system performs preliminary actions by pre-planning and optimizing transport sequences before execution. The central controller calculates optimal transport paths, anticipates future carrier needs, and coordinates movements in advance based on production requirements and current system state. This preliminary coordination reduces idle time and prevents disorganization without requiring overly complex real-time control mechanisms.
3Adaptability or versatility
If transport is performed without centralized scheduling, then the system is easier to operate, but as many products as possible cannot be produced within a given time unit
Solution Approach 1:
The control system implements dynamic scheduling that adapts to changing production requirements and system conditions. The central controller continuously optimizes transport sequences based on current workpiece priorities, assembly location availability, and carrier positions. This dynamic approach maintains flexibility in assembly sequences while maximizing productivity by coordinating transports to minimize idle time and optimize resource utilization.
Data Source
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AI summary
The invention relates to a method for controlling the transport of a large number of workpieces in a modular assembly system (1) with multiple assembly locations (2).To plan transport more flexibly and effectively, the following steps are provided: - Determining (S1) the respective production data for the workpieces, where the production data includes the specification of a position and a target assembly location for the respective workpiece, - Determining (S2) transport data (9) based at least on the production data, where each workpiece is assigned a transport to be carried out, - Determining (S3) a first planning data set (10) based on the transport data (9), where an assignment between transport units (3) and transports to be carried out is created, - Generating (S4) a multitude of second planning data sets (11, 12) from the first planning data set (10) by each recombination of the assignment differently, - Selecting (S5) a second planning data set (11, 12), and - Controlling (S6) the transport of the workpieces by the transport units (3) according to the selected planning data set (11, 12).